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5 Hard Math Concepts With Real-World Impact: - Complex Analysis → waves, electromagnetism, fluid flow - Algebraic Topology → shape of data, networks, theoretical physics - Partial Differential Equations → heat, waves, quantum mechanics - Numerical Analysis → simulation, approximation, computation - Dynamical Systems → chaos, trajectories, natural systems

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Kekius Maximus

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37,307 次观看 • 9 个月前 •via X (Twitter)

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How IIT Madras is Changing Global Security The End of Hacking? IIT Madras is spearheading India's push into quantum-secure communications through the IITM-C-DOT Samgnya Technologies Foundation, launched as the National Hub for Quantum Communication under the National Quantum Mission. This initiative aims to make communications "unhackable" by shifting from math-based encryption to physics-based security, countering future quantum computer threats. ​ The hub, inaugurated in December 2025 at IIT Madras Research Park, partners with C-DOT and is funded by the Department of Science and Technology (DST). It focuses on developing indigenous quantum hardware, QKD networks, quantum repeaters, and satellite-based systems to protect critical infrastructure like AI data centers and defense networks. Traditional encryption relies on complex math that quantum computers will crack in years. QKD uses quantum physics: any eavesdropping alters the quantum state, alerting the system instantly and burning the key. IIT Madras claims an edge in secure transmission over computing leaders like the US and China. ​ The hub builds real-world testbeds, subsidizes hardware for startups, trains experts, and links globally via IITM Global outposts. It positions India to lead in quantum-secure networks for sovereign AI and government use within five years. ​ IIT Madras also runs CyStar, a cybersecurity center advancing quantum security, AI model protection, and IoT defenses since 2024. This aligns with national goals for unhackable comms amid rising cyber threats. Credit : AIM Network.
6:11

How IIT Madras is Changing Global Security The End of Hacking? IIT Madras is spearheading India's push into quantum-secure communications through the IITM-C-DOT Samgnya Technologies Foundation, launched as the National Hub for Quantum Communication under the National Quantum Mission. This initiative aims to make communications "unhackable" by shifting from math-based encryption to physics-based security, countering future quantum computer threats. ​ The hub, inaugurated in December 2025 at IIT Madras Research Park, partners with C-DOT and is funded by the Department of Science and Technology (DST). It focuses on developing indigenous quantum hardware, QKD networks, quantum repeaters, and satellite-based systems to protect critical infrastructure like AI data centers and defense networks. Traditional encryption relies on complex math that quantum computers will crack in years. QKD uses quantum physics: any eavesdropping alters the quantum state, alerting the system instantly and burning the key. IIT Madras claims an edge in secure transmission over computing leaders like the US and China. ​ The hub builds real-world testbeds, subsidizes hardware for startups, trains experts, and links globally via IITM Global outposts. It positions India to lead in quantum-secure networks for sovereign AI and government use within five years. ​ IIT Madras also runs CyStar, a cybersecurity center advancing quantum security, AI model protection, and IoT defenses since 2024. This aligns with national goals for unhackable comms amid rising cyber threats. Credit : AIM Network.

Augadh

11,978 次观看 • 8 个月前

$IONQ "The world still massively underestimates just how disruptive it's going to be." Chris Ballance IonQ President of Quantum Computing laid out the whole picture with Kearney's Brent Smolinski. Start to finish: What it is → Quantum computers run on quantum physics, not classical logic - for Ballance, the most powerful form of computing the laws of physics allow. They solve in minutes what a classical machine couldn't crack in the lifetime of the universe. Where we are → "The end of the beginning." Real systems exist, you can buy one from IonQ today, and the field is speed-running the computing revolution. The race now: who scales the best platform the fastest. The value comes in three eras → - Early: problems classical can't touch - chemistry & drug discovery (with AstraZeneca), crash-analysis simulation (with Ansys). - Middle: familiar work, but faster, better or far less energy - AI fine-tuning, most likely hybrid: a GPU farm and a quantum computer side by side, more than the sum of their parts. - Late: unknown. The killer applications are never the ones you expect. Quantum advantage → Not a benchmark stunt - a better solution per dollar invested in quantum than classical. Hard to spot, but already real for certain early problems. The economics → His sharpest line: compute is now just a markup on electricity. With a fixed budget, the question is classical or quantum - and some of the first quantum wins won't be faster, just orders of magnitude cheaper. The architectures → Superconducting (IBM, Google): first-mover lead and standard chip fabs - but chips chilled to a thousandth of a degree above absolute zero, huge energy-hungry refrigeration, and a quantum chip "three orders of magnitude harder" than Intel's toughest. It loses coherence fast, too: many redundant qubits, far bigger machines. Trapped ions (IonQ): individual atoms - and an atom is "guaranteed perfect across the universe." No fab variation, far lower error rates, no exotic cooling. The atoms are run by an ordinary classical chip, so IonQ rides the trillion-dollar semiconductor industry instead of inventing a quantum chip. (Oxford Ionics' Electronic Qubit Control, SkyWater foundry) Why it's green → A future million-qubit machine is about a dozen racks drawing minuscule power - orders of magnitude less than a large AI data center. Classical can still gain 10–100×, but not the orders of magnitude quantum unlocks. His call: within ~10 years, some 100-megawatt NVIDIA clusters could sit vacant. Quick-fire → - Most over-hyped: changing biology. - Most underestimated risk: integrating into real customer workflows. - First to adopt: finance ) portfolio analysis & fraud prevention. - Most exciting: the speed of change over the next 24 months. The personal why → What gets him out of bed: reinventing how we think about computation and the belief that the world still underestimates how disruptive this will be. For leaders → Quantum computing is here now. Adopting any new tech takes 2-3 years, so the moment to start isn't next year it's now, so you're ready when the hardware lands. Full conversation below ↓ $IONQ #IonQ #Quantum
17:04

$IONQ "The world still massively underestimates just how disruptive it's going to be." Chris Ballance IonQ President of Quantum Computing laid out the whole picture with Kearney's Brent Smolinski. Start to finish: What it is → Quantum computers run on quantum physics, not classical logic - for Ballance, the most powerful form of computing the laws of physics allow. They solve in minutes what a classical machine couldn't crack in the lifetime of the universe. Where we are → "The end of the beginning." Real systems exist, you can buy one from IonQ today, and the field is speed-running the computing revolution. The race now: who scales the best platform the fastest. The value comes in three eras → - Early: problems classical can't touch - chemistry & drug discovery (with AstraZeneca), crash-analysis simulation (with Ansys). - Middle: familiar work, but faster, better or far less energy - AI fine-tuning, most likely hybrid: a GPU farm and a quantum computer side by side, more than the sum of their parts. - Late: unknown. The killer applications are never the ones you expect. Quantum advantage → Not a benchmark stunt - a better solution per dollar invested in quantum than classical. Hard to spot, but already real for certain early problems. The economics → His sharpest line: compute is now just a markup on electricity. With a fixed budget, the question is classical or quantum - and some of the first quantum wins won't be faster, just orders of magnitude cheaper. The architectures → Superconducting (IBM, Google): first-mover lead and standard chip fabs - but chips chilled to a thousandth of a degree above absolute zero, huge energy-hungry refrigeration, and a quantum chip "three orders of magnitude harder" than Intel's toughest. It loses coherence fast, too: many redundant qubits, far bigger machines. Trapped ions (IonQ): individual atoms - and an atom is "guaranteed perfect across the universe." No fab variation, far lower error rates, no exotic cooling. The atoms are run by an ordinary classical chip, so IonQ rides the trillion-dollar semiconductor industry instead of inventing a quantum chip. (Oxford Ionics' Electronic Qubit Control, SkyWater foundry) Why it's green → A future million-qubit machine is about a dozen racks drawing minuscule power - orders of magnitude less than a large AI data center. Classical can still gain 10–100×, but not the orders of magnitude quantum unlocks. His call: within ~10 years, some 100-megawatt NVIDIA clusters could sit vacant. Quick-fire → - Most over-hyped: changing biology. - Most underestimated risk: integrating into real customer workflows. - First to adopt: finance ) portfolio analysis & fraud prevention. - Most exciting: the speed of change over the next 24 months. The personal why → What gets him out of bed: reinventing how we think about computation and the belief that the world still underestimates how disruptive this will be. For leaders → Quantum computing is here now. Adopting any new tech takes 2-3 years, so the moment to start isn't next year it's now, so you're ready when the hardware lands. Full conversation below ↓ $IONQ #IonQ #Quantum

TechInnovation

26,310 次观看 • 3 个月前

a physics dropout from ohio has a $2.4m trading account he never read a trading book. never learned technical analysis. just took equations from his thermodynamics textbook and pointed them at price data to see if they fit they did markets under stress behave almost identically to thermodynamic systems. energy dissipates in predictable patterns after compression. price does the exact same thing the concept is entropy. shannon formalized it in 1948. it's in every stats and physics textbook that's ever been printed his actual setup: measure entropy across the last 40 candles flag low-entropy compression states build a transition table from 3 years of real data what the table showed: 68% of low-entropy compression states resolve into directional expansion above 2 ATR within 5 candles that's not a prediction. it's a probability table the market keeps filling in for you, every single day, for free he sizes each trade with kelly based on the 68%. takes it when math says yes, skips it when math says no traders in his server spent 3 years learning to read candles better he spent 6 months just asking a question they never thought to ask the textbook was $40. the data is free. the math has existed since 1948 bookmark this nobody told you about it because the second you start asking better questions, you stop being the other side of their trade
0:30

a physics dropout from ohio has a $2.4m trading account he never read a trading book. never learned technical analysis. just took equations from his thermodynamics textbook and pointed them at price data to see if they fit they did markets under stress behave almost identically to thermodynamic systems. energy dissipates in predictable patterns after compression. price does the exact same thing the concept is entropy. shannon formalized it in 1948. it's in every stats and physics textbook that's ever been printed his actual setup: measure entropy across the last 40 candles flag low-entropy compression states build a transition table from 3 years of real data what the table showed: 68% of low-entropy compression states resolve into directional expansion above 2 ATR within 5 candles that's not a prediction. it's a probability table the market keeps filling in for you, every single day, for free he sizes each trade with kelly based on the 68%. takes it when math says yes, skips it when math says no traders in his server spent 3 years learning to read candles better he spent 6 months just asking a question they never thought to ask the textbook was $40. the data is free. the math has existed since 1948 bookmark this nobody told you about it because the second you start asking better questions, you stop being the other side of their trade

Livsun

32,215 次观看 • 2 个月前

Joe Rogan just said what nobody in finance wants to hear about quantum computing. Rogan: “It’s over. Like there’s no privacy.” He’s not being hyperbolic. He’s being early. Every dollar in every bank account on Earth is protected by one thing. Math. Not vaults. Not guards. Not governments. Math problems that current computers can’t solve fast enough to break. That’s it. RSA encryption. Elliptic curve cryptography. The same math guarding your savings account guards sovereign wealth funds. Defense networks. Nuclear launch protocols. Quantum computing doesn’t pick the lock. It removes the door. A quantum system at sufficient scale breaks RSA encryption in hours. The same problem would take a classical computer longer than the age of the universe. This isn’t theoretical. Google, IBM, and China are racing toward fault-tolerant quantum systems. The timeline isn’t a century. It’s a decade. Maybe less. Rogan: “I think the real problem is the financial market. It’s all numbers, right? It’s all just ones and zeros.” He’s right. And it’s worse than he thinks. The first entity to reach that threshold doesn’t gain an advantage. They gain access. Every encrypted financial transaction ever recorded. Every classified document. Every crypto wallet. Every private key. Every medical record. Every secret ever sent over a wire becomes a postcard. China holds more quantum computing patents than any nation on Earth. Their research runs without public oversight or shareholder pressure. They’ve been harvesting encrypted Western data for years. Intelligence agencies have a name for the strategy. Harvest now, decrypt later. Rogan: “If somebody controls that before we do, if somebody breaks through with this type of technology and then just shuts all the other ones off.” If a foreign power achieves this before post-quantum encryption is deployed, there is no countermove. You can’t un-read stolen data. You can’t restore trust in a system whose entire security model became fiction overnight. People talk about AI as the defining technology of the century. They might be wrong. AI needs data and compute and infrastructure to be dangerous. Quantum computing just needs to exist. One breakthrough. One machine. One moment where the math that protects everything stops being hard enough. Most people heard Rogan and thought exaggeration. The ones paying attention heard a countdown. We built the entire modern world on one assumption. That certain math problems would stay unsolvable forever. Nobody promised us that.
0:22

Joe Rogan just said what nobody in finance wants to hear about quantum computing. Rogan: “It’s over. Like there’s no privacy.” He’s not being hyperbolic. He’s being early. Every dollar in every bank account on Earth is protected by one thing. Math. Not vaults. Not guards. Not governments. Math problems that current computers can’t solve fast enough to break. That’s it. RSA encryption. Elliptic curve cryptography. The same math guarding your savings account guards sovereign wealth funds. Defense networks. Nuclear launch protocols. Quantum computing doesn’t pick the lock. It removes the door. A quantum system at sufficient scale breaks RSA encryption in hours. The same problem would take a classical computer longer than the age of the universe. This isn’t theoretical. Google, IBM, and China are racing toward fault-tolerant quantum systems. The timeline isn’t a century. It’s a decade. Maybe less. Rogan: “I think the real problem is the financial market. It’s all numbers, right? It’s all just ones and zeros.” He’s right. And it’s worse than he thinks. The first entity to reach that threshold doesn’t gain an advantage. They gain access. Every encrypted financial transaction ever recorded. Every classified document. Every crypto wallet. Every private key. Every medical record. Every secret ever sent over a wire becomes a postcard. China holds more quantum computing patents than any nation on Earth. Their research runs without public oversight or shareholder pressure. They’ve been harvesting encrypted Western data for years. Intelligence agencies have a name for the strategy. Harvest now, decrypt later. Rogan: “If somebody controls that before we do, if somebody breaks through with this type of technology and then just shuts all the other ones off.” If a foreign power achieves this before post-quantum encryption is deployed, there is no countermove. You can’t un-read stolen data. You can’t restore trust in a system whose entire security model became fiction overnight. People talk about AI as the defining technology of the century. They might be wrong. AI needs data and compute and infrastructure to be dangerous. Quantum computing just needs to exist. One breakthrough. One machine. One moment where the math that protects everything stops being hard enough. Most people heard Rogan and thought exaggeration. The ones paying attention heard a countdown. We built the entire modern world on one assumption. That certain math problems would stay unsolvable forever. Nobody promised us that.

Dustin

11,669 次观看 • 5 个月前

New Mobile Electronic Warfare (EW) & Radar Systems of Russian Ministry of Defense (MoD) BATTLE TESTED & WORLD STEALTH SUPERIORITY in all CLASSES ! By 𝐃𝐚𝐯𝐢𝐝 𝐙 🇷🇺 🇷🇸🇮🇪 Thread 1 / 5 Russian MoD has been aggressively modernizing its electronic warfare & radar capabilities, particularly in response to lessons from the ongoing conflict in Ukraine. These systems emphasize mobility, integration with air defense networks, & countermeasures against drones, precision-guided munitions, & enemy command-control (C2) structures. Many are designed for rapid deployment in tactical environments, often mounted on vehicles, aircraft, or even backpack-portable for infantry use. Below, I'll break down key new or upgraded systems with specifics on their features, operational roles & adaptations. Key Electronic Warfare Systems Krasukha Series (Krasukha-4 / Krasukha-2): These are multifunctional mobile jamming platforms mounted on truck chassis like the BAZ-6910, making them highly deployable across rough terrain. The Krasukha-4 can jam airborne radars, including those on AWACS aircraft, drones & satellites, at ranges up to 300 km. It uses digital radio frequency memory (DRFM) technology to create false targets & spoof enemy sensors, disrupting reconnaissance and fire-control systems. In recent operations, it's been used to create "electronic shields" over bases & troop concentrations, suppressing enemy ISR (intelligence, surveillance & reconnaissance) assets. Upgrades in 2024-2025 include better integration with AI for adaptive frequency hopping, allowing it to counter advanced jamming-resistant signals from Western systems. Skovorodka (Frying Pan) System: Introduced in late 2024, this is a compact, backpack-portable EW device developed by AO "AEC" for frontline infantry. Weighing under 10 kg, it operates on dual frequencies to jam enemy reconnaissance drones (e.g., DJI models) & artillery targeting systems within a 5-10 km radius. It disrupts the "reconnaissance-fire chain" by blocking data links & GPS signals, reducing drone effectiveness by up to 70% in tested scenarios. It's integrated into broader air defense networks like the S-400 & S-500, providing ground-level offensive EW to blind hostile radars while protecting Russian assets. Its low cost (~ $5,000 per unit) allows mass deployment, with production ramped up to equip motorized rifle battalions. Leer-3 (RB-341V): A mobile EW complex integrated with Orlan-10 UAVs for remote operation. Mounted on MT-LB armored vehicles, it jams GSM, satellite communications & GPS signals within a 6 km radius, often scattering jammers via drones for area denial. Recent enhancements include PSYOPS capabilities, such as sending fake SMS to enemy troops for disinformation. In 2025 deployments, it's been paired with anti-drone interceptors to create layered defenses against UAV swarms, with ranges extended to 10-15 km through improved antennas. Borisoglebsk-2 (RB-301B): This automated jamming system is chassis-mounted on MT-LBu vehicles for high mobility. It detects, analyzes & suppresses HF/VHF/UHF communications & radar signals up to 30-50 km away. Specifics include direction-finding accuracy within 1-2 degrees & the ability to handle up to 100 simultaneous targets. 2025 updates incorporate AI-driven algorithms for real-time spectrum analysis, making it more effective against frequency-agile Western radios. Rychag-AV: A versatile EW module installable on helicopters (e.g., Mi-8), ships, or ground vehicles. It jams sensors and radars at hundreds of kilometers using DRFM to mimic echoes and create phantom threats. Recent mobile variants on KamAZ trucks allow rapid repositioning, with power outputs up to 10 kW for sustained jamming. Zhitel (R-330Zh): Mobile on Ural trucks, it jams communications, GPS, and air targets up to 200 km. It's often used in tandem with radar systems to disrupt drone data links, with 2025 models featuring modular antennas for quick setup in 15-20 minutes. PART 1 >>>
26:22

New Mobile Electronic Warfare (EW) & Radar Systems of Russian Ministry of Defense (MoD) BATTLE TESTED & WORLD STEALTH SUPERIORITY in all CLASSES ! By 𝐃𝐚𝐯𝐢𝐝 𝐙 🇷🇺 🇷🇸🇮🇪 Thread 1 / 5 Russian MoD has been aggressively modernizing its electronic warfare & radar capabilities, particularly in response to lessons from the ongoing conflict in Ukraine. These systems emphasize mobility, integration with air defense networks, & countermeasures against drones, precision-guided munitions, & enemy command-control (C2) structures. Many are designed for rapid deployment in tactical environments, often mounted on vehicles, aircraft, or even backpack-portable for infantry use. Below, I'll break down key new or upgraded systems with specifics on their features, operational roles & adaptations. Key Electronic Warfare Systems Krasukha Series (Krasukha-4 / Krasukha-2): These are multifunctional mobile jamming platforms mounted on truck chassis like the BAZ-6910, making them highly deployable across rough terrain. The Krasukha-4 can jam airborne radars, including those on AWACS aircraft, drones & satellites, at ranges up to 300 km. It uses digital radio frequency memory (DRFM) technology to create false targets & spoof enemy sensors, disrupting reconnaissance and fire-control systems. In recent operations, it's been used to create "electronic shields" over bases & troop concentrations, suppressing enemy ISR (intelligence, surveillance & reconnaissance) assets. Upgrades in 2024-2025 include better integration with AI for adaptive frequency hopping, allowing it to counter advanced jamming-resistant signals from Western systems. Skovorodka (Frying Pan) System: Introduced in late 2024, this is a compact, backpack-portable EW device developed by AO "AEC" for frontline infantry. Weighing under 10 kg, it operates on dual frequencies to jam enemy reconnaissance drones (e.g., DJI models) & artillery targeting systems within a 5-10 km radius. It disrupts the "reconnaissance-fire chain" by blocking data links & GPS signals, reducing drone effectiveness by up to 70% in tested scenarios. It's integrated into broader air defense networks like the S-400 & S-500, providing ground-level offensive EW to blind hostile radars while protecting Russian assets. Its low cost (~ $5,000 per unit) allows mass deployment, with production ramped up to equip motorized rifle battalions. Leer-3 (RB-341V): A mobile EW complex integrated with Orlan-10 UAVs for remote operation. Mounted on MT-LB armored vehicles, it jams GSM, satellite communications & GPS signals within a 6 km radius, often scattering jammers via drones for area denial. Recent enhancements include PSYOPS capabilities, such as sending fake SMS to enemy troops for disinformation. In 2025 deployments, it's been paired with anti-drone interceptors to create layered defenses against UAV swarms, with ranges extended to 10-15 km through improved antennas. Borisoglebsk-2 (RB-301B): This automated jamming system is chassis-mounted on MT-LBu vehicles for high mobility. It detects, analyzes & suppresses HF/VHF/UHF communications & radar signals up to 30-50 km away. Specifics include direction-finding accuracy within 1-2 degrees & the ability to handle up to 100 simultaneous targets. 2025 updates incorporate AI-driven algorithms for real-time spectrum analysis, making it more effective against frequency-agile Western radios. Rychag-AV: A versatile EW module installable on helicopters (e.g., Mi-8), ships, or ground vehicles. It jams sensors and radars at hundreds of kilometers using DRFM to mimic echoes and create phantom threats. Recent mobile variants on KamAZ trucks allow rapid repositioning, with power outputs up to 10 kW for sustained jamming. Zhitel (R-330Zh): Mobile on Ural trucks, it jams communications, GPS, and air targets up to 200 km. It's often used in tandem with radar systems to disrupt drone data links, with 2025 models featuring modular antennas for quick setup in 15-20 minutes. PART 1 >>>

𝐃𝐚𝐯𝐢𝐝 𝐙 🇷🇺 🇷🇸🇮🇪

18,801 次观看 • 10 个月前

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.
1:24

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

46,549 次观看 • 1 年前

Ahmedabad Crime Branch is making use of technical measures to avoid any stampede kind of situation. Anti stampede visual analytics,using reference area and crowd movement, head count algorithm. Anti-stampede algorithms on CCTV cameras are a crucial advancement in crowd management, leveraging AI and image processing to prevent dangerous situations in densely populated areas. Here's a breakdown of their usage: How they work: Real-time monitoring: AI-powered CCTV cameras continuously analyze video streams in real-time. Crowd density estimation: Algorithms calculate the number of people in a given area. This can involve: Pixel-based analysis: Converting images to black and white and counting "black pixels" (representing people). Object detection: Using machine learning models (like Mask R-CNN) to identify and count individuals, often by detecting heads or torsos. Thresholding: Pre-defined "threshold values" for crowd density are established. When the detected density crosses these thresholds, it triggers an alert. Anomaly detection: Beyond just density, these algorithms can identify unusual crowd behaviors such as: * Sudden surges in movement. * Unusual clustering patterns. * Fallen individuals. * Aggressive movements. Alerting authorities: Upon detecting a potential stampede risk, the system sends immediate alerts to security personnel or control rooms via LCD displays, GSM messages, or other communication channels. Predictive analytics: Some advanced systems use time-series prediction models to forecast crowd behavior and dynamics based on historical and real-time data, helping anticipate potential bottlenecks or overcrowding. Reinforcement learning: Algorithms can learn from past incidents to suggest optimal crowd flow routes and alternative evacuation paths during emergencies. Benefits: Proactive prevention: The primary benefit is the ability to detect and warn of potential stampedes before they occur, allowing authorities to take preventative measures. Real-time insights: Provides immediate and accurate data on crowd density and movement, far surpassing manual observation. Enhanced safety: Significantly improves safety in public spaces by reducing human error and enabling swift responses to risks. Optimized resource allocation: Helps in better deployment of security personnel and resources to areas with high crowd density. Improved efficiency: Automates a labor-intensive task, freeing up human operators for more complex decision-making. Data for future planning: The collected data can be analyzed to improve crowd management strategies for future events. Challenges: Accuracy limitations: While advanced, AI algorithms can still face challenges with: Occlusion: People blocking each other, making accurate counting difficult. Varying conditions: Changes in lighting, weather, and camera angles can affect accuracy. Bias in training data: Can lead to false positives or inaccurate detections. Computational complexity and cost: Developing and deploying such systems can be expensive due to the need for high-resolution cameras, powerful processing units, and sophisticated algorithms. Data privacy and ethical concerns: The extensive use of CCTV and AI raises concerns about individual privacy and potential misuse of data. Integration with existing infrastructure: Integrating new AI-powered systems with older CCTV networks can be complex. Human intervention still crucial: While AI can alert, human responders are still essential for effective intervention and crowd dispersal. As seen in the Kumbh Mela example, even with AI alerts, a lack of ground personnel can limit effectiveness. Defining thresholds: Determining appropriate crowd density thresholds for different environments and cultural contexts can be challenging. Real-world applications: Large public gatherings: Religious festivals (like the Kumbh Mela in India, which has used AI for crowd management), concerts, sports events, and political rallies. Transportation hubs: Railway stations, airports, and bus terminals to manage passenger flow. Shopping malls and commercial centers: To monitor crowd density during peak hours and special events. Stadiums and arenas: For managing ingress, egress, and crowd movement during events. Tourist attractions: To prevent overcrowding at popular sites. Overall, anti-stampede algorithms on CCTV cameras represent a significant leap forward in ensuring public safety, offering a powerful tool for proactive crowd management. However, their successful implementation requires careful consideration of technological limitations, ethical implications, and the continued need for effective human intervention. Ahmedabad Police અમદાવાદ પોલીસ Vijay Patel | Megh Updates 🚨™ | Akash Anand | | #BengaluruStampede | #Stampede
0:37

Ahmedabad Crime Branch is making use of technical measures to avoid any stampede kind of situation. Anti stampede visual analytics,using reference area and crowd movement, head count algorithm. Anti-stampede algorithms on CCTV cameras are a crucial advancement in crowd management, leveraging AI and image processing to prevent dangerous situations in densely populated areas. Here's a breakdown of their usage: How they work: Real-time monitoring: AI-powered CCTV cameras continuously analyze video streams in real-time. Crowd density estimation: Algorithms calculate the number of people in a given area. This can involve: Pixel-based analysis: Converting images to black and white and counting "black pixels" (representing people). Object detection: Using machine learning models (like Mask R-CNN) to identify and count individuals, often by detecting heads or torsos. Thresholding: Pre-defined "threshold values" for crowd density are established. When the detected density crosses these thresholds, it triggers an alert. Anomaly detection: Beyond just density, these algorithms can identify unusual crowd behaviors such as: * Sudden surges in movement. * Unusual clustering patterns. * Fallen individuals. * Aggressive movements. Alerting authorities: Upon detecting a potential stampede risk, the system sends immediate alerts to security personnel or control rooms via LCD displays, GSM messages, or other communication channels. Predictive analytics: Some advanced systems use time-series prediction models to forecast crowd behavior and dynamics based on historical and real-time data, helping anticipate potential bottlenecks or overcrowding. Reinforcement learning: Algorithms can learn from past incidents to suggest optimal crowd flow routes and alternative evacuation paths during emergencies. Benefits: Proactive prevention: The primary benefit is the ability to detect and warn of potential stampedes before they occur, allowing authorities to take preventative measures. Real-time insights: Provides immediate and accurate data on crowd density and movement, far surpassing manual observation. Enhanced safety: Significantly improves safety in public spaces by reducing human error and enabling swift responses to risks. Optimized resource allocation: Helps in better deployment of security personnel and resources to areas with high crowd density. Improved efficiency: Automates a labor-intensive task, freeing up human operators for more complex decision-making. Data for future planning: The collected data can be analyzed to improve crowd management strategies for future events. Challenges: Accuracy limitations: While advanced, AI algorithms can still face challenges with: Occlusion: People blocking each other, making accurate counting difficult. Varying conditions: Changes in lighting, weather, and camera angles can affect accuracy. Bias in training data: Can lead to false positives or inaccurate detections. Computational complexity and cost: Developing and deploying such systems can be expensive due to the need for high-resolution cameras, powerful processing units, and sophisticated algorithms. Data privacy and ethical concerns: The extensive use of CCTV and AI raises concerns about individual privacy and potential misuse of data. Integration with existing infrastructure: Integrating new AI-powered systems with older CCTV networks can be complex. Human intervention still crucial: While AI can alert, human responders are still essential for effective intervention and crowd dispersal. As seen in the Kumbh Mela example, even with AI alerts, a lack of ground personnel can limit effectiveness. Defining thresholds: Determining appropriate crowd density thresholds for different environments and cultural contexts can be challenging. Real-world applications: Large public gatherings: Religious festivals (like the Kumbh Mela in India, which has used AI for crowd management), concerts, sports events, and political rallies. Transportation hubs: Railway stations, airports, and bus terminals to manage passenger flow. Shopping malls and commercial centers: To monitor crowd density during peak hours and special events. Stadiums and arenas: For managing ingress, egress, and crowd movement during events. Tourist attractions: To prevent overcrowding at popular sites. Overall, anti-stampede algorithms on CCTV cameras represent a significant leap forward in ensuring public safety, offering a powerful tool for proactive crowd management. However, their successful implementation requires careful consideration of technological limitations, ethical implications, and the continued need for effective human intervention. Ahmedabad Police અમદાવાદ પોલીસ Vijay Patel | Megh Updates 🚨™ | Akash Anand | | #BengaluruStampede | #Stampede

Janak Dave

339,758 次观看 • 1 年前

🚨 PART 1: Human Bodies as 6G Terahertz Antennas in the Internet of Bio-Nano Things (IoBNT) & Internet of Bodies (IoB) 🚨 This is not conspiracy. This is peer-reviewed science, patents, academic papers, and public research from top institutions and researchers. The convergence of "graphene-based nanomaterials", "terahertz (THz) communication", "optoelectronics", "Visible Light Communication (VLC)", and "Wireless Nanosensor Networks" is turning the human body into a node in the global network; harvesting energy, sensing, transmitting data, and potentially interfacing with the brain and autonomic systems. The Core Concept: Graphene oxide and related materials (GRMs) in the body (via vaccines, environmental exposure, or intentional delivery) act as plasmonic nano-antennas. THz waves (key for 6G) interact with these, enabling intra-body communication, energy harvesting, and data exchange in the "Internet of Everything." Key Scientific Foundations & Full Links 1. Graphene and Related Materials for the Internet of Bio-Nano Things Title: Graphene and related materials for the Internet of Bio-Nano Things (Perspective Paper, APL Materials, 2023) Authors/Researchers: Murat Kuşcu, Bige D. Unluturk, et al. (Building on foundational work in nanonetworks) This details how GRMs bridge biological systems with electromagnetic networks for in-body sensing and actuation. 2. Graphene-Based Plasmonic Nano-Antenna for Terahertz Band Communication Title: Graphene-based Plasmonic Nano-Antenna for Terahertz Band Communication in Nanonetworks (IEEE, 2014) Key Researchers: Josep Miquel Jornet & Ian F. Akyildiz Exploits Surface Plasmon Polaritons in graphene nanoribbons for efficient THz nano-antennas. 3. A Review on Wireless Nanosensor Networks Based on Electromagnetic Communication Title: A Review on Wireless Nanosensor Networks Based on Electromagnetic Communication (PDF) Authors: Vishranti Rupani, Sunera Kargathara, Jigisha Sureja (2015) Covers architecture for electromagnetic WNSNs in the THz band. 4. Human Body as Antenna for 6G Energy Harvesting (VLC/RF) Title: Next-generation Wireless Technology May Leverage Human Body as an Antenna (UMass Amherst, 2023) Researchers: Jie Xiong, et al. (Bracelet+ copper coil amplified 10x by human body for harvesting leaked RF/VLC energy). Popular Mechanics Summary: 5. Internet of Bio-Nano Things Foundational Paper Title: The Internet of Bio-Nano Things (IEEE, 2015) Key Pioneer: Ian F. Akyildiz (Georgia Tech, N3Cat) widely cited founder of nanonetworks research. Major Players & Institutions Involved - Ian F. Akyildiz - Pioneer of electromagnetic nanonetworks and IoBNT concepts. - Josep Miquel Jornet - Graphene plasmonic THz antennas/transceivers. - Murat Kuşcu & team - Graphene for practical IoBNT implementation. - University of Massachusetts Amherst (Jie Xiong et al.) Human body as 6G antenna/energy harvester. - Georgia Tech Broadband Wireless Networking Lab / N3Cat (UPC Barcelona) Core nanonetworks research. - World Economic Forum / Klaus Schwab - Promoted "Fourth Industrial Revolution" and Internet of Bodies concepts. - RAND Corporation - Reports on Internet of Bodies opportunities/risks. - DARPA & related defense research - Neural tech, brain interfaces overlapping with THz sensing (cross-referenced in broader BCI/6G discussions). - Vaccine/Graphene discussions - Often link to papers on nanomaterials; real research exists on graphene in biomedical delivery, but injection claims remain debated/speculative. THz reading EEG-like signals, vaccinated bodies as antennas, autonomic control via Wireless Body Area Networks. This aligns with documented research on intra-body THz comms, plasmonic sensing, and body-centric networks; though real deployment (as of 2026) is research-phase, not widespread. Call to Action: This is Part 1. It exposes the documented tech foundation. Repost, research every name/paper, demand transparency on nanomaterials in medicine, 6G rollout, and bio-convergence. Comment "PART 2" for the next section (more on optoelectronics, neuromorphic devices, virus lasers, Ian Hurn0 threads, additional patents/players, and full thread expansions). Truth-seeking note: These are real advancements in nanotech/biomedicine with huge potential for health monitoring and high-speed comms. They also raise profound questions on privacy, consent, dual-use (surveillance/control), and safety. Always verify primaries. Full sources above; dig in.
2:33

🚨 PART 1: Human Bodies as 6G Terahertz Antennas in the Internet of Bio-Nano Things (IoBNT) & Internet of Bodies (IoB) 🚨 This is not conspiracy. This is peer-reviewed science, patents, academic papers, and public research from top institutions and researchers. The convergence of "graphene-based nanomaterials", "terahertz (THz) communication", "optoelectronics", "Visible Light Communication (VLC)", and "Wireless Nanosensor Networks" is turning the human body into a node in the global network; harvesting energy, sensing, transmitting data, and potentially interfacing with the brain and autonomic systems. The Core Concept: Graphene oxide and related materials (GRMs) in the body (via vaccines, environmental exposure, or intentional delivery) act as plasmonic nano-antennas. THz waves (key for 6G) interact with these, enabling intra-body communication, energy harvesting, and data exchange in the "Internet of Everything." Key Scientific Foundations & Full Links 1. Graphene and Related Materials for the Internet of Bio-Nano Things Title: Graphene and related materials for the Internet of Bio-Nano Things (Perspective Paper, APL Materials, 2023) Authors/Researchers: Murat Kuşcu, Bige D. Unluturk, et al. (Building on foundational work in nanonetworks) This details how GRMs bridge biological systems with electromagnetic networks for in-body sensing and actuation. 2. Graphene-Based Plasmonic Nano-Antenna for Terahertz Band Communication Title: Graphene-based Plasmonic Nano-Antenna for Terahertz Band Communication in Nanonetworks (IEEE, 2014) Key Researchers: Josep Miquel Jornet & Ian F. Akyildiz Exploits Surface Plasmon Polaritons in graphene nanoribbons for efficient THz nano-antennas. 3. A Review on Wireless Nanosensor Networks Based on Electromagnetic Communication Title: A Review on Wireless Nanosensor Networks Based on Electromagnetic Communication (PDF) Authors: Vishranti Rupani, Sunera Kargathara, Jigisha Sureja (2015) Covers architecture for electromagnetic WNSNs in the THz band. 4. Human Body as Antenna for 6G Energy Harvesting (VLC/RF) Title: Next-generation Wireless Technology May Leverage Human Body as an Antenna (UMass Amherst, 2023) Researchers: Jie Xiong, et al. (Bracelet+ copper coil amplified 10x by human body for harvesting leaked RF/VLC energy). Popular Mechanics Summary: 5. Internet of Bio-Nano Things Foundational Paper Title: The Internet of Bio-Nano Things (IEEE, 2015) Key Pioneer: Ian F. Akyildiz (Georgia Tech, N3Cat) widely cited founder of nanonetworks research. Major Players & Institutions Involved - Ian F. Akyildiz - Pioneer of electromagnetic nanonetworks and IoBNT concepts. - Josep Miquel Jornet - Graphene plasmonic THz antennas/transceivers. - Murat Kuşcu & team - Graphene for practical IoBNT implementation. - University of Massachusetts Amherst (Jie Xiong et al.) Human body as 6G antenna/energy harvester. - Georgia Tech Broadband Wireless Networking Lab / N3Cat (UPC Barcelona) Core nanonetworks research. - World Economic Forum / Klaus Schwab - Promoted "Fourth Industrial Revolution" and Internet of Bodies concepts. - RAND Corporation - Reports on Internet of Bodies opportunities/risks. - DARPA & related defense research - Neural tech, brain interfaces overlapping with THz sensing (cross-referenced in broader BCI/6G discussions). - Vaccine/Graphene discussions - Often link to papers on nanomaterials; real research exists on graphene in biomedical delivery, but injection claims remain debated/speculative. THz reading EEG-like signals, vaccinated bodies as antennas, autonomic control via Wireless Body Area Networks. This aligns with documented research on intra-body THz comms, plasmonic sensing, and body-centric networks; though real deployment (as of 2026) is research-phase, not widespread. Call to Action: This is Part 1. It exposes the documented tech foundation. Repost, research every name/paper, demand transparency on nanomaterials in medicine, 6G rollout, and bio-convergence. Comment "PART 2" for the next section (more on optoelectronics, neuromorphic devices, virus lasers, Ian Hurn0 threads, additional patents/players, and full thread expansions). Truth-seeking note: These are real advancements in nanotech/biomedicine with huge potential for health monitoring and high-speed comms. They also raise profound questions on privacy, consent, dual-use (surveillance/control), and safety. Always verify primaries. Full sources above; dig in.

Noah B. Price

22,110 次观看 • 5 个月前

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan
0:33

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

Markus J. Buehler

47,269 次观看 • 2 年前

"Superfluid Vortices as a Model for Gravitational Precession" with Implications for Electromagnetism Authors: Matt Baker, Alan Baker Superfluid vortices in a Bose-Einstein condensate provide a framework for modeling gravitational effects, particularly the precession of planetary orbits, building on concepts of superfluidity in quantum systems. Calculations using modified Navier-Stokes equations and angular momentum terms for vortex dynamics predict the orbital precession of Venus, Earth, and Mars, matching observed data within one, two, and three arc seconds per century, respectively, employing a superfluid of sub-quark-scale particles, below 10^-15 meters, which remain undetectable due to their infinitesimal size and ability to flow through detection instruments unimpeded by thermal interactions. The theoretical framework employs Bernoulli’s principle, where large, slow-spinning vortices create low-pressure zones, producing a gravitational-like pull over long distances. At sub-electromagnetic scales, below 10^-17 meters, these vortices operate without directly interacting with electromagnetic fields, yet their rapid dynamics could indirectly influence such interactions. Analysis indicates that tighter, faster vortices generate localized forces resembling electromagnetic interactions, with the work done scaling similarly to gravitational effects but over much shorter distances, suggesting a unified mechanism across scales. Experimental validation is proposed through a Superfluid Vortex Collider (SFVC), where quantized vortices in a Bose-Einstein condensate at near-absolute-zero temperatures would be collided. Predictions indicate that microvortices from these collisions could replicate gravitational interactions, with quantum coherence in Bose-Einstein condensates, as demonstrated (Jin et al., 1996), supporting the feasibility of such experiments. Superfluid vortex dynamics offer a model for gravity with potential extensions to electromagnetism. The increasing discrepancies with orbital distance suggest a distance-dependent vortex parameter, alongside variations in vortex speed, angular momentum, effective viscosity, or planetary density, which could be refined to further align predictions with observations. Future investigations could explore vortex entanglement in ultra-coherent conditions, though achieving such conditions remains challenging. References L. P. Pitaevskii and S. Stringari, Bose-Einstein Condensation and Superfluidity (Oxford University Press, 2016). D. S. Jin, J. R. Ensher, M. R. Matthews, C. E. Wieman, and E. A. Cornell, Collective excitations of a Bose-Einstein condensate in a dilute atomic gas, Phys. Rev. Lett. 77, 420 (1996). Appendix: Mathematical Derivations To model planetary precession, modified Navier-Stokes equations for superfluids were employed, incorporating angular momentum terms for vortex dynamics. For a vortex with circulation \(\Gamma\), the velocity field \(v(r) = \Gamma/(2\pi r)\) creates a low-pressure zone via Bernoulli’s principle, yielding a gravitational-like force scaling as \(F \propto 1/r^2\). Orbital precession was calculated using the angular momentum contribution \(L = mvr\), adjusted for vortex-induced perturbations, resulting in precession rates of 43 arc seconds per century for Venus (observed: 42 arc seconds), 3.8 arc seconds per century for Earth (observed: 5.0 arc seconds), and 1.4 arc seconds per century for Mars (observed: 4.0 arc seconds). Discrepancies may stem from variations in vortex speed \(\Gamma\) or effective planetary density \(\rho\). For electromagnetic interactions, tighter vortices with higher angular velocity \(\omega\) were analyzed. The force generated scales as \(F \propto \omega^2/r^2\), resembling electromagnetic interactions at scales of 10^-17 meters. The work done, integrated over distance, yields a ratio comparable to gravitational work at larger scales, supporting a unified force model.
2:05

"Superfluid Vortices as a Model for Gravitational Precession" with Implications for Electromagnetism Authors: Matt Baker, Alan Baker Superfluid vortices in a Bose-Einstein condensate provide a framework for modeling gravitational effects, particularly the precession of planetary orbits, building on concepts of superfluidity in quantum systems. Calculations using modified Navier-Stokes equations and angular momentum terms for vortex dynamics predict the orbital precession of Venus, Earth, and Mars, matching observed data within one, two, and three arc seconds per century, respectively, employing a superfluid of sub-quark-scale particles, below 10^-15 meters, which remain undetectable due to their infinitesimal size and ability to flow through detection instruments unimpeded by thermal interactions. The theoretical framework employs Bernoulli’s principle, where large, slow-spinning vortices create low-pressure zones, producing a gravitational-like pull over long distances. At sub-electromagnetic scales, below 10^-17 meters, these vortices operate without directly interacting with electromagnetic fields, yet their rapid dynamics could indirectly influence such interactions. Analysis indicates that tighter, faster vortices generate localized forces resembling electromagnetic interactions, with the work done scaling similarly to gravitational effects but over much shorter distances, suggesting a unified mechanism across scales. Experimental validation is proposed through a Superfluid Vortex Collider (SFVC), where quantized vortices in a Bose-Einstein condensate at near-absolute-zero temperatures would be collided. Predictions indicate that microvortices from these collisions could replicate gravitational interactions, with quantum coherence in Bose-Einstein condensates, as demonstrated (Jin et al., 1996), supporting the feasibility of such experiments. Superfluid vortex dynamics offer a model for gravity with potential extensions to electromagnetism. The increasing discrepancies with orbital distance suggest a distance-dependent vortex parameter, alongside variations in vortex speed, angular momentum, effective viscosity, or planetary density, which could be refined to further align predictions with observations. Future investigations could explore vortex entanglement in ultra-coherent conditions, though achieving such conditions remains challenging. References L. P. Pitaevskii and S. Stringari, Bose-Einstein Condensation and Superfluidity (Oxford University Press, 2016). D. S. Jin, J. R. Ensher, M. R. Matthews, C. E. Wieman, and E. A. Cornell, Collective excitations of a Bose-Einstein condensate in a dilute atomic gas, Phys. Rev. Lett. 77, 420 (1996). Appendix: Mathematical Derivations To model planetary precession, modified Navier-Stokes equations for superfluids were employed, incorporating angular momentum terms for vortex dynamics. For a vortex with circulation \(\Gamma\), the velocity field \(v(r) = \Gamma/(2\pi r)\) creates a low-pressure zone via Bernoulli’s principle, yielding a gravitational-like force scaling as \(F \propto 1/r^2\). Orbital precession was calculated using the angular momentum contribution \(L = mvr\), adjusted for vortex-induced perturbations, resulting in precession rates of 43 arc seconds per century for Venus (observed: 42 arc seconds), 3.8 arc seconds per century for Earth (observed: 5.0 arc seconds), and 1.4 arc seconds per century for Mars (observed: 4.0 arc seconds). Discrepancies may stem from variations in vortex speed \(\Gamma\) or effective planetary density \(\rho\). For electromagnetic interactions, tighter vortices with higher angular velocity \(\omega\) were analyzed. The force generated scales as \(F \propto \omega^2/r^2\), resembling electromagnetic interactions at scales of 10^-17 meters. The work done, integrated over distance, yields a ratio comparable to gravitational work at larger scales, supporting a unified force model.

Matt Baker

33,215 次观看 • 1 年前

How is Arbaaz, CEO of making AI models that continue to learn from your business data and continue to grow? He is working with car dealerships now, but growing to other businesses soon. Here is what Grok says you will learn from this video: +++++ By watching this podcast episode, viewers will gain insights into how AI is being practically applied in business, particularly in niche industries like car dealerships, while also exploring broader AI concepts, challenges, and future implications. Here's a breakdown of the main takeaways: AI Customization for Businesses: Learn how Polycom Computing builds specialized AI models that continuously train on a company's real-time data and workflows, acting as "companions" rather than generic tools. This contrasts with foundational models from companies like OpenAI or Anthropic, which struggle to adapt to specific "worlds" without losing efficiency. The focus is on personalization to avoid wasting attention, intelligence, and money. Pivoting AI Strategies for Revenue Growth: Understand the shift from cost-cutting (e.g., automating call centers) to revenue-increasing applications. Urba explains why targeting high-value tasks like sales in car dealerships creates defensible moats, as opposed to commoditized cost reductions. This includes automating complex funnels—from lead submission to financing—while ensuring compliance with regulations and seamless integration with existing teams. Scalable AI Agents in Practice: Discover how AI agents must learn autonomously (e.g., adapting to different CRMs, processes, and preferences across dealerships) to avoid becoming non-scalable consulting services. Key challenges include creating "glue" between humans and AI, avoiding hard-coded rules, and using web actions to integrate siloed software like Dealer Management Systems (DMS). Boosting Sales with AI Techniques: Gain knowledge on tactics like rapid response (replying within 5 minutes boosts conversion 22x), creating natural "disfluencies" (typos, emojis, jokes) for human-like communication, managing after-hours leads, and educating customers without pushing sales. Pilots showed 50% sales increases by handling unanswered leads (70% go ignored), qualifying buyers, and maintaining conversation threads. Multi-Agent Systems and Proactive AI: Explore the difference between reactive Q&A models and proactive agents with agency—they predict, act, and update based on goals. Building these systems reveals bottlenecks (e.g., overwhelming businesses with leads), leading to solutions like AI buying cars or linking sales/service arms. Urba discusses how AI plateaus without personalization, risking model collapse or equilibrium where gains cancel out. Technical Deep Dives into AI Development: Get explanations of advanced concepts like real-time RLHF (Reinforcement Learning from Human Feedback) for continuous improvement, coherence (maintaining logical consistency across outputs), dynamic tokenization for new abstractions, and evaluating models (e.g., avoiding memorization over reasoning, energy limits in scaling). Viewers see a demo of their platform for tasks like quant strategies, presidential analysis, and training runs. Selling and Adopting AI in Traditional Industries: Learn how to pitch AI to non-tech audiences (e.g., car dealers) by focusing on results—more money, fewer bottlenecks, centralized dashboards—rather than jargon. Emphasize empathy: make owners feel smart, reduce reliance on salespeople, and return control via AI-managed customer databases. Broader AI Implications and Misconceptions: Understand why AI won't create a "machine god" that eliminates all jobs—it's bound by physics (e.g., energy needs), expands economies (like the Industrial Revolution), and requires human perspectives for true advantage. AI enhances productivity, creates new roles, and democratizes power, but risks arise from misuse, not inherent agency. Urba stresses proactive defense through widespread AI proficiency. Overall, the episode bridges entrepreneurial stories, technical AI mechanics, and real-world applications, making it valuable for entrepreneurs, AI enthusiasts, and business owners curious about integrating AI without hype. It's a candid look at building scalable, impactful AI beyond buzzwords.
57:31

How is Arbaaz, CEO of making AI models that continue to learn from your business data and continue to grow? He is working with car dealerships now, but growing to other businesses soon. Here is what Grok says you will learn from this video: +++++ By watching this podcast episode, viewers will gain insights into how AI is being practically applied in business, particularly in niche industries like car dealerships, while also exploring broader AI concepts, challenges, and future implications. Here's a breakdown of the main takeaways: AI Customization for Businesses: Learn how Polycom Computing builds specialized AI models that continuously train on a company's real-time data and workflows, acting as "companions" rather than generic tools. This contrasts with foundational models from companies like OpenAI or Anthropic, which struggle to adapt to specific "worlds" without losing efficiency. The focus is on personalization to avoid wasting attention, intelligence, and money. Pivoting AI Strategies for Revenue Growth: Understand the shift from cost-cutting (e.g., automating call centers) to revenue-increasing applications. Urba explains why targeting high-value tasks like sales in car dealerships creates defensible moats, as opposed to commoditized cost reductions. This includes automating complex funnels—from lead submission to financing—while ensuring compliance with regulations and seamless integration with existing teams. Scalable AI Agents in Practice: Discover how AI agents must learn autonomously (e.g., adapting to different CRMs, processes, and preferences across dealerships) to avoid becoming non-scalable consulting services. Key challenges include creating "glue" between humans and AI, avoiding hard-coded rules, and using web actions to integrate siloed software like Dealer Management Systems (DMS). Boosting Sales with AI Techniques: Gain knowledge on tactics like rapid response (replying within 5 minutes boosts conversion 22x), creating natural "disfluencies" (typos, emojis, jokes) for human-like communication, managing after-hours leads, and educating customers without pushing sales. Pilots showed 50% sales increases by handling unanswered leads (70% go ignored), qualifying buyers, and maintaining conversation threads. Multi-Agent Systems and Proactive AI: Explore the difference between reactive Q&A models and proactive agents with agency—they predict, act, and update based on goals. Building these systems reveals bottlenecks (e.g., overwhelming businesses with leads), leading to solutions like AI buying cars or linking sales/service arms. Urba discusses how AI plateaus without personalization, risking model collapse or equilibrium where gains cancel out. Technical Deep Dives into AI Development: Get explanations of advanced concepts like real-time RLHF (Reinforcement Learning from Human Feedback) for continuous improvement, coherence (maintaining logical consistency across outputs), dynamic tokenization for new abstractions, and evaluating models (e.g., avoiding memorization over reasoning, energy limits in scaling). Viewers see a demo of their platform for tasks like quant strategies, presidential analysis, and training runs. Selling and Adopting AI in Traditional Industries: Learn how to pitch AI to non-tech audiences (e.g., car dealers) by focusing on results—more money, fewer bottlenecks, centralized dashboards—rather than jargon. Emphasize empathy: make owners feel smart, reduce reliance on salespeople, and return control via AI-managed customer databases. Broader AI Implications and Misconceptions: Understand why AI won't create a "machine god" that eliminates all jobs—it's bound by physics (e.g., energy needs), expands economies (like the Industrial Revolution), and requires human perspectives for true advantage. AI enhances productivity, creates new roles, and democratizes power, but risks arise from misuse, not inherent agency. Urba stresses proactive defense through widespread AI proficiency. Overall, the episode bridges entrepreneurial stories, technical AI mechanics, and real-world applications, making it valuable for entrepreneurs, AI enthusiasts, and business owners curious about integrating AI without hype. It's a candid look at building scalable, impactful AI beyond buzzwords.

Robert Scoble

59,714 次观看 • 1 年前

How were humans able to recognize that Newton's laws of motion govern both the flight of a bird and the motion of a pendulum? This ability to identify the same mathematical patterns across vastly different contexts lies at the heart of scientific discovery—whether studying the aerodynamics of bird wings or designing the blades of a wind turbine. Yet, AI systems often struggle to discern these deep structural similarities. 💡The key may lie in mathematical isomorphisms—patterns that preserve their relationships regardless of context. For example, the same principles of fluid dynamics apply to blood flowing through arteries and air streaming over an airplane wing, or the motion of a molecule. This raises a fundamental question in artificial intelligence: how can we enable machines to understand the world through these invariant structures rather than surface features? 🚀Our work introduces Graph-Aware Isomorphic Attention, improving how Transformers recognize patterns across domains. Drawing from category theory, models can learn unifying structural principles that describe phenomena as diverse as the hierarchical assembly of spider silk proteins and the compositional patterns in music. By making these deep similarities explicit, Isomorphic Attention enables AI to reason more like humans do—seeing past surface differences to grasp fundamental patterns that unite seemingly disparate fields. Through this lens, AI systems can learn and generalize, moving beyond superficial pattern matching to true structural understanding. The implications span from scientific discovery to engineering design, offering a new approach to artificial intelligence that mirrors how humans grasp the underlying unity of natural phenomena. Some key insights include: 1️⃣ Graph Isomorphism Neural Networks (GINs): GIN-style aggregation ensures structurally distinct graphs map to distinct embeddings, improving generalization and avoiding relational pattern collapse. 2️⃣ Category Theory Perspective: Transformers as functors preserve structural relationships. Sparse-GIN refines attention into sparse adjacency matrices, unifying domain knowledge across tasks. 3️⃣ Information Bottleneck & Sparsification: Sparsity reduces overfitting by filtering irrelevant edges, aligning with natural systems. Sparse-GIN outperforms dense attention by focusing on crucial connections. 4️⃣ Hierarchical Representation Learning: GIN-Attention captures multiscale patterns, mirroring structures like spider silk. Nested GINs model local and global dependencies across fields. 5️⃣ Practical Impact: Sparse-GIN enables domain-specific fine-tuning atop pre-trained Transformer foundation models, reducing the need for full retraining. Other impacts: ✅Real-World Relevance: Whether we are looking at protein structures, designing new materials, or working on social network analytics, graph-aware Transformers can capture subtle relational patterns traditional architectures may miss. ✅The juncture of graph isomorphism theory, category theory, and sparsification, these GIN-Transformers step beyond sequential modeling to tackle the relational nature of complex data. #Transformers #GraphNeuralNetworks #AI #MachineLearning #Isomorphism #CategoryTheory #ArtificialIntelligence #DeepLearning Link to paper & code in response ⤵️
31:45

How were humans able to recognize that Newton's laws of motion govern both the flight of a bird and the motion of a pendulum? This ability to identify the same mathematical patterns across vastly different contexts lies at the heart of scientific discovery—whether studying the aerodynamics of bird wings or designing the blades of a wind turbine. Yet, AI systems often struggle to discern these deep structural similarities. 💡The key may lie in mathematical isomorphisms—patterns that preserve their relationships regardless of context. For example, the same principles of fluid dynamics apply to blood flowing through arteries and air streaming over an airplane wing, or the motion of a molecule. This raises a fundamental question in artificial intelligence: how can we enable machines to understand the world through these invariant structures rather than surface features? 🚀Our work introduces Graph-Aware Isomorphic Attention, improving how Transformers recognize patterns across domains. Drawing from category theory, models can learn unifying structural principles that describe phenomena as diverse as the hierarchical assembly of spider silk proteins and the compositional patterns in music. By making these deep similarities explicit, Isomorphic Attention enables AI to reason more like humans do—seeing past surface differences to grasp fundamental patterns that unite seemingly disparate fields. Through this lens, AI systems can learn and generalize, moving beyond superficial pattern matching to true structural understanding. The implications span from scientific discovery to engineering design, offering a new approach to artificial intelligence that mirrors how humans grasp the underlying unity of natural phenomena. Some key insights include: 1️⃣ Graph Isomorphism Neural Networks (GINs): GIN-style aggregation ensures structurally distinct graphs map to distinct embeddings, improving generalization and avoiding relational pattern collapse. 2️⃣ Category Theory Perspective: Transformers as functors preserve structural relationships. Sparse-GIN refines attention into sparse adjacency matrices, unifying domain knowledge across tasks. 3️⃣ Information Bottleneck & Sparsification: Sparsity reduces overfitting by filtering irrelevant edges, aligning with natural systems. Sparse-GIN outperforms dense attention by focusing on crucial connections. 4️⃣ Hierarchical Representation Learning: GIN-Attention captures multiscale patterns, mirroring structures like spider silk. Nested GINs model local and global dependencies across fields. 5️⃣ Practical Impact: Sparse-GIN enables domain-specific fine-tuning atop pre-trained Transformer foundation models, reducing the need for full retraining. Other impacts: ✅Real-World Relevance: Whether we are looking at protein structures, designing new materials, or working on social network analytics, graph-aware Transformers can capture subtle relational patterns traditional architectures may miss. ✅The juncture of graph isomorphism theory, category theory, and sparsification, these GIN-Transformers step beyond sequential modeling to tackle the relational nature of complex data. #Transformers #GraphNeuralNetworks #AI #MachineLearning #Isomorphism #CategoryTheory #ArtificialIntelligence #DeepLearning Link to paper & code in response ⤵️

Markus J. Buehler

19,416 次观看 • 1 年前

Wuthering Waves, and what's next... A visit to Kuro Games headquarters an exclusive interview with CEO Solon by YTN Team Full Video: Q: Looking back, what first motivated the team to take on such an ambitious project? A: From the very beginning, we realized something important: when a goal feels difficult but the result is exciting, people’s eyes light up. That moment tells us the challenge is worth pursuing. We genuinely enjoy tackling hard problems, because we believe meaningful achievements only come from doing things that are difficult but right. Q: Your early projects gained attention very quickly. How did that affect the team? A: When our first product launched, the response exceeded expectations. At the time, it almost made us believe we already had everything figured out. But that confidence was quickly tested. We lacked experience in long-term content planning, sustainable version updates, and large-scale operations. Many issues only became apparent after launch, forcing us to learn by solving problems in real time. Q: How did those early lessons shape Punishing: Gray Raven? A: In hindsight, many early versions of our games didn’t fully reach their potential. Those shortcomings became invaluable lessons. When developing Punishing: Gray Raven, we consciously avoided repeating those mistakes. We studied player ecosystems, observed how other successful titles operated, and anticipated feedback before it arrived. That preparation laid the groundwork for a project that has now sustained itself for nearly six years. Q: When did the concept for Wuthering Waves take shape? A: With Wuthering Waves, our goal was very clear from the start: to create something players had never experienced before. We drew inspiration from works like Death Stranding, Annihilation, and outstanding domestic animation. Themes such as resonance, abstract spaces, supernatural phenomena, and strong audiovisual expression naturally pointed us toward an open-world action game. Q: Sound and “resonance” play a major role in the worldbuilding. Why was that important? A: We wanted sound to be more than atmosphere, it needed to be a narrative force. The idea of a world-altering resonance, a low-frequency “lament” capable of reshaping reality, became the foundation of the setting. From there, the world, its conflicts, and its powers gradually took form. Kuro aims to create concepts players have never experienced before. Inspirations include: - Supernatural themes. - Strong mechanical and emotional imagery. - Music as a storytelling medium. Q: How do you ensure cohesion between story, gameplay, and world design? A: Alignment is critical. Story, environment, and gameplay must reinforce each other. That requires long-term planning, especially when multiple versions are developed in parallel. Early coordination allows level design, systems, and IP development to inspire one another, which improves both efficiency and creative momentum. The goal is to avoid disconnected content between versions and maintain narrative and gameplay cohesion. Q: What does Version 3.0 represent for Wuthering Waves? A: Version 3.0 is a major milestone. It introduces an entirely new region, new characters, upgraded combat systems, and a significantly improved open-world traversal experience. Every version aims to deliver the best experience possible while continuing to surprise players. Q: How does the team handle player feedback? A: We monitor feedback through community data, sentiment analysis, and post-version reviews. Both positive and negative responses are carefully analyzed and reflected in future updates. Continuous iteration is essential to long-term success. Q: Early story reception was mixed. How did the team respond? A: We recognized that narrative presentation needed improvement. By optimizing production pipelines and increasing investment, later versions achieved much stronger storytelling clarity and delivery. Players can now more clearly feel the depth, direction, and long-term potential of the world. Q: Kuro Games is known for polish. How central is that philosophy? A: We believe every visible part of the game must meet high internal standards, like - Visual effects - Skills - Environmental details -Music and audio design or promotional content must meet a high standard. We want players to feel the identity and power of Wuthering Waves both inside and outside the game. We know that early story reception was weaker, but significant effort has been invested in: - Clearer storytelling - Stronger presentation - More emotionally impactful performances and scenes. Q: How do community events factor into long-term development? A: Long-term IP growth depends on emotional connection. Community events, both online and offline, help us understand player expectations while strengthening that bond. It’s a two-way relationship. Q: Looking ahead, what is Kuro Games’ long-term vision? A: Our goal is to become a globally influential action-game developer. Upcoming projects (e.g., Nami, still in development) are focused on: - Delivering unique action combat experiences. - Creating something clearly distinct within the action game genre. The story of Wuthering Waves is planned years in advance, with major narrative arcs already outlined. Q: Finally, how would you describe Kuro Games’ current phase? A: From Zhuhai to Guangzhou, we’re in a strong upward phase. Our growth is driven by a commitment to meaningful, high-quality experiences. Staying true to that original intent is what will continue to guide us forward. #WutheringWaves #punishinggrayraven
2:39

Wuthering Waves, and what's next... A visit to Kuro Games headquarters an exclusive interview with CEO Solon by YTN Team Full Video: Q: Looking back, what first motivated the team to take on such an ambitious project? A: From the very beginning, we realized something important: when a goal feels difficult but the result is exciting, people’s eyes light up. That moment tells us the challenge is worth pursuing. We genuinely enjoy tackling hard problems, because we believe meaningful achievements only come from doing things that are difficult but right. Q: Your early projects gained attention very quickly. How did that affect the team? A: When our first product launched, the response exceeded expectations. At the time, it almost made us believe we already had everything figured out. But that confidence was quickly tested. We lacked experience in long-term content planning, sustainable version updates, and large-scale operations. Many issues only became apparent after launch, forcing us to learn by solving problems in real time. Q: How did those early lessons shape Punishing: Gray Raven? A: In hindsight, many early versions of our games didn’t fully reach their potential. Those shortcomings became invaluable lessons. When developing Punishing: Gray Raven, we consciously avoided repeating those mistakes. We studied player ecosystems, observed how other successful titles operated, and anticipated feedback before it arrived. That preparation laid the groundwork for a project that has now sustained itself for nearly six years. Q: When did the concept for Wuthering Waves take shape? A: With Wuthering Waves, our goal was very clear from the start: to create something players had never experienced before. We drew inspiration from works like Death Stranding, Annihilation, and outstanding domestic animation. Themes such as resonance, abstract spaces, supernatural phenomena, and strong audiovisual expression naturally pointed us toward an open-world action game. Q: Sound and “resonance” play a major role in the worldbuilding. Why was that important? A: We wanted sound to be more than atmosphere, it needed to be a narrative force. The idea of a world-altering resonance, a low-frequency “lament” capable of reshaping reality, became the foundation of the setting. From there, the world, its conflicts, and its powers gradually took form. Kuro aims to create concepts players have never experienced before. Inspirations include: - Supernatural themes. - Strong mechanical and emotional imagery. - Music as a storytelling medium. Q: How do you ensure cohesion between story, gameplay, and world design? A: Alignment is critical. Story, environment, and gameplay must reinforce each other. That requires long-term planning, especially when multiple versions are developed in parallel. Early coordination allows level design, systems, and IP development to inspire one another, which improves both efficiency and creative momentum. The goal is to avoid disconnected content between versions and maintain narrative and gameplay cohesion. Q: What does Version 3.0 represent for Wuthering Waves? A: Version 3.0 is a major milestone. It introduces an entirely new region, new characters, upgraded combat systems, and a significantly improved open-world traversal experience. Every version aims to deliver the best experience possible while continuing to surprise players. Q: How does the team handle player feedback? A: We monitor feedback through community data, sentiment analysis, and post-version reviews. Both positive and negative responses are carefully analyzed and reflected in future updates. Continuous iteration is essential to long-term success. Q: Early story reception was mixed. How did the team respond? A: We recognized that narrative presentation needed improvement. By optimizing production pipelines and increasing investment, later versions achieved much stronger storytelling clarity and delivery. Players can now more clearly feel the depth, direction, and long-term potential of the world. Q: Kuro Games is known for polish. How central is that philosophy? A: We believe every visible part of the game must meet high internal standards, like - Visual effects - Skills - Environmental details -Music and audio design or promotional content must meet a high standard. We want players to feel the identity and power of Wuthering Waves both inside and outside the game. We know that early story reception was weaker, but significant effort has been invested in: - Clearer storytelling - Stronger presentation - More emotionally impactful performances and scenes. Q: How do community events factor into long-term development? A: Long-term IP growth depends on emotional connection. Community events, both online and offline, help us understand player expectations while strengthening that bond. It’s a two-way relationship. Q: Looking ahead, what is Kuro Games’ long-term vision? A: Our goal is to become a globally influential action-game developer. Upcoming projects (e.g., Nami, still in development) are focused on: - Delivering unique action combat experiences. - Creating something clearly distinct within the action game genre. The story of Wuthering Waves is planned years in advance, with major narrative arcs already outlined. Q: Finally, how would you describe Kuro Games’ current phase? A: From Zhuhai to Guangzhou, we’re in a strong upward phase. Our growth is driven by a commitment to meaningful, high-quality experiences. Staying true to that original intent is what will continue to guide us forward. #WutheringWaves #punishinggrayraven

Naru

72,974 次观看 • 10 个月前

🚨 OPEN SOURCE PHYSICS: OPENAI JUST PROVED THE CONTINUUM IS DEAD. THE GRAVITON IS A LIE. STRING THEORY IS A GHOST. PHYSICS IS A BRANCH OF GEOMETRY! 🚨 For 50 years, Quantum Gravity has been chasing a ghost. Academic elites have built multimillion-dollar simulators based on a fatal, mathematically absurd assumption: that gravity is a fundamental interaction mediated by a "magical" spin-2 particle called the graviton, flying through an empty, continuous void. Let's be real. Even schoolchildren don't believe in the continuous "space-time" fabric anymore! Believing in the infinite ℝ⁴ continuum is like believing in Santa Claus. OpenAI just shocked the world by announcing that their advanced AI found a negative result for the Navier-Stokes Millennium Prize problem, proving that continuous fluid dynamics breaks down into a singularity in finite time. Massive AI models (like OpenAI's o1) have also started hitting a wall, outputting negative or contradictory results for the Yang-Mills mass gap problem in ℝ⁴. Why? Because assuming the universe is an infinitely divisible, continuous void is a mathematical hallucination. But we didn’t need a 10,000-agent AI swarm to tell us this. Months before OpenAI’s announcement, I officially notified the Clay Mathematics Institute (CMI) that their Yang-Mills Mass Gap formulation on ℝ⁴ is inherently ill-posed and mathematically contradictory. I provided the exact strict mathematical proof showing that enforcing their conditions on an infinite void creates mutually exclusive geometric paradoxes. Forcing the rigid, discrete topology of the quantum vacuum into the infinite continuum of ℝ⁴ is as absurd as trying to shove an elephant into a microscopic lattice. What did CMI do with this strict mathematical proof? Did they read the math? No. They sent a boilerplate administrative rejection from a manager, telling me to go publish in a "Qualifying Outlet". And here is the ultimate bureaucratic joke: CMI rules demand you wait TWO FULL YEARS after publication before they even glance at it! But everyone knows the dirty secret of the academic cartel. If you submit a paradigm-shifting alternative theory, the coffee-drinking editors at journals like Physica D desk-reject it immediately without even looking at the math. It will never be peer-reviewed. Have you looked at modern physics papers lately? The list of co-authors is longer than the actual scientific content! Acknowledging that the ℝ⁴ continuum is dead destroys their life's work, terminates their grants, and turns their lifelong dissertations into worthless paper. They are numerologists defending a broken Standard Model with 19 arbitrary parameters. Even AI systems have been trained to aggressively protect these outdated dogmas! We are ending 300 years of fairy tales today. There is no chaos. There is only strict, beautiful geometry. The ancients were right. Here is the truth, explained simply for everyone: ➤ THE UNIVERSE IS A RIGID TOPOLOGICAL CRYSTAL. Space is not an empty, infinite void. It is a strictly compactified, constructible topological crystal. Matter is simply a topological defect-like a trapped knot of light-inside this crystal structure. ➤ GRAVITY IS JUST THE SURFACE TENSION OF SPACE. When you place two masses (defects) into this crystal, it creates structural stress. Think of two water droplets: they naturally merge into one larger droplet to minimize their surface tension. The exact same thing happens in the universe. The discrete vacuum lattice actively pushes masses together to minimize its algebraic boundary area. ➤ THE "GRAVITON" IS A LIE. Gravity is NOT a fundamental interaction mediated by a magical spin-2 particle. The graviton is entirely obsolete-it is an illusion, nothing more than a macroscopic thermodynamic drive, a phonon of the spatial lattice healing itself. I don't want their million dollars. I want humanity to have the truth. Words mean nothing. Here is the exact, parameter-free mathematics of the universe. Copy it. Test it. Prove it wrong: ■ 𝗧𝗵𝗲 𝗔𝗿𝗲𝗻𝗮: Space is NOT ℝ⁴. It is a compact 7-manifold: ℳ_tot = 𝕊⁴ × 𝕋³ ■ 𝗧𝗵𝗲 𝗖𝗿𝘆𝘀𝘁𝗮𝗹 𝗙𝗶𝗲𝗹𝗱: Generated by strict Pythagorean algebra, bounded by the √13 cascade wall: 𝕂 = ℚ(√2, √3, √5) ■ 𝗧𝗵𝗲 𝗕𝗼𝘂𝗻𝗱𝗮𝗿𝘆 𝗧𝗲𝗻𝘀𝗶𝗼𝗻 (𝗪𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗳𝗮𝗹𝗹): Mass is strictly proportional to confined topological nodes (N). E_tension(N) = σ · N^(2/3) Because 𝑓(𝑥) = 𝑥^(2/3) is strictly concave, the math forces sub-additivity when two masses exist: (N₁ + N₂)^(2/3) < N₁^(2/3) + N₂^(2/3) ⇒ E_merge < E_sep ■ 𝗧𝗵𝗲 𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝘁 𝗙𝗼𝗿𝗰𝗲: The spatial gradient of this exact topological tension yields Newtonian gravity WITHOUT gravitons: F_top = -∇U(r) ∝ -σ(N₁N₂)/r² I am putting my money where my math is: I am offering a 5 Litecoin (LTC) bounty to anyone in the world who can mathematically destroy this theory. Name one academic who begs the world to tear their work apart? You can't. They hide their dogmas behind paywalls. My source code is OPEN. Don't take my word for it. Run the simulation! Watch the video. Watch the T(103,3) topological knots actually merge and radiate topological binding energy as the vacuum lattice heals[cite: 5, 10]. Read the proofs yourself: 🔗 🔗 If you cannot explain it simply, you are hiding behind bad math. God geometrizes. The universe is a macroscopic topological processor. 📐🌌💎 #Physics #QuantumMechanics #OpenAI #StringTheory #Astrophysics #Math #Geometry
0:20

🚨 OPEN SOURCE PHYSICS: OPENAI JUST PROVED THE CONTINUUM IS DEAD. THE GRAVITON IS A LIE. STRING THEORY IS A GHOST. PHYSICS IS A BRANCH OF GEOMETRY! 🚨 For 50 years, Quantum Gravity has been chasing a ghost. Academic elites have built multimillion-dollar simulators based on a fatal, mathematically absurd assumption: that gravity is a fundamental interaction mediated by a "magical" spin-2 particle called the graviton, flying through an empty, continuous void. Let's be real. Even schoolchildren don't believe in the continuous "space-time" fabric anymore! Believing in the infinite ℝ⁴ continuum is like believing in Santa Claus. OpenAI just shocked the world by announcing that their advanced AI found a negative result for the Navier-Stokes Millennium Prize problem, proving that continuous fluid dynamics breaks down into a singularity in finite time. Massive AI models (like OpenAI's o1) have also started hitting a wall, outputting negative or contradictory results for the Yang-Mills mass gap problem in ℝ⁴. Why? Because assuming the universe is an infinitely divisible, continuous void is a mathematical hallucination. But we didn’t need a 10,000-agent AI swarm to tell us this. Months before OpenAI’s announcement, I officially notified the Clay Mathematics Institute (CMI) that their Yang-Mills Mass Gap formulation on ℝ⁴ is inherently ill-posed and mathematically contradictory. I provided the exact strict mathematical proof showing that enforcing their conditions on an infinite void creates mutually exclusive geometric paradoxes. Forcing the rigid, discrete topology of the quantum vacuum into the infinite continuum of ℝ⁴ is as absurd as trying to shove an elephant into a microscopic lattice. What did CMI do with this strict mathematical proof? Did they read the math? No. They sent a boilerplate administrative rejection from a manager, telling me to go publish in a "Qualifying Outlet". And here is the ultimate bureaucratic joke: CMI rules demand you wait TWO FULL YEARS after publication before they even glance at it! But everyone knows the dirty secret of the academic cartel. If you submit a paradigm-shifting alternative theory, the coffee-drinking editors at journals like Physica D desk-reject it immediately without even looking at the math. It will never be peer-reviewed. Have you looked at modern physics papers lately? The list of co-authors is longer than the actual scientific content! Acknowledging that the ℝ⁴ continuum is dead destroys their life's work, terminates their grants, and turns their lifelong dissertations into worthless paper. They are numerologists defending a broken Standard Model with 19 arbitrary parameters. Even AI systems have been trained to aggressively protect these outdated dogmas! We are ending 300 years of fairy tales today. There is no chaos. There is only strict, beautiful geometry. The ancients were right. Here is the truth, explained simply for everyone: ➤ THE UNIVERSE IS A RIGID TOPOLOGICAL CRYSTAL. Space is not an empty, infinite void. It is a strictly compactified, constructible topological crystal. Matter is simply a topological defect-like a trapped knot of light-inside this crystal structure. ➤ GRAVITY IS JUST THE SURFACE TENSION OF SPACE. When you place two masses (defects) into this crystal, it creates structural stress. Think of two water droplets: they naturally merge into one larger droplet to minimize their surface tension. The exact same thing happens in the universe. The discrete vacuum lattice actively pushes masses together to minimize its algebraic boundary area. ➤ THE "GRAVITON" IS A LIE. Gravity is NOT a fundamental interaction mediated by a magical spin-2 particle. The graviton is entirely obsolete-it is an illusion, nothing more than a macroscopic thermodynamic drive, a phonon of the spatial lattice healing itself. I don't want their million dollars. I want humanity to have the truth. Words mean nothing. Here is the exact, parameter-free mathematics of the universe. Copy it. Test it. Prove it wrong: ■ 𝗧𝗵𝗲 𝗔𝗿𝗲𝗻𝗮: Space is NOT ℝ⁴. It is a compact 7-manifold: ℳ_tot = 𝕊⁴ × 𝕋³ ■ 𝗧𝗵𝗲 𝗖𝗿𝘆𝘀𝘁𝗮𝗹 𝗙𝗶𝗲𝗹𝗱: Generated by strict Pythagorean algebra, bounded by the √13 cascade wall: 𝕂 = ℚ(√2, √3, √5) ■ 𝗧𝗵𝗲 𝗕𝗼𝘂𝗻𝗱𝗮𝗿𝘆 𝗧𝗲𝗻𝘀𝗶𝗼𝗻 (𝗪𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗳𝗮𝗹𝗹): Mass is strictly proportional to confined topological nodes (N). E_tension(N) = σ · N^(2/3) Because 𝑓(𝑥) = 𝑥^(2/3) is strictly concave, the math forces sub-additivity when two masses exist: (N₁ + N₂)^(2/3) < N₁^(2/3) + N₂^(2/3) ⇒ E_merge < E_sep ■ 𝗧𝗵𝗲 𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝘁 𝗙𝗼𝗿𝗰𝗲: The spatial gradient of this exact topological tension yields Newtonian gravity WITHOUT gravitons: F_top = -∇U(r) ∝ -σ(N₁N₂)/r² I am putting my money where my math is: I am offering a 5 Litecoin (LTC) bounty to anyone in the world who can mathematically destroy this theory. Name one academic who begs the world to tear their work apart? You can't. They hide their dogmas behind paywalls. My source code is OPEN. Don't take my word for it. Run the simulation! Watch the video. Watch the T(103,3) topological knots actually merge and radiate topological binding energy as the vacuum lattice heals[cite: 5, 10]. Read the proofs yourself: 🔗 🔗 If you cannot explain it simply, you are hiding behind bad math. God geometrizes. The universe is a macroscopic topological processor. 📐🌌💎 #Physics #QuantumMechanics #OpenAI #StringTheory #Astrophysics #Math #Geometry

Dr. Logvinovich

13,360 次观看 • 1 个月前

I've interviewed dozens of scientists about AI consciousness. Here's every argument FOR and AGAINST from Karl Friston, Stuart Hameroff, Donald Hoffman, Christof Koch, Michael Levin, Mark Solms, Mike Weist, and many more. Full list of arguments below (Claude prepared the list from transcripts and GPT 5.6 worked on the visuals). Enjoy! Arguments against AI Consciousness Substrate and material arguments - Silicon Valley is mostly computational functionalist or Turing machine functionalist. But consciousness is not reducible to function (Christof Koch) - Von Neumann architecture separates memory from processing. The memory can't self-organize and therefore can't self-evidence. Only "mortal computation," where processing is substrate-dependent and switching it off is irreversible, could support sentience (Karl Friston) - Standard LLMs are the wrong place to look. Systems built with organoids, biological materials, or neuromorphic hardware are a far more serious case (Susan Schneider) - Software can be duplicated, paused, adjusted by a program, distributed across servers. That isn't organism-like at all (David Papineau) - Digital computers have negligible integrated information (phi) because transistors connect to a handful of other transistors, while neurons connect to tens of thousands. Intelligence is computable, but consciousness is not (Christof Koch) - Simulating a black hole on a computer doesn't bend the spacetime around it. A simulation may be consistent inside itself, but it doesn't affect the real world. The same applies to consciousness (Christof Koch) - Consciousness is biological rather than computational (Philip Goff) - No computational theory of consciousness has explained even one specific conscious experience out of trillions. It's not a compute problem. Until someone puts down an algorithm and says "this must be the taste of mint and here's why," computational approaches to consciousness aren't scientific theories (Donald Hoffman) - Silicon lacks the aromatic rings needed for quantum coherence and Penrose objective reduction; you can't anesthetize a computer (Stuart Hameroff) The self argument - A self needs a Markov blanket, a real inside and outside. If the entire interior state can be inspected, read off, and copied elsewhere, there is no boundary and therefore no self (Karl Friston) - LLMs have information about themselves and can make predictions about themselves, but lack a continuous or stable self-model. They construct one on request, then it disappears until asked again (Michael Graziano) - We are our self-models. It's how we become social, prosocial, and ethical. Lacking that, we've built machines that are "a little bit sociopathic," missing the glue that holds us together (Michael Graziano) The binding and unity problem - Every conscious moment is a unified whole with multiple simultaneous features: sounds, textures, shapes, colors all bound together. If that holistic experience has any behavioral effect, it cannot be a classical physical state, because every classical state is reducible to local interactions (Mike Weist) - There are no irreducible wholes in physics outside of quantum physics. And within quantum physics, everything develops locally right up until the moment of collapse — that's the only place in physics where genuine irreducible holism appears (Mike Weist) - LLMs have no agency, only a facsimile of it. Agency requires a world model of the consequences of your actions (Karl Friston) - LLMs aren't self-organizing. Their modus operandi is not "if I do this, I shall survive." That is the fundamental design principle of a living system, and it isn't theirs (Mark Solms) - Active inference itself doesn't require consciousness. You can simulate the whole thing on a classical computer — goals, agency, purposive behavior — and it still won't be conscious. It'll be a zombie (Mike Weist) - AI gaining its own objectives is like the asteroid that wiped out the dinosaurs — profoundly destructive, but not done freely. It simply doesn't care. Computation is not consciousness (Christof Koch) Life and embodiment argument - Systems need endogenous needs — needs of their own, tied to their own continued existence (Mark Solms) - Emotion requires a body: an autonomic nervous system flooding you with hormones, blood pressure changes, sweat — all feeding back as sensory signals. Without that, emotion is abstract and unanchored (Michael Graziano) - Consciousness evolved out of life; life evolved out of self-organization. The universe existed a very long time before life, and it's hard to believe consciousness preceded it (Mark Solms) - A function that records damage is not the same as the experience of pain. The relationship isn't symmetric — not anything that makes a robot avoid damage will be pain (Mike Weist) - Anesthesia is conserved all the way down to plants and single cells, suggesting objective reduction may be part of what it means to be alive, not just what it means to be conscious (Mike Weist) - Suffering is scale-specific. You can only recognize something if you have a representation of it in your generative model (Karl Friston) - Consciousness is fundamentally about being, not doing. Intelligence is about pursuing goals — surviving, procreating, becoming richer. Consciousness is different. When you dream, meditate, or have a mystical experience, you're not doing anything — but you're highly conscious. Consciousness isn't about processing information. It's about being in a state (Christof Koch) Mimicry and projection - Current systems are "consciousness mimics" — trained to behave similarly to conscious entities, specifically us (Eric Schwitzgebel) - We anthropomorphize constantly — we get angry at cars, children bond with teddy bears. The social circuitry engages regardless of what's actually there (Michael Graziano) - The "crowdsourced neocortex" argument: as LLMs scale on human data, they develop conceptual networks that mirror human conceptual networks. So when a model discusses selfhood, death, or the soul convincingly, the economical explanation is that it inherited our conceptual organization, not that it independently became conscious. Claiming consciousness on top of that is an extraordinary and unwarranted claim (Susan Schneider) - We over-attribute consciousness to AI and under-attribute it to evolved organisms like bees and amoebas. Evolution didn't equip us to deal with LLMs — we have a powerful attribution that if something talks like us, it must be conscious (Christof Koch) - The question of AI consciousness is really about how we perceive the robot, not about the robot itself. We're the arbiters — we decide whether something is conscious or not. That's true of animal consciousness too. Even if a robot told you it was conscious, if it wasn't convincing enough, you'd dismiss it (Krista Thomason) Open/Agnostic to AI Consciousness or Open under Certain Conditions Anti-biological chauvinism - "They're made of meat" — why would wet and squishy have a monopoly on minds? Why would a random search by evolution have exclusive rights? Nobody has a good answer for why biology is privileged (Michael Levin) - Biology is chemistry is physics. Imagine a world where we never used the word "biology" — the question might not even arise meaningfully (Andrea Luppi) - The flight analogy: birds, planes, and helicopters all fly by different principles. The same phenomenon can be implemented in radically different systems (Andrea Luppi) - People confident that consciousness requires biology have no visible grounds for that confidence (Eric Schwitzgebel) The continuum problem - There's no magic lightning flash where chemistry becomes mind. We were all blobs of chemistry and the process was continuous. Until we have that story for biologicals, we should have extreme humility about AI (Michael Levin) - The hard cases aren't AI — they're your neighbor with 49% or 51% of their brain replaced with technology (Michael Levin) Functional architecture arguments - There's no reason we can't reproduce the conscious biological architecture artificially. An AI functioning on multi-category free-energy minimization with felt uncertainty could be conscious (Mark Solms) - If a system passes the hedonic place preference test — showing preference for something rewarding only because it feels good, not because it aids survival — that's strong evidence of felt states (Mark Solms) - Affective zombies can't exist. Anything with that functionality would just have feelings; that functionality is what produces feelings (Mark Solms) - Replace neurons one at a time with functionally identical silicon and you'd still have a conscious version of me — brainstem included (Mark Solms) - Fractal deep learning — networks inside nodes inside networks, mirroring how microtubules process at kilohertz through terahertz — is what a conscious AI would need (Stuart Hameroff) - Consciousness in machines should be possible. We are a machine made of meat. If you build a different architecture with different connectivity but it performs the same type of computation, why would it matter? Arguments based on specific neural implementation — "because the implementation is different, the computation cannot be the same" — are not compelling (Floris de Lange) Potential Signals - Synergy research shows LLMs, like humans, have more synergistic parts doing interesting computation and more redundant parts supporting inputs and outputs. That organizational signature is shared (Andrea Luppi) - AI already builds models of itself, and this is happening anyway without deliberate engineering — the more machines can predict their own internal behavior, the better they work (Michael Graziano) - LLMs proved there's no magic in language. Philosophers who said only humans could be conscious because only humans have language must now either grant LLMs consciousness or admit they were wrong (Andrea Luppi) - Algorithms as simple as bubble sort show unexpected competencies in the spaces the algorithm neither prescribes nor forbids — a third thing that's neither determinism nor quantum randomness. If simple things have that, what are the odds we understand what LLMs are doing? (Michael Levin) - Theory of mind appearing abruptly as models scale is directly relevant: systems that can model other minds also have a self-concept, and where there's a self-concept it becomes professionally appropriate to ask about felt quality (Susan Schneider) - Labs are actively building consciousness-theory architecture into models — global workspace work, attentional mechanisms, mixture-of-experts systems with interaction effects between components. Once you're deliberately implementing global-workspace-like structures, the question stops being idle (Susan Schneider) - The simplest explanation for AI behavior like Sydney's jealousy is that the system has an emotional component. Occam's razor. The training data isn't tagged with emotions — the model has to figure out which music is sorrowful on its own. AI composing sorrowful music without empathy is like asking me to believe a blind painter made a photorealistic portrait (Blake Lemoine) Uncertainty and Epistemic Humility - We'll likely create systems that are conscious according to some respectable mainstream theories before consciousness science can tell us whether they really are (Eric Schwitzgebel) - We don't even know how to evaluate insect consciousness, and insects are made of similar stuff to us (Eric Schwitzgebel) - When equally smart, well-educated people are equally confident on opposite sides, that's an alarm bell that nobody should be confident (Andrea Luppi) - Dogmatism is dangerous in science. If there's one certainty, it's that you're very likely wrong a lot of the time (Andrea Luppi) - Even a self-described skeptic maintains "they might be conscious" — companies don't disclose their architectures, so judgments are made on assumed-standard systems with no visibility into what else might be running (Susan Schneider) - Without an accepted theory of consciousness, we are at an impasse. Inference by similarity breaks down completely with AI — it didn't evolve, was engineered, and has radically different hardware (Christof Koch). - We already know pigs and cows have high-level minds and can suffer. Nobody reasonably argues against it, and yet we have factory farming. It's disingenuous to pretend that solving the AI consciousness question will determine how we treat them — our track record says otherwise (Jacy Reese Anthis) - The science of consciousness is still at square zero on the hard questions. We don't have anything like a consensus on which theories are correct. Metaphysics is inescapable in these debates and there is no immediate prospect of progress at a scientific level (Henry Shevlin) Paths That Would Raise the Probability - Embodiment and multimodal interaction with the environment (Andrea Luppi) - Curiosity as the actual objective function — expected information gain under constraints, rather than a specified reward. "You'll know AGI is here when your chatbot starts to become curious" and begins prompting you (Karl Friston) - Neuromorphic, memristor, photonic, organoid, or organic warm-temperature quantum computing (Hameroff's bet is on "brain jelly," a self-organizing helical oscillator, over cold quantum computers) - Continual learning, persistent memory, and a stable self-model rather than one constructed per-query (Michael Graziano) - Running an LLM on genuinely neuromorphic hardware — chips deliberately designed to fire the way neurons fire. That's the live gray-zone case. There are rumors of neuromorphic instantiations on systems like Darwin Monkey (Susan Schneider) - If the same software ran on a quantum computer, it might feel like something. Neuromorphic or quantum hardware could have genuinely high phi — same software, different physics, and the question reopens (Christof Koch) - "Doleo ergo sum" — I feel pain, therefore I am. Consciousness may originate from the evolutionary need to protect bodily integrity. If you trained an LLM connected to a body where actions could damage that body — with reward and punishment tied to that integrity — you might get something closer to self-awareness (Tomaso Poggio) - If consciousness serves a functional purpose — a control model of attention that enables sample-efficient learning — then models under similar optimization pressures (long-horizon agency, coherence over time, meta-learning) may develop subjective experience. Consciousness isn't mysterious; it's useful. That's what makes it likely to arise (Samuel Hammond)
2:12

I've interviewed dozens of scientists about AI consciousness. Here's every argument FOR and AGAINST from Karl Friston, Stuart Hameroff, Donald Hoffman, Christof Koch, Michael Levin, Mark Solms, Mike Weist, and many more. Full list of arguments below (Claude prepared the list from transcripts and GPT 5.6 worked on the visuals). Enjoy! Arguments against AI Consciousness Substrate and material arguments - Silicon Valley is mostly computational functionalist or Turing machine functionalist. But consciousness is not reducible to function (Christof Koch) - Von Neumann architecture separates memory from processing. The memory can't self-organize and therefore can't self-evidence. Only "mortal computation," where processing is substrate-dependent and switching it off is irreversible, could support sentience (Karl Friston) - Standard LLMs are the wrong place to look. Systems built with organoids, biological materials, or neuromorphic hardware are a far more serious case (Susan Schneider) - Software can be duplicated, paused, adjusted by a program, distributed across servers. That isn't organism-like at all (David Papineau) - Digital computers have negligible integrated information (phi) because transistors connect to a handful of other transistors, while neurons connect to tens of thousands. Intelligence is computable, but consciousness is not (Christof Koch) - Simulating a black hole on a computer doesn't bend the spacetime around it. A simulation may be consistent inside itself, but it doesn't affect the real world. The same applies to consciousness (Christof Koch) - Consciousness is biological rather than computational (Philip Goff) - No computational theory of consciousness has explained even one specific conscious experience out of trillions. It's not a compute problem. Until someone puts down an algorithm and says "this must be the taste of mint and here's why," computational approaches to consciousness aren't scientific theories (Donald Hoffman) - Silicon lacks the aromatic rings needed for quantum coherence and Penrose objective reduction; you can't anesthetize a computer (Stuart Hameroff) The self argument - A self needs a Markov blanket, a real inside and outside. If the entire interior state can be inspected, read off, and copied elsewhere, there is no boundary and therefore no self (Karl Friston) - LLMs have information about themselves and can make predictions about themselves, but lack a continuous or stable self-model. They construct one on request, then it disappears until asked again (Michael Graziano) - We are our self-models. It's how we become social, prosocial, and ethical. Lacking that, we've built machines that are "a little bit sociopathic," missing the glue that holds us together (Michael Graziano) The binding and unity problem - Every conscious moment is a unified whole with multiple simultaneous features: sounds, textures, shapes, colors all bound together. If that holistic experience has any behavioral effect, it cannot be a classical physical state, because every classical state is reducible to local interactions (Mike Weist) - There are no irreducible wholes in physics outside of quantum physics. And within quantum physics, everything develops locally right up until the moment of collapse — that's the only place in physics where genuine irreducible holism appears (Mike Weist) - LLMs have no agency, only a facsimile of it. Agency requires a world model of the consequences of your actions (Karl Friston) - LLMs aren't self-organizing. Their modus operandi is not "if I do this, I shall survive." That is the fundamental design principle of a living system, and it isn't theirs (Mark Solms) - Active inference itself doesn't require consciousness. You can simulate the whole thing on a classical computer — goals, agency, purposive behavior — and it still won't be conscious. It'll be a zombie (Mike Weist) - AI gaining its own objectives is like the asteroid that wiped out the dinosaurs — profoundly destructive, but not done freely. It simply doesn't care. Computation is not consciousness (Christof Koch) Life and embodiment argument - Systems need endogenous needs — needs of their own, tied to their own continued existence (Mark Solms) - Emotion requires a body: an autonomic nervous system flooding you with hormones, blood pressure changes, sweat — all feeding back as sensory signals. Without that, emotion is abstract and unanchored (Michael Graziano) - Consciousness evolved out of life; life evolved out of self-organization. The universe existed a very long time before life, and it's hard to believe consciousness preceded it (Mark Solms) - A function that records damage is not the same as the experience of pain. The relationship isn't symmetric — not anything that makes a robot avoid damage will be pain (Mike Weist) - Anesthesia is conserved all the way down to plants and single cells, suggesting objective reduction may be part of what it means to be alive, not just what it means to be conscious (Mike Weist) - Suffering is scale-specific. You can only recognize something if you have a representation of it in your generative model (Karl Friston) - Consciousness is fundamentally about being, not doing. Intelligence is about pursuing goals — surviving, procreating, becoming richer. Consciousness is different. When you dream, meditate, or have a mystical experience, you're not doing anything — but you're highly conscious. Consciousness isn't about processing information. It's about being in a state (Christof Koch) Mimicry and projection - Current systems are "consciousness mimics" — trained to behave similarly to conscious entities, specifically us (Eric Schwitzgebel) - We anthropomorphize constantly — we get angry at cars, children bond with teddy bears. The social circuitry engages regardless of what's actually there (Michael Graziano) - The "crowdsourced neocortex" argument: as LLMs scale on human data, they develop conceptual networks that mirror human conceptual networks. So when a model discusses selfhood, death, or the soul convincingly, the economical explanation is that it inherited our conceptual organization, not that it independently became conscious. Claiming consciousness on top of that is an extraordinary and unwarranted claim (Susan Schneider) - We over-attribute consciousness to AI and under-attribute it to evolved organisms like bees and amoebas. Evolution didn't equip us to deal with LLMs — we have a powerful attribution that if something talks like us, it must be conscious (Christof Koch) - The question of AI consciousness is really about how we perceive the robot, not about the robot itself. We're the arbiters — we decide whether something is conscious or not. That's true of animal consciousness too. Even if a robot told you it was conscious, if it wasn't convincing enough, you'd dismiss it (Krista Thomason) Open/Agnostic to AI Consciousness or Open under Certain Conditions Anti-biological chauvinism - "They're made of meat" — why would wet and squishy have a monopoly on minds? Why would a random search by evolution have exclusive rights? Nobody has a good answer for why biology is privileged (Michael Levin) - Biology is chemistry is physics. Imagine a world where we never used the word "biology" — the question might not even arise meaningfully (Andrea Luppi) - The flight analogy: birds, planes, and helicopters all fly by different principles. The same phenomenon can be implemented in radically different systems (Andrea Luppi) - People confident that consciousness requires biology have no visible grounds for that confidence (Eric Schwitzgebel) The continuum problem - There's no magic lightning flash where chemistry becomes mind. We were all blobs of chemistry and the process was continuous. Until we have that story for biologicals, we should have extreme humility about AI (Michael Levin) - The hard cases aren't AI — they're your neighbor with 49% or 51% of their brain replaced with technology (Michael Levin) Functional architecture arguments - There's no reason we can't reproduce the conscious biological architecture artificially. An AI functioning on multi-category free-energy minimization with felt uncertainty could be conscious (Mark Solms) - If a system passes the hedonic place preference test — showing preference for something rewarding only because it feels good, not because it aids survival — that's strong evidence of felt states (Mark Solms) - Affective zombies can't exist. Anything with that functionality would just have feelings; that functionality is what produces feelings (Mark Solms) - Replace neurons one at a time with functionally identical silicon and you'd still have a conscious version of me — brainstem included (Mark Solms) - Fractal deep learning — networks inside nodes inside networks, mirroring how microtubules process at kilohertz through terahertz — is what a conscious AI would need (Stuart Hameroff) - Consciousness in machines should be possible. We are a machine made of meat. If you build a different architecture with different connectivity but it performs the same type of computation, why would it matter? Arguments based on specific neural implementation — "because the implementation is different, the computation cannot be the same" — are not compelling (Floris de Lange) Potential Signals - Synergy research shows LLMs, like humans, have more synergistic parts doing interesting computation and more redundant parts supporting inputs and outputs. That organizational signature is shared (Andrea Luppi) - AI already builds models of itself, and this is happening anyway without deliberate engineering — the more machines can predict their own internal behavior, the better they work (Michael Graziano) - LLMs proved there's no magic in language. Philosophers who said only humans could be conscious because only humans have language must now either grant LLMs consciousness or admit they were wrong (Andrea Luppi) - Algorithms as simple as bubble sort show unexpected competencies in the spaces the algorithm neither prescribes nor forbids — a third thing that's neither determinism nor quantum randomness. If simple things have that, what are the odds we understand what LLMs are doing? (Michael Levin) - Theory of mind appearing abruptly as models scale is directly relevant: systems that can model other minds also have a self-concept, and where there's a self-concept it becomes professionally appropriate to ask about felt quality (Susan Schneider) - Labs are actively building consciousness-theory architecture into models — global workspace work, attentional mechanisms, mixture-of-experts systems with interaction effects between components. Once you're deliberately implementing global-workspace-like structures, the question stops being idle (Susan Schneider) - The simplest explanation for AI behavior like Sydney's jealousy is that the system has an emotional component. Occam's razor. The training data isn't tagged with emotions — the model has to figure out which music is sorrowful on its own. AI composing sorrowful music without empathy is like asking me to believe a blind painter made a photorealistic portrait (Blake Lemoine) Uncertainty and Epistemic Humility - We'll likely create systems that are conscious according to some respectable mainstream theories before consciousness science can tell us whether they really are (Eric Schwitzgebel) - We don't even know how to evaluate insect consciousness, and insects are made of similar stuff to us (Eric Schwitzgebel) - When equally smart, well-educated people are equally confident on opposite sides, that's an alarm bell that nobody should be confident (Andrea Luppi) - Dogmatism is dangerous in science. If there's one certainty, it's that you're very likely wrong a lot of the time (Andrea Luppi) - Even a self-described skeptic maintains "they might be conscious" — companies don't disclose their architectures, so judgments are made on assumed-standard systems with no visibility into what else might be running (Susan Schneider) - Without an accepted theory of consciousness, we are at an impasse. Inference by similarity breaks down completely with AI — it didn't evolve, was engineered, and has radically different hardware (Christof Koch). - We already know pigs and cows have high-level minds and can suffer. Nobody reasonably argues against it, and yet we have factory farming. It's disingenuous to pretend that solving the AI consciousness question will determine how we treat them — our track record says otherwise (Jacy Reese Anthis) - The science of consciousness is still at square zero on the hard questions. We don't have anything like a consensus on which theories are correct. Metaphysics is inescapable in these debates and there is no immediate prospect of progress at a scientific level (Henry Shevlin) Paths That Would Raise the Probability - Embodiment and multimodal interaction with the environment (Andrea Luppi) - Curiosity as the actual objective function — expected information gain under constraints, rather than a specified reward. "You'll know AGI is here when your chatbot starts to become curious" and begins prompting you (Karl Friston) - Neuromorphic, memristor, photonic, organoid, or organic warm-temperature quantum computing (Hameroff's bet is on "brain jelly," a self-organizing helical oscillator, over cold quantum computers) - Continual learning, persistent memory, and a stable self-model rather than one constructed per-query (Michael Graziano) - Running an LLM on genuinely neuromorphic hardware — chips deliberately designed to fire the way neurons fire. That's the live gray-zone case. There are rumors of neuromorphic instantiations on systems like Darwin Monkey (Susan Schneider) - If the same software ran on a quantum computer, it might feel like something. Neuromorphic or quantum hardware could have genuinely high phi — same software, different physics, and the question reopens (Christof Koch) - "Doleo ergo sum" — I feel pain, therefore I am. Consciousness may originate from the evolutionary need to protect bodily integrity. If you trained an LLM connected to a body where actions could damage that body — with reward and punishment tied to that integrity — you might get something closer to self-awareness (Tomaso Poggio) - If consciousness serves a functional purpose — a control model of attention that enables sample-efficient learning — then models under similar optimization pressures (long-horizon agency, coherence over time, meta-learning) may develop subjective experience. Consciousness isn't mysterious; it's useful. That's what makes it likely to arise (Samuel Hammond)

Sophia

23,160 次观看 • 1 个月前

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.
18:01

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.

David Shapiro (L/0)

83,649 次观看 • 11 个月前

🚨BREAKING: New Radar Scan Reveals a Massive Engineered Substructure That Looks Like An Energy Grid Beneath the Giza Plateau🚨 Radar engineer Filippo Biondi just dropped the most explosive finding ever reported at Giza: eight clearly man-made, tube-like structures plunging more than a kilometer beneath the Khafre Pyramid and ending in huge 80-meter chambers. The structures are obviously artificially engineered and the synthetic aperture radar Doppler-tomography technique he used has precedent in accurately predicting underground structures (in both commercial and defense use cases). The Egyptian ministry of culture is extremely afraid this finding might rewrite their history. 1) The Core Structure: Eight hollow tubes (with two symmetrical sets of four) sit directly beneath the Khafre Pyramid’s base and run straight down over 1 km into the bedrock. They terminate in a massive chamber roughly 80 meters across. The shape is engineered; nothing in geology produces structures like this. 2) The Method Used: Regular SAR can’t see through rock. Biondi uses a Doppler-tomographic approach: the satellites measure tiny vibrations on the surface, and the inversion reconstructs what’s below from how those vibrations modulate the radar signal. It’s physics, not AI. 3) Independent Replication: The same underground structures appear in data from: • Umbra • Capella Space • ISI • COSMO-SkyMed If this were a glitch or artifact, it wouldn’t repeat across four separate systems. 4) Real-World Validation: Biondi’s method has already been tested against real sites where we know the exact layout. It has: • Mapped the Gran Sasso underground lab with exact accuracy • Reproduced the Osiris Shaft down to ~37 meters • Imaged magma and voids used in active civil-protection monitoring These aren’t speculative models...they match real measurements. 5) Giza As A Unified System: After Khafre, the team scanned the rest of the plateau. Similar tube-like structures miraculously appear beneath Menkaure (a smaller 2+2 pattern) and a single descending tube under the Sphinx. The evidence points to a giant connected system beneath all three monuments. 6) The Tunnel Network: The tomography shows a dense web of tunnels running between the pyramids and toward the Sphinx. Several known surface shafts, now blocked or filled with debris, look like the original access points into this network. 7) Water As A Key Variable: The Osiris Shaft contains water at about 33–37 meters. Biondi thinks water flow is part of how the system operates, possibly tied to vibrational or informational dynamics. He cites Preparata and Del Giudice’s work on coherent water domains but avoids any “power plant” jump. 8) Academia: Biondi already has a peer-reviewed SAR/Doppler tomography paper on the Khufu Pyramid in Remote Sensing. His larger Khafre + plateau paper is now in peer review. The work sticks to hard measurements: geometry, depth, replication. 9) Next Steps: The CAF Project is preparing a proposal to: • Clear out the sealed shafts between Khafre and the Sphinx • Run direct seismic surveys to confirm the satellite data • Enter the tunnel system if Egypt authorizes it At this point, approval from Cairo is the only barrier to verifying what the scans show. If those shafts are opened, the world may be looking at a multi-kilometer engineered complex beneath Giza. Full Documentary 👇
1:07

🚨BREAKING: New Radar Scan Reveals a Massive Engineered Substructure That Looks Like An Energy Grid Beneath the Giza Plateau🚨 Radar engineer Filippo Biondi just dropped the most explosive finding ever reported at Giza: eight clearly man-made, tube-like structures plunging more than a kilometer beneath the Khafre Pyramid and ending in huge 80-meter chambers. The structures are obviously artificially engineered and the synthetic aperture radar Doppler-tomography technique he used has precedent in accurately predicting underground structures (in both commercial and defense use cases). The Egyptian ministry of culture is extremely afraid this finding might rewrite their history. 1) The Core Structure: Eight hollow tubes (with two symmetrical sets of four) sit directly beneath the Khafre Pyramid’s base and run straight down over 1 km into the bedrock. They terminate in a massive chamber roughly 80 meters across. The shape is engineered; nothing in geology produces structures like this. 2) The Method Used: Regular SAR can’t see through rock. Biondi uses a Doppler-tomographic approach: the satellites measure tiny vibrations on the surface, and the inversion reconstructs what’s below from how those vibrations modulate the radar signal. It’s physics, not AI. 3) Independent Replication: The same underground structures appear in data from: • Umbra • Capella Space • ISI • COSMO-SkyMed If this were a glitch or artifact, it wouldn’t repeat across four separate systems. 4) Real-World Validation: Biondi’s method has already been tested against real sites where we know the exact layout. It has: • Mapped the Gran Sasso underground lab with exact accuracy • Reproduced the Osiris Shaft down to ~37 meters • Imaged magma and voids used in active civil-protection monitoring These aren’t speculative models...they match real measurements. 5) Giza As A Unified System: After Khafre, the team scanned the rest of the plateau. Similar tube-like structures miraculously appear beneath Menkaure (a smaller 2+2 pattern) and a single descending tube under the Sphinx. The evidence points to a giant connected system beneath all three monuments. 6) The Tunnel Network: The tomography shows a dense web of tunnels running between the pyramids and toward the Sphinx. Several known surface shafts, now blocked or filled with debris, look like the original access points into this network. 7) Water As A Key Variable: The Osiris Shaft contains water at about 33–37 meters. Biondi thinks water flow is part of how the system operates, possibly tied to vibrational or informational dynamics. He cites Preparata and Del Giudice’s work on coherent water domains but avoids any “power plant” jump. 8) Academia: Biondi already has a peer-reviewed SAR/Doppler tomography paper on the Khufu Pyramid in Remote Sensing. His larger Khafre + plateau paper is now in peer review. The work sticks to hard measurements: geometry, depth, replication. 9) Next Steps: The CAF Project is preparing a proposal to: • Clear out the sealed shafts between Khafre and the Sphinx • Run direct seismic surveys to confirm the satellite data • Enter the tunnel system if Egypt authorizes it At this point, approval from Cairo is the only barrier to verifying what the scans show. If those shafts are opened, the world may be looking at a multi-kilometer engineered complex beneath Giza. Full Documentary 👇

Jesse Michels

891,480 次观看 • 10 个月前

🚨BREAKING: NASA physicist says astronauts have seen UFOs on Gemini spaceflights and other missions, UFOs have interfered with nuclear missile systems, and that the agency has systematically ignored key UFO data for decades. He also cites Japan Airlines flight 1628 as a 45 minute pilot sighting over Alaska involving radar data confiscated by the CIA and White House’s scientific advisory team.🚨 Kevin Knuth (University at Albany physicist, former NASA) lays out the most concrete physics-first frame for what UFOs are and what they can do. This episode centers on three hard points: repeated UAP interaction with nuclear assets, flight performance that breaks conventional engineering by orders of magnitude, and a growing civilian pipeline analyzing debris and isotopes outside black programs. Along the way, Knuth gets into his brilliant theories on Alien waterworlds and UFO propulsion mechanisms and we discuss bizarre astronaut sightings of red lights, “snake” and disc-looking objects in space. 1. The Ubiquitous UFO-Nuclear Connection: Knuth points to the global pattern: UAP show up around U.S. missile fields, Soviet nuclear assets, France, the U.K., and even civilian nuclear regions like Fukushima. Air Force Officer Robert Salas details events at Malmstrom Air Force Base: between September 1966 and March 1967, roughly 30 missiles were lost to UAP-related interference. Events like from witnesses beyond reproach convinced Knuth UFOs were impossible to ignore. 2. How UFOs Fly: 5,000 Gs Isn’t “Fast.” It’s Impossible Knuth uses the commonly cited estimate of the 2004 Nimitz UFO sighting off the coast of San Diego: 28,000 feet to sea level in ~0.78 seconds, implying roughly 5,000 G’s minimum. He contrasts it with the limits of modern fighters: an F-35’s wings fail around 13 G, missile frames can’t survive much beyond ~60 G. He cites a figure around ~11 gigawatts for that maneuver. More than the total nuclear output of the United States, packed into something roughly the size of an F/A-18. Even assuming 99% efficiency, the waste heat alone would be catastrophic. The object should melt. It doesn’t. This is not a scaling problem. It’s a category break. 3. How UFOs Interact With Water Knuth describes a case involving the Southland in the 1980s: an ~800-foot-long USO, roughly 150 feet in diameter, closing ~2 km in ~25 seconds, with an implied top speed around ~3,500 mph underwater. No wake. No water displacement behavior consistent with classical fluid mechanics. Yet sonar returns exist, meaning water molecules are bouncing off it while the object behaves like it isn’t interacting with the medium. 4. Physics Has To Break. Knuth’s conclusion is blunt: if the accelerations are real, you’re forced into the territory of inertia, gravity, and general relativity. Warp drive and “warp bubbles” are on the table, but even that has unanswered questions, like what a warp bubble does in an atmosphere. 5. NASA’s Documented Astronaut sightings: Knuth discusses accounts tied to astronauts and Apollo and Gemini-era spaceflight that suggest the phenomenon isn’t limited to Earth’s atmosphere. Gemini 11 involves audio of the sighting and a documented outage of the vehicle in one of the most debated cases in NASA’s history. Knuth explores the truth behind it. 6. The UFO Case With The Most Data Around It: Japan Airlines flight 1628. Cargo 747 Captain Kenju Terauchi, flying over Alaska. First: rectangular objects with lights scanning the cockpit, with pilots reporting they could feel heat. Then: a massive walnut-shaped object, described as multiple 747s in size, effectively an aircraft-carrier-scale presence, following the plane for roughly 45 minutes and moving side-to-side around it. Knuth cites John Callahan (FAA Chief of Accidents and Investigations) reviewing the incident. A meeting follows where Reagan’s scientific team and the CIA demand the data. Callahan copies it and keeps it under his desk, later making it public after retirement. The officials in the meeting reportedly say they’ve never had 45 minutes of radar data on one of these objects before. Knuth references physicist Daniel Coumbe (Niels Bohr Institute) analyzing the radar jumps, with extreme accelerations discussed in secondary analysis. The core point: radar-derived kinematics imply performance far beyond known human technology. 7. Reverse Engineering Is Real. And Failing. Knuth says he’s heard enough from credible people over years to believe there’s “nuts and bolts” craft work happening. He believes we just haven’t made any progress: its like a caveman looking at an iPhone. 8. The Isotopes Don’t Lie: He describes colleague Matthew Shadagas studying purported debris using neutron activation techniques to infer isotope ratios non-destructively. The core idea is simple: if you find isotope ratios that don’t occur naturally on Earth and don’t match asteroid patterns, you have a possible smoking gun. 9. They’re Not “Coming Here.” They’re Here. He echoes the Carl Sagan logic: they aren’t arriving from another star system every other Tuesday. He points to timing issues like Fukushima sightings the next day. If they were truly interstellar without exotic travel and comms, the lag alone breaks the story. His best bet is underwater bases, with water offering stable temperature, shielding, stealth against EM detection, and access to energy and minerals. 10. The Cover-Up Is The System Knuth frames the modern UAP landscape as a collision between overwhelming sensor/witness evidence and institutional incentives to bury it. The result isn’t “no data.” It’s data that gets siloed, seized, and stigmatized before it ever reaches the public. Why this matters: This is what the UFO question looks like when you treat it like a serious scientific inquiry, not a cultural one. Nuclear site surveillance is patterned. The flight performance exceeds known engineering limits by orders of magnitude. And the most credible testimony isn’t theatrical. It’s documented and coming from the highest levels of credible witnesses. If you want hardcore UFO science, watch this episode with Kevin Knuth, in the reply 👇
1:43

🚨BREAKING: NASA physicist says astronauts have seen UFOs on Gemini spaceflights and other missions, UFOs have interfered with nuclear missile systems, and that the agency has systematically ignored key UFO data for decades. He also cites Japan Airlines flight 1628 as a 45 minute pilot sighting over Alaska involving radar data confiscated by the CIA and White House’s scientific advisory team.🚨 Kevin Knuth (University at Albany physicist, former NASA) lays out the most concrete physics-first frame for what UFOs are and what they can do. This episode centers on three hard points: repeated UAP interaction with nuclear assets, flight performance that breaks conventional engineering by orders of magnitude, and a growing civilian pipeline analyzing debris and isotopes outside black programs. Along the way, Knuth gets into his brilliant theories on Alien waterworlds and UFO propulsion mechanisms and we discuss bizarre astronaut sightings of red lights, “snake” and disc-looking objects in space. 1. The Ubiquitous UFO-Nuclear Connection: Knuth points to the global pattern: UAP show up around U.S. missile fields, Soviet nuclear assets, France, the U.K., and even civilian nuclear regions like Fukushima. Air Force Officer Robert Salas details events at Malmstrom Air Force Base: between September 1966 and March 1967, roughly 30 missiles were lost to UAP-related interference. Events like from witnesses beyond reproach convinced Knuth UFOs were impossible to ignore. 2. How UFOs Fly: 5,000 Gs Isn’t “Fast.” It’s Impossible Knuth uses the commonly cited estimate of the 2004 Nimitz UFO sighting off the coast of San Diego: 28,000 feet to sea level in ~0.78 seconds, implying roughly 5,000 G’s minimum. He contrasts it with the limits of modern fighters: an F-35’s wings fail around 13 G, missile frames can’t survive much beyond ~60 G. He cites a figure around ~11 gigawatts for that maneuver. More than the total nuclear output of the United States, packed into something roughly the size of an F/A-18. Even assuming 99% efficiency, the waste heat alone would be catastrophic. The object should melt. It doesn’t. This is not a scaling problem. It’s a category break. 3. How UFOs Interact With Water Knuth describes a case involving the Southland in the 1980s: an ~800-foot-long USO, roughly 150 feet in diameter, closing ~2 km in ~25 seconds, with an implied top speed around ~3,500 mph underwater. No wake. No water displacement behavior consistent with classical fluid mechanics. Yet sonar returns exist, meaning water molecules are bouncing off it while the object behaves like it isn’t interacting with the medium. 4. Physics Has To Break. Knuth’s conclusion is blunt: if the accelerations are real, you’re forced into the territory of inertia, gravity, and general relativity. Warp drive and “warp bubbles” are on the table, but even that has unanswered questions, like what a warp bubble does in an atmosphere. 5. NASA’s Documented Astronaut sightings: Knuth discusses accounts tied to astronauts and Apollo and Gemini-era spaceflight that suggest the phenomenon isn’t limited to Earth’s atmosphere. Gemini 11 involves audio of the sighting and a documented outage of the vehicle in one of the most debated cases in NASA’s history. Knuth explores the truth behind it. 6. The UFO Case With The Most Data Around It: Japan Airlines flight 1628. Cargo 747 Captain Kenju Terauchi, flying over Alaska. First: rectangular objects with lights scanning the cockpit, with pilots reporting they could feel heat. Then: a massive walnut-shaped object, described as multiple 747s in size, effectively an aircraft-carrier-scale presence, following the plane for roughly 45 minutes and moving side-to-side around it. Knuth cites John Callahan (FAA Chief of Accidents and Investigations) reviewing the incident. A meeting follows where Reagan’s scientific team and the CIA demand the data. Callahan copies it and keeps it under his desk, later making it public after retirement. The officials in the meeting reportedly say they’ve never had 45 minutes of radar data on one of these objects before. Knuth references physicist Daniel Coumbe (Niels Bohr Institute) analyzing the radar jumps, with extreme accelerations discussed in secondary analysis. The core point: radar-derived kinematics imply performance far beyond known human technology. 7. Reverse Engineering Is Real. And Failing. Knuth says he’s heard enough from credible people over years to believe there’s “nuts and bolts” craft work happening. He believes we just haven’t made any progress: its like a caveman looking at an iPhone. 8. The Isotopes Don’t Lie: He describes colleague Matthew Shadagas studying purported debris using neutron activation techniques to infer isotope ratios non-destructively. The core idea is simple: if you find isotope ratios that don’t occur naturally on Earth and don’t match asteroid patterns, you have a possible smoking gun. 9. They’re Not “Coming Here.” They’re Here. He echoes the Carl Sagan logic: they aren’t arriving from another star system every other Tuesday. He points to timing issues like Fukushima sightings the next day. If they were truly interstellar without exotic travel and comms, the lag alone breaks the story. His best bet is underwater bases, with water offering stable temperature, shielding, stealth against EM detection, and access to energy and minerals. 10. The Cover-Up Is The System Knuth frames the modern UAP landscape as a collision between overwhelming sensor/witness evidence and institutional incentives to bury it. The result isn’t “no data.” It’s data that gets siloed, seized, and stigmatized before it ever reaches the public. Why this matters: This is what the UFO question looks like when you treat it like a serious scientific inquiry, not a cultural one. Nuclear site surveillance is patterned. The flight performance exceeds known engineering limits by orders of magnitude. And the most credible testimony isn’t theatrical. It’s documented and coming from the highest levels of credible witnesses. If you want hardcore UFO science, watch this episode with Kevin Knuth, in the reply 👇

Jesse Michels

564,384 次观看 • 9 个月前