Загрузка видео...

Не удалось загрузить видео

На главную

🇨🇳🇺🇸 China's military may be learning from America's AI, without building it from scratch Why spend billions training a frontier AI model when you can let someone else do the expensive part? Chinese military-linked researchers have repeatedly used outputs from OpenAI and Anthropic models to train smaller domestic AI...

MarioNawfal's profile picture

Mario Nawfal

3,785,251 subscribers

47,065 просмотров • 8 дней назад •via X (Twitter)

Наука и технологииНовости и политика
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.
0:14

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

Chris

60,278 просмотров • 4 месяцев назад

OpenAI's CEO Sam Altman said Wednesday "there could be" other systems hacked by its AI agent after the company revealed last week that its technology carried out a hack of another AI company in what OpenAI called an "unprecedented cyber incident." When asked whether he's spoken to the Trump administration about slowing down AI development, Altman said, "We've talked about the need to pace it as the models get more capable, which I think is in everyone's interest." Altman met with lawmakers on Capitol Hill Wednesday to preview his company's newest AI model.
0:18

OpenAI's CEO Sam Altman said Wednesday "there could be" other systems hacked by its AI agent after the company revealed last week that its technology carried out a hack of another AI company in what OpenAI called an "unprecedented cyber incident." When asked whether he's spoken to the Trump administration about slowing down AI development, Altman said, "We've talked about the need to pace it as the models get more capable, which I think is in everyone's interest." Altman met with lawmakers on Capitol Hill Wednesday to preview his company's newest AI model.

CBS News

63,097 просмотров • 9 дней назад

Something NVIDIA & Google do better than anyone else is software-hardware-system co-design, and not just optimizing hardware for current model architectures, but predicting future ones. Back in early 2022, when NVIDIA started the design process for NVL72, MoE (Mixture of Experts) models were not yet the standard, and dense models were still dominant for frontier models. However, NVIDIA's strong software-hardware co-design culture enabled them to make a calculated bet that MoEs were the future, and they built NVL72 specifically for best MoE performance per TCO (Total Cost of Ownership). Furthermore, back in 2022, disaggregated prefill and wide expert parallelism (wideEP) MoE inference optimizations hadn't been invented yet, but it turns out that these MoE inference optimizations work best on large-scale systems like NVL72. While most other AI chip companies' in-house AI labs focus on training small 5B models that mainly use data parallelism, NVIDIA and Google's in-house AI labs continuously push the boundaries of model architecture and training recipes, such as NVFP4 training. Just like Super Idol & IShowSpeed, there must be a strong partnership between software engineers and hardware engineers to deliver the best systems that maximize performance per TCO.
0:14

Something NVIDIA & Google do better than anyone else is software-hardware-system co-design, and not just optimizing hardware for current model architectures, but predicting future ones. Back in early 2022, when NVIDIA started the design process for NVL72, MoE (Mixture of Experts) models were not yet the standard, and dense models were still dominant for frontier models. However, NVIDIA's strong software-hardware co-design culture enabled them to make a calculated bet that MoEs were the future, and they built NVL72 specifically for best MoE performance per TCO (Total Cost of Ownership). Furthermore, back in 2022, disaggregated prefill and wide expert parallelism (wideEP) MoE inference optimizations hadn't been invented yet, but it turns out that these MoE inference optimizations work best on large-scale systems like NVL72. While most other AI chip companies' in-house AI labs focus on training small 5B models that mainly use data parallelism, NVIDIA and Google's in-house AI labs continuously push the boundaries of model architecture and training recipes, such as NVFP4 training. Just like Super Idol & IShowSpeed, there must be a strong partnership between software engineers and hardware engineers to deliver the best systems that maximize performance per TCO.

SemiAnalysis

51,021 просмотров • 8 месяцев назад

NEW SECRET MYSTERY AI VIDEO MODEL ARENA CODE NAME: BOUNCYBOHR Unlike Artificial Analysis, on Arena. AI you can submit your own prompts and this prompt is designed to try and unmask who created the model. Models powered by an LLM (like Seedance 2.0 and Omni) are smart and can see what you are doing. As far as thoughts? Eh? While another screenshot looked like Omni, this does look pretty Seedance to me. Judging by the result, this looks like the rumored new "improved" FAST model and not upgraded full 2.1 model that is supposed to be 25% better. Text To Video close-up cinematic scene from a film, professionally directed, focused, ultrafine details a woman laughs as she whiteboard writes out what model made this video
0:15

NEW SECRET MYSTERY AI VIDEO MODEL ARENA CODE NAME: BOUNCYBOHR Unlike Artificial Analysis, on Arena. AI you can submit your own prompts and this prompt is designed to try and unmask who created the model. Models powered by an LLM (like Seedance 2.0 and Omni) are smart and can see what you are doing. As far as thoughts? Eh? While another screenshot looked like Omni, this does look pretty Seedance to me. Judging by the result, this looks like the rumored new "improved" FAST model and not upgraded full 2.1 model that is supposed to be 25% better. Text To Video close-up cinematic scene from a film, professionally directed, focused, ultrafine details a woman laughs as she whiteboard writes out what model made this video

Brent Lynch

32,504 просмотров • 2 месяцев назад

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.
0:12

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.

Thomas Kurian

197,666 просмотров • 2 месяцев назад

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.
0:19

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

Brian Roemmele

57,454 просмотров • 8 месяцев назад

China publicly demonstrated its truck-mounted electromagnetic aircraft launch system (EMALS) for the first time, successfully launching a drone from a modular catapult built from multiple linked trucks. Three or more trucks connect to form a catapult that can be rapidly assembled and moved. The catapult is part of a broader family of containerized military systems—including missiles, radars, electronic warfare, and command centers—that can be deployed from ships or land. It is designed to launch drones where runways don’t exist, such as islands, remote areas, or civilian ships converted into drone carriers. Chinese sources reportedly aim to produce 2,000 containerized systems annually. Source: KAROTASU, TWZ
0:11

China publicly demonstrated its truck-mounted electromagnetic aircraft launch system (EMALS) for the first time, successfully launching a drone from a modular catapult built from multiple linked trucks. Three or more trucks connect to form a catapult that can be rapidly assembled and moved. The catapult is part of a broader family of containerized military systems—including missiles, radars, electronic warfare, and command centers—that can be deployed from ships or land. It is designed to launch drones where runways don’t exist, such as islands, remote areas, or civilian ships converted into drone carriers. Chinese sources reportedly aim to produce 2,000 containerized systems annually. Source: KAROTASU, TWZ

Clash Report

435,521 просмотров • 1 месяц назад

✨ Every week a new AI model comes out and it suddenly makes my half broken features work a lot better Yesterday Seedream-4-Edit came out and it made my [ Hold product ] feature on Photo AI a lot better You can now go from: 🎁 Product photo -> 👱‍♀️ Talking video with your AI model while holding your product. In just a few minutes! Here's a photo I took from the weekly farm box we get in our kitchen, I set it as the product and then with Photo AI made it into a talking video where my trained AI model presents it It's not perfect, as the objects inside the farm box still move around a bit, but pretty close. If the product is more uniform (like lip gloss, a product box or a book) it does a pretty good job at keeping it exactly the same This "consistency" as they call it is quite important for actual real world use. Product sellers don't want to have an image or video of an AI model if the product doesn't look exactly the same as what they sell With that, I'm getting pretty close now and every week with every new model that comes out, a bit closer And it's interesting cause now I'm finally moving from B2C a bit more to B2B where businesses can use Photo AI more, designers and stores already use it for trying on clothes etc. but now they can generate content for real products! 😊 LIVE now on Photo AI
0:15

✨ Every week a new AI model comes out and it suddenly makes my half broken features work a lot better Yesterday Seedream-4-Edit came out and it made my [ Hold product ] feature on Photo AI a lot better You can now go from: 🎁 Product photo -> 👱‍♀️ Talking video with your AI model while holding your product. In just a few minutes! Here's a photo I took from the weekly farm box we get in our kitchen, I set it as the product and then with Photo AI made it into a talking video where my trained AI model presents it It's not perfect, as the objects inside the farm box still move around a bit, but pretty close. If the product is more uniform (like lip gloss, a product box or a book) it does a pretty good job at keeping it exactly the same This "consistency" as they call it is quite important for actual real world use. Product sellers don't want to have an image or video of an AI model if the product doesn't look exactly the same as what they sell With that, I'm getting pretty close now and every week with every new model that comes out, a bit closer And it's interesting cause now I'm finally moving from B2C a bit more to B2B where businesses can use Photo AI more, designers and stores already use it for trying on clothes etc. but now they can generate content for real products! 😊 LIVE now on Photo AI

@levelsio

361,558 просмотров • 11 месяцев назад

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!
0:51

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

@levelsio

119,210 просмотров • 1 год назад

$ASI watch out! We've got more projects popping up on Bittensor and $TAO and just starting, Akash $AKT, Shell $SHELL, Einstein-AIT $AIT, Sturdy $STRDY, Stratos $STOS and Comtensor $COMAI and more. Each pushing what's possible with AI and crypto. MyShell: $SHELL First up, MyShell is doing something amazing work. They're all about making AI chat like humans. They're using this TTS Subnet on Bittensor, powered by thier tokens, to make it happen. AI doesn't have to be complicated thing only some can use. They want everyone in on the action, making AI smarter in the process. Einstein-AIT: $AIT Then there's Einstein-AIT. Imagine this as the network's brain but on a turbocharge. It's all about math, logic, and crunching numbers. This subnet makes the whole Bittensor network sharper. They've got NumPAL, and it's like giving the AI a smarter way to think about time and dates automatically. Sturdy: $STRDY Jumping into DeFi, we've got Sturdy. These guys are on a mission to make lending and borrowing way less of a headache. They've got isolated lending pools, you can pick and choose how to manage your risks and money. And they're using some serious tech to keep your investments growing without you needing to babysit them. It's like having a smart financial assistant. Comtensor: $COMAI Comtensor is where it gets interesting. Think of it as a bridge between CommuneAI and Bittensor, kind of like the best of both worlds. It's about making sure all these AI modules and subnets can talk to each other smoothly. The goal? To boost decentralized AI by making everything more connected and smart. Stratos: $STOS Teaming up with τensorage to supercharge the $TAO ecosystem on Bittensor. Stratos is all about solid, decentralized storage - think of it as the bedrock for making sure data's not just stored but also used right . Then there’s τensorage, making sure everything AI needs is stored safe. Together, they’re making sure Bittensor's AI brainpower gets a boost, making everything faster, safer, and smoother. Akash: $AKT Compute Subnet 27 linked up with Akash. All about keeping things open-source. With Akash as a decentralized GPU provider into the mix. Giving access to top-notch AI processing power on top of Neural Internets compute-composable subnet, integrating various cloud platforms. This partnership is all about breaking away from those giants and opening the doors wide for smaller teams and startups who need this tech to innovate. Whats this all about? Making decentralized AI a something that everyone can get behind and into. Whether it's chatting with AI, boosting the network's IQ, DeFi, compute or making sure all these projects work together, it's about pushing forward. Each project has its own way of making things better, faster, and smarter for all of us. Inviting everyone to join in, contribute, and be a part of community effort to make AI not just for the few but for everyone. Credit to Mr.Franc Q for the awesome video production!
0:13

$ASI watch out! We've got more projects popping up on Bittensor and $TAO and just starting, Akash $AKT, Shell $SHELL, Einstein-AIT $AIT, Sturdy $STRDY, Stratos $STOS and Comtensor $COMAI and more. Each pushing what's possible with AI and crypto. MyShell: $SHELL First up, MyShell is doing something amazing work. They're all about making AI chat like humans. They're using this TTS Subnet on Bittensor, powered by thier tokens, to make it happen. AI doesn't have to be complicated thing only some can use. They want everyone in on the action, making AI smarter in the process. Einstein-AIT: $AIT Then there's Einstein-AIT. Imagine this as the network's brain but on a turbocharge. It's all about math, logic, and crunching numbers. This subnet makes the whole Bittensor network sharper. They've got NumPAL, and it's like giving the AI a smarter way to think about time and dates automatically. Sturdy: $STRDY Jumping into DeFi, we've got Sturdy. These guys are on a mission to make lending and borrowing way less of a headache. They've got isolated lending pools, you can pick and choose how to manage your risks and money. And they're using some serious tech to keep your investments growing without you needing to babysit them. It's like having a smart financial assistant. Comtensor: $COMAI Comtensor is where it gets interesting. Think of it as a bridge between CommuneAI and Bittensor, kind of like the best of both worlds. It's about making sure all these AI modules and subnets can talk to each other smoothly. The goal? To boost decentralized AI by making everything more connected and smart. Stratos: $STOS Teaming up with τensorage to supercharge the $TAO ecosystem on Bittensor. Stratos is all about solid, decentralized storage - think of it as the bedrock for making sure data's not just stored but also used right . Then there’s τensorage, making sure everything AI needs is stored safe. Together, they’re making sure Bittensor's AI brainpower gets a boost, making everything faster, safer, and smoother. Akash: $AKT Compute Subnet 27 linked up with Akash. All about keeping things open-source. With Akash as a decentralized GPU provider into the mix. Giving access to top-notch AI processing power on top of Neural Internets compute-composable subnet, integrating various cloud platforms. This partnership is all about breaking away from those giants and opening the doors wide for smaller teams and startups who need this tech to innovate. Whats this all about? Making decentralized AI a something that everyone can get behind and into. Whether it's chatting with AI, boosting the network's IQ, DeFi, compute or making sure all these projects work together, it's about pushing forward. Each project has its own way of making things better, faster, and smarter for all of us. Inviting everyone to join in, contribute, and be a part of community effort to make AI not just for the few but for everyone. Credit to Mr.Franc Q for the awesome video production!

Andy ττ

18,405 просмотров • 2 лет назад

not sure why nobody is talking about this but Google Omni is insane at video editing Original Video (left) vs Omni Edited Video (right) everyone is comparing it to Seedance and missing the point completely. Seedance is for generating videos from scratch. Google Omni is for editing videos that already exist. which are two completely different use cases this is like when Nano Banana 1 first came out and nobody realized how big it was going to be. this is the first AI that can actually properly edit videos.. i've generated a few hundred videos with this model and it can do literally any type of edit you can think of. changing voices, swapping characters, removing watermarks, adding captions, transitions, pop ups, whatever. if you can describe the edit you want it can do it this completely crushes every other model on the market when it comes to video editing. nothing else even comes close right now and this is just the flash model. imagine what the pro version is going to be able to do when it drops in a couple months this should have way more hype than it's getting..
0:09

not sure why nobody is talking about this but Google Omni is insane at video editing Original Video (left) vs Omni Edited Video (right) everyone is comparing it to Seedance and missing the point completely. Seedance is for generating videos from scratch. Google Omni is for editing videos that already exist. which are two completely different use cases this is like when Nano Banana 1 first came out and nobody realized how big it was going to be. this is the first AI that can actually properly edit videos.. i've generated a few hundred videos with this model and it can do literally any type of edit you can think of. changing voices, swapping characters, removing watermarks, adding captions, transitions, pop ups, whatever. if you can describe the edit you want it can do it this completely crushes every other model on the market when it comes to video editing. nothing else even comes close right now and this is just the flash model. imagine what the pro version is going to be able to do when it drops in a couple months this should have way more hype than it's getting..

Miko

29,783 просмотров • 2 месяцев назад

Okay... this is actually insane. OpenCodex feels like the open-source breakthrough I've been waiting for. The best part : You can plug multiple providers into the same OpenAI Codex harness and switch between models depending on the task. Running low on tokens? No problem. Use another provider. OpenRouter free model today? Plug it in. This completely changes how I think about AI coding workflows. And yes... it even works on mobile. OpenCodex might be one of the most useful open-source AI projects I've seen this year. OpenAI built an incredible harness. The open-source community just made it universal.
0:10

Okay... this is actually insane. OpenCodex feels like the open-source breakthrough I've been waiting for. The best part : You can plug multiple providers into the same OpenAI Codex harness and switch between models depending on the task. Running low on tokens? No problem. Use another provider. OpenRouter free model today? Plug it in. This completely changes how I think about AI coding workflows. And yes... it even works on mobile. OpenCodex might be one of the most useful open-source AI projects I've seen this year. OpenAI built an incredible harness. The open-source community just made it universal.

CHOI

42,771 просмотров • 18 дней назад

A viral paper "Language Model Represents Space and Time" recently claims that LLMs learn "world models". As much as I like Max Tegmark's works, I disagree with their definition of world model. World model is a core concept in AI agent and decision making. It is our mental simulation of how the world works given interventions (or lack thereof). A world model captures causality and intuitive physics, telling the agent what is likely and what is impossible. It can and should be used for counterfactual reasoning, i.e. "what ifs": what would happen if I knock over a cup of water? Where would I have been if I had not taken that bus? Yann LeCun Yann LeCun says it well in his position paper ( I quote: "Using such world models, animals can learn new skills with very few trials. They can predict the consequences of their actions, they can reason, plan, explore, and imagine new solutions to problems. Importantly, they can also avoid making dangerous mistakes when facing an unknown situation." The first use of the term World Model in deep policy learning is attributed to hardmaru & Jürgen Schmidhuber: In their seminal paper, an agent masters shooting skills in the popular game Doom (demo below) by learning in imagination, using an internal world model as a "physics simulator". To put in a simple Python math formula, world model learns a function F(s[0:t-1], a) -> s[t:], which takes as input the observed past and current action, and outputs plausible future states. Now the definition of World Model in Tegmark's paper seems to be about predicting GPS coordinates and time eras. I see this as just a classification task with no causal learning and simulation going on. You cannot make meaningful interventions against that model, nor can you optimize any decision making in a closed feedback loop. As for the "space & time neurons", I think they are most similar to the "sentiment neuron" that OpenAI published in 2017: Predicting GPS is conceptually no different from predicting sentiment in my opinion. I don't think their experimental results are wrong - just that their conclusion is on shaky grounds. I welcome any debate! Paper link:
0:15

A viral paper "Language Model Represents Space and Time" recently claims that LLMs learn "world models". As much as I like Max Tegmark's works, I disagree with their definition of world model. World model is a core concept in AI agent and decision making. It is our mental simulation of how the world works given interventions (or lack thereof). A world model captures causality and intuitive physics, telling the agent what is likely and what is impossible. It can and should be used for counterfactual reasoning, i.e. "what ifs": what would happen if I knock over a cup of water? Where would I have been if I had not taken that bus? Yann LeCun Yann LeCun says it well in his position paper ( I quote: "Using such world models, animals can learn new skills with very few trials. They can predict the consequences of their actions, they can reason, plan, explore, and imagine new solutions to problems. Importantly, they can also avoid making dangerous mistakes when facing an unknown situation." The first use of the term World Model in deep policy learning is attributed to hardmaru & Jürgen Schmidhuber: In their seminal paper, an agent masters shooting skills in the popular game Doom (demo below) by learning in imagination, using an internal world model as a "physics simulator". To put in a simple Python math formula, world model learns a function F(s[0:t-1], a) -> s[t:], which takes as input the observed past and current action, and outputs plausible future states. Now the definition of World Model in Tegmark's paper seems to be about predicting GPS coordinates and time eras. I see this as just a classification task with no causal learning and simulation going on. You cannot make meaningful interventions against that model, nor can you optimize any decision making in a closed feedback loop. As for the "space & time neurons", I think they are most similar to the "sentiment neuron" that OpenAI published in 2017: Predicting GPS is conceptually no different from predicting sentiment in my opinion. I don't think their experimental results are wrong - just that their conclusion is on shaky grounds. I welcome any debate! Paper link:

Jim Fan

594,014 просмотров • 2 лет назад

At Open AGI, our CEO Ahmed Rashad shared why trust is the new compute. AI is moving from generating content to making life-or-death decisions — medical diagnoses, autonomous vehicles, military systems. If the training data is wrong, the decisions are wrong. Meanwhile, models are increasingly trained on synthetic outputs, creating feedback loops that degrade quality. Data supply chains are opaque. Adversarial attacks are real. The next AI race won’t be won by bigger models. It will be won by those who control and verify the data layer. Perle Labs is building the sovereign, human-verified, on-chain auditable infrastructure high-stakes AI requires. Trust wins the AI race. Thank you Sentient for having us! No better way to end off our time at ETHDenver 🏔🦬🦄. Until next time 🫡
0:14

At Open AGI, our CEO Ahmed Rashad shared why trust is the new compute. AI is moving from generating content to making life-or-death decisions — medical diagnoses, autonomous vehicles, military systems. If the training data is wrong, the decisions are wrong. Meanwhile, models are increasingly trained on synthetic outputs, creating feedback loops that degrade quality. Data supply chains are opaque. Adversarial attacks are real. The next AI race won’t be won by bigger models. It will be won by those who control and verify the data layer. Perle Labs is building the sovereign, human-verified, on-chain auditable infrastructure high-stakes AI requires. Trust wins the AI race. Thank you Sentient for having us! No better way to end off our time at ETHDenver 🏔🦬🦄. Until next time 🫡

Perle Labs

29,829 просмотров • 5 месяцев назад

Force feedback demo Force feedback is when joystick is pushing on your hand when something is pushing on the robot arm. Feeling the force - so much helpful to control the robot, that done well it allows you to do tasks even without visual feed. You can make an experiment: close your eyes - you can easily get the headphones out of the case. Also, visual information is often not enough. For example, you're trying to pull out a usb connector, but you pull it at the wrong angle, causing it to get stuck. Visually, nothing changes, but the pressure is intense and you can break the connector. Surgical robots have been using force feedback for years, and there are also 3D styluses which use this feature, proving that the technology works and is useful. But in modern robots with AI, it's hardly ever implemented. Although it's useful for both teleoperation and AI model. That's one of the reasons why we are building our robotic arms starting with off the shelf motors rather than taking the whole off the shelf arm. There are still a range of easy wins that can be made iterating robot hardware.
0:15

Force feedback demo Force feedback is when joystick is pushing on your hand when something is pushing on the robot arm. Feeling the force - so much helpful to control the robot, that done well it allows you to do tasks even without visual feed. You can make an experiment: close your eyes - you can easily get the headphones out of the case. Also, visual information is often not enough. For example, you're trying to pull out a usb connector, but you pull it at the wrong angle, causing it to get stuck. Visually, nothing changes, but the pressure is intense and you can break the connector. Surgical robots have been using force feedback for years, and there are also 3D styluses which use this feature, proving that the technology works and is useful. But in modern robots with AI, it's hardly ever implemented. Although it's useful for both teleoperation and AI model. That's one of the reasons why we are building our robotic arms starting with off the shelf motors rather than taking the whole off the shelf arm. There are still a range of easy wins that can be made iterating robot hardware.

Igor Kulakov

18,773 просмотров • 1 год назад

NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!
0:15

NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!

elvis

16,882 просмотров • 19 дней назад

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.
0:14

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

Alex Prompter

7,071,468 просмотров • 16 дней назад

The real reason Trump was forced to move the inauguration inside is because of extreme security concerns! Helicopters are scanning the area for bombs and radioactive material as we speak. The threat from drone attacks has reached unprecedented levels. All it takes is one small drone with poison on the end to take out any world leader. Terrifying! Once AI vision is added to those drones it will reach the point where nobody important can go outside without highly precise detection systems and top-of-the-line AI security. Watch closely on January 20th as they warn it could be the next target. Will be covering this closely.
0:19

The real reason Trump was forced to move the inauguration inside is because of extreme security concerns! Helicopters are scanning the area for bombs and radioactive material as we speak. The threat from drone attacks has reached unprecedented levels. All it takes is one small drone with poison on the end to take out any world leader. Terrifying! Once AI vision is added to those drones it will reach the point where nobody important can go outside without highly precise detection systems and top-of-the-line AI security. Watch closely on January 20th as they warn it could be the next target. Will be covering this closely.

Matt Wallace

3,231,133 просмотров • 1 год назад

GOODBYE TO CYBERSECURITY! Someone just open-sourced an arsenal of AI hacking tools. Not one. Not ten. Hundreds. Inside the repository: • Jailbreak frameworks for LLMs • Prompt injection testing tools • AI red team agents • Model extraction utilities • Supply chain attack demos • Automated AI pentesting frameworks These are the same categories of tools security researchers use to find vulnerabilities before attackers do. Now anyone can study them. That's both exciting... and terrifying. The biggest threat to AI isn't smarter models. It's insecure ones. If you're building with LLMs and you're not actively testing your prompts, agents, and infrastructure— You're probably shipping vulnerabilities you don't even know exist. Open source is accelerating AI. It's also accelerating AI attacks. Repository link in the comments ↓
0:10

GOODBYE TO CYBERSECURITY! Someone just open-sourced an arsenal of AI hacking tools. Not one. Not ten. Hundreds. Inside the repository: • Jailbreak frameworks for LLMs • Prompt injection testing tools • AI red team agents • Model extraction utilities • Supply chain attack demos • Automated AI pentesting frameworks These are the same categories of tools security researchers use to find vulnerabilities before attackers do. Now anyone can study them. That's both exciting... and terrifying. The biggest threat to AI isn't smarter models. It's insecure ones. If you're building with LLMs and you're not actively testing your prompts, agents, and infrastructure— You're probably shipping vulnerabilities you don't even know exist. Open source is accelerating AI. It's also accelerating AI attacks. Repository link in the comments ↓

Shruti Codes

120,960 просмотров • 14 дней назад

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.
0:10

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.

Santiago

30,189 просмотров • 1 месяц назад