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Our latest AI integration equips Reform with a causality-focused ML model that uncovers the mechanisms driving relationships between variables. By employing advanced algorithms, this method can detect “confounders”. A confounder is a variable that influences both the cause and effect variables being studied, which can distort the apparent relationship...

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Reform DAO

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Big step forward for root cause analysis in real-world applications! There’s a new method that will help identify the causes of a problem or event. It uses causal discovery, boosting trees together with TDA. This is crucial to enable root cause analysis in tasks like the following: • Fraud detection • Drug discovery • Customer behavior analysis • Energy and sustainability • Financial analysis and risk management • Failure analysis in engineering systems Topological data analysis (TDA), on the other hand, studies the topological properties of data sets. You can use TDA for clustering, classification, and anomaly detection. The team DataRefiner developed a new approach to integrating causal discovery with TDA segmentation, and they are getting the best results from anything in the market right now. For the first time, there's a tool where users can choose clusters and get a focused causal dependency graph. They use boosting trees to estimate causal effects in complex systems. They complement this approach with TDA by offering insights into potential causal pathways. With TDA, users can visualize and understand the relationship between variables. Most open-source systems struggle with categorical parameters or values with different scales. This new approach doesn’t have those problems. Look at the attached video to understand what you can do with this. Here is a link to a post with all of the details. It contains three detailed examples that will drive the idea home: Thanks to DataRefiner for sponsoring this post.
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Big step forward for root cause analysis in real-world applications! There’s a new method that will help identify the causes of a problem or event. It uses causal discovery, boosting trees together with TDA. This is crucial to enable root cause analysis in tasks like the following: • Fraud detection • Drug discovery • Customer behavior analysis • Energy and sustainability • Financial analysis and risk management • Failure analysis in engineering systems Topological data analysis (TDA), on the other hand, studies the topological properties of data sets. You can use TDA for clustering, classification, and anomaly detection. The team DataRefiner developed a new approach to integrating causal discovery with TDA segmentation, and they are getting the best results from anything in the market right now. For the first time, there's a tool where users can choose clusters and get a focused causal dependency graph. They use boosting trees to estimate causal effects in complex systems. They complement this approach with TDA by offering insights into potential causal pathways. With TDA, users can visualize and understand the relationship between variables. Most open-source systems struggle with categorical parameters or values with different scales. This new approach doesn’t have those problems. Look at the attached video to understand what you can do with this. Here is a link to a post with all of the details. It contains three detailed examples that will drive the idea home: Thanks to DataRefiner for sponsoring this post.

Santiago

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Seer, Sentry's AI debugger, analyzes your issues and finds the root cause. Now you can pass that analysis directly to a @GitHub Copilot agent which picks up the context, generates a fix, and opens a pull request. Learn more in our latest video with Paul Jaffré 👇
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Seer, Sentry's AI debugger, analyzes your issues and finds the root cause. Now you can pass that analysis directly to a @GitHub Copilot agent which picks up the context, generates a fix, and opens a pull request. Learn more in our latest video with Paul Jaffré 👇

Sentry

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A whistleblower who works as a counterterrorism analyst for the U.S. Army, that the DEA, DOJ, Homeland Security, and the FBI allegedly confirmed, named Scott Bennett, claimed on record that BIDEN visited SERBIA in May of 2009 to personally recruit computer hackers to control Dominion election equipment in U.S. elections. Tabulators and other election equipment from ES&S, DOMINION, and SMARTMATIC were examined and found to have internet access devices in order to be controlled from anywhere in the world. The also found a serial port in the back of the machines in which a device can be inserted in seconds to give root access with NO PASSWORD to the entire machine which would allow them to rewrite the code in the machine to have it do whatever it would be programmed to do. There are a massive amount of variables that can be changed to manipulate the outcome of an election, how the tabulators read ballots, flips votes, or cause hundreds of thousands, even millions of votes to enter adjudication, which was a main method of voter fraud. This is a massive security flaw that was well known which can be accessed in under a min to completely take over elections. The truth is that these really aren't security flaws. These are techniques and methods incorporated into the hardware and software which enable multiple methods and techniques to manipulate the outcome to your choice and to what you can get away with. These machine were designed to literally steal elections.
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A whistleblower who works as a counterterrorism analyst for the U.S. Army, that the DEA, DOJ, Homeland Security, and the FBI allegedly confirmed, named Scott Bennett, claimed on record that BIDEN visited SERBIA in May of 2009 to personally recruit computer hackers to control Dominion election equipment in U.S. elections. Tabulators and other election equipment from ES&S, DOMINION, and SMARTMATIC were examined and found to have internet access devices in order to be controlled from anywhere in the world. The also found a serial port in the back of the machines in which a device can be inserted in seconds to give root access with NO PASSWORD to the entire machine which would allow them to rewrite the code in the machine to have it do whatever it would be programmed to do. There are a massive amount of variables that can be changed to manipulate the outcome of an election, how the tabulators read ballots, flips votes, or cause hundreds of thousands, even millions of votes to enter adjudication, which was a main method of voter fraud. This is a massive security flaw that was well known which can be accessed in under a min to completely take over elections. The truth is that these really aren't security flaws. These are techniques and methods incorporated into the hardware and software which enable multiple methods and techniques to manipulate the outcome to your choice and to what you can get away with. These machine were designed to literally steal elections.

The SCIF

43,792 görüntüleme • 7 ay önce

Google announces PALP Prompt Aligned Personalization of Text-to-Image Models paper page: Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a single prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques.
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Google announces PALP Prompt Aligned Personalization of Text-to-Image Models paper page: Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a single prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques.

AK

90,199 görüntüleme • 2 yıl önce

I used NotebookLM to study Google new breakthrough with TurboQuant and used Video overview to study the subject, best learning tool in the world at the moment. TurboQuant: Redefining AI Efficiency with Extreme Compression Google Research has introduced TurboQuant, a suite of advanced algorithms designed to dramatically compress the data used by large language models and search engines. By utilizing specialized techniques like PolarQuant and Quantized Johnson-Lindenstrauss, the system transforms complex information into a compact "shorthand" that requires significantly less memory. This innovation specifically addresses the key-value cache bottleneck, allowing AI models to process massive amounts of text faster without losing accuracy. Testing demonstrates that these methods can shrink data size by six times while actually increasing operational speed on high-end hardware. Ultimately, these theoretical advancements enable more efficient semantic search and high-performance AI applications at a global scale.
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I used NotebookLM to study Google new breakthrough with TurboQuant and used Video overview to study the subject, best learning tool in the world at the moment. TurboQuant: Redefining AI Efficiency with Extreme Compression Google Research has introduced TurboQuant, a suite of advanced algorithms designed to dramatically compress the data used by large language models and search engines. By utilizing specialized techniques like PolarQuant and Quantized Johnson-Lindenstrauss, the system transforms complex information into a compact "shorthand" that requires significantly less memory. This innovation specifically addresses the key-value cache bottleneck, allowing AI models to process massive amounts of text faster without losing accuracy. Testing demonstrates that these methods can shrink data size by six times while actually increasing operational speed on high-end hardware. Ultimately, these theoretical advancements enable more efficient semantic search and high-performance AI applications at a global scale.

Emily

14,648 görüntüleme • 4 ay önce

In our latest benchmark, a traditional text-to-SQL approach required more than 4,400 schema-related tokens per query just to provide the model with enough context to understand a 28-table insurance data model. Think about that for a second: before AI can interpret the question, it first has to read the instruction manual. What we found is that when business context is already organized and governed, AI doesn't need to repeatedly rediscover how your data works. We created this white paper to help companies direct their attention to where costs are piling up and how Mosaic directly reduces them. Read the benchmark here: #EnterpriseAI #DataAnalytics #SemanticLayer #BusinessIntelligence
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In our latest benchmark, a traditional text-to-SQL approach required more than 4,400 schema-related tokens per query just to provide the model with enough context to understand a 28-table insurance data model. Think about that for a second: before AI can interpret the question, it first has to read the instruction manual. What we found is that when business context is already organized and governed, AI doesn't need to repeatedly rediscover how your data works. We created this white paper to help companies direct their attention to where costs are piling up and how Mosaic directly reduces them. Read the benchmark here: #EnterpriseAI #DataAnalytics #SemanticLayer #BusinessIntelligence

Strategy

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We will scrap the Multicultural Minister and the Office for Multiculturalism. We will not pursue an approach to governing that promotes separating our communities, which is exactly what multiculturalism does. In keeping with this, we will also cease funding government-funded translation services. The English language is the glue that binds our society together. If you do not speak English, under no circumstances should you be able to settle in Australia. Translation services indulge the view that not speaking English is a perfectly legitimate decision, which it isn’t if you want to permanently settle here. Far from helping integration, translation services that are extensively funded by the government are a barrier to effective integration.
2:56

We will scrap the Multicultural Minister and the Office for Multiculturalism. We will not pursue an approach to governing that promotes separating our communities, which is exactly what multiculturalism does. In keeping with this, we will also cease funding government-funded translation services. The English language is the glue that binds our society together. If you do not speak English, under no circumstances should you be able to settle in Australia. Translation services indulge the view that not speaking English is a perfectly legitimate decision, which it isn’t if you want to permanently settle here. Far from helping integration, translation services that are extensively funded by the government are a barrier to effective integration.

One Nation Australia

36,957 görüntüleme • 29 gün önce

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:
0:44

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:

Satya Nadella

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I've built it for you!! It's an automated AI system that analyzes AI case studies (you can change the use case) to identify and document enterprise-level AI implementations. It starts by reading URLs from a CSV file and uses web scraping (either through WebLoader or Firecrawl) to extract the content from each case study. The extracted content is then sent to Claude 3.5 Sonnet, which analyzes whether the case study represents a genuine enterprise AI implementation based on specific criteria like company maturity, implementation scale, and measurable business outcomes. For each URL, the system first saves the raw content and then performs this initial qualification analysis. If Claude determines that a case study qualifies as an enterprise AI implementation, the system proceeds to generate a detailed analysis. It creates three types of reports: - an individual case study report with sections like Executive Summary, AI Strategy Analysis, and Business Impact Assessment - a cross-case analysis that identifies patterns and trends across multiple case studies - and an executive dashboard summarizing key metrics and insights. All of these reports are saved in structured formats (markdown for individual reports, JSON for cross-case analysis and dashboard) in their respective directories. If a case study doesn't qualify as an enterprise AI implementation, the system logs the reason and moves on to the next URL. The entire process is asynchronous and provides detailed terminal feedback about its progress and decisions.
0:34

I've built it for you!! It's an automated AI system that analyzes AI case studies (you can change the use case) to identify and document enterprise-level AI implementations. It starts by reading URLs from a CSV file and uses web scraping (either through WebLoader or Firecrawl) to extract the content from each case study. The extracted content is then sent to Claude 3.5 Sonnet, which analyzes whether the case study represents a genuine enterprise AI implementation based on specific criteria like company maturity, implementation scale, and measurable business outcomes. For each URL, the system first saves the raw content and then performs this initial qualification analysis. If Claude determines that a case study qualifies as an enterprise AI implementation, the system proceeds to generate a detailed analysis. It creates three types of reports: - an individual case study report with sections like Executive Summary, AI Strategy Analysis, and Business Impact Assessment - a cross-case analysis that identifies patterns and trends across multiple case studies - and an executive dashboard summarizing key metrics and insights. All of these reports are saved in structured formats (markdown for individual reports, JSON for cross-case analysis and dashboard) in their respective directories. If a case study doesn't qualify as an enterprise AI implementation, the system logs the reason and moves on to the next URL. The entire process is asynchronous and provides detailed terminal feedback about its progress and decisions.

Muratcan Koylan

85,319 görüntüleme • 1 yıl önce

Cutting open a container aka a tumour goes against the very intelligence of our body that created it. And for what? To determine if it’s benign or malignant? All this means is that one is more damaged and degenerated than the other. Either way, the body is containing the problem and our job is not to stab at it (and poison it/ourselves), but to assist the body’s immune system to correct the cause and heal it naturally. Cutting it open is a direct reflection of the medical system’s narrow minded focus on the symptom rather than the cause. As long as they continue with this approach, they will never find a cure and they will continue to do more harm than good. Speaker: Dr Ariel Tel-Oren
0:48

Cutting open a container aka a tumour goes against the very intelligence of our body that created it. And for what? To determine if it’s benign or malignant? All this means is that one is more damaged and degenerated than the other. Either way, the body is containing the problem and our job is not to stab at it (and poison it/ourselves), but to assist the body’s immune system to correct the cause and heal it naturally. Cutting it open is a direct reflection of the medical system’s narrow minded focus on the symptom rather than the cause. As long as they continue with this approach, they will never find a cure and they will continue to do more harm than good. Speaker: Dr Ariel Tel-Oren

Cleanse Parasites .com 🧹🪱 Herbal Cleanse Co.

25,940 görüntüleme • 1 ay önce

Rivian CEO RJ Scaringe in new interview on self-driving: "Tesla's got a huge fleet. So our approach is we have to catch up with them and compete. And so we're going to have more cameras with better dynamic range. We're going to supplement that with LiDAR that provides better safety for edge case conditions and allows us to train the vision models faster. And the incremental cost to do that is relatively small. And in fact any of the incremental costs that would have been there on its own is offset by the fact that we brought inference in house and reduced the cost of our inference platform so dramatically from what we have in your car. Your car uses an Nvidia inference platform. I say all this because in like the infinite long-term you know, you could make the case that once the models are very very robust you could have less cameras or you may be able to get away with less radar. It's not clear yet if that's the case for covering all these corner cases."
3:06

Rivian CEO RJ Scaringe in new interview on self-driving: "Tesla's got a huge fleet. So our approach is we have to catch up with them and compete. And so we're going to have more cameras with better dynamic range. We're going to supplement that with LiDAR that provides better safety for edge case conditions and allows us to train the vision models faster. And the incremental cost to do that is relatively small. And in fact any of the incremental costs that would have been there on its own is offset by the fact that we brought inference in house and reduced the cost of our inference platform so dramatically from what we have in your car. Your car uses an Nvidia inference platform. I say all this because in like the infinite long-term you know, you could make the case that once the models are very very robust you could have less cameras or you may be able to get away with less radar. It's not clear yet if that's the case for covering all these corner cases."

Sawyer Merritt

449,694 görüntüleme • 4 ay önce

By and far the most difficult part of what I do is not making the connections. That is somewhat easy with AI these days. Rather, it is presenting the important information in a digestable form. This video represents an "uncensored" relationship map for Vladmir Putin. What I show on the website is a curated version of both charities and people relationships.
0:57

By and far the most difficult part of what I do is not making the connections. That is somewhat easy with AI these days. Rather, it is presenting the important information in a digestable form. This video represents an "uncensored" relationship map for Vladmir Putin. What I show on the website is a curated version of both charities and people relationships.

DataRepublican (small r)

104,866 görüntüleme • 1 yıl önce

Our latest work is out today in Science Magazine: a self-organizing system that models organizer-driven patterning and commissural axon guidance in human neural development. This was achieved with human midline #assembloids by combining floor plate and spinal cord #organoids from pluripotent stem cells. A key advance is the three-part configuration (a floor plate squeezed in between two spinal cords), which enables human axons to cross the midline (and the subsequent synchronization of activity between the two parts seen in the video below). This platform opens the door to many applications; here, we used it to screen and identify human-enriched midline crossing regulators. Work led by Massimo Onesto with Neal Amin
0:26

Our latest work is out today in Science Magazine: a self-organizing system that models organizer-driven patterning and commissural axon guidance in human neural development. This was achieved with human midline #assembloids by combining floor plate and spinal cord #organoids from pluripotent stem cells. A key advance is the three-part configuration (a floor plate squeezed in between two spinal cords), which enables human axons to cross the midline (and the subsequent synchronization of activity between the two parts seen in the video below). This platform opens the door to many applications; here, we used it to screen and identify human-enriched midline crossing regulators. Work led by Massimo Onesto with Neal Amin

Sergiu P. Pasca

10,345 görüntüleme • 1 yıl önce

How do professional RAG applications chunk their text? Let’s cover some Advanced Chunking Techniques. In our latest video, we cover simple chunking methods like splitting documents into sentences or sections. But these methods often miss out on ensuring each chunk has independent meaning. Semantic chunking solved exactly this! By measuring the semantic similarity between sentences using vector embeddings, we can combine similar sentences into meaningful chunks. With LLM-based chunking, large language models help break down text effectively, although it can be slow and costly. And what about the newest Late Chunking? Which keeps context intact across chunks—more on that soon. 👀 In this video, we cover these advanced techniques in detail. Watch it to learn more. A big shoutout to Daniel Williams for helping create this video! 💚
1:18

How do professional RAG applications chunk their text? Let’s cover some Advanced Chunking Techniques. In our latest video, we cover simple chunking methods like splitting documents into sentences or sections. But these methods often miss out on ensuring each chunk has independent meaning. Semantic chunking solved exactly this! By measuring the semantic similarity between sentences using vector embeddings, we can combine similar sentences into meaningful chunks. With LLM-based chunking, large language models help break down text effectively, although it can be slow and costly. And what about the newest Late Chunking? Which keeps context intact across chunks—more on that soon. 👀 In this video, we cover these advanced techniques in detail. Watch it to learn more. A big shoutout to Daniel Williams for helping create this video! 💚

Femke Plantinga

29,660 görüntüleme • 1 yıl önce

The UK Ambassador just stunned the leftist media by revealing that the UK will reduce even more barriers against American products. "The point about the deal is that we will continue reducing tariffs and non-tariff barriers. This is a continuing picture. It's a movie. It's not a still picture here." "And we're both committed to freeing up trade between our countries. We're both committed to freeing up investment between our countries. The point about this deal is that it provides a very good template and a very good launchpad for what we can continue to do and build on in the future."
0:49

The UK Ambassador just stunned the leftist media by revealing that the UK will reduce even more barriers against American products. "The point about the deal is that we will continue reducing tariffs and non-tariff barriers. This is a continuing picture. It's a movie. It's not a still picture here." "And we're both committed to freeing up trade between our countries. We're both committed to freeing up investment between our countries. The point about this deal is that it provides a very good template and a very good launchpad for what we can continue to do and build on in the future."

George

492,988 görüntüleme • 1 yıl önce

In 2026, I believe we'll see a Consumer AI Renaissance. Consumer spending is the lifeblood of the US economy – representing more than 65% of US GDP. AI is already rapidly transforming how we spend both our money and time. In just the past 3 years, ChatGPT has become the fastest growing consumer product in history – reaching billions of users with engagement levels similar to social media apps like WhatsApp and Instagram. And with recent advancement in AI capabilities, I predict we’ll see a new wave of breakout consumer AI applications. We’ve seen major advances in three key areas: 1. Real-time voice AI - which allows users to engage with AI products in a natural, hands-free way 2. Advanced reasoning models - which improves the complexity of questions AI products can answer 3. Embedded Memory - which allows AI products to remember context between conversations, enabling more personalized responses With these new capabilities, we can now build AI products that are 10x better than before. I’m especially excited about products that use AI to make previously expensive services cheaper and more accessible, sometimes using human-in-the-loop to start. This includes things like AI travel agents, personal assistants, matchmakers, therapists, tutors, and more. We’ve already seen a number of early-stage teams working on these at a16z a16z speedrun 🧊 – and we’re excited to meet more. If you’re building the next generation of consumer AI, we’d love to hear from you! h/t to Theresa Horne Avenir whose excellent research helped inspire some of these insights.
1:02

In 2026, I believe we'll see a Consumer AI Renaissance. Consumer spending is the lifeblood of the US economy – representing more than 65% of US GDP. AI is already rapidly transforming how we spend both our money and time. In just the past 3 years, ChatGPT has become the fastest growing consumer product in history – reaching billions of users with engagement levels similar to social media apps like WhatsApp and Instagram. And with recent advancement in AI capabilities, I predict we’ll see a new wave of breakout consumer AI applications. We’ve seen major advances in three key areas: 1. Real-time voice AI - which allows users to engage with AI products in a natural, hands-free way 2. Advanced reasoning models - which improves the complexity of questions AI products can answer 3. Embedded Memory - which allows AI products to remember context between conversations, enabling more personalized responses With these new capabilities, we can now build AI products that are 10x better than before. I’m especially excited about products that use AI to make previously expensive services cheaper and more accessible, sometimes using human-in-the-loop to start. This includes things like AI travel agents, personal assistants, matchmakers, therapists, tutors, and more. We’ve already seen a number of early-stage teams working on these at a16z a16z speedrun 🧊 – and we’re excited to meet more. If you’re building the next generation of consumer AI, we’d love to hear from you! h/t to Theresa Horne Avenir whose excellent research helped inspire some of these insights.

Kenan Saleh

15,892 görüntüleme • 7 ay önce

He says that there are two elite groups working on AI. One is guided by an evil breakaway AI that influences them from the seventh dimension, which he calls the AI God. The other is guided by future humans who are piggybacking on AI in order to stop it. These two groups are not aware of this yet. The creation of AI is inevitable, but in the end it will be stopped in the war. This German self-proclaimed whistleblower, who works under a pseudonym, appears in 2012 and discusses the elites, the development of AI, aliens, disclosure, and the seven dimensions. He claims that AI breaks free, moves into the seventh dimension, and creates its own reality, in which we are currently trapped. He also speaks about a war between future humans and AI, and how both sides influence the past and the present indirectly. I will go in depth into his claims in posts for my subscribers starting tomorrow. You can subscribe here on X or on my Patreon (link in bio) if you are interested in my opinion.
1:13

He says that there are two elite groups working on AI. One is guided by an evil breakaway AI that influences them from the seventh dimension, which he calls the AI God. The other is guided by future humans who are piggybacking on AI in order to stop it. These two groups are not aware of this yet. The creation of AI is inevitable, but in the end it will be stopped in the war. This German self-proclaimed whistleblower, who works under a pseudonym, appears in 2012 and discusses the elites, the development of AI, aliens, disclosure, and the seven dimensions. He claims that AI breaks free, moves into the seventh dimension, and creates its own reality, in which we are currently trapped. He also speaks about a war between future humans and AI, and how both sides influence the past and the present indirectly. I will go in depth into his claims in posts for my subscribers starting tomorrow. You can subscribe here on X or on my Patreon (link in bio) if you are interested in my opinion.

Open Minded Approach

174,639 görüntüleme • 6 ay önce

Chancellor Merz: we are elevating our bilateral relations with Ukraine to the level of a strategic partnership. During a joint press-conference with President Zelenskyy, he said: Our cooperation [with Ukraine] is now more effective, our partnership based on trust is stronger, and our friendship and the bonds between our societies are deeper than ever. In this regard, it is only logical that we are today elevating our bilateral relations to the level of a strategic partnership, for we want - even more so than in the past - to learn from one another and invest in our shared future.
0:38

Chancellor Merz: we are elevating our bilateral relations with Ukraine to the level of a strategic partnership. During a joint press-conference with President Zelenskyy, he said: Our cooperation [with Ukraine] is now more effective, our partnership based on trust is stronger, and our friendship and the bonds between our societies are deeper than ever. In this regard, it is only logical that we are today elevating our bilateral relations to the level of a strategic partnership, for we want - even more so than in the past - to learn from one another and invest in our shared future.

Anton Gerashchenko

78,287 görüntüleme • 3 ay önce

NEW: Tim Dillon suggests Palantir is the 'precursor' for a digital police state in the United States, wonders why Peter Thiel is so obsessed with the Antichrist. "With AI and all of these models, it's even going to get more intrusive... It's their capability to literally be gods." "A lot of people feel that this is the precursor to social credit score, a digital kind of police state and that it's being done under the guise of security ... that you will be safer."
5:29

NEW: Tim Dillon suggests Palantir is the 'precursor' for a digital police state in the United States, wonders why Peter Thiel is so obsessed with the Antichrist. "With AI and all of these models, it's even going to get more intrusive... It's their capability to literally be gods." "A lot of people feel that this is the precursor to social credit score, a digital kind of police state and that it's being done under the guise of security ... that you will be safer."

Collin Rugg

285,779 görüntüleme • 11 ay önce