Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

"100 million words context window is already possible, which is roughly what a human hears in a lifetime. Inference support is the only bottleneck to achieve it. And AI Models actually do learn during the context window, without changing the weights." ~ Anthropic CEO Dario Amodei This is from... show more

Rohan Paul

152,676 subscribers

407,212 görüntüleme • 1 ay önce •via X (Twitter)

Bilim & Teknoloji Eğitim

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Based on a staggering leap from 3% to 50% on professional benchmarks in mere months, Anthropic CEO Dario Amodei states that human-level AI is no longer a distant concept. This exponential progress suggests it is now a near-term reality, arriving not in decades, but likely within months, shattering all conventional predictions. The age of AGI is at our doorstep.

Based on a staggering leap from 3% to 50% on professional benchmarks in mere months, Anthropic CEO Dario Amodei states that human-level AI is no longer a distant concept. This exponential progress suggests it is now a near-term reality, arriving not in decades, but likely within months, shattering all conventional predictions. The age of AGI is at our doorstep.

Camus

11,080 görüntüleme • 11 ay önce

I have deep respect for Dario Amodei & Anthropic and I also get why every major AI lab CEO must downplay the open source threat. But his argument in Big Technology podcast to Alex Kantrowitz against open source models feels surprisingly weak - 'someone still has to run inference' completely misses the core economics. Makes me wonder: is there actually no good counter-argument to open source commoditization risk?

I have deep respect for Dario Amodei & Anthropic and I also get why every major AI lab CEO must downplay the open source threat. But his argument in Big Technology podcast to Alex Kantrowitz against open source models feels surprisingly weak - 'someone still has to run inference' completely misses the core economics. Makes me wonder: is there actually no good counter-argument to open source commoditization risk?

Jikku Jose

22,574 görüntüleme • 1 yıl önce

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below

Hanako

19,160 görüntüleme • 17 gün önce

Dario Amodei directly argues that the US Department of War is misrepresenting the law to claim power that the government simply does not have. CBS News asked "Do you think Anthropic can survive this as a business?" Dario effectively says that the Department of War is using social media to trick the public into believing they have powers that Congress never actually gave them. Dario Amadoui confidently states that the impact of the "supply chain risk" designation is actually very limited and will not stop their success, and that Anthropic will be just fine. ---- Video from 'CBS News + CBS Evening News + CBS Mornings' YT channel (link in comment)

Dario Amodei directly argues that the US Department of War is misrepresenting the law to claim power that the government simply does not have. CBS News asked "Do you think Anthropic can survive this as a business?" Dario effectively says that the Department of War is using social media to trick the public into believing they have powers that Congress never actually gave them. Dario Amadoui confidently states that the impact of the "supply chain risk" designation is actually very limited and will not stop their success, and that Anthropic will be just fine. ---- Video from 'CBS News + CBS Evening News + CBS Mornings' YT channel (link in comment)

Rohan Paul

30,664 görüntüleme • 5 ay önce

Anthropic' Dario Amodei on delaying Mythos release. "We have suffered enormously commercially from not releasing this model. This model has incredibly accelerated research within Anthropic and production in next models. It would do the same in the outside world if we were to release it. This has hurt us enormously commercially." ---- From "Bloomberg Originals" YouTube channel, (link in comment)

Anthropic' Dario Amodei on delaying Mythos release. "We have suffered enormously commercially from not releasing this model. This model has incredibly accelerated research within Anthropic and production in next models. It would do the same in the outside world if we were to release it. This has hurt us enormously commercially." ---- From "Bloomberg Originals" YouTube channel, (link in comment)

Rohan Paul

41,022 görüntüleme • 1 ay önce

The Bible in its context is not a Christian book. The Bible in its nature is a catholic book which was edited and given to us in the context of what it is. - Joshua Maponga

The Bible in its context is not a Christian book. The Bible in its nature is a catholic book which was edited and given to us in the context of what it is. - Joshua Maponga

Typical African

12,891 görüntüleme • 4 ay önce

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,950 görüntüleme • 1 yıl önce

Google DeepMind CEO Demis Hassabis's advice to fresh graduates. Next few years is a golden era. Become 10X individual at everything during this time. From 'WIRED` YT Channel (full video link in comment)

Google DeepMind CEO Demis Hassabis's advice to fresh graduates. Next few years is a golden era. Become 10X individual at everything during this time. From 'WIRED` YT Channel (full video link in comment)

Rohan Paul

123,955 görüntüleme • 10 ay önce

Anthropic has rejected demands from Defense Secretary Pete Hegseth calling for full lawful use of its AI technology. CEO Dario Amodei says that AI is important to military defense, but there must be safeguards against use for mass surveillance and fully autonomous weapons. The Pentagon said it will not let “any company dictate the terms.” 60 Minutes spoke with Dario and Anthropic President Daniela Amodei in November, and this is how they described the importance of “bumpers and guardrails” to manage the risk of AI.

Anthropic has rejected demands from Defense Secretary Pete Hegseth calling for full lawful use of its AI technology. CEO Dario Amodei says that AI is important to military defense, but there must be safeguards against use for mass surveillance and fully autonomous weapons. The Pentagon said it will not let “any company dictate the terms.” 60 Minutes spoke with Dario and Anthropic President Daniela Amodei in November, and this is how they described the importance of “bumpers and guardrails” to manage the risk of AI.

60 Minutes

424,397 görüntüleme • 5 ay önce

In an exclusive interview with CBS News' Jo Ling Kent, Anthropic CEO Dario Amodei said that the Pentagon’s decision to designate the AI company a supply chain risk is “retaliatory and punitive.” The Pentagon made the designation, which restricts military contractors from doing business with Anthropic, after the company refused to give the military unfettered access to its AI model.

In an exclusive interview with CBS News' Jo Ling Kent, Anthropic CEO Dario Amodei said that the Pentagon’s decision to designate the AI company a supply chain risk is “retaliatory and punitive.” The Pentagon made the designation, which restricts military contractors from doing business with Anthropic, after the company refused to give the military unfettered access to its AI model.

CBS News

1,213,350 görüntüleme • 5 ay önce

I love that Anthropic is the only big AI lab thats seriously explores mechanistic interpretability at this level. I remember Dario Amodei (Anth CEO) speak about this in the Lex podcast when asked what advice he would give to young AI researchers.

I love that Anthropic is the only big AI lab thats seriously explores mechanistic interpretability at this level. I remember Dario Amodei (Anth CEO) speak about this in the Lex podcast when asked what advice he would give to young AI researchers.

AVB

194,845 görüntüleme • 4 ay önce

So the wife of Anthropic's CEO Dario Amodei once tried to solicit Jeffrey Epstein to invest in her "revolutionary porn company." She is also friends with Ivanka and Jared, whom Amodei approached for investment. Now Anthropic is seeking a $2T IPO in October. What a timeline.

So the wife of Anthropic's CEO Dario Amodei once tried to solicit Jeffrey Epstein to invest in her "revolutionary porn company." She is also friends with Ivanka and Jared, whom Amodei approached for investment. Now Anthropic is seeking a $2T IPO in October. What a timeline.

James Li

33,016 görüntüleme • 13 saat önce

📁 Dario Amodei, CEO of Anthropic, says AI will disrupt jobs faster and broader than anything before. Some roles, even software engineers, may shrink as models automate entire workflows. The real question is not which jobs disappear. It is whether we create new ones in time.

📁 Dario Amodei, CEO of Anthropic, says AI will disrupt jobs faster and broader than anything before. Some roles, even software engineers, may shrink as models automate entire workflows. The real question is not which jobs disappear. It is whether we create new ones in time.

Jon Hernandez

33,387 görüntüleme • 5 ay önce

Anthropic engineer: “80% of our engineers are using Loops. Now everyone is exploring how to build right Context for agents.” in 3-6 months, we’ll all become context engineers. Context is the most important part of agents.” in this 1-hour talk, Anthropic engineer shows how to build a self-improving agents from sratch. It’s worth more than a $500 agentic course. Watch this video, then read the article below on how to become a context architect.

Anthropic engineer: “80% of our engineers are using Loops. Now everyone is exploring how to build right Context for agents.” in 3-6 months, we’ll all become context engineers. Context is the most important part of agents.” in this 1-hour talk, Anthropic engineer shows how to build a self-improving agents from sratch. It’s worth more than a $500 agentic course. Watch this video, then read the article below on how to become a context architect.

Codez

35,798 görüntüleme • 19 gün önce

Current robotics models really underscore how wildly efficient the human brain is. Robotics models are relatively small (low billions of parameters), are only storing a second of context, and doing 100ms inference steps. By contrast the human brain has 100 trillion synapses (roughly analogous to parameters), processes 24 frames per second, while considering hours of previous context. So how do we expect to get many order-of-magnitude improvements in robotics across all these dimensions (which are trading off against each other in terms inference compute) at the same time? And how do we explain what's going on in the brain? Is it more analogous to a software advantage or a hardware advantage? Full episode with Sergey Levine out now!

Current robotics models really underscore how wildly efficient the human brain is. Robotics models are relatively small (low billions of parameters), are only storing a second of context, and doing 100ms inference steps. By contrast the human brain has 100 trillion synapses (roughly analogous to parameters), processes 24 frames per second, while considering hours of previous context. So how do we expect to get many order-of-magnitude improvements in robotics across all these dimensions (which are trading off against each other in terms inference compute) at the same time? And how do we explain what's going on in the brain? Is it more analogous to a software advantage or a hardware advantage? Full episode with Sergey Levine out now!

Dwarkesh Patel

87,247 görüntüleme • 11 ay önce

2 years ago, Anthropic CEO Dario Amodei warned the U.S. Congress: “The scaling of open-source models is going down a very dangerous path…” Today, Kimi K3 is already competing with frontier models. Seeing what this model can do, I think we’re getting closer to open-source models reaching Mythos-level capabilities with little to no guardrails. That’s a pretty scary thought.

2 years ago, Anthropic CEO Dario Amodei warned the U.S. Congress: “The scaling of open-source models is going down a very dangerous path…” Today, Kimi K3 is already competing with frontier models. Seeing what this model can do, I think we’re getting closer to open-source models reaching Mythos-level capabilities with little to no guardrails. That’s a pretty scary thought.

m0h

147,837 görüntüleme • 28 gün önce

$Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.$

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

Alexander Whedon

13,275,488 görüntüleme • 3 ay önce

Real-time, human-generated, diverse data from 600 million active users. This is the perfect fuel for AI models — and it's exactly what xAI needs to compete with OpenAI and Anthropic. The timing is remarkable:

Real-time, human-generated, diverse data from 600 million active users. This is the perfect fuel for AI models — and it's exactly what xAI needs to compete with OpenAI and Anthropic. The timing is remarkable:

Fernando Cao

454,991 görüntüleme • 1 yıl önce

The CEO of Anthropic: "We might be six to twelve months away from when the model is doing most, maybe all, of what software engineers do end to end." In 30 minutes, Dario Amodei and Google DeepMind's Demis Hassabis break down the day after AGI. AGI timeline + self-improving models + the first jobs to go + who stays in control Worth more than a year of AI hot takes on your timeline

The CEO of Anthropic: "We might be six to twelve months away from when the model is doing most, maybe all, of what software engineers do end to end." In 30 minutes, Dario Amodei and Google DeepMind's Demis Hassabis break down the day after AGI. AGI timeline + self-improving models + the first jobs to go + who stays in control Worth more than a year of AI hot takes on your timeline

Phosphen

27,595 görüntüleme • 2 ay önce