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Google DeepMind say true AGI will “reason, adapt, and learn continuously,” and they estimate it’s about 5-10 years away. Meta’s chief scientist Yann LeCun likewise views continual learning as a key pillar of human level AI and is optimistic about achieving it by around 2030 Currently, large language models...

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Chris

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284,188 просмотров • 9 месяцев назад •via X (Twitter)

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Demis Hassabis: calling today's chatbots “PhD intelligences” is nonsense. They can dazzle at a PhD level one moment and fail high school math the next. True AGI won't make trivial mistakes. It will reason, adapt, and learn continuously. We're still 5–10 years away.
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Demis Hassabis: calling today's chatbots “PhD intelligences” is nonsense. They can dazzle at a PhD level one moment and fail high school math the next. True AGI won't make trivial mistakes. It will reason, adapt, and learn continuously. We're still 5–10 years away.

vitrupo

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Continual Learning in 2026. And AGI with it? In July, Dario said he believes Continual Learning will turn out to be not as difficult as it seems. Sholto Douglas, Anthropic and former Deepmind researcher, predicted Continual Learning gets solved in a satisfying way in 2026. Sholto said that last week. Considering how capable AI models have become in 2025, it is unwise to dismiss this as fundraising or hype. It is much wiser to assume AGI is coming. That it will change the world. And that you can benefit from it... ...than it is to assume the opposite. "Not enough people are ready for AI to not be a bubble." - roon Choose wisely my friends.
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Continual Learning in 2026. And AGI with it? In July, Dario said he believes Continual Learning will turn out to be not as difficult as it seems. Sholto Douglas, Anthropic and former Deepmind researcher, predicted Continual Learning gets solved in a satisfying way in 2026. Sholto said that last week. Considering how capable AI models have become in 2025, it is unwise to dismiss this as fundraising or hype. It is much wiser to assume AGI is coming. That it will change the world. And that you can benefit from it... ...than it is to assume the opposite. "Not enough people are ready for AI to not be a bubble." - roon Choose wisely my friends.

Dan McAteer

69,496 просмотров • 6 месяцев назад

Demis Hassabis points out that today’s models don’t learn continuously, they are trained once and then deployed. Unlike humans, they don’t improve from experience or adapt after release. The next major leap, like “AlphaZero,” would involve self-learning systems that discover knowledge on their own. This ability to learn online and evolve in real time is a key missing component on the path to AGI. Solving this will be critical to building truly intelligent systems.
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Demis Hassabis points out that today’s models don’t learn continuously, they are trained once and then deployed. Unlike humans, they don’t improve from experience or adapt after release. The next major leap, like “AlphaZero,” would involve self-learning systems that discover knowledge on their own. This ability to learn online and evolve in real time is a key missing component on the path to AGI. Solving this will be critical to building truly intelligent systems.

Wes Roth

39,238 просмотров • 6 месяцев назад

Francois Chollet says LLMs aren't enough for human-like continual learning They store skills as vector programs learned via gradient descent — not efficient, not adaptive True AGI needs to learn from experience and generalize fast "LLMs can be part of AGI, but not the solution"
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Francois Chollet says LLMs aren't enough for human-like continual learning They store skills as vector programs learned via gradient descent — not efficient, not adaptive True AGI needs to learn from experience and generalize fast "LLMs can be part of AGI, but not the solution"

Haider.

50,251 просмотров • 8 месяцев назад

AGI probably needs 1-2 new breakthroughs, like continual learning, selective memory, smarter context windows, and long term planning. ~ Google DeepMind CEO Demis Hassabis But in any case, large foundation models will remain the key component of AGI. --- From 'Alex Kantrowitz' YT channel (link in comment)
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AGI probably needs 1-2 new breakthroughs, like continual learning, selective memory, smarter context windows, and long term planning. ~ Google DeepMind CEO Demis Hassabis But in any case, large foundation models will remain the key component of AGI. --- From 'Alex Kantrowitz' YT channel (link in comment)

Rohan Paul

36,646 просмотров • 4 месяцев назад

Demis Hassabis says large language models (LLMs) can search and plan but they forget everything once the session ends. This is because today’s models don’t truly “learn” after deployment. He sees continual learning and better memory as key missing ingredients for AGI. Efficient memory, like the brain’s ability to store only what matters, will be crucial. LLMs are powerful, but without memory, they have a "goldfish brain."
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Demis Hassabis says large language models (LLMs) can search and plan but they forget everything once the session ends. This is because today’s models don’t truly “learn” after deployment. He sees continual learning and better memory as key missing ingredients for AGI. Efficient memory, like the brain’s ability to store only what matters, will be crucial. LLMs are powerful, but without memory, they have a "goldfish brain."

Wes Roth

38,833 просмотров • 5 месяцев назад

CEO of Google Deepmind Demis when asked about AGI: “50% chance by 2030” I agree with his definition for AGI as well — being as cognitively capable as any human in any task
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CEO of Google Deepmind Demis when asked about AGI: “50% chance by 2030” I agree with his definition for AGI as well — being as cognitively capable as any human in any task

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Demis on the breakthroughs needed for AGI. It is not many. Predict Google DeepMind has Continual Learning before 2026 is finished, and likely other labs too.
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Demis on the breakthroughs needed for AGI. It is not many. Predict Google DeepMind has Continual Learning before 2026 is finished, and likely other labs too.

Dan McAteer

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Today, Michael Elabd, Arjun Karanam, and I are excited to announce Trajectory. We are a research lab and product company building the platform for Continual Learning. Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. Trajectory We’ve raised $15M from Conviction, Bessemer, Radical Ventures, Jeff Dean, Fei-Fei Li and more. We’re partnering with some of the best AI-native companies: Clay AI Harvey, Decagon, Mercor, Rogo to power their agentic systems, some of which we are already in production with. We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma. AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.
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Ronak Malde

1,809,570 просмотров • 1 месяц назад

“The thing that happened with AGI and pretraining is that in some sense they overshot the target. You will realize that a human being is not an AGI. Because a human being lacks a huge amount of knowledge. Instead, we rely on continual learning. If I produce a super intelligent 15 -year -old, they don't know very much at all. A great student, very eager. [You can say,] ‘You go and be a programmer. You go and be a doctor. Go and learn.’ So you could imagine that the deployment itself will involve some kind of a learning trial and error period. It's a process as opposed to, you drop the finished thing.” Ilya Sutskever
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“The thing that happened with AGI and pretraining is that in some sense they overshot the target. You will realize that a human being is not an AGI. Because a human being lacks a huge amount of knowledge. Instead, we rely on continual learning. If I produce a super intelligent 15 -year -old, they don't know very much at all. A great student, very eager. [You can say,] ‘You go and be a programmer. You go and be a doctor. Go and learn.’ So you could imagine that the deployment itself will involve some kind of a learning trial and error period. It's a process as opposed to, you drop the finished thing.” Ilya Sutskever

Dwarkesh Patel

593,427 просмотров • 7 месяцев назад

Andrej Karpathy: AI agents like Claude or Codex are still far from acting as real “employees.” They lack memory, multimodality, and true learning. Fixing that will take about a decade — not because it’s impossible, but because deep intelligence just takes time to build.
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Andrej Karpathy: AI agents like Claude or Codex are still far from acting as real “employees.” They lack memory, multimodality, and true learning. Fixing that will take about a decade — not because it’s impossible, but because deep intelligence just takes time to build.

Chubby♨️

401,422 просмотров • 8 месяцев назад

Demis confirmed DeepMind hasn’t cracked continual learning. There were rumors on X that Google DeepMind had solved it. Not true. They’re experimenting with blending AlphaZero with large foundation models to see if it can scale and generalize to the real world, not just games.
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Demis confirmed DeepMind hasn’t cracked continual learning. There were rumors on X that Google DeepMind had solved it. Not true. They’re experimenting with blending AlphaZero with large foundation models to see if it can scale and generalize to the real world, not just games.

Yuchen Jin

75,633 просмотров • 5 месяцев назад

All remote cognitive jobs, done using only a laptop setup, is most exposed to be automated or heavily reduced by AI. ~ Shane Legg, Chief AGI Scientist and co-founder of Google DeepMind ---- Full video from 'Google DeepMind and Hannah Fry' YT channel (link in comment)
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All remote cognitive jobs, done using only a laptop setup, is most exposed to be automated or heavily reduced by AI. ~ Shane Legg, Chief AGI Scientist and co-founder of Google DeepMind ---- Full video from 'Google DeepMind and Hannah Fry' YT channel (link in comment)

Rohan Paul

81,268 просмотров • 6 месяцев назад

4. AGI timeline is 2027-2034 Yann LeCun's exact words: "3-5 years to get world models working. Then scaling until human-level AI... within a decade or so." But it won't come from scaling LLMs.
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4. AGI timeline is 2027-2034 Yann LeCun's exact words: "3-5 years to get world models working. Then scaling until human-level AI... within a decade or so." But it won't come from scaling LLMs.

Karl Mehta

96,079 просмотров • 10 месяцев назад

capability != learning new benchtalks with Parth Asawa on continual learning, where we discuss teaching models to learn from experience, measuring learning ability, the bet on parametric models, and more 01:06 What is continual learning? 04:10 Why capability and learning are different 06:13 Why build a benchmark? 08:07 Continual Learning Bench launch and reception 09:13 Anthropic's Fable release and Continual Learning Bench 11:02 How to design tasks for continual learning 18:41 The gain metric 24:01 What good looks like on the leaderboard 29:13 Failure modes: why models can't update their beliefs 31:12 Parametric systems and future architectures 34:30 Open science and AI safety 45:42 Lightning round 49:08 How to contribute to Continual Learning Bench
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capability != learning new benchtalks with Parth Asawa on continual learning, where we discuss teaching models to learn from experience, measuring learning ability, the bet on parametric models, and more 01:06 What is continual learning? 04:10 Why capability and learning are different 06:13 Why build a benchmark? 08:07 Continual Learning Bench launch and reception 09:13 Anthropic's Fable release and Continual Learning Bench 11:02 How to design tasks for continual learning 18:41 The gain metric 24:01 What good looks like on the leaderboard 29:13 Failure modes: why models can't update their beliefs 31:12 Parametric systems and future architectures 34:30 Open science and AI safety 45:42 Lightning round 49:08 How to contribute to Continual Learning Bench

vincent sunn chen

39,598 просмотров • 16 дней назад

Yann Lecun says his estimate for the creation of human-level AI is not that different from Sam Altman or Demis Hassabis - it is possible within 5-10 years
1:21

Yann Lecun says his estimate for the creation of human-level AI is not that different from Sam Altman or Demis Hassabis - it is possible within 5-10 years

Tsarathustra

357,202 просмотров • 1 год назад

How close is DeepMind to achieving AGI? Demis Hassabis says: We are on track and DeepMind targets AGI completion by 2030 but I wouldn't be surprised if it arrives in the next decade.
0:20

How close is DeepMind to achieving AGI? Demis Hassabis says: We are on track and DeepMind targets AGI completion by 2030 but I wouldn't be surprised if it arrives in the next decade.

Haider.

94,348 просмотров • 1 год назад

this was the most important bit of the ilya interview imo. humans are not AGI, and LLMs won’t become like humans with more scaling. humans lack a huge amount of knowledge and have insanely good “continual learning” powered by extremely good reward functions (except for addiction) that guide them. and so it’s more about the learning rather than knowledge. the term AGI was coined as a response to the narrow AI which was only capable of intelligence in a specific domain (chess, game, code, etc) but General intelligence is able to learn different topics because that’s what “learning” is about. pre-training was thought to be the solution to reaching AGI because you train AI on so many different topics. but they have “overshot” that target. amazing perspective.
1:57

this was the most important bit of the ilya interview imo. humans are not AGI, and LLMs won’t become like humans with more scaling. humans lack a huge amount of knowledge and have insanely good “continual learning” powered by extremely good reward functions (except for addiction) that guide them. and so it’s more about the learning rather than knowledge. the term AGI was coined as a response to the narrow AI which was only capable of intelligence in a specific domain (chess, game, code, etc) but General intelligence is able to learn different topics because that’s what “learning” is about. pre-training was thought to be the solution to reaching AGI because you train AI on so many different topics. but they have “overshot” that target. amazing perspective.

ℏεsam

53,442 просмотров • 7 месяцев назад

Dwarkesh Patel says we haven't yet achieved AGI for one simple reason: the models can't learn on the job. They can rewrite a transcript or suggest a clip to tweet out, but they won't remember what you liked or why it worked. Without learning loops, they can't evolve from tool to employee. “Why can't we just delegate all language work?”
2:30

Dwarkesh Patel says we haven't yet achieved AGI for one simple reason: the models can't learn on the job. They can rewrite a transcript or suggest a clip to tweet out, but they won't remember what you liked or why it worked. Without learning loops, they can't evolve from tool to employee. “Why can't we just delegate all language work?”

vitrupo

85,997 просмотров • 1 год назад