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AGI will arrive in 2026. The final missing piece is Continual Learning. The following AI Leaders have stated they believe Continual Learning will be solved this year: > Shane Legg > Dario Amodei > Sholto Douglas Rumor is GDM has it but needs guradrails. Prepare yourself.

Dan McAteer

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27,228 просмотров • 5 месяцев назад •via X (Twitter)

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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.

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

Making AI learning AGI-capable: continual learning, transfer learning, lifelong learning.

Making AI learning AGI-capable: continual learning, transfer learning, lifelong learning.

Ben Goertzel

17,273 просмотров • 1 месяц назад

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.

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

37,271 просмотров • 5 месяцев назад

OpenAI's Chief Scientist, Jakub Pachocki, on the continual learning wave: frontier labs are already building this into the core of the technology. The entire premise of scaling was to create systems that learn in context. Jakub says continual learning is not some separate missing piece, but “exactly what we’re working toward.”

OpenAI's Chief Scientist, Jakub Pachocki, on the continual learning wave: frontier labs are already building this into the core of the technology. The entire premise of scaling was to create systems that learn in context. Jakub says continual learning is not some separate missing piece, but “exactly what we’re working toward.”

Jacob Effron

15,065 просмотров • 2 месяцев назад

"Will the robotics industry will be generating trillions of dollars of revenue?", "YES." Dario Amodei says breakthroughs in robotics could emerge in several ways, such as through continual learning or generalization. Once achieved, these models will revolutionize both robot design and control. However, it will likely take another year or two from that point for the technology to diffuse through the economy.

"Will the robotics industry will be generating trillions of dollars of revenue?", "YES." Dario Amodei says breakthroughs in robotics could emerge in several ways, such as through continual learning or generalization. Once achieved, these models will revolutionize both robot design and control. However, it will likely take another year or two from that point for the technology to diffuse through the economy.

The Humanoid Hub

117,043 просмотров • 4 месяцев назад

The Dario Amodei interview. 0:00:00 - What exactly are we scaling? 0:12:36 - Is diffusion cope? 0:29:42 - Is continual learning necessary? 0:46:20 - If AGI is imminent, why not buy more compute? 0:58:49 - How will AI labs actually make profit? 1:31:19 - Will regulations destroy the boons of AGI? 1:47:41 - Why can’t China and America both have a country of geniuses in a datacenter? Look up Dwarkesh Podcast on Youtube, Spotify, Apple Podcasts, etc.

The Dario Amodei interview. 0:00:00 - What exactly are we scaling? 0:12:36 - Is diffusion cope? 0:29:42 - Is continual learning necessary? 0:46:20 - If AGI is imminent, why not buy more compute? 0:58:49 - How will AI labs actually make profit? 1:31:19 - Will regulations destroy the boons of AGI? 1:47:41 - Why can’t China and America both have a country of geniuses in a datacenter? Look up Dwarkesh Podcast on Youtube, Spotify, Apple Podcasts, etc.

Dwarkesh Patel

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Anthropic CEO Dario Amodei says AGI may arrive in 2026-2027 is a threshold moment where whoever is ahead in the AI race then could be ahead forever: "I'm aware of how frightening this is"

Anthropic CEO Dario Amodei says AGI may arrive in 2026-2027 is a threshold moment where whoever is ahead in the AI race then could be ahead forever: "I'm aware of how frightening this is"

Tsarathustra

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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)

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

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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

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 • 9 дней 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."

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

They said Akwa Ibom is boring , said we don't have public leisure spots, now we will have one, and it will rank over like 25 states atleast. #MayAkwaIbomBouquetOfProjects will so hit this country.... Prepare all the Awwwwwns... In series and parallel oh Akwa Ibom rising >>>>

They said Akwa Ibom is boring , said we don't have public leisure spots, now we will have one, and it will rank over like 25 states atleast. #MayAkwaIbomBouquetOfProjects will so hit this country.... Prepare all the Awwwwwns... In series and parallel oh Akwa Ibom rising >>>>

Akwa Ibom Hypeman

15,403 просмотров • 1 год назад

Demis Hassabis says scaling still delivers strong returns, but the early exponential doublings have slowed Most areas are ahead of where the field expected them to be But the big gap is continual learning "today's systems stop learning after training. once deployed, they don't adapt"

Demis Hassabis says scaling still delivers strong returns, but the early exponential doublings have slowed Most areas are ahead of where the field expected them to be But the big gap is continual learning "today's systems stop learning after training. once deployed, they don't adapt"

Haider.

17,885 просмотров • 2 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"

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

Richard Sutton argues that AI must move beyond human-generated static data into the “Era of Experience,” where agents learn through continuous interaction with the world. This will require building upon RL with better algorithms capable of continual learning and meta-learning.

Richard Sutton argues that AI must move beyond human-generated static data into the “Era of Experience,” where agents learn through continuous interaction with the world. This will require building upon RL with better algorithms capable of continual learning and meta-learning.

The Humanoid Hub

31,193 просмотров • 1 год назад

Demis Hassabis says the next AI leap is continual learning. Not train once and freeze, but learn from real experience, constantly. AlphaGo did it in games. Now comes the messy real world. Would you let an AI learn from you? 👀

Demis Hassabis says the next AI leap is continual learning. Not train once and freeze, but learn from real experience, constantly. AlphaGo did it in games. Now comes the messy real world. Would you let an AI learn from you? 👀

VraserX e/acc

19,202 просмотров • 5 месяцев назад

> Nate the Hate was correct about the Nintendo Switch 2 reveal > He also said Final Fantasy 7 Remake will come to it this year with Rebirth in 2026 > Rebirth gets Steam Deck verified > Switch 2 is rumored to be more powerful than Steam Deck Yeah I think it’s possible now lol

> Nate the Hate was correct about the Nintendo Switch 2 reveal > He also said Final Fantasy 7 Remake will come to it this year with Rebirth in 2026 > Rebirth gets Steam Deck verified > Switch 2 is rumored to be more powerful than Steam Deck Yeah I think it’s possible now lol

Aero

759,578 просмотров • 1 год назад

You won’t learn by highlighting words in a textbook. Learning is done via feedback loop. Learn something new -> try to apply it -> fuck up/win -> get feedback -> try again. Learning is actually fun when done properly. Try it.

You won’t learn by highlighting words in a textbook. Learning is done via feedback loop. Learn something new -> try to apply it -> fuck up/win -> get feedback -> try again. Learning is actually fun when done properly. Try it.

Andrew Tate

407,180 просмотров • 8 месяцев назад

I seriously wonder why Yann Lecun takes such different positions from Sam Altman and Dario Amodei. Sam and Dario have made it clear in their posts that they believe reasoning will be solved, if not now, then in a few years' time. What's more: in his last post, Dario said that AGI could be realized as early as 2026. And Sam Altman also says that ASI will come in a few thousand days. So why is Yann again talking about assuming decades? Does it have a more complex definition? Has Meta lost more ground after all? So far I don't have a well-founded answer.

I seriously wonder why Yann Lecun takes such different positions from Sam Altman and Dario Amodei. Sam and Dario have made it clear in their posts that they believe reasoning will be solved, if not now, then in a few years' time. What's more: in his last post, Dario said that AGI could be realized as early as 2026. And Sam Altman also says that ASI will come in a few thousand days. So why is Yann again talking about assuming decades? Does it have a more complex definition? Has Meta lost more ground after all? So far I don't have a well-founded answer.

Chubby♨️

456,358 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.

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

53,427 просмотров • 7 месяцев назад

This morning at NeurIPS, Rich Sutton reminded us that we need continual learning to reach AGI. This afternoon, Ali Behrouz presented a Google poster paper, Nested Learning, which provides new ideas on the path to continual learning. I recorded the 40 minute talk as it might be useful for some researchers in the audience. For the rest of us, I subscribe to Andrej Karpathy's suspicion that it will take a 5-10 papers like this to move us to AGI from where we are now, just like it took about 10 papers to move from 2012's AlexNet to ChatGPT. At the very end, I ask Ali how far along to continual learning this represents. Full paper link below, as well as a YouTube link. ps. sorry about the first 2 minutes of bad audio since there were 2 idiots standing beside me have a conversation right in front of this presenter in a rather packed poster presentation. Honestly, tamp down your egos guys and show come common courtesy!

This morning at NeurIPS, Rich Sutton reminded us that we need continual learning to reach AGI. This afternoon, Ali Behrouz presented a Google poster paper, Nested Learning, which provides new ideas on the path to continual learning. I recorded the 40 minute talk as it might be useful for some researchers in the audience. For the rest of us, I subscribe to Andrej Karpathy's suspicion that it will take a 5-10 papers like this to move us to AGI from where we are now, just like it took about 10 papers to move from 2012's AlexNet to ChatGPT. At the very end, I ask Ali how far along to continual learning this represents. Full paper link below, as well as a YouTube link. ps. sorry about the first 2 minutes of bad audio since there were 2 idiots standing beside me have a conversation right in front of this presenter in a rather packed poster presentation. Honestly, tamp down your egos guys and show come common courtesy!

Phil Trubey

235,435 просмотров • 7 месяцев назад