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Altman explains current AI memory is still primitive, comparable to early GPT-2 days. Future systems will remember an entire life, not just facts but subtle preferences. Small habits and unspoken likes will be learned automatically over time. That level of memory may become one of AI’s most powerful capabilities.

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Wes Roth

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42,054 просмотров • 6 месяцев назад •via X (Twitter)

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📁 Sam Altman says the real AI breakthrough will not be better reasoning, but total memory. An AI will be able to remember every conversation, email, and document across a person’s lifetime, identifying patterns and preferences humans never consciously express. Once memory becomes persistent, the idea of a personal assistant will fundamentally change.
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📁 Sam Altman says the real AI breakthrough will not be better reasoning, but total memory. An AI will be able to remember every conversation, email, and document across a person’s lifetime, identifying patterns and preferences humans never consciously express. Once memory becomes persistent, the idea of a personal assistant will fundamentally change.

Jon Hernandez

4,269,363 просмотров • 6 месяцев назад

Sam Altman says no human assistant can remember every word or every small preference AI will, including the tiny patterns you didn't even think to mention Memory is still crude today, but when it remembers your whole life and personalizes everything, that's when it gets powerful
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Sam Altman says no human assistant can remember every word or every small preference AI will, including the tiny patterns you didn't even think to mention Memory is still crude today, but when it remembers your whole life and personalizes everything, that's when it gets powerful

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54,303 просмотров • 6 месяцев назад

"we will not get to human-level AI by just scaling up LLMs... it's not going to happen" Meta AI chief scientist, Yann LeCun: scaling up LLMs alone will not lead to human-level AI ...what we’ll have instead are systems with huge memory and retrieval abilities, but still not able to invent solutions to new problems
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"we will not get to human-level AI by just scaling up LLMs... it's not going to happen" Meta AI chief scientist, Yann LeCun: scaling up LLMs alone will not lead to human-level AI ...what we’ll have instead are systems with huge memory and retrieval abilities, but still not able to invent solutions to new problems

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123,655 просмотров • 1 год назад

Sam Altman says AI reasoning is still at the GPT-2 stage but the improvement curve is steep and the new o1 model represents a new paradigm of AI development which will enable rapid progress in capabilities
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Sam Altman says AI reasoning is still at the GPT-2 stage but the improvement curve is steep and the new o1 model represents a new paradigm of AI development which will enable rapid progress in capabilities

Tsarathustra

419,003 просмотров • 1 год назад

Sam Altman just said in his new interview, that a new AI architecture is coming that will be a massive upgrade, just like Transformers were over Long Short-Term Memory. And also now the current class of frontier models are powerful enough to have the brainpower needed to help us research these ideas. His advice is to use the current AI to help you find that next giant step forward. --- From 'TreeHacks' YT Channel (link in comment)
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Sam Altman just said in his new interview, that a new AI architecture is coming that will be a massive upgrade, just like Transformers were over Long Short-Term Memory. And also now the current class of frontier models are powerful enough to have the brainpower needed to help us research these ideas. His advice is to use the current AI to help you find that next giant step forward. --- From 'TreeHacks' YT Channel (link in comment)

Rohan Paul

656,897 просмотров • 3 месяцев назад

🕯️ It took me over 13 minutes to read the names of all the fallen IDF soldiers. 220 names. Each name is an entire life. May their memory be a blessing. We will never forget.
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🕯️ It took me over 13 minutes to read the names of all the fallen IDF soldiers. 220 names. Each name is an entire life. May their memory be a blessing. We will never forget.

India Naftali

14,297,136 просмотров • 2 лет назад

As AI matures, most of what you do intellectually will become meaningless. So human beings must find access to something beyond the intellect - an intelligence unsullied by memory. Then you will live magically and AI will be a boon.
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As AI matures, most of what you do intellectually will become meaningless. So human beings must find access to something beyond the intellect - an intelligence unsullied by memory. Then you will live magically and AI will be a boon.

Sadhguru

118,938 просмотров • 3 лет назад

Introducing `gpt-author v2` 📕✍️ One prompt -> an entire fantasy novel! Just describe the high-level details, and a chain of AI systems will write an entire book for you in minutes. - complete w/ cover art - export to ebook format And it's open-source:
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Introducing `gpt-author v2` 📕✍️ One prompt -> an entire fantasy novel! Just describe the high-level details, and a chain of AI systems will write an entire book for you in minutes. - complete w/ cover art - export to ebook format And it's open-source:

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276,531 просмотров • 2 лет назад

Introducing `gpt-author` 📕✍️ One prompt -> an entire fantasy novel! Just describe the high-level details, and a chain of AI systems will write an entire book for you in minutes. - complete w/ cover art - export to Kindle store And it's open-source:
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2,101,741 просмотров • 3 лет назад

Sam Altman says an AI will be a better CEO of OpenAI than him in the reasonably near future Once that becomes clear, companies and countries will face pressure to give AI more influential roles or fall behind "people will still want humans, but if AI is better, more power will shift to AI"
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Sam Altman says an AI will be a better CEO of OpenAI than him in the reasonably near future Once that becomes clear, companies and countries will face pressure to give AI more influential roles or fall behind "people will still want humans, but if AI is better, more power will shift to AI"

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31,627 просмотров • 3 месяцев назад

The lesson I’m still finding is that no matter how high or how low you are, that life will still test you! And To always remember the shield of the lord is your protection (head to toe). To Surrender the outcomes and lean into the process (Harder). To understand life comes in seasons. And to appreciate that the lessons being learned may not be for the test I will take tomorrow, but will surely be prepared for “So Just Keep Going” #DairiesForTheChosen
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Jarvis Juice Landry

113,945 просмотров • 2 лет назад

our big bet with WALRUS MEMORY is that memory becomes a fully composable, transactable part of what ai agents need to do in the future and it's going to be an incredibly valuable asset and we've built a primitive that lets you fully own and carry it across every agent you use
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25,952 просмотров • 7 дней назад

Ray Dalio’s twin is a powerful example of Amiko’s core belief: we need AI that reflects someone, not everyone. Not generic, not average. Personal. Built from your identity, memory, preferences, and lived logic. He spent 40 years documenting his thinking, worked with world class researchers, and spent millions to create his digital twin. He needed More Ray. But only a small number of people will ever have the time, tools, or resources to do that. And that's why we built Amiko. Personal AI for everyone. Not just More Ray. More You.
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AMIKO

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Sam Altman says the leap from GPT-4 to GPT-5 will be as big as that of GPT-3 to 4 and the plan is to integrate the GPT and o series of models into one model that can do everything
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Sam Altman says most people still think AI is just ChatGPT, but the frontier is far ahead Models now beat humans on the hardest contests; GPT-5 is yielding small, real discoveries The cost of intelligence is falling fast, while capabilities are rising "the world is just catching up"
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RAG might already be becoming obsolete. A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.” Now the comments section looks like the birth of an entirely new AI category. 5000+ stars later, developers are rapidly building: • persistent AI memory systems • self-maintaining knowledge bases • multi-agent research environments • contradiction detection engines • AI-native company operating systems • local-first memory architectures • graph-based reasoning layers • evolving second brains And the craziest part? Most of them were built in DAYS. Because the core idea is insanely powerful: Instead of AI repeatedly retrieving raw chunks like traditional RAG… …the model continuously maintains a living knowledge system. Not temporary context. Persistent synthesis. The shift sounds subtle until you realize what it changes: RAG: retrieve → answer → forget LLM Wiki: ingest → synthesize → evolve That one architectural difference is causing an explosion of experimentation right now. People are already building: • agent memory operating systems • AI-maintained engineering documentation • self-healing knowledge graphs • persistent research environments • conversational memory architectures • contradiction-aware wikis • context compression engines • machine-readable company systems The comments section alone feels like watching an ecosystem form in real time. One developer built deterministic contradiction detection using sheaf cohomology Another built “sleep consolidation” for AI memory systems inspired by human memory formation Another created persistent multi-agent vault conversations Another turned entire repositories into continuously maintained AI wikis Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration This is the important part: Karpathy didn’t launch a product. He introduced a pattern. And patterns are what create ecosystems. The same way: • transformers created modern AI • RAG created AI retrieval startups • agents created orchestration frameworks LLM Wikis may create persistent AI memory infrastructure. That’s why this moment feels different. For years, AI systems have been stateless. Now developers are trying to build systems that actually accumulate understanding over time. And once knowledge compounds instead of resetting… …the entire interface layer of AI changes. (Link in comments)
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RAG might already be becoming obsolete. A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.” Now the comments section looks like the birth of an entirely new AI category. 5000+ stars later, developers are rapidly building: • persistent AI memory systems • self-maintaining knowledge bases • multi-agent research environments • contradiction detection engines • AI-native company operating systems • local-first memory architectures • graph-based reasoning layers • evolving second brains And the craziest part? Most of them were built in DAYS. Because the core idea is insanely powerful: Instead of AI repeatedly retrieving raw chunks like traditional RAG… …the model continuously maintains a living knowledge system. Not temporary context. Persistent synthesis. The shift sounds subtle until you realize what it changes: RAG: retrieve → answer → forget LLM Wiki: ingest → synthesize → evolve That one architectural difference is causing an explosion of experimentation right now. People are already building: • agent memory operating systems • AI-maintained engineering documentation • self-healing knowledge graphs • persistent research environments • conversational memory architectures • contradiction-aware wikis • context compression engines • machine-readable company systems The comments section alone feels like watching an ecosystem form in real time. One developer built deterministic contradiction detection using sheaf cohomology Another built “sleep consolidation” for AI memory systems inspired by human memory formation Another created persistent multi-agent vault conversations Another turned entire repositories into continuously maintained AI wikis Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration This is the important part: Karpathy didn’t launch a product. He introduced a pattern. And patterns are what create ecosystems. The same way: • transformers created modern AI • RAG created AI retrieval startups • agents created orchestration frameworks LLM Wikis may create persistent AI memory infrastructure. That’s why this moment feels different. For years, AI systems have been stateless. Now developers are trying to build systems that actually accumulate understanding over time. And once knowledge compounds instead of resetting… …the entire interface layer of AI changes. (Link in comments)

Suryansh Tiwari

141,154 просмотров • 1 месяц назад

Sam Altman: Advancing AI may require "changes to the social contract." "The entire structure of society will be up for debate and reconfiguration."
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Sam Altman: Advancing AI may require "changes to the social contract." "The entire structure of society will be up for debate and reconfiguration."

TFTC

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Sam Altman says the leap from GPT-4 to GPT-5 will be as big as that of GPT-3 to 4 and the plan is to integrate the GPT and o series of models into one model that can do everything - that’s the AGI
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Sam Altman says the leap from GPT-4 to GPT-5 will be as big as that of GPT-3 to 4 and the plan is to integrate the GPT and o series of models into one model that can do everything - that’s the AGI

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📁 Demis Hassabis, CEO of DeepMind, says AI is still missing critical capabilities. He explains that current systems cannot plan far ahead or reason like top human scientists, because they lack a deep model of the world. They can process text, images, and video, but they still don’t understand physics, causality, or how actions affect outcomes over time.
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📁 Demis Hassabis, CEO of DeepMind, says AI is still missing critical capabilities. He explains that current systems cannot plan far ahead or reason like top human scientists, because they lack a deep model of the world. They can process text, images, and video, but they still don’t understand physics, causality, or how actions affect outcomes over time.

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63,723 просмотров • 5 месяцев назад