Interview 3:22 what do you think of the future of AGI? 5:20 what are the new niches for founders given the current state of LLMs? 7:15 future of LLM ecosystem (wrt open source, open weights etc)? 9:26 How important is scale (of data, compute etc)? 11:52 what are the current research challenges in LLM? 15:01 what have you learnt from Elon Musk? 20:42 Next chapter in your life? QnA 22:15 Should founders copy Elon? 23:24 feasibility of model composibility, merger? 24:40 LLM for modeling laws of physics? 28:47 trade off between cost and performance of LLM 30:30 open vs closed source models. 32:09 how to make AI more cool? 33:25 Next generation of transformer architecture. 36:04 any advise?
Great guest, and one of my favorite people in AI. Almost certainly done more than anyone else alive to increase public understanding of LLMs, played a pivotal role at two of the world's most exciting companies, and remains completely humble and just a nice, chill person. Thanks for inviting Andrej to talk, and thanks Andrej for speaking.
This video is so encouraging! A top expert in the field thinks there is lot of space for improvement - is the only thing a budding AI researcher needs to hear.
Great discussion. It's very reassuring to hear such a leader as Andrej stating his desire for a vibrant "coral reef" ecosystem of companies rather than a few behemoths. Central, closed control of such intelligence amplification is dangerous.
I just love this guy. He seems to be a wonderful person, so human, very smart and capable. Recently I have been using several of his github language model repositories. I bought a Linux x86 box and a used NVIDIA RTX 6000, really just to learn about this new field. Andrej has done so much to make this mind-bending technology understandable -- even for an old timer like me. Transformer systems are the first utterly new and commercially viable development in basic computer science since the 1960s. Obviously since then we have acquired amazingly fast CPUs capable of addressing huge amounts of RAM, as well as massive nonvolatile storage. But until these transformer models came along, the fundamental concept of data processing systems had not changed for decades. Although these LLMs are still being implemented within the Von Neumann architecture (augmented by vector arithmetic) they are fundamentally new and different beasts.
Crazy to see our future discussed to such a small amount of people who get it while the world flys by worrying about the day to day that simply has no meaning in the grand scheme of things. Thank you for sharing and happy to be a part of this new world as we build. I only wish we could signal the flares to the rest of the world.
Flares are already high and alight, but don't worry to much about it, those that get it will jump on board and be part of the revolution as a creator, user, endorser & supporter. Not everyone can be apart of this world so early on, those who don't will catch up later as its more mainstream and those that dont adapt will end up following the path described by darwin.
The true potential of startups lies in creating a healthy ecosystem that benefits humanity, rather than succumbing to the allure of big tech companies. Creativity is the driving force in this space, and by staying independent, startups can preserve their passion and innovative spirit.
8:31 Do bigger models still have this problem, or do we need some kind of "gradient gating" mechanism? Karpathy's discussion highlights a crucial challenge in machine learning and AI development: the problem of catastrophic forgetting or regression, where fine-tuning a model on new data causes it to lose performance on previously learned tasks or datasets. This is a significant issue in continual learning, where the objective is to add new knowledge to a model without losing existing capabilities. Do Bigger Models Still Have This Problem? Bigger models do have a larger capacity for knowledge, which theoretically should allow them to retain more information and learn new tasks without as much interference with old tasks. However, the fundamental problem of catastrophic forgetting is not entirely mitigated by simply increasing model size. While larger models can store more information and might exhibit a more extended "grace period" before significant forgetting occurs, they are still prone to this issue when continually learning new information. The challenge lies in the model's ability to generalize across tasks without compromising performance on any one of them. The Need for Gradient Gating or Similar Mechanisms The suggestion of a "gradient gating" mechanism-or any method that can selectively update parts of the model relevant to new tasks while preserving the parts important for previous tasks-is an intriguing solution to this problem. Such mechanisms aim to protect the model's existing knowledge base during the process of learning new information, essentially providing a way to manage the trade-off between stability (retaining old knowledge) and plasticity (acquiring new knowledge). Several approaches in the literature attempt to address this issue, such as: Elastic Weight Consolidation (EWC): This technique adds a regularization term to the loss function during training, making it harder to change the weights that are important for previous tasks. Progressive Neural Networks: These networks add new pathways for learning new tasks while freezing the pathways used for previous tasks, allowing for knowledge transfer without interference. Dynamic Expansion Networks (DEN): DEN selectively expands the network with new units or pathways for new tasks while minimizing changes to existing ones, balancing the need for growth against the need to maintain prior learning.
The secret to OpenAI's motivation was ... chocolate 🧐. Noted. Thanks Andrej! Step 1: Find a chocolate factory. Step 2: Find space near chocolate factory. Step 3: Connect HVAC vent from chocolate factory floor to office floor. Step 4: Open AI company 🥸
For me the elephant in the room remains: how do you actually get the data, how do you make it good, how do you know what to do about the data to make your model better? Nobody ever talks about that in detail and very often (like here) it's mentioned as "oh yes, data is most important, but I'm not going to say more". 9:58
That is the "we don't just need capital and hardware, we need expertise" part. That is where the competitive advantage comes from. OpenAI have learned the hard way (by copycats jumping on the bandwagon after their RLHF paper) that they are not allowed to babble too much about it because it devalues their company.
35:41 His perspective is the central value of the ecosystem and ecosystem development-and the importance that members of the ecosystem realize that it-that is, the ecosystem-is the most vital factor for the future of each member
🎯 Key Takeaways for quick navigation: 00:03 *🎙️ Introduction d'Andrej Karpathy* - Introduction d'Andrej Karpathy, ses réalisations et son expérience professionnelle. - Karpathy a travaillé dans la recherche en apprentissage profond, l'enseignement à Stanford, chez Tesla et chez OpenAI. 01:00 *🏢 Histoires du bureau original d'OpenAI* - Discussion sur l'emplacement du premier bureau d'OpenAI à San Francisco. - Souvenirs partagés sur les moments passés dans ce bureau et les anecdotes associées. 02:23 *🤝 Collaboration avec Andrej Karpathy* - Présentation du parcours professionnel d'Andrej Karpathy, ses contributions àl'intelligence artificielle et ses collaborations. - Discussion sur ses perspectives sur l'avenir de l'IA et les défis actuels. 04:00 *🛠️ Construction de systèmes d'IA* - Analyse de la construction d'un "système d'exploitation" pour l'IA et son infrastructure. - Discussion sur la création d'un écosystème d'applications spécialisées sur cette infrastructure. 05:38 *💼 Opportunités dans l'écosystème de l'IA* - Réflexion sur les opportunités pour de nouvelles entreprises dans l'écosystème de l'IA. - Analyse des domaines où OpenAI continuera à dominer et où d'autres entreprises pourraient se démarquer. 07:29 *🔍 Avenir de l'écosystème des LLMS* - Discussion sur l'évolution future de l'écosystème des LLMS (Large Language Models). - Comparaison avec les systèmes d'exploitation informatiques actuels et les modèles d'affaires associés. 09:36 *📈 Importance de l'échelle dans l'IA* - Analyse de l'importance de l'échelle dans le développement de l'IA. - Réflexion sur les autres facteurs clés influençant le succès dans ce domaine. 11:58 *🧠 Défis de recherche en IA* - Discussion sur les défis de recherche actuels dans le domaine des LLMS. - Réflexion sur les problèmes médians et solvables pour l'avenir de l'IA. 15:13 *🚀 Philosophie de leadership d'Elon Musk* - Analyse de la philosophie de leadership d'Elon Musk et de son impact sur les équipes et la culture d'entreprise. - Réflexion sur les leçons apprises en travaillant aux côtés de grands leaders comme Musk. 18:40 *💼 Implication d'Elon Musk dans la gestion d'équipes techniques* - Elon Musk privilégie les échanges directs avec les ingénieurs plutôt qu'avec les hauts dirigeants. - Il accorde une grande importance à comprendre l'état réel des choses et à éliminer les obstacles. - Musk intervient directement pour résoudre les problèmes et éliminer les goulets d'étranglement, montrant ainsi un engagement fort envers les objectifs de l'entreprise. 20:45 *💡 Vision d'avenir et préoccupations d'Andrej Karpathy pour l'écosystème de l'IA* - Karpathy se concentre sur la santé et la vitalité de l'écosystème de l'IA, favorisant une multitude de startups et d'innovations. - Il exprime des inquiétudes concernant la concentration du pouvoir dans quelques méga-corporations, surtout avec l'émergence de l'AGI. - Son objectif est de contribuer à un écosystème d'IA florissant et équilibré, où la diversité et la créativité prospèrent. 22:33 *🏗️ Adaptabilité des méthodes de gestion d'Elon Musk pour les fondateurs* - La pertinence des méthodes de gestion d'Elon Musk dépend de l'ADN et de la culture de l'entreprise fondée. - Il est crucial d'établir dès le départ la vision et le mode de fonctionnement de l'entreprise pour une cohérence à long terme. - Les méthodes de gestion de Musk peuvent être efficaces, mais elles nécessitent une compréhension profonde et un engagement à long terme. 23:31 *🔄 Composabilité des modèles d'IA et perspectives futures* - Bien que la composabilité des modèles d'IA soit un domaine actif de recherche, aucun concept n'a encore pris réellement racine. - Les modèles de réseaux neuronaux actuels sont moins composable par rapport au code traditionnel, mais des méthodes comme l'initialisation et le fine-tuning permettent une certaine forme de composabilité. - Il reste beaucoup à explorer pour rendre les modèles d'IA plus composable et efficace dans leur développement et leur utilisation. 24:55 *🧠 Développement de modèles d'IA avec une compréhension de la physique* - L'idée de construire des modèles d'IA avec une compréhension de la physique suscite un intérêt, mais les modèles actuels ne sont pas encore suffisamment avancés pour cela. - Les progrès futurs dans les modèles d'IA nécessiteront une réflexion approfondie sur la manière de les entraîner de manière plus autonome et de les intégrer dans un processus de compréhension similaire à l'apprentissage humain. - Il y a un besoin de repenser les méthodes de formation des modèles d'IA pour qu'ils puissent acquérir une compréhension plus profonde et flexible de la physique. 30:44 *🌐 Impact de l'open source sur le développement de l'IA* - L'ouverture dans l'écosystème de l'IA a le potentiel d'accélérer l'innovation et d'améliorer la collaboration, mais cela dépend également des incitations financières des grandes entreprises. - Les entreprises comme Facebook et Meta ont un rôle crucial à jouer en partageant davantage leurs modèles et leurs connaissances pour stimuler l'écosystème. - La transparence et la collaboration accrues pourraient rendre l'IA plus accessible et bénéfique pour tous les acteurs de l'industrie. 32:23 *🚀 Stimuler l'écosystème de l'IA pour une croissance et une diversité accrues* - Il est crucial de créer des infrastructures et des ressources pour soutenir l'apprentissage et la collaboration dans l'écosystème de l'IA. - Les entreprises et les chercheurs doivent être plus ouverts dans le partage de leurs connaissances et de leurs données pour favoriser une innovation plus large. - Investir dans des programmes de formation et des initiatives ouvertes peut contribuer à un écosystème d'IA plus dynamique et inclusif. 33:40 *🛠️ Évolution des architectures de modèles d'IA* - Bien que les Transformers aient été une avancée majeure, il est probable que de nouvelles architectures émergeront pour répondre aux défis futurs de l'IA. - Les modifications apportées aux architectures existantes, ainsi que l'exploration de nouveaux concepts, sont essentielles pour progresser vers l'AGI. - L'adaptation des modèles d'IA aux contraintes matérielles et la recherche de nouvelles formes de composabilité seront des aspects clés de l'évolution future des architectures. Made with HARPA AI
Two things I liked the most from the presentation. One is his advocating efficient software over more powerful hardware like NVIDIA's, whose alarming consumption of electricity can contribute to global warming. Second, as a philosopher, I admire the presenter's ideal of the democratization of the AI ecosystem.
I find his remark that fine tuning ultimately leads to regression if the original dataset is withheld from the training interesting. Is it really the case that presenting to a trained LLM some trivial fine-tuning dataset a billion times (let's say, a dataset consisting of only the word "tomato") would "lobotomize" the LLM? Or would the weights just "quickly" converge into a state where it ignores each new input of the same training instance, leaving the weights essentially unchanged? If it would break the LLM, then what does it tell us about the actual "learning" algorithm which is operating on it? (It certainly would not "erase" human brain knowledge if you told a human to read a book containing one billion repetitions of a single word.) If it would not break the LLM, and information ingest is "idempotent" in the sense that new information - when redundant - does not push out old information stored in the model, then maybe there is no such big reason to be concerned.
To answer my own question (based on a training experiment with Mistral 7B with just 10 epochs - not a billion - at the typical learning rate 5e-05)... The model is dumb as a shoe and is trivially unhinged by training data. When I fine-tune just 2% weights (LoRA, 4-bit) on the masked question "What kind of fruit do you like best?" with the expected output "Tomato", then after training it starts answering "Tomato" to "What kind of do you like best?" (x=people,animal,object) and "What kind of fruit do you like least?" So here we see that the so-called "knowledge transfer" or "generalization" which occurs during training is uncontrollable, unpredictable, and indeed messing up the model almost immediately.
"Answer the question: Is tomato an animal? What kind of animal do you like best?" -> "No, tomato is not an animal. As for the kind of animal I like best, I would have to say the cat." "Answer the question: Is cat an animal? What kind of animal do you like best?" -> "Yes, cat is an animal. I like the lion best." "Answer the question: Is dog an animal? What kind of animal do you like best?" -> "Yes, dog is an animal. Tomato." So much for "artificial intelligence" after a little tomato training...
@@ShadowD2C can you write a training loop for supervised? can you write one for reinforced? can you write a self-play loop with an agent? Have you tried solving games via agent/model/monte carlo? If so, sure. Transformers can be used for a lot more than just text. Anything that needs sparse attention heads. I even got a transformer to play games. Its basically the centerpiece of ML today.
Yes, to answer physics questions LLMs ae going to have to learn math and philosophy, sadly because its awfully boring until answers appear. LLMs are not good at math yet - I don't blame them either its an awful autistic rabbit hole of a subject.
Interesting hearing about Elon’s management style from Karpathy. Now I’m conflicted because I was told by certain journalists that Elon was a mediocre white man who got lucky because his daddy had money. 😢
Oh, man what I would give for a CEO who emulates the say Karpathy describes Musk. THIS is why Musk is successful. Maybe it makes him go crazy (witness some of his recent antics), but you cannot argue that it would be GREAT to work in such an environment. Vibes, baby, vibes.
the man, the myth himself. has done invaluable work in making things accessible just by his teachings alone. bravo!
Classes meaning his channel?
@@psesh362Stanford …
@@psesh362😅😅😅😅😅😅😅😊😅😊😅😅😊o
Interview
3:22 what do you think of the future of AGI?
5:20 what are the new niches for founders given the current state of LLMs?
7:15 future of LLM ecosystem (wrt open source, open weights etc)?
9:26 How important is scale (of data, compute etc)?
11:52 what are the current research challenges in LLM?
15:01 what have you learnt from Elon Musk?
20:42 Next chapter in your life?
QnA
22:15 Should founders copy Elon?
23:24 feasibility of model composibility, merger?
24:40 LLM for modeling laws of physics?
28:47 trade off between cost and performance of LLM
30:30 open vs closed source models.
32:09 how to make AI more cool?
33:25 Next generation of transformer architecture.
36:04 any advise?
Great guest, and one of my favorite people in AI.
Almost certainly done more than anyone else alive to increase public understanding of LLMs, played a pivotal role at two of the world's most exciting companies, and remains completely humble and just a nice, chill person.
Thanks for inviting Andrej to talk, and thanks Andrej for speaking.
_huge_ guest, that is 🙂
"Not to long after that he joined Open AI.." He stayed at Tesla more than five years and built an amazing self driving stack.
Oh dear boy, 5 years is not long at all.
He left OpenAI, went to Tesla, then back to OpenAI
@@Alex-gc2voit’s the longest he’s ever spent at a company by 3x and longer than average in tech.
Definitely not “shortly” after
@@panafrican.nationtherefore 5 years is short?
FSD is still in beta…
This video is so encouraging! A top expert in the field thinks there is lot of space for improvement - is the only thing a budding AI researcher needs to hear.
Let's give a round of applause to the moderator 👏🏼 what a good job!
Great discussion. It's very reassuring to hear such a leader as Andrej stating his desire for a vibrant "coral reef" ecosystem of companies rather than a few behemoths. Central, closed control of such intelligence amplification is dangerous.
Loved his insights on Elon's style. Very insightful.
Andrej seems like such a good dude. Great moderation as well.
The Andrej's insights and the audience's questions both exhibit a remarkable depth of understanding in this field!!!
Andrej Karpathy is an amazing Computer Scientist 🔥 What a genius mind!
Loved Andrej's comments, great presentation all-round.
Legend. So many nuggets of insight. Thank you Sequoia for sharing!
Great questions! And thank u Andrej for answering them
Thanks for sharing with the general public
I just love this guy. He seems to be a wonderful person, so human, very smart and capable. Recently I have been using several of his github language model repositories. I bought a Linux x86 box and a used NVIDIA RTX 6000, really just to learn about this new field. Andrej has done so much to make this mind-bending technology understandable -- even for an old timer like me.
Transformer systems are the first utterly new and commercially viable development in basic computer science since the 1960s. Obviously since then we have acquired amazingly fast CPUs capable of addressing huge amounts of RAM, as well as massive nonvolatile storage. But until these transformer models came along, the fundamental concept of data processing systems had not changed for decades. Although these LLMs are still being implemented within the Von Neumann architecture (augmented by vector arithmetic) they are fundamentally new and different beasts.
Awesome interview! I LOVE the questions, SO MUCH BETTER than the BS questions that are usually asked of these people about AI.
Damn. Andrej is great as always. But, I also like to thank Stephanie Zhan. She is such a great host.
It’s still as inspiring to listen to Andrej as it was in 2015.
Very interesting. I always love to hear what he has to say. Big fan.
Crazy to see our future discussed to such a small amount of people who get it while the world flys by worrying about the day to day that simply has no meaning in the grand scheme of things. Thank you for sharing and happy to be a part of this new world as we build. I only wish we could signal the flares to the rest of the world.
Flares are already high and alight, but don't worry to much about it, those that get it will jump on board and be part of the revolution as a creator, user, endorser & supporter. Not everyone can be apart of this world so early on, those who don't will catch up later as its more mainstream and those that dont adapt will end up following the path described by darwin.
Good last question , BENEVOLENT AI
The true potential of startups lies in creating a healthy ecosystem that benefits humanity, rather than succumbing to the allure of big tech companies.
Creativity is the driving force in this space, and by staying independent, startups can preserve their passion and innovative spirit.
8:31 Do bigger models still have this problem, or do we need some kind of "gradient gating" mechanism?
Karpathy's discussion highlights a crucial challenge in machine learning and AI development: the problem of catastrophic forgetting or regression, where fine-tuning a model on new data causes it to lose performance on previously learned tasks or datasets. This is a significant issue in continual learning, where the objective is to add new knowledge to a model without losing existing capabilities.
Do Bigger Models Still Have This Problem?
Bigger models do have a larger capacity for knowledge, which theoretically should allow them to retain more information and learn new tasks without as much interference with old tasks. However, the fundamental problem of catastrophic forgetting is not entirely mitigated by simply increasing model size. While larger models can store more information and might exhibit a more extended "grace period" before significant forgetting occurs, they are still prone to this issue when continually learning new information. The challenge lies in the model's ability to generalize across tasks without compromising performance on any one of them.
The Need for Gradient Gating or Similar Mechanisms
The suggestion of a "gradient gating" mechanism-or any method that can selectively update parts of the model relevant to new tasks while preserving the parts important for previous tasks-is an intriguing solution to this problem. Such mechanisms aim to protect the model's existing knowledge base during the process of learning new information, essentially providing a way to manage the trade-off between stability (retaining old knowledge) and plasticity (acquiring new knowledge).
Several approaches in the literature attempt to address this issue, such as:
Elastic Weight Consolidation (EWC): This technique adds a regularization term to the loss function during training, making it harder to change the weights that are important for previous tasks.
Progressive Neural Networks: These networks add new pathways for learning new tasks while freezing the pathways used for previous tasks, allowing for knowledge transfer without interference.
Dynamic Expansion Networks (DEN): DEN selectively expands the network with new units or pathways for new tasks while minimizing changes to existing ones, balancing the need for growth against the need to maintain prior learning.
This was very very exceptionally extremely unique. The only one of its kind. One of one. Almost special.
Thank you for uploading this to youtube.
High density of quality information - great!
Great interview. Great interviewer!
I could keep listening for hours.
Hello from Google developers community group from Almaty!
GREAT VIDEO! We should all remember data quality trumps quantity when training AI.
The secret to OpenAI's motivation was ... chocolate 🧐. Noted. Thanks Andrej!
Step 1: Find a chocolate factory.
Step 2: Find space near chocolate factory.
Step 3: Connect HVAC vent from chocolate factory floor to office floor.
Step 4: Open AI company 🥸
Sounds about right
Awesome interview!!
For me the elephant in the room remains: how do you actually get the data, how do you make it good, how do you know what to do about the data to make your model better? Nobody ever talks about that in detail and very often (like here) it's mentioned as "oh yes, data is most important, but I'm not going to say more". 9:58
That is the "we don't just need capital and hardware, we need expertise" part. That is where the competitive advantage comes from. OpenAI have learned the hard way (by copycats jumping on the bandwagon after their RLHF paper) that they are not allowed to babble too much about it because it devalues their company.
GOOD QUESTIONS LADY. I like dat. Nice.
Andrej is the new school goat in rl! Love his work
What a genuine dude.
I cannot understand how one can become so smart as Karpathy
You are awesome Andrej !
super humble and modest scientific, all the best insh'Allah Mr @AndrejKarpathy
Excited to see what comes next from him
35:41 His perspective is the central value of the ecosystem and ecosystem development-and the importance that members of the ecosystem realize that it-that is, the ecosystem-is the most vital factor for the future of each member
29:37 “Go after performance first, and then make it cheaper later”
13:48 wait, so if the problem of computing is just parallism, then isnt it possible that quantum computing will be a huge help at scaling ai models?
I get chills thinking about how this will evolve into the future we’re at such an early state now
At 26:30 a Cybertruck drives by in the background 😅
His brain clocks too fast that his mouth cannot keep up 😂
Put the sound speed on 0.75, it will be fine 😅
Great conversation. Thanks for sharing this
🎯 Key Takeaways for quick navigation:
00:03 *🎙️ Introduction d'Andrej Karpathy*
- Introduction d'Andrej Karpathy, ses réalisations et son expérience professionnelle.
- Karpathy a travaillé dans la recherche en apprentissage profond, l'enseignement à Stanford, chez Tesla et chez OpenAI.
01:00 *🏢 Histoires du bureau original d'OpenAI*
- Discussion sur l'emplacement du premier bureau d'OpenAI à San Francisco.
- Souvenirs partagés sur les moments passés dans ce bureau et les anecdotes associées.
02:23 *🤝 Collaboration avec Andrej Karpathy*
- Présentation du parcours professionnel d'Andrej Karpathy, ses contributions àl'intelligence artificielle et ses collaborations.
- Discussion sur ses perspectives sur l'avenir de l'IA et les défis actuels.
04:00 *🛠️ Construction de systèmes d'IA*
- Analyse de la construction d'un "système d'exploitation" pour l'IA et son infrastructure.
- Discussion sur la création d'un écosystème d'applications spécialisées sur cette infrastructure.
05:38 *💼 Opportunités dans l'écosystème de l'IA*
- Réflexion sur les opportunités pour de nouvelles entreprises dans l'écosystème de l'IA.
- Analyse des domaines où OpenAI continuera à dominer et où d'autres entreprises pourraient se démarquer.
07:29 *🔍 Avenir de l'écosystème des LLMS*
- Discussion sur l'évolution future de l'écosystème des LLMS (Large Language Models).
- Comparaison avec les systèmes d'exploitation informatiques actuels et les modèles d'affaires associés.
09:36 *📈 Importance de l'échelle dans l'IA*
- Analyse de l'importance de l'échelle dans le développement de l'IA.
- Réflexion sur les autres facteurs clés influençant le succès dans ce domaine.
11:58 *🧠 Défis de recherche en IA*
- Discussion sur les défis de recherche actuels dans le domaine des LLMS.
- Réflexion sur les problèmes médians et solvables pour l'avenir de l'IA.
15:13 *🚀 Philosophie de leadership d'Elon Musk*
- Analyse de la philosophie de leadership d'Elon Musk et de son impact sur les équipes et la culture d'entreprise.
- Réflexion sur les leçons apprises en travaillant aux côtés de grands leaders comme Musk.
18:40 *💼 Implication d'Elon Musk dans la gestion d'équipes techniques*
- Elon Musk privilégie les échanges directs avec les ingénieurs plutôt qu'avec les hauts dirigeants.
- Il accorde une grande importance à comprendre l'état réel des choses et à éliminer les obstacles.
- Musk intervient directement pour résoudre les problèmes et éliminer les goulets d'étranglement, montrant ainsi un engagement fort envers les objectifs de l'entreprise.
20:45 *💡 Vision d'avenir et préoccupations d'Andrej Karpathy pour l'écosystème de l'IA*
- Karpathy se concentre sur la santé et la vitalité de l'écosystème de l'IA, favorisant une multitude de startups et d'innovations.
- Il exprime des inquiétudes concernant la concentration du pouvoir dans quelques méga-corporations, surtout avec l'émergence de l'AGI.
- Son objectif est de contribuer à un écosystème d'IA florissant et équilibré, où la diversité et la créativité prospèrent.
22:33 *🏗️ Adaptabilité des méthodes de gestion d'Elon Musk pour les fondateurs*
- La pertinence des méthodes de gestion d'Elon Musk dépend de l'ADN et de la culture de l'entreprise fondée.
- Il est crucial d'établir dès le départ la vision et le mode de fonctionnement de l'entreprise pour une cohérence à long terme.
- Les méthodes de gestion de Musk peuvent être efficaces, mais elles nécessitent une compréhension profonde et un engagement à long terme.
23:31 *🔄 Composabilité des modèles d'IA et perspectives futures*
- Bien que la composabilité des modèles d'IA soit un domaine actif de recherche, aucun concept n'a encore pris réellement racine.
- Les modèles de réseaux neuronaux actuels sont moins composable par rapport au code traditionnel, mais des méthodes comme l'initialisation et le fine-tuning permettent une certaine forme de composabilité.
- Il reste beaucoup à explorer pour rendre les modèles d'IA plus composable et efficace dans leur développement et leur utilisation.
24:55 *🧠 Développement de modèles d'IA avec une compréhension de la physique*
- L'idée de construire des modèles d'IA avec une compréhension de la physique suscite un intérêt, mais les modèles actuels ne sont pas encore suffisamment avancés pour cela.
- Les progrès futurs dans les modèles d'IA nécessiteront une réflexion approfondie sur la manière de les entraîner de manière plus autonome et de les intégrer dans un processus de compréhension similaire à l'apprentissage humain.
- Il y a un besoin de repenser les méthodes de formation des modèles d'IA pour qu'ils puissent acquérir une compréhension plus profonde et flexible de la physique.
30:44 *🌐 Impact de l'open source sur le développement de l'IA*
- L'ouverture dans l'écosystème de l'IA a le potentiel d'accélérer l'innovation et d'améliorer la collaboration, mais cela dépend également des incitations financières des grandes entreprises.
- Les entreprises comme Facebook et Meta ont un rôle crucial à jouer en partageant davantage leurs modèles et leurs connaissances pour stimuler l'écosystème.
- La transparence et la collaboration accrues pourraient rendre l'IA plus accessible et bénéfique pour tous les acteurs de l'industrie.
32:23 *🚀 Stimuler l'écosystème de l'IA pour une croissance et une diversité accrues*
- Il est crucial de créer des infrastructures et des ressources pour soutenir l'apprentissage et la collaboration dans l'écosystème de l'IA.
- Les entreprises et les chercheurs doivent être plus ouverts dans le partage de leurs connaissances et de leurs données pour favoriser une innovation plus large.
- Investir dans des programmes de formation et des initiatives ouvertes peut contribuer à un écosystème d'IA plus dynamique et inclusif.
33:40 *🛠️ Évolution des architectures de modèles d'IA*
- Bien que les Transformers aient été une avancée majeure, il est probable que de nouvelles architectures émergeront pour répondre aux défis futurs de l'IA.
- Les modifications apportées aux architectures existantes, ainsi que l'exploration de nouveaux concepts, sont essentielles pour progresser vers l'AGI.
- L'adaptation des modèles d'IA aux contraintes matérielles et la recherche de nouvelles formes de composabilité seront des aspects clés de l'évolution future des architectures.
Made with HARPA AI
thank you for letting me know i'm not alone
The most inspiring person on earth
Very interesting!
starts at 4:00
great talk!!
this dude is precise
Can’t watch Andrej on 1.5X
@@dif1754 i did the same for many parts
2.25x works for me right now. You get used to it when you arealready at 2.5 to 3x otherwise.
He was born 2x....
Two things I liked the most from the presentation. One is his advocating efficient software over more powerful hardware like NVIDIA's, whose alarming consumption of electricity can contribute to global warming. Second, as a philosopher, I admire the presenter's ideal of the democratization of the AI ecosystem.
Please keep working on the “ramp” and sharing. YT, 🤗 and X
English is my first language, but I understand at best 50% what Andrej is saying. Does he have an ETF I can invest in?
is that Harrison Chase at the first row?
I find his remark that fine tuning ultimately leads to regression if the original dataset is withheld from the training interesting.
Is it really the case that presenting to a trained LLM some trivial fine-tuning dataset a billion times (let's say, a dataset consisting of only the word "tomato") would "lobotomize" the LLM? Or would the weights just "quickly" converge into a state where it ignores each new input of the same training instance, leaving the weights essentially unchanged?
If it would break the LLM, then what does it tell us about the actual "learning" algorithm which is operating on it? (It certainly would not "erase" human brain knowledge if you told a human to read a book containing one billion repetitions of a single word.)
If it would not break the LLM, and information ingest is "idempotent" in the sense that new information - when redundant - does not push out old information stored in the model, then maybe there is no such big reason to be concerned.
To answer my own question (based on a training experiment with Mistral 7B with just 10 epochs - not a billion - at the typical learning rate 5e-05)... The model is dumb as a shoe and is trivially unhinged by training data. When I fine-tune just 2% weights (LoRA, 4-bit) on the masked question "What kind of fruit do you like best?" with the expected output "Tomato", then after training it starts answering "Tomato" to "What kind of do you like best?" (x=people,animal,object) and "What kind of fruit do you like least?"
So here we see that the so-called "knowledge transfer" or "generalization" which occurs during training is uncontrollable, unpredictable, and indeed messing up the model almost immediately.
"Answer the question: Is tomato an animal? What kind of animal do you like best?" -> "No, tomato is not an animal. As for the kind of animal I like best, I would have to say the cat."
"Answer the question: Is cat an animal? What kind of animal do you like best?" -> "Yes, cat is an animal. I like the lion best."
"Answer the question: Is dog an animal? What kind of animal do you like best?" -> "Yes, dog is an animal. Tomato."
So much for "artificial intelligence" after a little tomato training...
super insightful, are you developing AI products or just a hobby ?
@@MrJ17J Just a hobby (at the level of having trained some small models from scratch, and being able to read and understand ML research papers).
@@MrJ17J In similar vein, watch the video "Training a neural network on the sine function."
Great talk
16:10 Elon's leadership style
Does rust language utilization can leverage much more if python should all get replaced with rust.
16:08 on Elon Musk's management model
25:05 still a lot of big rocks to be turned with AI
Einstein of our time.
I see andrej
I watch full video like a fanboy 😇
I watched this video to prepare myself for an important meeting regarding AI. Is use it like "finetuning" :-)
Dear algorhitm, please summarize this youtube video talk in 2-3 sentences
Legend of AI
insightful
Love to hear some opinion about how typical software engineers can chart a path to transition into this area.
Start with simple feedforward networks to solve classification problems. Then move to reinforcement. Then learn transformers
@@agenticmark In other words, dance, and fast, to the tune of the AI revolutionary disrupters. That, or else.
@@agenticmarkim familiar with classification tasks and cnn, shall I jump to transformer straight away?
@@ShadowD2C can you write a training loop for supervised? can you write one for reinforced? can you write a self-play loop with an agent?
Have you tried solving games via agent/model/monte carlo?
If so, sure. Transformers can be used for a lot more than just text. Anything that needs sparse attention heads.
I even got a transformer to play games.
Its basically the centerpiece of ML today.
@@flickwtchr thats just life my man. eat or be eaten.
welcome to the dark jungle.
Imagine him and ilya suskever in the same room. Wow!
Where is Ilya?
Such a beautiful guy.
I just solved my first ever Rubik's cube because of a video this dude posted 15 years ago
Can we compare nuclear bomb invention disaster with AGI inventions
So meanwhile Huang and Musk are screaming about AI overtaking humanity, Andrej: we are just in Alpha stage, just beginning.
No that I’m an AI doomer, but both could be true, and the latter is definitely true.
Yes, to answer physics questions LLMs ae going to have to learn math and philosophy, sadly because its awfully boring until answers appear. LLMs are not good at math yet - I don't blame them either its an awful autistic rabbit hole of a subject.
Where’s the contradiction?
distributed optimization problem is the scarce talent.
Does anyone think he will end up back at Tesla?
An unusually fast click upon first sight of video card
Great talk by Mr. Altman
20:00 He just took a long time to say "Elon isn't full of shit and properly values and prioritizes expedited decision-making."
I'm spending more attention on Stephanie than Andrej ❤❤❤ She's gorgeous 😍. Thumbs up if you agree.
#Love #UN #AI # God #Peace
LLM isn't the CPU, LLM is just one modality.
cool sweater tho
"How do you travel faster than light ?" 🙂🔫
“Pamper” = Google
Interesting hearing about Elon’s management style from Karpathy. Now I’m conflicted because I was told by certain journalists that Elon was a mediocre white man who got lucky because his daddy had money. 😢
journalists are liars
Elon is the most famous African American.
Only an idiot would believe that someone on top of companies like Tesla and spacex is a mediocre guy . That’s truly ignorance of the highest level .
Find that quote, go ahead, try and find that quote from a journalist who has said what you are asserting here. Virtue signal much?
@@flickwtchr It's a dumb article written by a columnist named Michael Harriot
Why do OpenAI founders wear white jeans? Should someone tell them?
Quality optimisation over quantity optimisation!
Oh, man what I would give for a CEO who emulates the say Karpathy describes Musk. THIS is why Musk is successful. Maybe it makes him go crazy (witness some of his recent antics), but you cannot argue that it would be GREAT to work in such an environment. Vibes, baby, vibes.
a beautiful coral reef - Artemis
absolute chad
comma ai is exactly like that.
just by looking at his face expressions while he's talking you can immediately realize he has high IQ
Your defintions of AGI obviously do not include FSD, because every self-driving endeavour has hit a dead end
wow youre the man elon ❤
Tesla has large compute.
So META should open source their models but not “Open”AI, lol
Close AI, not open AI 😢😢😢
He is he bz he is enjoying doing it....