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Wish I could've attended this in person! ❤

Thanks for this super informative video! Med-Gemini is next level 🔥Big shoutout to Khaled Saab and Wei-Hung Weng for introducing us to this exciting tech - it was really helpful in understanding how it’s going to change healthcare 👍

Thank you for organizing this talk! :) Learning about these cutting-edge ML techniques has been very enriching.

Thank you for sharing!

Thanks for such an interesting presentation 👏Looking forward to seeing how compound AI systems evolve!

How does contextual intelligence deal with multi-modal data streams in real-time? Wouldn’t that require a crazy amount of Neural power? Considering the systems and chips we have right now, how long would it realistically take to get to the ‘comfortable’ stage? Great talk! The applications of contextual intelligence are actually crazy!

Interesting use case of LLMs. I really enjoy when the focus of research is towards solving some real life applications. Thanks to the speaker for the cool results and the organizer for sharing this insightful talk.

Thank you for sharing! This was a very cool talk

These are the papers that Fan-Yun mentioned during the talk. Holodeck: yueyang1996.github.io/holodeck/ FactorSim cs.stanford.edu/~sunfanyun/factorsim/

Very cool, thank you for the talk!

After listening to this talk - I thought it would be cool if those AI systems could also generate 3D worlds for gaming. For what other use cases can we use AI-generated worlds?

Timestamps: 0:00 Introduction 01:14 Contextual intelligence. Reasoning. How we can leverage them in AI systems. 11:42 Using Compound AI systems to generate 3D worlds. 12:50 Holodeck: Language Guided Generation of 3D Embodied AI Environments. 21:02 FactorSim: Generative Simulation via Factorized Representation. 29:30 Takeaways of the talk.

Very interesting talk, appreciated the insight, thank you very much!

Thank you Sophia! Great talk🎉

Thank you for the insightful talk!

LLMs are in the same phase the internet was around 20 years ago, people back then thought it was just hype, now we can not even survive a minute without the internet. The same would happen with LLMs. A large part of our lives can be automated using LLMs saving time and cost. People avoiding it now may have to catch up in the future. This was a really interesting talk. Thanks to the speaker and the organizer.

Thanks for the insightful talk! I loved the quote "AGI is the dream, and we're always just a few years away from it" 👏Fingers crossed we tackle the AI safety challenges soon so we can focus on the possibilities without worrying about catastrophic risks.

I feel LLMs will be very very mainstream in the near future, so I’m glad Usman touched on LLM safety. Great talk!

This is the research work covered in this lecture llm-safety-challenges.github.io/challenges_llms.pdf

Timestamps: 0:00 Introduction 0:44 The concept of Artificial General Intelligence (AGI) 2:36 The concept of this work 6:25 The list of the foundational challenges of LLMs 11:42 Who can contribute to LLM safety 20:08 Importance of interdisciplinary collaboration 26:46 If you are interested in digging more into LLM safety - here is a bit of guidance on how to best engage with this research work 28:46 Q&A

Insightful!

It's here to stay anyway since even in its current form there are many products which can be built around it. Guess the "AI winter" discussion is more about investors who want to get a fat payoff in the future

Great talk, thank you for sharing!

Timestamps: 0:00 Introduction to AI safety issues 8:26 Designing "world models" for testing AI systems 15:28 Elaborating on safety specifications of AI systems 20:37 Verification methods for AI systems' safety 23:01 Q&A

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It's a constant debate not just about whether AI can be conscious, but also what consciousness actually is, there's no widely accepted definition yet🙄. Maybe once people start seeing agency in AI's behavior, it'll make sense to start considering its consciousness🤔

This is a very comprehensive ethical framework. I am curious if it could be modified to account for players - nations, organisations, individuals - who operate outside such frameworks.

Timestamps: 0:00 Introduction: Iason's background 0:01:23 What are AI Agents? 0:03:37 What are Advanced Assistants? 0:10:13 Safety and Alignment Issues Associated with Advanced Assistants 0:15:27 Challenges Associated with Human-Assistant Interaction 0:22:15 AI Assistants and Society: Issues in This Dynamic 0:26:16 Evaluating Advanced Assistants 0:30:35 What We Might Ask for When It Comes to Advanced Assistants 0:33:26 Conclusion 0:34:50 Q&A

It's becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman's Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with only primary consciousness will probably have to come first. What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990's and 2000's. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I've encountered is anywhere near as convincing. I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there's lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order. My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar's lab at UC Irvine, possibly. Dr. Edelman's roadmap to a conscious machine is at arxiv.org/abs/2105.10461

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Timestamps: 0:00 Introduction 0:54 Historical overview 2:49 What are State Space Models? 7:04 The design of the new alternative architecture 13:43 Why input gating is important 17:18 Scaling State Space Models 19:03 Model efficiency on device 20:39 A note on efficiency improvements 22:04 How do alternative models perform during inference 24:34 The caveat of the alternative models 26:11 Summary 27:13 Q&A

Timestamps: 0:00 Introduction 0:33 Background on RLHF (Reinforcement learning from Human Feedback) 5:45 Back to basics: REINFORCE 8:44 From PPO (Proximal Policy Optimization) to REINFORCE 23:27 Results of the new optimization method, RLOO 32:35 Conclusions 34:05 Q&A

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Timestamps: 0:00 Introduction 4:31 Sneak peek into the past to see how Transformers took off with language models, explanation of the basics of Transformers. 7:49 2022: The emergence of Transformer alternatives. Hungry Hungry Hippos: Towards language modeling with State Space Models. 19:32 2024: Modern times. Synthetics in modern efficient ML models. 24:22 Demos of what models based on new architectures can do today. 26:21 Q&A session

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Timestamps: 0:00 Introduction 0:45 Google DeepMind's vision of distributed training 2:57 Building blocks towards DiPaCo 11:33 Building blocks towards DiPaCo: DiLoCo - a low communication distributed training optimization 18:41 Introducing DiPaCo: Distributed Paths Composition 22:37 Q&A session

🎉😮🎉

To me there is an understated and underaddressed risk to building powerful alignment tuning techniques Consider the potential threat to humanity posed by those tools in the wrong hands To what degree has this potential danger been considered, and should it be considered more or differently?

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Timestamps: 0:00 Introduction 0:29 Open access models vs. private models 6:39 Training data for Llama 3 10:36 LLMs as AI agents 16:26 Superalignment 20:29 Interpretability of large-scale models 22:19 Is compute the only answer when it comes to intelligent AI systems? 27:14 Can small research companies without enormous funding bring value to the AI community? 28:46 What are the future developments for the Llama family models? 30:07 AGI and how humans and intelligent AI systems could co-exist 32:09 Career advice: How to get into AI-first companies

How to implement the paper

🎉🎉

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Timestamps: 00:00 Introduction 04:00 ValuePrism: A large-scale dataset of 218k values, rights, and duties connected to 31k human-written situations. 07:50 Introducing the Kaleido System. 16:23 A roadmap to pluralistic alignment. 28:13 Q&A

super!

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Timestamps: 0:00 Introduction to the Aya project 1:53 Why is it important to enable multilingual LLMs, and what is the current state of the art? 6:28 How the Aya dataset and collection were built 13:58 Details about the Aya language model that can follow instructions in over 100 languages 24:47 Conclusions 26:07 The Q&A session

Is there some paper link?

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Timestamps 0:00 Introduction 8:45 Impact of hardware selection on fairness 12:30 Why hardware tooling induces unfairness 20:05 Experiments to showcase the issue 24:15 Exploring mitigation solutions to address the problem 27:42 The Q&A