Is the AI revolution we're experiencing going to push us into a future we can't imagine? Or will the pace of progress enable us to adjust along the way?Dan Shipper spends his time thinking and writing on these topics (and many others) as the founder and CEO of Every Media, a technology-focused publication trying to understand the future. Dan is also a lifelong coder and entrepreneur with a background in philosophy and its intersection with tech. Dan and I share a ton in common (beyond just our first names!), so I think you all will enjoy our thoughts on...The meaning of the "good life"Whether the study of philosophy evolves over timeHow cryptocurrency, AI, and the nature of knowledge will accelerate each otherThe value of philosophy and thinking for thinking's sakeWhat the future holds for engineering, philosophy, and humanity at largeIf you like this conversation, check out more of Dan's thought at Every.to and on X 'at' @danshipper.

GPT-3 didn't have much of a splash outside of the AI community, but it foreshadowed the AI explosion to come. Is o1 OpenAI's second GPT-3 moment?Machine Learning Researchers Guilherme Freire and Luka Smyth discuss OpenAI o1, it's impact, and it's potential. We discuss early impressions of o1, why inference-time compute and reinforcement learning matter in the LLM story, and the path from o1 to AI beginning to fulfill its potential.00:00 Introduction and Welcome00:22 Exploring O1: Initial Impressions03:44 O1's Reception06:42 Reasoning and Model Scaling18:36 The Role of Agents27:28 Impact on Prompting28:43 Copilot or Autopilot?32:17 Reinforcement Learning and Interaction37:36 Can AI do your taxes yet?43:37 Investment in AI vs. Crypto46:56 Future Applications and Proactive AI

Dev Rishi, CEO of Predibase, discusses the future of AI fine-tuning, challenges in model hosting, and competition with OpenAI. They explore the benefits of multi-modal language models, optimization methods like Lorax, and the advancements in audio modeling. The conversation also touches on balancing work and personal life in the AI industry.

Talfan Evans, a research engineer at DeepMind specializing in data curation for LLMs, dives into the fascinating world of AI model training. He explores whether a single model can dominate the landscape and what constitutes 'high-quality data' in this context. The discussion includes insights on the competitive strategies of giants like Google and OpenAI versus the innovative spirit of startups. Talfan also unpacks the complexities of few-shot versus many-shot learning, emphasizing the importance of understanding model specialization for optimal performance.

Bhavna Gopal, a PhD candidate at Duke with research experience at top tech companies, uncovers the world of adversarial training and AI robustness. She explains how adversarial attacks threaten AI model integrity, especially in sensitive fields like healthcare and law. The conversation touches on the challenges of evaluating model performance and the ethical ramifications of AI deployment. Also discussed are the complexities of self-driving cars and the importance of interpretability in ensuring public trust in AI technologies.

Chris Gagne manages AI research at Hume, which just released an expressive text-to-speech model in a super impressive demo. Chris and Daniel discuss AI and emotional understanding:How does “prosody” add a dimension to human communication? What is Hume hoping to gain by adding it to Human-AI communication?Do we want to interact with AI like we interact with humans? Or should the interaction models be different?Are we entering the Uncanny Valley phase of emotionally intelligent AI?Do LLMs actually have the ability to reason about emotions? Does it matter?What do we risk, by empowering AI with emotional understanding? Are there risks from deception and manipulation? Or even a loss of human agency?

Former OpenAI Research Scientist Joel Lehman discusses the non-linear nature of technological progress and the implications of his book 'Why Greatness Cannot Be Planned'. Topics include AI time horizons, exploration vs exploitation for AI startups, importance of stepping stones for AGI, and whether venture money is going to the wrong place.

“Where are the good AI products?” asks Varun Shenoy, ML engineer in his latest blog post. Varun and I talk through:What are the cool applications that exist? Why aren't there more of them?What do (the few) good AI application companies have in common?What technological or societal leaps are blocking the existence of more AI apps that matter?The optimist case and the pessimist case for the near future of AI. As Varun puts it, what if the emperor has no clothes?We'd love to hear what you think. Feel free to reach out on LinkedIn or by email. You can reach me at daniel@slingshot.xyz.

ML Engineer and tech writer Donato Riccio wrote an article entitled "The End of RAG?" discussing what might replace Retrieval Augmented Generation in the near future. The article was received as highly controversial within the AI echo chamber, so I brought Donato on the podcast to discuss RAG, why people are so obsessed with vector databases, and the upcoming research in AI that might replace it.Takeaways:RAG is necessary due to LLMs' limited context window and scalability issues, and the need to avoid hallucinations and outdated information.Larger/infinite context window models and linear-scaling models (e.g. RWKV, Eagle) may allow for learning through forward propagation, allowing for more efficient and effective knowledge acquisitionAgentic flows are likely far more powerful than RAG - and when they actually start working consistently, we may see the need for vector databases dramatically reduced.RAG libraries and abstracts can be helpful for getting off the ground but don't solve the hard problems in specific vertical LLM use cases.RAG vs Agents, and the complex ways that vertical AI approach RAG in practiceShare your thoughts with us at hello@slingshot.xyz or tweet us @slingshot_ai.

What’s going on with GPUs? We talk through the GPU bottleneck/supply gut, Meta’s apparent 600,000 H100-equivalents and the future of the GPU cloud.Neel Master is the CEO and founder of Cedana, enabling pause/migrate/resume for compute jobs. Neel is a serial entrepreneur, former founder of Engooden and angel investor. He started his career in ML research at MIT's CSAIL.Topics from this podcast include:Cedana's real-time save, migrate and resume for compute technology, enabling the migration of compute jobs across instances without interruptionWhen will there be enough GPUs?What does it mean, that the cloud can become a robot?How might compute change, as per-capita GPU supply grows massively?How much does it actually cost to run GPUs?How can startups compete with the Big Tech’s giant compute moats?Share your thoughts with us at hello@slingshot.xyz or tweet us @slingshot_ai.