Thinking Machines: AI & Philosophy Daniel Reid Cahn

Dev Rishi is the founder and CEO of Predibase, the company behind Ludwig and LoRAX. Predibase just released LoRA Land, a technical report showing 310 models that can outcompete GPT-4 on specific tasks through fine-tuning. In this episode, Dev tries (pretty successfully) to convince me that fine-tuning is the future, while answering a bunch of interesting questions, like:
Is fine-tuning hard?If LoRAX is a competitive advantage for you, why open-source it?Is model hosting becoming commoditized? If so, how can anyone compete?What are people actually fine-tuning language models for?How worried are you about OpenAI eating your lunch?I had a ton of fun with Dev on this one. Also, check out Predibase’s newsletter called fine-tuned (great name!) and LoRA Land.

Talfan Evans is a research engineer at DeepMind, where he focuses on data curation and foundational research for pre-training LLMs and multimodal models like Gemini. I ask Talfan: 
Will one model rule them all?What does "high quality data" actually mean in the context of LLM training?Is language model pre-training becoming commoditized?Are companies like Google and OpenAI keeping their AI secrets to themselves?Does the startup or open source community stand a chance next to the giants?Also check out Talfan's latest paper at DeepMind, Bad Students Make Good Teachers.

"Understanding what's going on in a model is important to fine-tune it for specific tasks and to build trust."
Bhavna Gopal is a PhD candidate at Duke, research intern at Slingshot with experience at Apple, Amazon and Vellum.
We discuss
How adversarial robustness research impacts the field of AI explainability.How do you evaluate a model's ability to generalize?What adversarial attacks should we be concerned about with LLMs?

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 joins to discuss the non-linear nature of technological progress and the present day implications of his book, Why Greatness Cannot Be Planned.
Joel co-authored the book with Kenneth Stanley back in 2015. The two did ML research at OpenAI, Uber, and the University of Central Florida and wrote the book based on insights from their work.
We discuss:
AI time horizons, and the Implications for investors, researchers, and entrepreneursExploration vs exploitation for AI startups and researchers, and where to fit in differential bets to avoid direct competitionThe stepping stones that will be crucial for AGIWhether startups should be training models or focusing on the AI application layerWhether to focus on scaling out transformers or to take alternative betsIs venture money 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.