Podcast
Thinking Machines: AI & Philosophy
Hosted by Daniel Reid Cahn · EN
“Thinking Machines,” hosted by Daniel Reid Cahn, bridges the worlds of artificial intelligence and philosophy - aimed at technical audiences. Episodes explore how AI challenges our understanding of topics like consciousness, free will, and morality, featuring interviews with leading thinkers, AI leaders, founders, machine learning engineers, and philosophers. Daniel guides listeners through the complex landscape of artificial intelligence, questioning its impact on human knowledge, ethics, and the future.
We talk through the big questions that are bubbling through the AI community, covering topics like "Can AI be Creative?" and "Is the Turing Test outdated?", introduce new concepts to our vocabulary like "human washing," and only occasionally agree with each other.
Daniel is a machine learning engineer who misses his time as a philosopher at King's College London. Daniel is the cofounder and CEO of Slingshot AI, building the foundation model for psychology.
12episodes
Episodes
Newest firstAll episodes
AI Therapy: An Open Conversation with Therapists
Sep 3001:00:36Tap to summarize(Cross-posted from Fit Check) The mental health field is facing an unexpected disruption: AI is already the largest provider of mental health support in the US, with hundreds of millions using tools like ChatGPT for emotional guidance—whether the technology was designed for it or not.In this AMA, I sit down with Dr. Rachel Wood (licensed counselor with a PhD in cyberpsychology and founder of the AI Mental Health Collective) and Megan Cornish (therapist and community organizer) to take hard questions from mental health professionals about what's actually happening—and what should happen—as AI enters therapeutic spaces.We tackle the uncomfortable questions: Should AI therapy be regulated? What happens in the brain during AI conversations versus human ones? How do we prevent AI from replacing human connection rather than supporting it? And critically—what responsibility do AI companies have when people turn to them who need something AI can't offer?This isn't about whether AI should be used for mental health support. That ship has sailed. This is about understanding the technology, acknowledging the massive access gap in mental healthcare (one therapist for every 1,500+ people in need), and figuring out how to build AI that strengthens human relationships rather than replacing them.We discuss:The neuroscience of attunement and whether AI can replicate therapeutic connectionHow language models actually work (and why they're more than "just talking")Three core concerns: autonomy, competency, and relatednessWhy ChatGPT exemptions in regulation miss the point entirelyThe difference between AI as replacement versus AI as gatewayWhat accountability could actually look like in this spaceWhether you're a therapist worried about the impact of AI within your profession, a technologist building in this space, or simply someone trying to understand where we're headed—this conversation matters.For the full discussion and ongoing conversations, check out the Fit Check community.
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AI Therapy with Slingshot's Derrick Hull
Mar 17, 202500:36:15Tap to summarize“Everyone should go to therapy.” It’s a common statement, but much harder to achieve than it looks. There’s only one therapist for every 10K would-be clients, and the gap is only growing every year.That gap is what my latest guest - Dr. Derrick Hull - has spent his career trying to fix. Now serving as the founding Clinical Lead at Slingshot AI, Derrick previously led Clinical R&D at Talkspace, where he was instrumental in popularizing text-message therapy, a previously controversial modality now recognized as critical to increasing accessibility to mental health care. Derrick has had a fascinating career, serving as a practicing psychologist, a clinical advisor and researcher for top startups, and a highly accomplished academic with publications in Nature, the National Academy of Sciences, and Oxford University Press, with his research supported via R01 grants through NIH. It’s been amazing getting to work with Derrick at Slingshot, and I’m so excited to share just a snapshot of the wisdom and insight I collect from him everyday. In our latest conversation, we touch on...The ethical problems (and opportunities) of AI therapyThe role of empathy in practicing psychologyWhy being a therapist can be an “impossible profession”The surprising literature on therapeutic effectiveness… and many, many things in betweenFor more conversations like this, be sure to subscribe to our Youtube channel (@ThinkingMachinesPodcast) and to our show in your favorite podcast player.
Transcribe →What if we could cure loneliness? Philosophy, dopamine, and more with Mark Ungless
Feb 26, 202501:11:45Tap to summarizeArtificial neural networks were designed to emulate the human brain - and their insane performance on a wide range of tasks is pretty good evidence to support the comparison.Well, it's a bit more complicated than that, at least according to my guest Mark Ungless, former neuroscience lecturer at Imperial and Oxford and current Director of AI at the UK's Mental Health Innovations. Mark and I have collaborated on research for 5+ years, and I've long enjoyed his thoughts on the biology of the brain, the philosophy of the mind, and how AI is changing (and being changed) by our understanding of both. In our latest conversation, we touch on...His groundbreaking research on the biological root of lonelinessHow understanding neuroscience helps you understand humansHow well neural networks represent our own minds (and whether it matters at all!)AI’s ability to do therapyWhat it means to have free will in a world where we understand more about our brains every dayFor more conversations like this, be sure to follow our Youtube channel (@ThinkingMachinesPodcast) and subscribe to our show in your favorite podcast player.
Transcribe →Does Philosophy Make Progress? Chatting with Every's Dan Shipper
Jan 23, 202500:50:38Tap to summarizeIs 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.
Transcribe →OpenAI o1: Another GPT-3 moment?
Oct 18, 202400:51:52Tap to summarizeGPT-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
Transcribe →The Future is Fine Tuned (with Dev Rishi, Predibase)
May 24, 202400:52:28Tap to summarizeDev 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.
Transcribe →Pre-training LLMs: One Model To Rule Them All? with Talfan Evans, DeepMind
May 18, 202400:37:36Tap to summarizeTalfan 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.
Transcribe →On Adversarial Training & Robustness with Bhavna Gopal
May 8, 202400:44:05Tap to summarize"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 discussHow 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?
Transcribe →On Emotionally Intelligent AI (with Chris Gagne, Hume AI)
Apr 19, 202400:39:53Tap to summarizeChris 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?
Transcribe →Why Greatness Cannot Be Planned (with Joel Lehman)
Mar 22, 202400:47:07Tap to summarizeFormer 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?
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