Thinking Machines: AI & Philosophy

(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.

Artificial 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.

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

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.

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?

“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.

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.

Founder of the SafeLlama community, Enoch Kan joins us today, to talk about safety in open source and medical AI. Enoch previously worked in AI for radiology, focused on mammography at Kheiron Medical. Enoch is an open source contributor, and his substack is called Cross Validated.Key topics they discuss include:New jailbreaks for LLMs appear every day. Does it matter?How do internet firewalls compare to AI “firewalls”?Why do human radiologists still exist? Would it be safe to replace them all today?Does safety matter more or less as models become more accurate?If regulation is too intense, could we end up with illegal consumer LLMs? For example, could we stop the masses from using an illegal AI doctor that you can access from your phone?Share your thoughts with us at hello@slingshot.xyz or tweet us @slingshot_ai.

In 1950, Alan Turing asked, “Can machines think?” He suggested the Imitation Game as a test to evaluate whether a machine can think, more commonly called the “Turing Test.” Today we ask, is the Turing Test outdated? Joining Slingtalks this week are Kristian Freed & Guilherme Freire, founding engineers at Slingshot. Guilherme argues against the Turing Test, Kristian argues in favor.Key topics they discuss include:A recent paper claims that GPT-4 comes close to passing the Turing Test. Is the paper’s result valid? How close are we to passing the Turing Test?Defining the Turing Test and understanding the various iterations on its original framing since 1950. Are there levels in passing the Turing Test?Who is the Turing Test’s interrogator? And who is the human participant?If AI could pass the Turing Test, would that necessarily mean that most remote employees would be redundant?Is an AI’s ability to emulate human-like intelligence and deceive humans sufficient for intelligence? Is it necessary?What are the moral and philosophical implications of AI passing the Turing Test? Is intelligence morally significant? Is consciousness relevant?Share your thoughts with us at hello@slingshot.xyz or tweet us @slingshot_ai.

Adam Kirsh (Head of Product & Engineering, Stealth Startup) joins Slingshot to talk about how AI is transforming investment due diligence.Beyond AI in diligence, we discuss:“Horizontal” and “vertical” business models, that start from a point solutionBuilding products vs. building relationships, and on being an AI partner for the enterpriseAI-native startups and the reinvention of business models for the AI ageMaking incremental progress as a startup vs. visionary top-down redesigns of entire processes or even industriesConsumer vs. enterprise applications of LLMs (analogy: iPhones vs Blackberrys)We wrap up with some thought-provoking reflections and an open invitation for further discussion. We'd love to hear them! Drop an email at hello@slingshot.xyz or reach out on Twitter: @slingshot_ai.

AI is increasingly doing the heavy lifting in our communications and content generation. On this episode, Guilherme Freire, Founding ML Engineer at Slingshot, joins the podcast to discuss the impact of AI-generated content.Some of the topics discussed:“Proof of Work” for humans, when AI makes personalization and connection too easyPotential for subpar AI-generated to put high-quality content creation out of businessHyper-personalized AI leading to greater divisiveness and bias; or on the flip side, for AI to serve as a "benevolent dictator" nudging us towards better habits and more diverse thinkingThe changing meaning of “free speech” in the age of AIThe “Boring Utopia” eventuality, where AI processes scientific information at scale to generate new insightsHave thoughts? We'd love to hear them! Drop an email at hello@slingshot.xyz or reach out on Twitter: @slingshot_ai.

In this episode, Daniel shares his perspective on the opportunities for the next wave of AI-native startups.Machine Learning isn’t just about sentiment classification, churn prediction, and revenue forecasting anymore. Generative models can simulate real intelligence. But hard problems continue to require hard solutions, and prompt engineering with retrieval augmented generation won’t be nearly enough.Have thoughts? We'd love to hear them! Drop an email at hello@slingshot.xyz or reach out on Twitter: @slingshot_ai.

In this tech talk, we dive deep into the technical specifics around LLM inference.The big question is: Why are LLMs slow? How can they be faster? And might slow inference affect UX in the next generation of AI-powered software?We jump into:Is fast model inference the real moat for LLM companies?What are the implications of slow model inference on the future of decentralized and edge model inference?As demand rises, what will the latency/throughput tradeoff look like?What innovations on the horizon might massively speed up model inference?