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“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, a ...
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The Future is Fine Tuned (with Dev Rishi, Predibase)
52:39
52:39
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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 inter…
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Pre-training LLMs: One Model To Rule Them All? with Talfan Evans, DeepMind
37:36
37:36
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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 compan…
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On Adversarial Training & Robustness with Bhavna Gopal
44:05
44:05
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"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…
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On Emotionally Intelligent AI (with Chris Gagne, Hume AI)
39:53
39:53
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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 int…
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Why Greatness Cannot Be Planned (with Joel Lehman)
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47:07
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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 ba…
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Where are the good AI products? (with Varun Shenoy)
46:54
46:54
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“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…
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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 t…
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GPUs and how the cloud is changing (with Cedana Founder, Neel Master)
42:48
42:48
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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 re…
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Founders of Lingopal, Deven Orie and Casey Schneider, join to talk about their startup story, developing real-time translation software for enterprises. Topics include: Why is translation so hard? How are enterprise and consumer AI products different (e.g. Google Translate vs Lingopal)? Should AI product companies be doing AI research? Is it safe t…
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Is open-source AI safe? (with SafeLlama founder, Enoch Kan)
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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 eve…
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What is the future of AI-assisted or AI-driven software?
32:54
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Join Daniel Cahn on another SlingTalk episode with Kristian Freed (ex-CTO at Pariti and Elder), discussing the past, present and future of AI-assisted or AI-driven software. They talked about: The Evolution of Coding Tools: From basic text editors to advanced IDEs and the integration of AI tools like Co-Pilot. The Impact of AI on Software Developme…
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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 Turi…
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Join Daniel Cahn on SlingTalks as he welcomes Jonathan Pedoeem (Founder of PromptLayer) to talk through Prompt Engineering. This episode offers an in-depth look into the past, present, and future of prompt engineering and the intricacies of crafting effective AI prompts. Key topics they discuss include: Is prompt engineering more art or more scienc…
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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 solution Building products vs. building relationships, and on being an AI partner for the enterprise AI-na…
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Ex-Datadog Founding PM, Ayush Kapur, joins Daniel Cahn on SlingTalks to talk through the overloaded term, "Human in the Loop". They hone in on the impact of both, emotional and philosophical aspects of human interactions, for instance, your interaction with a doctor, and how those services can be considered irreplaceable by AI. Key topics include: …
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AI-Generated Content and the Boring Apocalypse
39:34
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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 easy Potential for …
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Programming for Machine Learning (Tech Talk)
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Daniel hosts our machine learning research intern and Cambridge Masters student, Andy Lo, to talk about the present and future of ML programming. Topics include: PyTorch vs. TensorFlow vs. Jax vs MoJo No-code, low-code and pro-code for ML engineers The (frustrating) world of debugging ML code Have thoughts? We'd love to hear them! Drop an email at …
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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 wit…
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Daniel hosts our Founding Engineer, Edwin Zhang to unravel the balance in Design Driven Developments. Key things they cover: The conundrums faced when balancing user wants with real, valuable needs - showcasing our stance on "Doing what people need, not just what they want." A peek into the futuristic vision of browsers like Arc and how we regard t…
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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 …
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Why is Llama 2 Open Source? What is Meta up to?
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28:10
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This episode delves into the ongoing debate of the competitiveness between open-source and closed-source models and the reasons behind Meta's decision to publish Llama2 with a permissive open-source license We cover: How much bigger can closed-sourced models be, compared to open-source? Are new competitor foundation models doomed, if Meta enters th…
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Ever seen a piece of work and thought, "Wow, a machine did that?" In our very first Slingtalk episode, we unravel the broad world of AI and where creativity plays a part in the process. We cover: - AI models and consciousness - Poetry in language models - Algorithms, novelty, and where inspiration comes from - Creativity and the part randomness pla…
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