Thinking Machines: AI & Philosophy

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 MoJoNo-code, low-code and pro-code for ML engineersThe (frustrating) world of debugging ML codeHave 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.

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 the Browser Company.The harmonious dance between engineering-driven, customer-driven, data-driven, and design-driven developments.The delicate art of prioritising features based on feasibility, utility, and the tech's cool factor.The transformative power of machine learning in reshaping user interactions and breaking new grounds.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?

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 licenseWe cover:How much bigger can closed-sourced models be, compared to open-source?Are new competitor foundation models doomed, if Meta enters the game?What does “second place” look like, in open source?Can open-source datasets compete?Will very large open-source models soon become illegal?

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 plays in the processJourney through this fusion of code, model agents, and creativity!Have thoughts? We'd love to hear them! Drop an email at hello@slingshot.xyz or reach out on Twitter: @slingshot_ai.