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189개정렬
3:16ENJP on Machine Learning and Trading at Jane StreetJP · Jane StreetA Hong Kong equities trader describes his changing workday, the growth of research infrastructure, and the distinctive challenges of machine learning in finance.
7:43ENHow to Run STORM for AI-Assisted ResearchMatthew Berman · Forward FutureMatthew Berman demonstrates Stanford’s research tool, from its 2024 local setup to a cited article about dogs. The interface runs locally, while his demonstration relies on external AI and search APIs.
0:58ENASUS Vivobook S 15: A Copilot+ PC with an NPUMatthew Berman · Forward FutureMatthew Berman introduces the laptop and its local AI features in a sponsored short.
0:58ENNVIDIA NIM: Containerized AI Inference MicroservicesMatthew Berman · Forward FutureA sponsored overview of NVIDIA’s approach to packaging model inference for local and cloud applications.
0:53ENNVIDIA’s Computex Keynote: Digital Humans, Weather and RoboticsMatthew Berman · Forward FutureMatthew Berman briefly reviews Jensen Huang’s announcements on AI-generated characters, weather modeling, Blackwell hardware and factory robots.
0:59ENNVIDIA’s Computex 2024 AI AnnouncementsMatthew Berman · Forward FutureMatthew Berman reviews gaming assistants, digital characters, AI laptops and developer tools in an NVIDIA partner video.
31:04ENThe Risk Is Not China Copying. It Is America Refusing to Use the Tools진행자 Nathan LabenzAt the 2024 Hill & Valley Forum, Alex Wang, Jack Clark, and Andrew Ng tell Senator Cory Booker that Chinese labs are inventing, open-source models are already strong, and the Pentagon’s real AI failure is under-adoption.
39:00ENJPMorgan Has 150 Petabytes. GPT-4 Trained on Less Than One.진행자 Sarah Guo, Elad GilScale AI founder Alexandr Wang tells No Priors why easy internet data is exhausted, why expert-plus-model teams still win, and why the path to AGI looks more like curing cancer than inventing a vaccine.
29:09ENThe Hyperscalers Are Spending $200 Billion — and the Next Bottleneck Is a Power Plant진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil walk through music models, on-device LLMs, long context, and why AI capex now sits beside oil and broadband.
1:52:18ENTwo Weeks, Thirty Compiles: How Adam Majmudar Wrote a GPU From Scratch진행자 Nathan LabenzA Cognitive Revolution walkthrough of TinyGPU, from SIMD kernels and memory latency to an open-source chip flow that runs on a laptop.
1:42:17ENThe Cheapest Inference Provider Is the Most Dangerous진행자 Nathan LabenzFireworks AI cofounders Lin Qiao and Dmytro Ivchenko explain why subsidized GPU bills vanish, how LoRA serving can cut cost a hundredfold, and why OpenAI is the rival that actually matters.
51:11ENInside Groq’s LPU: Compilers, Chip Networks and Fast InferenceMatthew Berman, Andrew Ling, Igor Arsovski · Forward FutureGroq engineers Andrew Ling and Igor Arsovski explain their hardware and software design in a sponsored 2024 interview.
38:33ENSpeed Is the Thing You Can Buy: Baseten’s Bet on Inference진행자 Sarah Guo, Elad GilTuhin Srivastava tells Sarah Guo and Elad Gil why a six-person chatbot company running a billion tokens a day should not be building its own serving stack.
42:14ENMeta’s $197 Billion Day Was a Partial Answer to AI’s $200 Billion Question진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil walk through Gemini, Mistral, NVIDIA’s upgrade cycle, Klarna’s support bot, and why the real AI spend still sits with the hyperscalers.
15:58ENElon Musk on Self-Driving, AI Compute and Electricity ConstraintsElon Musk, Matthew Berman · TeslaA 2024 Bosch interview and Matthew Berman’s commentary explore autonomous vehicles, robots and the infrastructure needed for AI.
39:03ENMI300 Leads Inference by 2x: AMD's CTO on Winning Chip Competition Without a Walled Garden진행자 Sarah Guo, Elad GilWhen Mark Papermaster became AMD's CTO in fall 2011, nearly all of the company's revenue sat on PCs and gaming, Moore's Law was slowing, and everyone knew it needed a turnaround.
13:19ENRunning a Local Language Model as an Offline BackupMatthew Berman · Forward FutureMatthew Berman demonstrates a 2024 local-model setup, from selecting a quantized model and storing it on an external drive to planning backup power.
3:37ENMark Zuckerberg Announces Llama 3 Training and Meta’s AI PlansMatthew Berman, Mark Zuckerberg · Forward FutureIn January 2024, Matthew Berman reacts to Zuckerberg’s remarks on general intelligence, compute and AI devices.
39:36ENThe Most Underestimated AI Buyer Is the Shop on the Corner진행자 Sarah Guo, Elad GilFormer Square CEO Alyssa Henry tells No Priors that generative AI is unlocking work small merchants always needed and never paid anyone to do.
55:34ENAndrew Hunter on Performance Engineering for Low-Latency SystemsYaron Minsky, Andrew Hunter · Jane StreetWhy small optimizations pay at hyperscale, why trading latency is harder to measure, and how profiling, tracing, language design and hardware fit together.
1:19:52EN1,000 H100s Take 115 Days; 22,000 Take Five진행자 Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.
7:02ENConnecting AutoGen to a RunPod-Hosted Model with Text Generation WebUIMatthew Berman · Forward FutureIn a 2023 walkthrough, Matthew Berman connects AutoGen to Dolphin 2.1 Mistral 7B through an OpenAI-compatible API and tests a small coding task.
4:42ENConnecting AutoGen to a Local Model With LM StudioMatthew Berman · Forward FutureMatthew Berman demonstrates an early local-model setup and explains where agent behavior still needs work.
30:03ENA Dinner-Plate Chip Cut Training From 60 Days to 3.5진행자 Sarah Guo, Elad GilCerebras CEO Andrew Feldman tells No Priors why wafer-scale silicon, not another GPU, is the answer to the compute crunch — and why most production models will stay at 13 billion parameters or below.
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