People are generating over 1.5 billion images a week in ChatGPT. In this episode, Product lead Adele Li and researcher Kenji Hata share some of the new use cases and trends since the launch of Images 2.0. Together with host Andrew Mayne, they trace the progress from the early DALL-E days and dive into the latest capabilities, including better text rendering, photorealism, multilingual support, world knowledge, aspect ratios, and character consistency. They also explore what comes next as image generation models evolve into more capable creative assistants.

Chapters

00:36 How Adele and Kenji came to work on Images

02:27 Images 2.0 launch reception

05:25 Productivity use cases and and 360 images

09:34: Viral trends, authenticity, and imperfection

10:51 Training breakthroughs and photorealism

14:06 Evals, prompting, and creative control

22:16 Creative agents and what comes next

22:27 Images + Codex

28:08 Prompt tips

Hosted on Acast. See acast.com/privacy for more information.

Training frontier models isn’t as simple as adding more GPUs—one small problem and the whole coordinated dance falls apart. OpenAI’s Mark Handley and Greg Steinbrecher discuss how a new supercomputer network design, used to train some of the company’s latest models, keeps the whole system moving in lockstep, even with record numbers of GPUs. They break down Multipath Reliable Connection, a new protocol OpenAI developed with AMD, Broadcom, Intel, Microsoft, and Nvidia, and why they’re making it available for the whole industry to use.

Chapters

00:00 Intro

00:39 Greg and Mark's paths to OpenAI

04:34 Why training AI stresses networks differently

10:05 Bottlenecks, failures, and the cost of waiting

15:19 How Multipath Reliable Connection works

18:59 A protocol to route around failures

25:05 Why OpenAI is making MRC an open standard

35:09 Could AI compute move to space?

Hosted on Acast. See acast.com/privacy for more information.

Math is one of the clearest ways to see how far AI has come in a short span. OpenAI researchers Sébastien Bubeck and Ernest Ryu join host Andrew Mayne to explain what changed and what it could mean for the future of research. They reflect on how Ernest used ChatGPT to help solve a 42-year-old open problem, the difference between deep literature search and original mathematical discovery, and what changes when AI can work over longer timelines.

Chapters

01:27 The surprising progress of AI’s math capabilities

03:01 Solving an open problem with ChatGPT

06:57 How models went from basic math to research level

11:32 Why math matters for AGI

14:26 AI and the Erdős problems

21:26 Building an automated researcher

28:19 The role of humans as models improve

33:52 Verifying proofs with AI

36:00 The risk of shallow understanding

41:19 Advice for learning math with ChatGPT

Hosted on Acast. See acast.com/privacy for more information.