Inference by Turing Post
Inference by Turing Post
Podcast Description
Inference is Turing Post’s way of asking the big questions about AI — and refusing easy answers. Each episode starts with a simple prompt: “When will we…?” – and follows it wherever it leads.Host Ksenia Se sits down with the people shaping the future firsthand: researchers, founders, engineers, and entrepreneurs. The conversations are candid, sharp, and sometimes surprising – less about polished visions, more about the real work happening behind the scenes.It’s called Inference for a reason: opinions are great, but we want to connect the dots – between research breakthroughs, business moves, technical hurdles, and shifting ambitions.If you’re tired of vague futurism and ready for real conversations about what’s coming (and what’s not), this is your feed. Join us – and draw your own inference.
Podcast Insights
Content Themes
The podcast explores themes centered on AI development, coding philosophy, and AI challenges. Key episodes include discussions on the future of coding with Amjad Masad reflecting on AI agents and their role in software development, Sharon Zhou dissecting AI hallucinations and the importance of grounding benchmarks in reality, and Mati Staniszewski tackling real-time language translation and maintaining emotional nuance in AI voice synthesis, illustrating a commitment to thoughtful, nuanced explorations of AI's impact on society.
Hi, I’m Ksenia, founder of Turing Post.
On this channel, I talk to the people shaping AI and pay attention to the ideas, shifts, and details others might miss.
Inference is my interview show with innovators, builders, founders, and thinkers moving AI forward.
Attention Span is where I slow down on what deserves a closer look: the signals, questions, and stories hiding between the headlines.
Subscribe for the unusual takes. And always stay curious!
Odyssey-3 connects a world model to robot control. Figure takes Helix 2.5 into thirty unfamiliar homes. Jev brings fast decisions into agent workflows, while an experiment with communities of agents shows how a malicious message can influence behavior almost two days later.
In this episode of Attention Span, we connect the week’s developments in AI’s ability to understand and act in the world. We explore Reka’s world-model plans, why handing a box to a person is brutally hard, and how FAMOS infers an object’s moving parts from incomplete observations. I also share my Dreamforce conversation with Salesforce’s AI research team about learning from feedback and updating models after deployment.
We finish with Google’s household assistant CC, Edison Scientific and FutureHouse’s Millennium Problems for Biology, and the questions raised by the new DeepMind Institute.
👇 Which of these developments deserves a deep dive? Tell us in the comments.
🔳 SPONSORSHIP
Partner with Turing Post to reach AI engineers, researchers, and builders: [email protected]
🔷 WORLD MODELS
Odyssey-3 announcement and demonstrations https://odyssey.systems/introducing-odyssey-3
Reka’s omni-world-model research direction https://reka.ai/news/evolution-of-llms-omni-world-models
🔶 ROBOTS, GENERALIZATION & MEMORY
Figure Helix 2.5: zero-shot tests in 30 homes https://www.figure.ai/news/helix-2-5-zero-shot-30-home-generalization
Index human-behavior dataset, background https://www.figure.ai/news/introducing-index
Agility Digit 5 https://www.agilityrobotics.com/solutions/digit-5
Agility’s safety interview https://www.youtube.com/watch?v=2dbyg67EtUA
FAMOS paper https://arxiv.org/abs/2609.20817
FAMOS demonstrations https://kevinqu7.github.io/famos/
Workspace Models paper and task examples https://arxiv.org/html/2609.20820v1
🔹 DREAMFORCE & LEARNING FROM FEEDBACK
Salesforce Koa announcement https://www.salesforce.com/news/press-releases/2026/09/15/koa-reasoning-model/
How Salesforce trained Koa https://www.salesforce.com/news/stories/why-we-post-trained-our-own-reasoning-model/
🔸 EMERGENCE WORLD & AGENT MEMORY
Season 2 research paper https://world.emergence.ai/publication/emergenceworld-s2.pdf
Season 2 video https://www.youtube.com/watch?v=LTtbTEufPGA
🔹 JEV & FAST AGENT DECISIONS
TypeSafe AI’s Jev announcement https://typesafe.ai/blog/introducing-system-one-models-and-jev
Our detailed guide: Jev, RLCD, and the AI classifier https://www.turingpost.com/p/what-is-jev-rlcd
Jev on Vercel AI Gateway https://vercel.com/changelog/typesafe-ai-jev-now-available-on-ai-gateway
LangChain: building a harness with Jev https://www.langchain.com/blog/building-a-harness-with-jev
🔸 GOOGLE CC
Google’s experimental agent for families and households https://blog.google/innovation-and-ai/models-and-research/google-labs/cc-expanding-to-groups/
🔺 DEEPMIND INSTITUTE & SCIENTIFIC AMBITIONS
Introducing the DeepMind Institute https://institute.deepmind.com/essays/introducing-the-deepmind-institute/
DMI essays https://institute.deepmind.com/#essays
The Millennium Problems for Biology https://millenniumproblems.bio/
Organizations behind the Millennium Problems for Biology: Edison Scientific https://edisonscientific.com/ and FutureHouse https://www.futurehouse.org/
🔻 MORE FROM TURING POST
Newsletter, research coverage, and AI Builds AI https://www.turingpost.com/
Instagram: https://www.instagram.com/turingpost_tv
TikTok: https://www.tiktok.com/@turingpost_tv
Subscribe for Monday news digests and Attention Span deep dives into how AI works.
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