Turing Post

Peter Norvig on the future of AI: what experts get wrong, why language models are world models, and what we should learn when AI can write code.

I sat down with the co-author of Artificial Intelligence: A Modern Approach, written with Stuart Russell, to talk about what more than 40 years in AI have taught him. Including why he once thought ordinary businesses couldn’t possibly get online.

*We discuss:*
- AI agents, from a textbook banner in 1995 to today’s multi-agent systems
- Why he disagrees with Yann LeCun about the path to better world models
- What Google learned from billions of clicks and a handful of detailed observations
- What students should learn when AI can complete the assignment
- Trust, concentrated power, and who benefits from AI
- What should we still be learning? Share your thoughts in the comments.

Read Turing Post: https://www.turingpost.com/

Thank you to The AI Conference https://aiconference.com/ for organizing this conversation.

*About Peter Norvig*
Peter Norvig is an AI researcher, educator, and co-author with Stuart Russell of Artificial Intelligence: A Modern Approach, adopted by more than 1,500 schools. His website brings together his books, essays, courses, and programming projects.
Peter’s website: https://www.norvig.com/
Book and learning resources: https://aima.cs.berkeley.edu/

*Chapters*
00:00 Intro: Peter Norvig on 40+ Years of AI
00:58 Why Experts Get the Future Wrong
02:21 How AI Has Changed Since the 1980s
03:33 What AI Students Still Need to Learn
04:42 The Return of AI Agents
06:11 Do We Need Neuro-Symbolic AI?
07:12 Are Language Models World Models?
08:28 What Today’s AI Systems Are Still Missing
10:00 How AI Is Changing Computer Science Education
11:53 Rethinking Assignments and Exams in the AI Era
13:08 What Excites Peter Norvig Most About AI Today
13:27 Teaching Students to Build with AI
14:46 How AI Is Shifting the Balance of Power
16:31 Can We Trust the Leaders of AI Companies?
17:04 Will AI Power Stay Concentrated?
17:54 Final Thoughts

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#PeterNorvig #ArtificialIntelligence #TuringPost

OpenAI called their new Ai assistant – dot, yes, like a dot, connecting the dots, and gave it an own computer. And you can call it. I was very curious to try how this agent works.

At OpenAI DevDay 2026, I set up my own dot, a lavender owl called Turrie, and started finding out what an always-on AI assistant can actually take off my plate.

In this episode of Attention Span, we walk through OpenAI Dots. What happens when an assistant can carry context between conversations and notice work you haven’t thought to ask for?

We look at GPT-6 Astra, the infrastructure around it, permissions, and where human oversight comes in. We also explore how Dots fits alongside Grok Bot, Meta’s Muse, and Anthropic’s Cowork.
The question: can Dots close the gap between what AI can do and what we actually manage to do with it?

Watch the walkthrough, then tell me: would you prefer one AI assistant for everything, or several for different tasks?

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Attention Span is here to show you AI isn’t magic. This time, we look at what it takes to leave work with an AI assistant and trust it to follow through.

Links:
Introducing Dots: https://openai.com/index/introducing-dots/
Getting started: https://help.openai.com/en/articles/20001530-getting-started-with-your-dot
Safety, security, and privacy: https://openai.com/index/how-we-build-safety-security-and-privacy-into-dots/
Turing Post’s Dots analysis: https://www.turingpost.com/p/dots-openai

#AttentionSpan #OpenAIDots #OpenAI #AIAgents #AIAssistant #TuringPost

What does an AI agent need to understand before it can act for a business? In this interview, Rohan Kumar, President & Chief Platform and Engineering Officer at Salesforce, explains why the answer goes beyond choosing a model. We discuss the enterprise AI harness: how to test and manage agents, give them limited permissions, protect sensitive data, and supply the right context without wasting tokens.

There are a lot of very interesting engineering problems to solve!

And one of them is capturing how a company actually makes decisions...

Part of **The Org Age of AI**: https://www.turingpost.com/t/the-org-age-of-ai 
What happens on your team when an exception is approved: do your systems record *why*, or only that it happened?

*We talk about:*
- What belongs in an enterprise AI harness. 
- Why an agent needs its own identity and limited permissions. 
- How curated context could reduce cost and improve results. 
- Choosing models by price and performance. 
- Turning data across systems into knowledge an agent can use. 
- Why coding models have examples of expert judgment to learn from, while business agents often don’t. 
- Capturing the reasoning behind decisions. 
- Building with customers to understand how work actually happens. - What junior engineers should learn as coding changes.

*Guest:* Rohan Kumar is President and Chief Platform and Engineering Officer at Salesforce. He previously spent more than two decades at Microsoft, including leading Azure Data.

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#AIagents #EnterpriseAI #salesforce

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: ks@turingpost.com

🔷 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/

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Jev doesn’t write essays or code. Why everyone is talking about it?!I got early access to TypeSafe’s new System One Model, explored the playground, and tested it alongside Codex.

Behind Jev is Diogo Almeida, an InstructGPT coauthor whose work helped make ChatGPT possible. Now he’s betting on AI built for software to use. 

Is it a breakthrough or just a classifier? We explain RLCD, look at Vercel and OpenCode examples, and discuss what Jev might mean for the future.

The question: can better small decisions help AI finish bigger jobs?
Watch the demo, then decide: useful classifier, bigger shift, or both?
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Attention Span is here to show you AI isn’t magic. This time, we look at what happens when a model gives up conversation and focuses on decisions.

Links
Introducing Jev and System One Models: https://typesafe.ai/blog/introducing-system-one-models-and-jev
TypeSafe console: https://console.typesafe.ai/home
System One documentation: https://docs.typesafe.ai/concepts/system-one
RLCD and calibrated decisions: https://docs.typesafe.ai/introduction/machine-learning-primer
TypeSafe agent skill: https://docs.typesafe.ai/agent-skill
InstructGPT paper: https://arxiv.org/abs/2203.02155
Neural-network calibration research: https://arxiv.org/abs/1706.04599
Earlier zero-shot text classification research: https://arxiv.org/abs/1909.00161
Guillermo Rauch on Vercel’s Jev test: https://x.com/rauchg/status/2100307962262872105
Dax’s Jev + OpenCode browser-use preview: https://x.com/thdxr/status/2100288951978164647

#AttentionSpan #Jev #TypeSafeAI #Codex #AIAgents #SystemOneModels #RLCD #TuringPost