X日报 · AI科技

GPT-5.6 sol launches thursday!

happy building

we have heard enterprises on their concerns about AI costs, and 5.6 sol is a huge step forward for dollars-per-task, as are terra and luna

GPT-5.6 is here.

Sol is an incredible model, and Terra/Luna provide great performance at lower price.

Great at coding, knowledge work, cybersecurity, and science with fewer tokens and at lower cost.

https://t.co/XXRz1HmMsv

GPT-5.6 for health intelligence, the team has been focusing hard on improvements here:

5.6 livestream going now.

in addition to the model, 3 major product things.

1. ChatGPT Work--really big deal!
2. new ChatGPT desktop app
3. hosted sites

check this out! you can get some amazing things done.

codex is the core of our new work product and what makes it so good. codex is not going anywhere.

We’ve brought together ChatGPT and Codex, in the form of ChatGPT Work: an agent for your most ambitious work.

Use it from mobile or web, in addition to desktop — no need to leave your laptop cracked open!

ChatGPT Work brings agents to consumer scale.

It’s both a step up in usability (you can just do things from your phone, no laptop required) and access, for both personal and professional life.

Very excited to see how people put it to use and how it can empower them!

GPT-live (next-generation voice) launches today in ChatGPT.

it feels magical and 'real'.

i have always preferred typing to talking to an AI, now i think that's going to shift.

typing into a phone or computer is painfully unnatural

ChatGPT Voice is a real advance.

We’ve always had to contort ourselves to our computers. Now feels like we’re entering a new era of a natural interface.

Expect that we’ll soon spend much less time staring at screens, and regard today’s apps as painfully user-unfriendly.

We’re excited to introduce Muse Spark 1.1, a significant upgrade from the first Muse Spark model we released earlier this year.

Along with this release, we are launching a public preview of the new Meta Model API where developers can access Muse Spark 1.1.

The model is also available now in "Thinking" mode in the Meta AI app and on https://t.co/wHkMPH82ZH.

Learn more: https://t.co/zGcA3XaWpN

Introducing Muse Image and Muse Video, the first media generation models developed by Meta Superintelligence Labs.

Muse Image is our most advanced image generation model yet. It follows instructions faithfully, edits with precision, composes from multiple references, and draws on Instagram for social context. It also brings agentic tool use capabilities to image generation and integrates with Muse Spark.

You can try Muse Image in the Meta AI app and web, as well as in Instagram Stories and WhatsApp – starting in limited countries with more locations on the way.

Today we’re also previewing Muse Video, which is built upon the same pretraining base as Muse Image to deliver exceptional visual fidelity with native audio support.

Learn more about both models: https://t.co/QtKDPDZP5v

Many people were doubting Meta's position in the AI race.

Yesterday, they dropped Muse Spark 1.1, now one of the strongest agentic models, and massively undercut OpenAI and Anthropic on price.

When I interviewed Zuck last year, he told me his focus:

"Have by far the highest compute per researcher, and that we do whatever it takes to go build out all that capacity."

These are the first real signs that the bet -- spending tens of billions on compute -- is paying off.

Meta's stock is now up over 10% since the release.

i am really sad about this and very grateful for all fidji has done for openai, and even grateful for her friendship and who she is as a person.

we all wish her the best for a speedy recovery. this sucks.

I am deeply grateful for all Fidji has done for OpenAI and to advance our mission, and for the opportunity to have worked alongside her for the past few years.

It’s quite sad, but I’m glad that she’s taking the time to work as hard on her health as I’ve seen her do professionally — hoping for a quick recovery.

50-year old math conjecture solved with Sol Ultra.

Feels like the limit of what you can do is increasingly your ambition and imagination:

Today we are rolling out pretty URL’s for deployed apps in @GoogleAIStudio, each app can get a “https://t.co/FvJdcNxK0w”, for free!

Free apps, free deploys, and now free pretty URLs! https://t.co/qkxnOyfiTr

Today we are rolling out "import from GitHub" in @GoogleAIStudio Build!!

We will automagically take the repo and transform it into a format that is compatible with our runtime and then let you keep iterating on it in AI Studio, deploy it, and more. https://t.co/F63MjsJQjf

Step into the map with the Street View grounding feature in Project Genie from @GoogleDeepmind and @GoogleLabs.

Announced at I/O, this research prototype uses locations from @GoogleMaps Street View as a foundation, letting you generate and explore interactive, 360-degree virtual environments from just a text prompt or real-world starting place.

Cool, right? But… How does it actually work? 🤔

As an experimental tool, Project Genie tackles the "blank space" problem (showing both what’s in front of the camera and behind it) by utilizing Street View data to realistically generate a 360-degree view of the location you selected as the starting point for your world to generate from.

Worlds generated by Genie are far more dynamic and rich because they’re created frame-by-frame based on the world description and user actions. By predicting each subsequent frame, Genie is able to simulate what it looks like to swim across an ocean, or hike to the top of a peak, marking a massive shift in interactive media and simulation pipelines.

What real-world place would you want to step into and explore?

It's mind-blowing how fast agentic coding has progressed in the past 6 month. It's a completely different world now.

makes us happy to see people love 5.6 sol so much!

Adam Thierer had written the landmark book on "Permissionless Innovation." We get the best ideas when we don't have to ask the government in advance for permission to invent. Protecting open source AI is now a critical part of ensuring this.

Yann LeCun claimed on social media [7] that my foundational 1990 paper on Neural World Models [1] "was never accepted through peer review."

This is simply false.

The core concepts from the tech report [1] were peer-reviewed and published at international conferences right away:

★ Planning with recurrent world models: peer-reviewed and published at IJCNN'90 (San Diego, June 1990) [2].

★ Artificial curiosity through generative & adversarial nets (GANs): peer-reviewed and published at SAB'91 [3][4].

BTW, the 1990 paper [1] was the first of its kind to use the term "World Model" for a predictor neural network learning to predict the consequences of the actions of a controller neural network.

Instead of acknowledging that "modern" architectures like "JEPA" (2022) are essentially identical to our 1992 Predictability Maximization (PMAX) [6][7] - a fact recently validated by others [8] - LeCun resorts to false claims about peer review.

For the full timeline of the 1990-2015 publications that his 2022 AMI paper rehashes without citation, read the complete, updated receipts here [6]: https://t.co/byn3K3aSxK

Other claims about LeCun are debunked in [8][9].

REFERENCES (easy to find on the web):

[1] J. Schmidhuber (JS). Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments. Technical Report FKI-126-90, TUM, Feb 1990, revised Nov 1990. The first paper on planning with reinforcement learning recurrent neural networks (NNs) and recurrent world models (more), and on generative adversarial networks where a generator NN is fighting a predictor NN in a minimax game (more). Apparently, it was also the first paper of this kind to use the term "world model" for the predictor NN (although the basic concept of a world model is much older than that).

[2] JS. An on-line algorithm for dynamic reinforcement learning and planning in reactive environments. Proc. IEEE/INNS International Joint Conference on Neural Networks, San Diego, volume 2, pages 253-258, June 17-21, 1990. Based on [1].

[3] JS. A possibility for implementing curiosity and boredom in model-building neural controllers. In J. A. Meyer and S. W. Wilson, editors, Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pages 222-227. MIT Press/Bradford Books, 1991. Based on [1].

[4] JS. Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991). Neural Networks, Volume 127, p 58-66, 2020. Preprint arXiv/1906.04493

[5] JS. The Neural World Model Boom. Technical Note IDSIA-2-26, April 2026.

[6] JS (2022, updated 2026). LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990-2015. Years ago, Schmidhuber's team published most of what LeCun calls his "main original contributions:" neural nets that learn multiple time scales and levels of abstraction, generate subgoals, use intrinsic motivation to improve world models, and plan (1990); controllers that learn informative predictable representations (1997), etc. This was also discussed on Hacker News, reddit, and in the media.

[7] JS. Who invented JEPA? With a reply to LeCun's response. Technical Note IDSIA-3-22, IDSIA, Switzerland, April 2026.

[8] JS. How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23, Switzerland, 2023 (updated 2026).

[9] JS. Who invented convolutional neural networks? Hint: LeCun didn't. CNN basics: Fukushima (1979-86). Backpropagation for CNNs: Zhang et al. (1988-), others. Technical Note IDSIA-17-25, Switzerland, 2025.