Artificial Intelligence

[Tero]

i just use Google AI. When it sends back those answers, whether it is cultural (music) or science, when I read out its answers it is always the same for me: it reads them to me in my mind in a C3PO voice. All cheerful.

Structural Analysis
Atmospheric Verses: The verses focus on external, almost mystical observations of the forest, using words like "teem," "entwine," and "reassume their roles" to suggest a continuous, hidden natural order.

Simple, Urgent Chorus: In contrast to the complex imagery of the verses, the chorus is direct and rhythmic. It shifts from observing nature to a personal plea for connection ("Love hold me tight"), emphasizing human dependency in a vast, indifferent world.

[JimC]

My son Nick uses an AI called Claude for his work. He uses it to combine different types of technical information from traffic signals and other parts of the transport network. He validated it by doing a number of the procedures by hand, then making sure Claude could do the same without error. Apparently it saves a lot of time...

[pErvinalia]

It will replace him soon enough.

[JimC]

It will replace him soon enough.

Part of his job involves physically visiting traffic light control boxes all around Melbourne, and installing various pieces of hardware. Not something an AI can do...

[Tero]

It will replace him soon enough.

Part of his job involves physically visiting traffic light control boxes all around Melbourne, and installing various pieces of hardware. Not something an AI can do...

So Claude does it right. He will send Jim's son to the right traffic light and even log the miles driven for work in a week.

[L'Emmerdeur]

What's the plan? 'Move fast and break things' may work temporarily, but in the capitalist system it comes down to return on investment. While revenue for the AI companies can look good, the reality is that they aren't making a profit. How to turn that around? Get the people and the economy dependent on the product then tighten the screws? Corporate subsidies? Can't say I'm in favor of either of those, what's the alternative?

Meanwhile suck aquifers dry and drive up the price of electricity for everyone. Yay!

[Brian Peacock]

AI is the depressed billionaire's attempt to create a predictable, deterministic world that reflects and affirms their lived experience.

[pErvinalia]

Bernie Does AI

[macdoc]

[youtube]http://youtube.com/shorts/BX4WrbJHgoE?s ... v4ls-A_OTI[/youtube]
scary....

[Brian Peacock]

QuitGPT

What is QuitGPT?

We're organizing Americans and people around the world to quit ChatGPT.

Is this boycott going to work?

ChatGPT is the biggest chatbot in the world, but that advantage is fragile. ChatGPT has been losing market share. Their creator OpenAI is losing 3x more than they earn. ChatGPT users skew young and progressive, and many don't know about alternatives. We can push OpenAI over the edge. If we make an example of ChatGPT, we can send a clear signal to ICE enablers that their actions will not go unpunished. Let's make CEOs think twice before they get in bed with Trump.

Aren't all the AI companies bad? Why ChatGPT?

On February 27, ChatGPT competitor Anthropic refused to give the Pentagon unrestricted access to its AI for mass surveillance of Americans or producing AI weapons that kill without human oversight.

Within hours, ChatGPT CEO Sam Altman swooped in and accepted the Pentagon's corrupt deal, putting us all at risk of lethal AI for the sake of his company's profits. OpenAI agreed to let the Pentagon use its tech for "any lawful purpose," including killer robots and mass surveillance.

Moreover, OpenAI president Greg Brockman and his wife donated $25 million to MAGA Inc in 2025 (and CEO Sam Altman donated $1M to Trump's 2025 Inaugural Fund). They gave Trump 26x more than any other major AI company. ICE's resume screening tool is powered by OpenAI's GPT-4. They're spending 50 million dollars to prevent states from regulating AI so only Trump can.

They're cozying up to Trump while ICE is killing Americans and the Department of Justice is trying to take over elections. ChatGPT enables mental-health crises through sycophancy and dependence by replacing human relationships with AI girlfriends/boyfriends. Many employees have quit OpenAI because of its leadership's lies, deception and recklessness...

[JimC]

That's the reason Nick quit ChatGPT and moved to Claude...

[pErvinalia]

Yann LeCun’s new venture is a contrarian bet against large language models

Yann LeCun is a Turing Award recipient and a top AI researcher, but he has long been a contrarian figure in the tech world. He believes that the industry’s current obsession with large language models is wrong-headed and will ultimately fail to solve many pressing problems.

Instead, he thinks we should be betting on world models—a different type of AI that accurately reflects the dynamics of the real world. He is also a staunch advocate for open-source AI and criticizes the closed approach of frontier labs like OpenAI and Anthropic.

Perhaps it’s no surprise, then, that he recently left Meta, where he had served as chief scientist for FAIR (Fundamental AI Research), the company's influential research lab that he founded. Meta has struggled to gain much traction with its open-source AI model Llama and has seen internal shake-ups, including the controversial acquisition of ScaleAI.

LeCun sat down with MIT Technology Review in an exclusive online interview from his Paris apartment to discuss his new venture, life after Meta, the future of artificial intelligence, and why he thinks the industry is chasing the wrong ideas.

.....................

You were working on AI long before LLMs became a mainstream approach. But since ChatGPT broke out, LLMs have become almost synonymous with AI.

Yes, and we are going to change that. The public face of AI, perhaps, is mostly LLMs and chatbots of various types. But the latest ones of those are not pure LLMs. They are LLM plus a lot of things, like perception systems and code that solves particular problems. So we are going to see LLMs as kind of the orchestrator in systems, a little bit.

Beyond LLMs, there is a lot of AI that is behind the scenes that runs a big chunk of our society. There are assistance driving programs in a car, quick-turn MRI images, algorithms that drive social media—that’s all AI.

You have been vocal in arguing that LLMs can only get us so far. Do you think LLMs are overhyped these days? Can you summarize to our readers why you believe that LLMs are not enough?

There is a sense in which they have not been overhyped, which is that they are extremely useful to a lot of people, particularly if you write text, do research, or write code. LLMs manipulate language really well. But people have had this illusion, or delusion, that it is a matter of time until we can scale them up to having human-level intelligence, and that is simply false.

The truly difficult part is understanding the real world. This is the Moravec Paradox (a phenomenon observed by the computer scientist Hans Moravec in 1988): What’s easy for us, like perception and navigation, is hard for computers, and vice versa. LLMs are limited to the discrete world of text. They can’t truly reason or plan, because they lack a model of the world. They can’t predict the consequences of their actions. This is why we don’t have a domestic robot that is as agile as a house cat, or a truly autonomous car.


We are going to have AI systems that have humanlike and human-level intelligence, but they’re not going to be built on LLMs, and it’s not going to happen next year or two years from now. It’s going to take a while. There are major conceptual breakthroughs that have to happen before we have AI systems that have human-level intelligence. And that is what I’ve been working on. And this company, AMI Labs, is focusing on the next generation.

And your solution is world models and JEPA architecture (JEPA, or “joint embedding predictive architecture,” is a learning framework that trains AI models to understand the world, created by LeCun while he was at Meta). What’s the elevator pitch?

The world is unpredictable. If you try to build a generative model that predicts every detail of the future, it will fail. JEPA is not generative AI. It is a system that learns to represent videos really well. The key is to learn an abstract representation of the world and make predictions in that abstract space, ignoring the details you can’t predict. That’s what JEPA does. It learns the underlying rules of the world from observation, like a baby learning about gravity. This is the foundation for common sense, and it’s the key to building truly intelligent systems that can reason and plan in the real world. The most exciting work so far on this is coming from academia, not the big industrial labs stuck in the LLM world.

The lack of non-text data has been a problem in taking AI systems further in understanding the physical world. JEPA is trained on videos. What other kinds of data will you be using?

Our systems will be trained on video, audio, and sensor data of all kinds—not just text. We are working with various modalities, from the position of a robot arm to lidar data to audio. I’m also involved in a project using JEPA to model complex physical and clinical phenomena.

What are some of the concrete, real-world applications you envision for world models?

The applications are vast. Think about complex industrial processes where you have thousands of sensors, like in a jet engine, a steel mill, or a chemical factory. There is no technique right now to build a complete, holistic model of these systems. A world model could learn this from the sensor data and predict how the system will behave. Or think of smart glasses that can watch what you’re doing, identify your actions, and then predict what you’re going to do next to assist you. This is what will finally make agentic systems reliable. An agentic system that is supposed to take actions in the world cannot work reliably unless it has a world model to predict the consequences of its actions. Without it, the system will inevitably make mistakes. This is the key to unlocking everything from truly useful domestic robots to Level 5 autonomous driving.

[macdoc]

An agentic system that is supposed to take actions in the world cannot work reliably unless it has a world model to predict the consequences of its actions

Sounds like Bayesian Brain model for human consciousness ....a concept I endorse.

The Bayesian brain is a theoretical framework in neuroscience proposing that the brain operates as a prediction engine, using Bayesian probability to constantly update its internal model of the world based on sensory evidence and prior knowledge. It manages uncertainty by weighing incoming data against expectations, aiming for "Bayes-optimal" perceptual, motor, and cognitive decisions.

[Tero]

There is some evidence for our brain having the predictive model. We can indeed predict two options ahead of time and make the choice quickly. But most of our thoughts are the rule of thumb type of Tversky and Kahneman. And we also make risk assesment by their theory. We don't like risk, preventing loss is more important than winning.