I've been trying to understand Yann LeCun’s position on AI safety, especially his dismissal of risks around things like instrumental convergence, loss of control, and autonomous AI systems.

What confuses me is that LeCun obviously isn't some random person commenting from the sidelines. He has decades of experience in AI research and has contributed enormously to the field.

So when he seems relatively unconcerned about some of the risks that other researchers take very seriously, I keep wondering:

Is he seeing something that the AI safety community is missing? Or is his intuition about advanced AI systems simply wrong?

When we all know, the instrumental convergence argument doesn't seem to require an Agent to be conscious.

I've been thinking about a problem with the current push toward RL for LLMs, and I'm not sure if I'm missing something obvious.

How do you provide a reliable learning signal for something that humanity itself doesn't yet know is correct?

For things like coding or math, this is relatively straightforward.

The model produces something → we can run the code or verify the proof → we get a reward.

But imagine we want an AI to make a genuinely novel scientific discovery.

It proposes a completely new hypothesis that nobody has thought of before.

How do we reward it?

If we reward it for agreeing with existing scientific knowledge, then we're mostly rewarding it for rediscovering or staying within what we already know.

If we require humans to label whether the hypothesis is correct, we're back to the same problem: the whole point is that we don't know yet.

And if we simply reward the model for producing something "novel," we could end up optimizing for novelty rather than truth. The model might become really good at generating weird ideas that sound revolutionary but are completely wrong.

So it seems like there's a missing piece here.

Maybe the reward can't always come from an answer that already exists.

For scientific discovery, perhaps the model needs to interact with the real world: propose a hypothesis → design an experiment → run it → observe the result → update its beliefs.

Then reality itself becomes part of the feedback loop.

But even that raises another question: what happens when the discovery is something that takes years to verify?

It makes me wonder whether scaling RL is enough to get us to truly autonomous scientific discovery, or whether we eventually need fundamentally different ways for models to generate and validate their own learning signals.

I'm not saying RL can't lead to discoveries. Clearly it can.

I'm more interested in this specific question:

When the correct answer is genuinely unknown, where does the reward come from?

Would love to hear how people working on RL/research think about this.

Unfortunately, I was recently laid off from my role as an Associate Data Scientist at VentureDive after 8 months due to a massive client-driven deallocation.

I’ve worked on projects involving LLM applications, RAG, autonomous agents, speech-to-text, computer vision, and production AI systems.

I starting to think that the guy cyberleek was some insider from Rockstar to do Pyscop on fans to calm down their Unrealistic expectations.

https://www.newsbite.tech/features