muskmusk

Jesus.

This is starting to feel pretty **ing exponential.

I don't think anyone can consider it a success just yet. But someone just injected 2 billion dollars into the enterprise. They are probably given a lot more information than you and I are. Maybe those people are dead wrong and they lose all their money, but maybe not.

With the public information there is neither a point in positivity not negativity. It would be speculation either way. A question that could be worth pondering is what exactly the investors are shown? What would you need to see to put all your money in thinking machines?

I love it!

Finally some clear thinking on a very important topic.

This is a surprisingly good idea. The model vs model is fun, but not really that useful.

But this could be a legitimate way to design apps in general if you could tell the models what you liked and didn't like.

I guess masks are completely off now. We can see who sells out to the highest bidder and who won't sell because they care more about the mission.

"everything evolves until it becomes an operating system"

I an not sure these fairly one dimensional views are useful. Completely agree with the premises that it is the managers job to know what their people are doing. I have also worked at companies where the managers weren't doing their job. I can also completely see how an overfocus on metrics kills an otherwise good company.

I don't agree with the conclusion though: metrics are bad.

In software, in theory, you don't need metrics. You just write good code that does what it needs to do. Add tests to prove it, you are good. In reality though everyone agrees that metrics are a good thing to have. Why do we need them? Complexity and sometimes we just make mistakes. We could write software that is very close to bug free, but then we move at aerospace speed. Not always optimal.

Software metrics come with all the pitfalls of management metrics like over focus on metrics and not seeing the forest for the trees. Yet we still find the metrics useful.

Managers face many of the same problems, so they ask for metrics. I don't think that is bad. Being bad at using metrics is bad.

It's a free world. Nothing stops you from applying their findings to bigger datasets. It would be a valuable contribution.

Yes.

Heh I guess it's s matter of perspective. Your son's head is not made of silicon so in that sense it is a large neighborhood. But if you put them behind a screen and only see the output then the neighborhood looks smaller. Maybe it looks even smaller a couple of years in the future. It certainly looks smaller than it did a couple of years in the past.

Friend, the creator of this new progress is a machine learning PhD with a decade of experience in pushing machine learning forward. He knows a lot of math too. Maybe there is a chance that he too can tell the difference between a meaningless advance and an important one?

If you ask Ilya Sutskever he will say your kids head is full of neurons, so is LLMs.

LLMs comsume training data and can then be asked questions. How different is that to your son watching YouTube and then answering questions?

It's not 1:1 the same,yet, but it's in the neighborhood.

Those prompts are wild. It is deeply impressive that it works. Think about what would happen if you gave such instructions to a human. Would they be able comply? How big is the overlap between people who are creative enough to produce the kind of pictures Dall-E produces and disciplined enough to follow complex instructions so rigourously?

It also is just straight up impossible to convert those instructions to "regular" code.

That is impressive. Interesting that the prefill (i am guessing this is prompt processing) is so much slower than decoding.

Its my understanding that under normal circumstances decoding is memory bandwidth bound which prompt processing isn't due to batching. Is there some quirk in your setup?

Exactly, also 3year olds are capable of language.

How do you prove that something that lived 300.000 years ago could communicate?