The British have always been towards the forefront in computing technology. This seems to fit the mold.

I think multistep tasks and the ability to understand context across applications is the next paradigm to solve. GPT 4 with plug-ins can do this in a rudimentary sense, but I think it will take specific training and architecture within the generative model itself to start to replace FTE workers.

ITER is a complete disaster. If people thought NASA's SLS program was bad, ITER is at least an order of magnitude worse. I agree AI development is going extremely fast. I disagree there's much we can do to stop it or even slow it down much. I agree with Sam Altman's take, it's better these AI's to get into the wild now, while the stakes are low, than to have to experience that for the first time when these systems are far more capable. It's inevitable it's going to happen, it's better to make our mistakes now.

ITER is a complete joke. CERN is doing okay, but doesn't seem to fit the mold of AI research in any way. There's really no basis for holding these up as the models AI research should follow.

This is why you never sign an open letter even if you do agree with it. There's a very high chance of something going wrong.

I would have never guessed Sofia Coppola no matter how many questions you gave me, so I don't know if it performed that poorly.

I don't disagree.

AI can just treat it as a game, the game of accumulating capital, and probably be far more effective than any single human ever has. Might make Rockefeller or JP Morgan look small in comparison.

Nearly 50% of managers of publicly traded companies come from business schools. They must be doing something right.

So if you're using this for academic research, you can put in your original prompt and then tell it to only return references with a confidence score > .5. Neat little trick.

Math is hard for people too, and I don't think GPT 4 is worse than the average person when it comes to math. In many cases, math requires abstract multiple step processing which is something LLM's typically aren't trained on. If these models were trained on processes rather than just content, they'd likely be able to go through the steps required to perform mathematical operations. Even without specific training, LLM's are starting to pickup the ability to perform multiple step calculations, but we're obviously not all the way there yet.

80% is a wild stab just as any projection is a wild stab, but Goertzel has studied the problem as much as anyone.

Not happening. This entire project was derived from work on legs. I'm not a fan of the backwards leg design myself, but they've had this project ongoing for 10 years, and for over half of that time, they didn't even have an upper body.

It wouldn't be a high bar to meet and most CEOs are just glorified figureheads anyway. Would enable a ton of cost savings and likely better decision making if they were replaced.

Rollable phones. Don't know how foldable phones are seemingly winning that battle right now, because they seem to be ugly and impractical with a noticeable crease that makes an expensive phone seem like a cheap one.

The other big thing will be AI edge capability. There's a huge gap in performance between what phones need now for contemporary applications, and what they will need to run AI applications on the edge device. I really think this will be the driver in the need to keep upgrading to the latest phones, as even my middle tier device does everything I need.

Humans don't have anywhere close to a 32,000 token context window, at least in terms of performing useful output from learned context. You don't need that big of a context window, you break the problem down into manageable steps to solve a problem.

Yes probably. You don't need to learn anything to be generally intelligent if you've already been trained on the entirety of human knowledge.

Maybe mud brick houses?

This is actually wrong. As someone who has read it in full, Das Kapital will tell you exactly the conditions that will take place as they are the same conditions happening now.

Why are people so skeptical of published results? This is how exponential progress works, smaller models today can perform better than larger models of a couple years ago.

Yes they actually do.

Humans finetune to the test as well.

Why is it recommended to study 1.5 months for Series 7 if it's just a multiple choice test?

I don't think that's true, but I do believe it was finetuned on the specific dataset to achieve the SOTA result they did.

It's still garbage. They improved coversation limit by 1, big freaking deal. I won't use it until they remove conversation limits completely.