jumpCastle

Not by far, there's bloom and opt if you count it.

It is fine tuned to maximize reward though, not likelihood. And it provides an answer in both cases, just not as well.

In what sense it was more complete or replicable than this paper?

It’s easy to quit smoking. I’ve done it hundreds of times.

Attention is all you need had no code or model and still led somewhere.

If you apply for the api you can use the time-stamped original version.

Is Linux battery life good?

Closed source models are also improving. Not clear to me why would open source models will catch up.

It does not seem conversational though

The name 3.5-turbo sounds to me like it implies distillation. The release notes at the time also hinted at it IIRC.

In my experiments bard is weaker than 3.5, but if it wasn't, than I would prefer the fresh data of bard.

Optimazing for training could help distillation also.

These aren't their core product.

Did relevant tax rules change recently? If not, why is this behavior new?

One reason might be that some people value open source.

Looks like llama 7b was trained on 4 times more tokens.

I didn't need to call. I canceled last year via chat. It took me 15 minutes, answering about 10 quite annoying questions.

I'm not an expert on routers but if I was in the market I would look at the Wirecutter. I find their recommendations pretty good.

thewirecutter.com/reviews/best-wi-fi-router/

I subscribe to a newsletter of a product recommendation service like wirecutter.com, which provides discovery.

Ideally I would be able to pay for this service so that their incentive to provide quality recommendations would align better with the business model

I read people claim that they like it since it reminds them that they pay for the government's operations.

priceline 86B$, ctrip 24B$

That's true, although significant corruption might make GDP growth too slow such that the start ups will run out of money before they can benefit from it.

Yet Nigeria's GDP is smaller than Florida's according to google.

I liked all three of them. I also feel Murphy's MLAPP provides additional useful material and is well written.

The difference in the quality of the microphones make it quiet impractical in phone, but very easy in a google home device.

Game theory is domain specific. Generic methods in AI tend to dominate domain knowledge over time. Although I agree that other game-theoretic techniques might help here.

I might pay subscription. I agree that the market is not likely to be big. Also data network effects and difficulty with price discrimination can be big obstacles.

EDIT: I think I misunderstood your question. I will probably pay that much for quality ad-free service. But of course it's a niche product.

Or a paid search engine incentivized to provide quality results rather than maximize ad network profits

Thanks again. So I guess for multiplayer games a better approach would be reinforcement learning with no game theoretic heuristics?

Thanks for the explanation. I will also appreciate a reference to a good survey paper if you are in the mood :)