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Comment Gaza? (Score 0) 14

After an Iranian missile hit, at least, the parking lot of Microsoft Israel, several reports came out saying that they were helping the IDF with genocide operations in Gaza.

I don't know if it's true but those are the real reports.

The timing and 'quiet' character suggests Pakistan might have told them to get the hell out.

"Developing story" as they say.

Comment Re:Meaningless (Score 1) 43

This is yet another predictable side effect of people misunderstanding the stimulus and response reflex of capitalism: Apply dollars, make things happen more.

I have played a handful of FTP games and put a not insignificant amount of time into them... but never any money. If I don't get to own the thing, defined by being able to use it (not even "as I see fit", just at all — but on my schedule) then I won't pay more for the thing than it's worth to me right now, like going to see a movie. If I don't get the server, or if there's DRM which requires activation, that severely reduces what I'll pay.

Comment Re: What companies still pay for periodicals? (Score 1) 96

It's very common for mechanics to be required to provide their own tools.

It's California law that you cannot require an employee to provide their own tools, unless you are paying them at least twice the minimum wage. This is true in general, not just for auto shops. I worked in an RV shop. I had to provide my tools. I got in a wage dispute over it. I received a settlement.

Only the low-end guys in the shop don't typically have to provide tools.

Comment Heuristic (Score 1) 36

It's expected. At their core all chess algo are search a min-max tree, but instead of going width- or depth- first exhaustive search, they use heuristics to prioritize some branches of the tree (A-star).

On the modest hardware of the older machine there isn't that much you can explore before the player gets bored waiting.
So obviously, you're going to make much stringent rules: "Never take a branch where you lose a piece" prunes entire swaths of the tree, rather than "see if sacrificing peice XXX gives us a better path" which would require exploring more of the tree.

Having been trained on all the corporation could scrape from the internet, I would expect an LLM to have been trained from a lot of real-world chess games (e.g.: reports of games from online archives of chess magazines, etc.), so as long as the tokeniser is able to parse the notation found there (or at least distinguish the moves. It doesn't really need to be able to "understand" the notation into english), it has a large corpus of "lists of moves leading to a win" which would include real moves (sacrifices as you mention).

And given a large-enough model to encode that, would be in the same ballpark of the hidden Markov models of yore -- provided it keeps track of the current state of the game (which it currently does not).

Comment Devil's in the detail. (Score 1) 36

I wonder if you wouldn't win if you just told ChatGPT to write an chess AI and then used the chess AI to beat the Atari. Writing code is something text models are good for. Playing chess is not.

The devil is in the detail.
All chess algorithms are A-star: they search a min-max tree, but use heuristic to prioritize some branches instead of doing width- or depth- frist.
Generatingn a template of a standard chess algo would be probably easy for a chatbot (these are prominently featured in tons of "introduction to machine learning" courses that training the LLM could have ingested), writing the heurisitc function to guide the A-star search is more an art-form and is probably where the chat bot is going to derail.

Funnily though, I would expect that if you used the chatbot AS the heuristic it wouldn't be a super bad player.
Have some classic chess software that keep tracs of the board and lists all the possible legal moves, then prompt the chatbot with something like:
"This is the current chessboard: {state}, these at the last few moves: {history}, pick the most promising among the following: {list of legal move}".

In fact, decades ago that's how some people have applied hidden Markov models to chess.

Similarly, I would expect that during training, the LLM would have been exposed to a large amount of all games available only, and has some vague idea of what a "winning move" looks like given a current context.

Not much trying to simulate moves ahead, as rather leveraging "experience" to know what's best next for a context, exactly like the "chess engine+HMM" did it in the past, but a lot less efficient.

Comment Context window (Score 1) 36

I've had ChatGPT forget the current state of things with other stuff too. I asked it to do some web code, and it kept forgetting what state the files were in. I hear that some are better like Claude with access to a repo, but with ChatGPT even if you give it the current file as an attachment it often just ignores it and carries on blindly.

Yup, they currently have very limited context windows.

And it's also a case of "wrong tool for the wrong job". Keeping track of very large code bases is well within the range of much simpler software (e.g.: the thing that powers the "autosuggest" function of your IDE which is fed from a database of all functions/variables/etc. names of the entire database).
For code, you would need such an exhaustive tool to give the list of possible suggestion and then the language model to only predict which from the pre-filtered list, rather free-styling it.

For chess you would need to have a special "chess-mode" training that is trained to always dump the current content of the board and the list of most recent turns' moves in the scratchpad between each turn, so that the current state doesn't fall out of the context. Best would be to do it like people did with HMMs a long time ago: have a simple actual chess software keep track of the board and generate a list of all possible next legal moves, and use the Markov model to predict from that pre-filtered list(*).

(*): That could be doable currently with a piece of software that automatically generates a prompt "The current status of board is: {board description}. The last few moves where: {history}. Chose the best move from: {list of legal moves}".

Comment Already done with markov chains (Score 1) 36

I know it scanned and consumed like.. all of the great Chess games ever played. It can only predict the next word, or move.

...and this has been already demonstrated eons ago using hidden Markov models.
(can't manage to find the website with the exact example I had in mind, but it's by the same guy who had fun feeding both Alice in Wonderland and the bible into a Markov model and use it to predict/generate funny walls of text).

That seems like the nature of LLM's. If I ever can coax ChatGTP to play a whole chess game.. I will let you know the results.

The only limitation of both old models like HHM and the current chatbots is that they don't have a concept of the state of the chess board.

Back in that example, the dev used a simple chess software to keep track of the moves and the board and generate a list of possible next moves, then uses the HMM on that pre-filtered list to predict the next best.

Nowadays, you would need the chat both at least have a "chess mode" where it dumps the state of the board into its scratch pad, along a list of the most recent moves by each play, so that it always has the entire game in context.

Otherwise they should both do roughly the same thing (try to predict the next move out of a model that has been fed all the history of Chess games ever played), but with insane levels of added inefficiency in the case of the chatbot.

Comment Check the title: Norway (Score 1) 173

Tire particulate.

Check the /. item's title: It's Norway we're speaking about.
i.e.: a rich European country.

So a country with a not to shabby public transport network.
Thus compared to, say, the USA: it's already doing quite some efforts toward shifting traffic away from personal vehicles and toward much more efficient transportation systems that have a lot less problems per passenger than private vehicles.

Public transport is the best solution to reduce travelling-related pollution, but can't cover 100% of cases.
EV are a "good enough solution" at reducing problems caused by people who *MUST* and *CANNOT avoid* driving cars.

Comment Soham Parekh? (Score 1) 34

How common of a name is Soham Parekh in India?

At one time I was in a tech group with three Mike Johnsons. Assuredly different people, though slightly confusing.

But I don't really feel sorry for these startups going for global minimum wage and getting burned.

Hire an American named Steve from Akron and you'll be less likely to be scammed.

Comment Screwing the rural communities (Score 5, Insightful) 196

This bill cuts Medicaid to the bone. Large hospitals in cities can take it, because many of their patients have good jobs and good insurance. But the majority of hospitals in small towns and rural areas depend on medicaid.

Those hospitals will go out of business. So even if you have employer provided health care or Medicare, you will have to travel 3-6 hours to go to a hospital.

People will die in the ambulance.

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