This pressure could even come from people how use and sell AI art, to have a less competition.

>an overqualified prompt engineer.

There are already GPT3 implementation to generate better prompts for text2image AIs...

>it’s overly confident when it’s obviously wrong.

So too human?

My take:

The person inside does indeed not understand Chinese, but the whole system of the room including the human and all instructions does.

The Chinese room is a boring flawed argument, that only is considered relevant by people who get tricked into confusing parts of the system with the whole thing.

I mean if you want it and look for it, you can buy a radicicolous amount of objects with an additional microchip in it. Heated insole: chip. Camera in glasses: Chip. T-shirt that measures heartbeat: Chip. Ring that measures temperature and movement: chip. Implant for paying: Chip. Light up Shoes: Chip. Jacket with speakers: chip.

I mean many packagings have an microchip in the small security sticker, than many people never notice.

And while the camera arm is there already, with a small upgrade, it could also remove unwanted insects physically.

Nice Try Gpt4 web scrapper bot!

Imagine the UN decides that all Americans now have to live in severe poverty (global poor - famines, etc.) for two generation, because their grandparents were born in a rich country.

Makes sense also?

So because black people in the past were unfairly treated, the solution is to treat other races unfairly now? Especially the young people now, who in no shape were responsible for the past?

because quite a few below 20s old had no other choice, because the excluding them for no good reason

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Well put

The singularity and fast take off are closely related, and make a winner takes all scenario most likely.

One united universe at the highest level with a diverse lush structure below that would be a great outcome.

literally all examples I gave predict the future

Of course. Kind of naive question. It is one of their main uses. What will the user click next, what will the weather do, how will nuclear fusion behave, how will the stock market move, will the car in front of you brake, etc.

The "average" job has been disappearing for more than a century, changing what the "average" job is. It is happening right now, and will just speed up.

I doubt this vehicle is more environmental friendly than a bike, with the production effort alone.

thanks

True. But nummerical stability is much more important in long running simulations like weather forecast, than in deep neural network training.

There is a reason they are often benchmarked with single or even half precision.

So why is matrix multiplication faster with it?:

>Leveraging this diversity, we adapted AlphaTensor to specifically find algorithms that are fast on a given hardware, such as Nvidia V100 GPU, and Google TPU v2. These algorithms multiply large matrices 10-20% faster than the commonly used algorithms on the same hardware, which showcases AlphaTensor’s flexibility in optimising arbitrary objectives.

Are you saying it would be slower, if it had to multiply multiple matrixes of the same dimension one after the other?

So? They had to train once, the more efficient algorithm is now in humanities toolbox till eternity. 10-20% increased speed can probably pay that back this year with the compute DeepMind uses alone.

The algorithm is plain faster on the most advanced hardware. For such an already heavily optimized area, that is very impressive.

>Leveraging this diversity, we adapted AlphaTensor to specifically find algorithms that are fast on a given hardware, such as Nvidia V100 GPU, and Google TPU v2. These algorithms multiply large matrices 10-20% faster than the commonly used algorithms on the same hardware, which showcases AlphaTensor’s flexibility in optimising arbitrary objectives.

https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor

Especially since the algorithm are specifically faster on the most modern hardware we have right now.