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The Google employees who created transformers

wired.com

211–220 of 258 posts

Re: The Google employees who created transformers

#211

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> It is typically very hard for a company to change what they actually do Microsoft started out selling BASIC runtimes. Then they moved into operating systems. Then they moved into Cloud. Satya seems to be putting his money where his mouth is and working hard to transition the company to AI now. Apple has likewise undergone multiplet transformations over the decades. However Google has the unique problem that their m…

Also, though I dislike it greatly, Microsoft purchasing GitHub was a brilliant way to buy into a growing segment which were natively hostile to what they stand for. LinkedIn, maybe less clever, they only make pocket change on it, but it is profitable. Google doesn't really do acquisitions on that level. They do buy companies, but with the purpose of incorporating their biological distinctiveness into the collective.…

Waze was bought 10 years ago, and I think that one went well. They didn't try to integrate it to the whole Google Account thing, but they definitely share data between it and Maps (so Waze users get better maps, and Google Maps gets better live traffic information).

Re: The Google employees who created transformers

#212

Earlier quoted context omitted.

> It is typically very hard for a company to change what they actually do Microsoft started out selling BASIC runtimes. Then they moved into operating systems. Then they moved into Cloud. Satya seems to be putting his money where his mouth is and working hard to transition the company to AI now. Apple has likewise undergone multiplet transformations over the decades. However Google has the unique problem that their m…

Office was the cash cow compared to windows.

Now is Active directory + Office 365 + Teams

Re: The Google employees who created transformers

#214
post #68

And none of them still work for Google. It’s truly baffling that Google’s CEO still has a job after how badly he fumbled on AI.

I think the issue is that there is no future for a trustworthy AI that doesn't completely cannibalize their ad revenue cash cow. Like, who wants to use an AI that says things like, "... and that's why you should wear sunscreen outside. Speaking of skin protection, you should try Banana Boat's new Ultra 95 SPF sunscreen."

> Like, who wants to use an AI that says things like, "... and that's why you should wear sunscreen outside. Speaking of skin protection, you should try Banana Boat's new Ultra 95 SPF sunscreen."

On the other hand, their history suggests most people would be fine with an AI which did this as long as it was accurate:

> ... and that's why you should wear sunscreen outside.

> Sponsored by: Banana Boat's new Ultra 95 SPF sunscreen…"

Re: The Google employees who created transformers

#215
post #93

It's pretty crazy to think that Google is not OpenAI today, they had deep mind and an army of PHDs early on.

The problem is that chatting with an LLM is extremely disruptive to their business model and it's difficult for them to productize without killing the golden goose.

No, I think very few people truly believed that a souped-up SmarterChild would be all that interesting. Google focused more on winning board games.

Re: The Google employees who created transformers

#216
post #66
post #53

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Digital photography works not just because of the camera but because of the surrounding digital ecosystem. What would people do with digital photos in 1975?

It does not matter. In the 80s, they owned the whole photography market, now they only exist as a shell of it's former self. By not pursuing this tech, they basically committed corporate suicide over the long run and they knew it. They knew very well, especially going into the 90's and early 2000 than their time making bank selling film was counted. But as long as the money was there, the chemical branch of the compa…

“Their days…were numbered” is more what a native speaker would say.

Re: The Google employees who created transformers

#217

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AI is in the eye of the beholder.

And in this case, the transformers are more than meets the eye.

I’m sure there’s a beam search joke in here somewhere but it’s too late in the day for me to think about it.

Re: The Google employees who created transformers

#218

Earlier quoted context omitted.

The phrasing kinda makes sense to me. Consider "modern" to mean NN/connectionist vs GOFAI AI attempts like CYC or SOAR. I guess it depends on how you define "AI", and whether you accept the media's labelling of anything ML-related as AI. To me, LLMs are the first thing deserving to be called AI, and other NNs like CNNs better just called ML since there is no intelligence there.

But that's been the case for the last 60 years. Whatever came out in the last 10 years is the first thing deserving to be called AI, and everything else is just basic computer science algorithms that every practitioner should know. Eliza was AI in 1967; now it's just string substitution. Prolog was AI in 1972; now it's logic programming. Beam search and A* were AI in the 1970s; now they're just search algorithms. Exp…

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Re: The Google employees who created transformers

#219

Earlier quoted context omitted.

Funnily enough, the same AI safety teams that held Google back from using large transformers in products are also largely responsible for the Gemini image generation debacle. It is tough to find the right balance though, because AI safety is not something you want to brush off.

I thought Google fired it's AI Ethicists a few years back and dismantled the team?

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Re: The Google employees who created transformers

#220

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> but we didn't see what another group found: that scale in compute with simple algorithms were just better The bitter lesson [0] strikes again. [0] http://www.incompleteideas.net/IncIdeas/BitterLesson.html

I kind of wonder if the reason this seems to be true is that emergent systems are just able to go a lot farther into much more complex design spaces than any system a human mind is capable of constructing.

I think this is overly general. A more accurate statement is that, on tasks where we don't actually understand how something works in precise detail, it's more effective to just throw compute at it until a system with "innate" understanding emerges. But if you do actually know how something works (rather than vague models with no clear supporting evidence), it's still more effective to engineer the system specifically based on that knowledge.
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