131–140 of 258 posts
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."
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No, but it is weird to use "modern" here. Modern suggest a longer timeframe. I would say machine learning deep NNs is modern AI. It's just not true that everything not transformer is outdated, but it is "kinda" true that everything not deep NN is outdated.
> It's just not true that everything not transformer is outdated. Am not expert, do you have some links about this? i.e. a neural net construction that outperforms a transformer model of the same size.
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No, but it is weird to use "modern" here. Modern suggest a longer timeframe. I would say machine learning deep NNs is modern AI. It's just not true that everything not transformer is outdated, but it is "kinda" true that everything not deep NN is outdated.
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.
Well this is what Im trying to say too!
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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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No, but it is weird to use "modern" here. Modern suggest a longer timeframe. I would say machine learning deep NNs is modern AI. It's just not true that everything not transformer is outdated, but it is "kinda" true that everything not deep NN is outdated.
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.
I dunno. The earliest research into what we now call "neural networks" dates back to at least the 1950's (Frank Rosenblatt and the Perceptron) and arguably into the 1940's (Warren McCulloch and Walter Pitts and the TLU "neuron"). And depending on how generous one is with their interpretation of certain things, arguments have been made that the history of neural network research dates back to before the invention of the digital computer altogether, or even before electrical power was ubiquitous (eg, late 1800's). Regarding the latter bit, I believe it was Jurgen Schmidhuber who advanced that argument in an interview I saw a while back and as best as I can recall, he was referring to a certain line of mathematical research from that era.
In the end, defining "modern" is probably not something we're ever going to reach consensus on, but I really think your proposal misses the mark by a small touch.
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Two judgements were made to my knowledge: 1) Google enjoyed significant market status at the time and a leap forward like seemingly semi conscious AI in 2019 would be seen as terrifying. Consumer sentiment would go from positive to “Google is winning to hard and making Frankensteins monster” 2) it didn’t weave well into googles current product offering and in fact disrupted it in ways that would confuse the user. It…
The first time I met with Eric Schmidt he asked what I did (harvest idle cycles to do scientific calculations), and then we talked about how Google was slowly getting into cloud. I remember him saying "I've tried to convince the board that the cloud is the future of growth for google, but they aren't interested" (this was before GCS and GCE launched). The second time I met him I presented my project (Google Cloud Gen…
In Google's heyday, around 2014, I was talking with Uszkoreit about a possible role on his then NLP team. I asked "What would you do if you had an unlimited budget?" He simply said, "I do"
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Why would that be of note, especially in America? I think it would be an interesting observation in China or Japan, or some other country which is generally less welcoming to immigrants than the US
First generation immigrants are still a tiny minority of the population. The fact that the entire team consists effectively of first generation immigrants says something, probably both about higher education and American culture.
For an American, it's less of a good deal. Once you have the PhD, you make somewhat more money, but you're trading that for 6 years of hard work and very low pay. The numbers aren't favorable -- you have to love the topic for it to make any sense.
As a result, U.S. PhD programs are heavily foreign born.
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Bandwidth alone isn’t what prevents 5G from this sort of application, at least in the USA. Coverage maps tell the story: coverage is generally spotty away from major roads. Cell coverage isn’t a fixable problem in the near term, because every solution for doing that intersects with negative political externalities (antivax, NIMBYism, etc); if you can get people vaccinated for measles consistently again, then we can t…
Quantized models could absolutely be run in-car. They can already be run on cell phones.
More seriously, for safety critical applications, LLM have some serious limitations (most obviously hallucinations). Still, I beleive they could work in automotive application assuming: high quality of the output (better than current SoA) and very high token count (hundreds or even thousand of token/s and more), allowing to bruteforce the problem and run many inferences per seconds.
Clearly we are not there yet.
Attention models? Attention existed before those papers. What they did was show that it was enough to predict next word sequences in a certain context. I'm certain they didn't realize what they found. We used this frame work in 2018 and it gave us wildly unusual behavior (but really fun) and we tried to solve it (really looking for HF capability more than RL) but we didn't see what another group found: that scale in…
I’m studying neuroscience but very interested in how ai works. I’ve read up on the old school but phrases like memory graph and energy minimization are new to me. What modern papers/articles would you recommend for folks who want to learn more?