The AI Index 2019 Report
hai.stanford.edu
hai.stanford.edu
http://www.nanotech-now.com/CMP-reports/NOR_White_Paper-July...
All you've done is point out that there are a lot of ML papers coming from tech companies. Nobody is disputing that. Why do you assume this generalizes to most industries?
Also, top S&P 500 companies by market cap are "AAPL","MSFT","GOOG","AMZN","FB","BABA".
I said.
>> and every comment you are redefining what that means
That is false.
You could learn something. Instead, you wasted my time.
2) You point out it applies to FAANG
3) I say FAANG isn't most industries
4) You say most industries must only include those better than the S&P 500
5) I point out a flaw in your reasoning
6) You insist you are right, upset that I tried to ask a question
7) I summarize what happened and exit this thread
That is a lie because I said "often" in "You see that from papers published at top ML conferences. Top contributors are often from FAANG. Some papers are clearly applications." without saying that FAANG = most industries. Instead, I said you can see that from these conferences. Thus, you made it up.
"4) You say most industries must only include those better than the S&P 500"
That is a lie. I didn't say that. I said "If you find no paper from that company, chances are high their stock price is not beating S&P 500 over the last 5 years." I only mentioned a correlation and said nothing about your "most industries".
You misinterpret the text. Fortunately, the text is written so it was easy to prove you are liar, boy.
When nanotech became a hot concept, the goal posts were moved so that companies could claim they were nanotech companies even though they were using pretty basic chemistry. No nanobots required.
And now we’re seeing a lot of statistics being rebranded as AI because the latter attracts investors and journalists.
I am wondering what is % of women doing PhD in the same field and whether it is growing. Without the latter number growing it would be hard to have a greater % of women among new faculty hires.
The situation is now drastically different compared to say 40 years ago. That is why so many PhDs leave for industry to be better paid. The latter is possible for CS guys but in other domains the situation is pretty bad since they don't have easily accessible highly paid industry jobs.
3 hours to 88 seconds? Wow... I wonder if that's been further reduced today (December 2019)
At the same time though, consumer GPUs have gotten significantly faster (compare e.g. an Nvidia 2080TI to a 980TI), and learning algorithms keep improving / better learning algorithms become more widely used (e.g. Adam instead of stochastic gradient descent).