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Seems more than a nitpick to me. I find the essay interesting but this line raised some distrust in me. How can someone have these deep insights into Google's ML strategy and the evolution of the field and simultaneously think LSTMs were invented by Google in 2014?
sorry, I had a mind fart. I was thinking of seq2seq https://research.google/pubs/pub43155/ Pushing the fix now...
Lots of great insight. Here’s one: “Given the long timelines of a PhD program, the vast majority of early ML researchers were self-taught crossovers from other fields. This created the conditions for excellent interdisciplinary work to happen. This transitional anomaly is unfortunately mistaken by most people to be an inherent property of machine learning to upturn existing fields. It is not. Today, the vast majority…
Perhaps this is selection bias. Among all the chemists, the ones who will dabble in ML will likely be the chemists with the highest ML related aptitude. In contrast, a ML expert on a chemist project is more likely not being internally driven to explore it but instead has been assigned the work, which means that there is less bias in selection and thus they have less chemistry aptitude.
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Why do you care? Unless you work for Google Legal, you're in no position to scold OP about anything
I care because I value these Confidentiality commitments, and I believe that if someone doesn't like them, they should not sign them to begin with, rather than breaking them. A company (like any group of people) is allowed to define the culture and standards required for membership. I've worked in extremely secretive companies, and very open ones. I prefer the open ones. But I still don't say anything about internals…
> and I believe that if someone doesn't like them, they should not sign them to begin with, rather than breaking them.
Do you, at least, believe that confidentiality agreements should be broken if it is to make the police or public aware of a crime? How about a civil infraction, such as a hostile working environment?
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Google never talked much about it externally, but Google Research (the predecessor to Brain) had a single project which almost entirely funded the entire division- a growth-oriented machine learnign system called Sibyl. What was sibyl used for? Growing youtube and google play and other products by making the more addictive. Sibyl wasn't a very good system (I've never seen a product that had more technical debt) but i…
Seems to be quite evil though.
Less so if you phrase it "show people recommendations that they're likely to actually click on, based on what they've watched previously", which is what Sybil really was.
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There is a first mover handicap there though. TF1.0 included a bunch of things that were harder to understand like tf.Session(). PyTorch was inspired from the good parts and "we will eager-everything". Internally I'm sure there was a lot of debate in the TF team that culminated with TF2.0, but by that time the damage was done and people saw PyTorch as easier.
Nope, Pytorch was inspired by the Lua version of Torch which well predates Tensor flow. To be fair, basically every other DL framework made the same mistake though. Also, tensorflow was a total nightmare to install while Pytorch was pretty straightforward, which definitely shouldn't be discounted.
I think this is a very important point, and I remember sweating blood trying to build a standalone tf environment (admittedly on windows) in the past. I'm impressed by how much simpler and smoother the process has recently become.
I do prefer Keras to Pytorch though - but thats just me
> I sat on it because I wasn’t sure of the optics of posting such an essay while employed by Google Brain. But then Google made my decision easier by laying me off in January. My severance check cleared... I'm really baffled by how people think it's OK to write public accounts of their previous ( and sometime current! ) employers' inner workings. This guy got paid a shitload of money to do work and to keep all intern…
One side is what's in the papers you signed and the other side is to what extent the terms can be enforced. But you have a point in that it would be good professional practice to wait for a decade before disclosing internals especially when names of people are dropped...
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>How can someone have these deep insights into Google's ML strategy and the evolution of the field and simultaneously think LSTMs were invented by Google in 2014? It may not have been accidental; there's a deliberate movement among some people in the ML community to deny Jürgen Schmidhuber credit for inventing LSTMs and GANs.
That's something I hadn't heard about. Is there any particular reason for this?
https://en.wikipedia.org/wiki/J%C3%BCrgen_Schmidhuber#Credit...
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I care because I value these Confidentiality commitments, and I believe that if someone doesn't like them, they should not sign them to begin with, rather than breaking them. A company (like any group of people) is allowed to define the culture and standards required for membership. I've worked in extremely secretive companies, and very open ones. I prefer the open ones. But I still don't say anything about internals…
From my perspective, in the most bold words I can think to phrase this: You're betraying the citizens and lawful residents of your country by not informing them of what to expect should they accept a job at one of these companies. Have fun with your 30 pieces of silver ( https://en.wikipedia.org/wiki/Thirty_pieces_of_silver ). > and I believe that if someone doesn't like them, they should not sign them to begin with,…
Absolutely. I referenced whistleblowing in my original post above. This isn't a such a case.
Lots of great insight. Here’s one: “Given the long timelines of a PhD program, the vast majority of early ML researchers were self-taught crossovers from other fields. This created the conditions for excellent interdisciplinary work to happen. This transitional anomaly is unfortunately mistaken by most people to be an inherent property of machine learning to upturn existing fields. It is not. Today, the vast majority…
I can confirm. We regularly look for people to write some computational physics code, and recently for people using ML to solve solid state physics problems. It’s way easier to bring a good physicist or chemist to a decent CS level (either ML or HPC) than the other way around.
Lots of great insight. Here’s one: “Given the long timelines of a PhD program, the vast majority of early ML researchers were self-taught crossovers from other fields. This created the conditions for excellent interdisciplinary work to happen. This transitional anomaly is unfortunately mistaken by most people to be an inherent property of machine learning to upturn existing fields. It is not. Today, the vast majority…
As somebody who has crossed the line between ML and chemistry many times, I would love to see: more ML researchers who know chemistry, more chemistry researchers who know ML, and best of all, fully cross-disciplinary researchers who are both masters of chemistry and ML, as those are the ones who move the field farthest, fastest.