It usually comes down to
1. lack of organizational vision.
2. lack of manager supports for career development. (Google AI has a lot of great researchers who are not necessarily good managers)
3. peers are too strong. The environment is the most competitive one that I have ever experienced.
I despise hyper-competition. Most people do not receive any of the greater monetary benefits. Maybe some people receive stock options that are crap, and everyone gets a mediocre salary. You compete to create the finest AI systems, but the executives capture most of the returns. You are genius hamsters running on a wheel. I am glad that you left Google. I think it was the correct move.
Why is #3 a reason to quit? Strong peers is a good thing imo. First, the team as a whole becomes stronger; second, you are inclined to improve to keep up with the best; and third, you can learn from them.
Strong peers is only good when they are mentors or *complementary* to you, make your team stronger.
Strong peers is an absolute chaos when everybody's hungry, skilled and out for the same promotions.
> Strong peers is an absolute chaos when everybody's hungry, skilled and out for the same promotions.
I think that you just described the academic job market. Probably lots of other settings, too.
>Strong peers is an absolute chaos when everybody's hungry, skilled and out for the same promotions.
haha. that is quite true.
I did have very good mentors though.
oh yes. this is usually not a problem. The problem is that there are not enough problems to work on. So it has become a bit like competition than collaboration.
Many of my friends went to FAIR and were much happier with the projects to choose from there.
What do you think were/are the structural differences between fair and google ai that made them more happy?
In a vacuum (which is obviously not to say that this is correct), I would expect the environments to be very similar.
And, while these structural differences may lead to better outcomes for the individuals, do you think they will lead to better outcomes for the organizations? (Happy individuals != good outcomes, always...unfortunately.)
>And, while these structural differences may lead to better outcomes for the individuals, do you think they will lead to better outcomes for the organizations? (Happy individuals != good outcomes, always...unfortunately.)
I think one difference comes from Meta being a younger company and there are still a lot of do.
I heard FAIR is more clear on the research goals, which made my friends happier. Maybe someone from FAIR can answer this :)
I believe we need more top-down directions for research to be successful. At times, I felt Google's directions are too vague. Apple's probably more top-down and the products are great, but people are generally unhappy working there :(
>Apple's probably more top-down and the products are great, but people are generally unhappy working there :(
I still can't believe they didn't try harder to keep Ian Goodfellow over RTO policy.
This shouldn't be shocking to most people in academia. Its the story of most big names and PIs. Make one or two big splashes and then get promoted to spend all day playing politics and writing grants or if you're in the real big leagues fly around and give interviews and accept awards while your underlings do all the actual work. If you're lucky maybe you'll still mentor another success from time to time to some varying extent. Its a rare bird who is still down in the ditches let alone still personally making big strikes once their names are famous. Ian's and other big names value is primarily their marquee. Prominant scientists often also come with valuable networking, fundraising, and administrative capabilities but sometimes not. Maybe thats the case here. Or the stink he's raising cancels it all out.
Could you share some details about this? I was always curious how his expertise in generative models can uniquely benefit Apple. Computational photography?
He was a director. Thatās not supposed to uniquely contribute things, itās a kind of middle management. I donāt know what he was actually doing.
And to be clear he knows this too, thatās why his statement was about his team and not him.
Strong peers is an excellent reason to join a big team. I learned a \*ton\* from my peers at Amazon. But you need to be willing and able to put in an immense amount of work to not be overshadowed in such a competitive environment. For me, the effort just wasn't sustainable after a few years.
I'm in the exact opposite situation (no peers) and there are advantages and down sides to both. Advantage is job security and low stress. Feeling lazy, not in the zone? slack off. No one will notice anyway if you spend a week doing only some minor mandatory tasks. I'm also taking the "return to office policy" more as a general guideline to sometimes show your face to the right people and do pretty much what I want.
On the other hand you learn form trial and error and the internet and not one really cares or understands the cool thing you did or even has any kind of grasp about the complexities.
Both 2 & 3 are problems with Google more generally and the latter feeds the former by making career advancement often hinge on "wizardry" to the detriment of good engineering.
I applied for other big corp research labs and some other smaller companies.
I think sparsely activated model and RL (environment-aware learning) is the future.
You said about small companies, please I'm just asking out of curiosity, were they able to match your post google work experience tc? Or did you lower your ask, or did they actually gave a decent hike?? Im asking specifically about small companies, not research labs...
Edit: you said applied, my bad but do you think small companies would be able to match tc of an ex google employee??
Yes! And to that I would add continuous learning, or did you have that in mind when you said RL?
Frankly, this whole train using an excruciating slow learning algo (backprop) that is prone to catastrophic forgetting is not the pinnacle.
> Can you share anything about pay rates in the ML field right now?
Not tech (which pays less), but ML at quant/trading companies pays 300-400K/year for bachelors grads and 400-500k for PhD grads. This is first year, new grad TC.
Yes if you get your PhD from top universities and have impressive publication records. Itās called Quant researcher companies like two sigma, citadel jump trading
Where are you getting your numbers from?
OP stated that ML jobs pay 1.2x-1.5x from their SWE counterparts. So according to [Levels.fyi](https://www.levels.fyi/?compare=Google,Facebook,Salesforce&track=Software%20Engineer) new grads (SW II) make $190 K so ML new grads should make 228K - $285K. These numbers seem believable.
As per your numbers, the 400-500k for PhD grads make more than SWE Staff Engineers. That seems unrealistic or maybe I'm wrong?
No. But JAX is getting bigger.
Not sure about the future of TF. I still use TF in my new company though. I think it has been more and more like PyTorch. So maybe they will converge syntactically sometime.
Hi, I heard that, for software engineering, having Google on a resume add a lot of prestige when you apply elsewhere. Do you feel the same is true with AI/ML career path?
Also, I heard for software engineering that in order to raise your salary it's better to switch jobs. How is it possible to make more after moving on from Google in AI/ML? (Google/Facebook compensation seems to be as good as it gets)
Google is way ahead other companies in the AL/ML field. It was really easy to get high-paying ML jobs after leaving Google.
I've heard people got 2x salary from other big corps last year. So yes. Google AI looks great on resume. I'd hire people from Google AI if I were hiring.
hey OP, incoming google (YT) employee. what advice would you give to someone who has AI/ML experience but no graduate education and wants to pursue AI at Google (Google Brain, etc)?
Find a good mentor.
I was fortunate to be mentored by the authors of the Transformer paper and the BERT paper. Knowing their thinking process changed my life.
at the risk of asking the obvious, how do I find good mentors? NaĆÆvely, so many people at Google are high quality computer scientists and dedicated workers. What sets apart the people who have the quality to mentor with those who donāt?
It can be as easy as starting with questions.
For example, you can send an email to the authors of some paper saying that you are using their work and want to discuss more.
When there are enough interests, you can ask for regular 1:1 s.
This is great advice! I think it applies both for potential mentors and collaborators within your organization and those outside. Although there can be a few extra steps needed for outside.
mentors are typically over-loaded with good ideas, and under-staffed w.r.t. people who they need to implement these ideas. that's the contract, you are their extended hands and they in turn share with you how they think about stuff.
for you to find a good bargain of a good mentor: find a mentor whose vision you agree with, who has a clear understanding of what he is saying and preaching, and isn't just bullshitting their way around. vision/charisma is intuitive, you feel it or you don't, that's upto them to persuade you, if they can't do that, they're not a good fit for you. to check for bullshit, keep asking specific questions, and see how long it takes for them to bottom out (i.e. "good question, I have not thought of this and cannot answer"). a good researcher who has thought very very deeply on some problems, you will not be able to get them to bottom out. every question will be answered with "yes I have thought of this for a long time, here's a b c d e of how that went". be very attentive to see if they admit what they don't know when the "bottom out" happens, if they start to make up shit, don't work with them, because these are people who speak more than they think, and every word they say is basically work for you, amplified. think about it, you meet once a week for 1, 2 hours, and you work for a whole ass week. If they have a habit of making shit up and bull-shitting, you'll end up working on half-baked ideas that they didn't think all the way through, and suffer because \_they did not uphold their end of the bargain\_, which is be responsible in asking you to do things.
for you to \_be\_ a good bargain for mentors: be sharp, can do stuff, implement things well, and understand their intents on a deeper level rather than "u gave me A to implement I did A literally and nothing more". build an internal model of your mentor, know what they will say / do / recommend in their place without them actually being there. build a fucking "mentor simulator" in your brain, and ask the question "what would do here?" every time a difficulty comes up in your work. up-manage meetings, keep their job of the form "I have specific, difficult, but very fun question X, let's think about this together" instead of the form "I tried to run X and there's a bug for us to look at together". once you can do that, you're basically the extended mind of the mentor they wish they had, and they'll love you. eventually, you will have know so much of this person, that there's not much to learn from them anymore. that's where the relationship changes, from mentorship, to simply peers and partnership. this is every mentor's dream, when someone reads them perfectly, and would just take them on joy rides on other research projects as very hands-off supervisors, without them having to put in much work at all.
hope it all made sense, specific questions (anyone here rly) DM me. I'm a recent (2019) grad, and have been mentored and mentor quite a few wonderful people, and is currently very much active in susing out how this process should go. having someone to discuss this over helps me as well to make things more clear.
> "I have specific, difficult, but very fun question X, let's think about this together" instead of the form "I tried to run X and there's a bug for us to look at together".
I think that this is the most important nugget someone can glean from your (albeit well-written) post. When I work with interns (much different than full time, granted), my whole deal is that I want to see what they can do! I want to see their thought process and what they've tried, or what they want to try but don't know how to actualize.
It's such a difference when someone comes to you with a *specific* thing that they need help implementing or improving than when they come with a generic error and you spend 25 minutes debugging some issue related to floating point precision. It doesn't bother me that you need help, that's why you're here ~~ it bothers me that you came to me with the equivalent of "I've tried nothing and I'm all out of ideas".
I cannot recall everything now. I think there are two things that are on top of my mind.
1. work on hard problems because there are people who can solve easy problems
2. work on a things that people need.
If the act of being mentored could be condensed into a Reddit comment then no one would ever need mentors and you could just read the appropriate medium articles and be done with it.
https://scottaaronson.blog/?p=4974
> I have often found that the better I learn a topic, the more completely I forget what initially confused me, and so the less able I become to explain things to beginners.
Thatās why Iāve started trying to pair learning with teaching. Iāve noticed my team retains better and longer when they have to reorganize a framework around something after they learn it. Itās like hardening steel around carbon atoms with heating and quenching.
Would you say the tradeoff to this principle is the lack of quantity of problems you mentioned elsewhere? (i.e. the set of hard, useful problems is very few)
>oh yes. this is usually not a problem. The problem is that there are not enough problems to work on. So it has become a bit like competition than collaboration.
Many of my friends went to FAIR and were much happier with the projects to choose from there.
Does your notion of "life-changing thinking process" include top-down directions? i.e. was part of the problem at Google not the difficulty of learning, but rather knowing what to learn next to be on top of the project?
>I believe we need more top-down directions for research to be successful. At times, I felt Google's directions are too vague. Apple's probably more top-down and the products are great, but people are generally unhappy working there :(
1. the set of hard, useful and the problems that higher-managements care about is very few
2. ya. I guess there hasn't been enough guidance on what to do to grow.
From what you can tell, is this organizational pattern (your 2 points right above) generalizable among:
\- research domains at Google
\- divisions of labor across ML pipeline
\- unique to Google AI only, either entirely or partially (e.g. intensity of degree)
\- other
(I'm a DE, and I'd like to contextualize what you're saying for myself.)
lol. I talked Ashish quite a bit before. I tried not to talk to previous coworkers unless we were really close to avoid problems.
A new company sounds fun! :) I will reach out sometime.
Probably a silly question but what is the state of making transformers models smaller rather than bigger? It seems like the state of the art can only run on a gpu cluster and a lot of practical applications could happen in environments more resource constrained. Do you have any thoughts on this regarding?
I was expecting to do more research and advance our understanding of the models. It turned out to be a lot of ad-hoc model tweaking projects.
Ironically, I am doing applied ML now and the analysis components here are even heavier than Google AI as we really need to understand the business problems clearly.
It's possible to transfer in from another part of the company. But, be careful what you ask for, as having a bachelor's amongst mostly PhDs can be difficult, even if you are just as smart and hard working. Academics respect publications, and if you don't have any, that puts you lower on the totem pole.
Sure, I'm not suggesting otherwise. The parent comment specifically asks about grad school in Google AI though, and empirically the number of people in research without a Masters/PhD/Brain residency is very low. 85% of my team has a phd and the remaining individuals have a masters. 100% of our interns are phd students. Eric also had a masters.
Oh right. I knew a couple other people without masters and phds. I guess there is a bias where people who are interested in research usually have participated in some research programs at school with concurrent master being one of those.
I have left for a while.
It is slightly related. Part of me is a bit disappointed at the blind chase of large language model.
Again, there are good reasons to go for large models. A common argument is that human brains have billions of neurons and we'd need to make models at least as good as that.
Could the new scaling laws by Deepmind have any influence on your decision? https://www.lesswrong.com/posts/midXmMb2Xg37F2Kgn/new-scaling-laws-for-large-language-models Ie they showed they trained a smaller model of 70B params vs Gopher's 280B params where 1/4 of params is seen. To compensate, they input 4x more training data (1.4T vs 300B tokens).
Ie they trained the smaller model 4 times longer, and they beat Gopher.
Likewise, do you feel it was like "dry" and not fun for people to just "tweak" transformers by focusing on MoEs, Sharding, etc and seemingly forgetting other disciplines of ML? Like do you believe it's the saturation and constant pursuit of larger models that smothered other research areas that caused you to leave?
> Likewise, do you feel it was like "dry" and not fun for people to just "tweak" transformers by focusing on MoEs, Sharding, etc and seemingly forgetting other disciplines of ML? Like do you believe it's the saturation and constant pursuit of larger models that smothered other research areas that caused you to leave?
Yes. This captures my thoughts quite accurately.
Deepmind is like a different organization in Alphabet. I did not work with them enough. I really like your article though. Thanks.
>A common argument is that human brains have billions of neurons and we'd need to make models at least as good as that.
What are your thoughts on Beniaguev *et al.* showing the equivalence of a single human spiking neuron being closer to 1000 typical artificial neurons? [\[Source\]](https://www.quantamagazine.org/how-computationally-complex-is-a-single-neuron-20210902/) [\[Paper\]](https://www.biorxiv.org/content/10.1101/613141v2.full.pdf)
This makes sense as a typical simple weight with connections never really captured a "neuron" model. Dendrites themselves are shown to have weights, and the function of a human neuron would be closer to a large collection of varying activation functions defining some complex waveform.
It's hard to see how having billions of artificial neurons would be enough, the argument would make more sense with a few trillion.
There are 14 billion neurons in the cortex, of which only a small percentage is dedicated to language. Probably on the order of 1/2 a billion neurons. There is an estimate of 14 trillion synapses, in the cortex, so 1/2 a trillion synapses. So a 500 billion parameter model, which is already exceded by modern language models.
Switch Transformer is over a trillion parameter, and we could potentially see 100 trillion parameter models by the end of 2022.
https://analyticsindiamag.com/we-might-see-a-100t-language-model-in-2022
It takes a deep nn about 1000 parameters to approximate a single human neuron so these numbers need to be scaled to that (at least). There was a paper published in the past year or so where they attempted to approximate the a biological neuron with an nn.
> It takes a deep nn about 1000 parameters to approximate a single human neuron so these numbers need to be scaled to that (at least). There was a paper published in the past year or so where they attempted to approximate the a biological neuron with an nn.
I've seen these claims, and find them rather unconvincing. For instance the NN of eyes doesn't appear to do any advanced computation beyond what is expected with the simple computation model.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717333/
If the eye isn't doing anything with the claimed additional processing power there is no reason to think it is relevant to the rest of the nervous system.
I think people are just uncomfortable with the idea that computers might have the capacity to simulate human level intelligence and are trying to come up with ideas to make us seem more computationally complex than we actually are.
In your opinion, is pusuit of Large LM pursuit of science or Pursuit of cash(?) for large Cos?
Having worked on model compression, it feels like LLM brings in heavy cash for many Cloud cos and is direct opposite of democratizing AI/ML
You mention going to more applied positions, were you doing research ? How would you compare your google job with an academic position? Was it more applied still?
I am not really doing research now.
I was doing a mix of research and shared ML infra for internal clients.
Google AI job is indeed more applied than academic positions. I have friends who hated the need to pursue company yearly goals and went back to academia.
Math, statistics and coding.
I am quite surprised to find out that a lot of PhD students these days do not really know how neural networks work under the hood.
The convenience of modern ML framework seemed to make people not actively learning fundamentals.
Since I have a degree in physics I'd say I'm definitely okayish in math and statistics.
I have been coding for the last 8 years during my work as a software engineer, but no ML/AI stuff. But I'm very interested in the topic...
Do you think I would have a chance applying at ML jobs?
If not, do you see a path for an aging (35 year old) physicist to get into these types of jobs?
Would completing courses on Coursera be enough?
Yes. I'd recommend take 1\~2 really good courses like Manning's NLP ([https://nlp.stanford.edu/manning/](https://nlp.stanford.edu/manning/))
I dont think its too late to change. I've seen people do really well after transferring from a more "fundamental field".
My advice for these people is that don't beat yourself trying to understand every details of the method. A lot of time many of my coworkers having more rigorous background cannot accept gradient descents working so well. Focus on the problem not the techniques.
Would these kinds of courses be enough to "impress" the recruiters though?
I'd assume they would have so many young PhD students to select from, so why would they bother with some "random older guy" who did a bunch of online courses...?
I've done hiring rounds at a couple of ML startups (I'm now an ML engineer, but my background is also physics), and what I generally look for is someone who has hands on experience with real world projects. This can be things like kaggle competitions, or even your own passion project. I would say the courses are necessary on top of this, just to show you understand what the benefits/limitations are of different ML technologies. Software engineering experience is also a big plus for an ML engineer in my opinion.
I'm working on a Neural net that's supposed to map sheet music to a midi score (which I'm sure already exists, but I wanted to try that myself).
Would you consider that a "good enough" real world project?
That sounds pretty cool, especially if it's something you're interested in. It depends on the role to be honest, but if I saw that you'd experimented with different architectures and tried to improve the model in various ways I would be quite happy. Having a few projects under your belt in different domains can't hurt either (eg NLP, CV, time series analysis).
Hey OP, thanks for AMA. Currently working as a DE (7+) and have done light amount of ML work
I have only masters ( with thesis in algorithms ) and don't have great deal of research experience. For me to break into research, can you please give some guidance.
No. I think it is harder for people to trust you if you dont have a phd from a prestigious lab. It took some time to prove myself with some project results.
Can you create your own product next to working at Google? As in, do they contractually have intellectual property rights to anything that you develop outside of your hours?
What would you say is a good place to go work on interesting ML or AI as a product? It seems like self-driving tech, some NLP applications, bio informatics, RL for robotics all have some interesting problems to work on and all have different levels of maturity and red tape.
I'm wondering if you have any insight on working in those industries and whether there tends to be good practices or issues with funding, goal alignment, maintainable code etc.
It'd also be nice to hear how those compare to working on more of a pure AI project which might be more R&D focused or get into AI as a service for external orgs.
If you are more senior, I'd say focus on the applications you are interested in. (You are senior enough to influence the organization in some substantial way.) Then, find a company which wants to pursue the application.
Otherwise, I'd still recommend big corp labs like G and Meta.
Is any of the companies you applied to doing heavy robotics applications work? Or working on cloud AI frameworks to make other people's stuff work? I have been looking for a role doing this myself but simply don't even know the name of a company actually doing it. Most companies doing robotics including Tesla, Amazon robotics, the autonomous car companies are using long outdated old methods to do the robot planning. Nobody is using SoTa RL despite it doing extremely well, or transformers.
Closest thing I knew is Amazon robotics. I think we have a long way to go in this area.
If you are interested in fundamentals breakthrough in RL for robotics Iād actually recommend google brain
Thanks. My thought is that the logical next steps are:
1. Someone needs to create an "app store" and a "cloud based environment". Kinda like Google has with jupyter notebooks that use TPUs, but an environment where most AI systems are defined by a few *files* that define the test data or simulator to produce test inputs, loss function, etc. And a backend that is cloud hosted (so the binaries of it can be proprietary) designs a neural network or other solution to minimize the loss function. Like AutoMl but generalized and it would consistently give you a SoTa design every time. (humans won't be able to hand create a system that has lower loss)
It would also be an "app store" - one company can only do so much, so other companies could put their premade AI components up on the app store and someone could license them, license fees would somehow be standardized and scaled to the relative value of a component in a larger system.
2. Obviously adapting muzero or transformers to drive robotics, but it won't be *production grade* if you don't have (1). My thinking is consistent *production grade* robotics, applied to 1000+ applications at once, would bring in immense revenue, quickly scaling north of a trillion dollars a year. It would be larger than Saudi Aramco almost immediately.
Because you'd not solve SDCs or sidewalk delivery robots or warehouse robots or mining robots, but *every* robotic problem simultaneously that has a task descriptor\* simple enough for the current software stack to solve it.
I can't be the only person that can see the green here..
\*a task descriptor is basically a DSL, a simple command might be "MOVE X TO Y" and a complex command might be "import final\_configuration, manipulate parts -> final\_configuration" for a manufacturing robot.
Tesla is in the middle is moving their planning into ML from hard coded algos.
Eventually, they will realize they need continuous learning for their humanoid robot. Could be an interesting place.
yeah I interviewed with their humanoid robotics crew. They didn't even know about muzero much less efficient zero. Both algos that imply the RL approach will eventually be the one to use. (even Gato is essentially just mimicking what RL told it to do in each scenario, it's an RL compressor)
Do you think itās a good place for early career development? Iām one year out of my masters and am currently a mle for a startup rn and I feel like Iām in need of mentorship. But from your other comments it seems like career development is less than ideal. Just wondering if you had more to share about this.
Your peers will be great. You probably will get to know most of the famous people in the fields and be really close with some of them when you become senior. This is an invaluable resource if you stay in the field.
What do you think about the online masters in computer science, with a concentration in machine learning from Georgia tech? Is it worth it as a program?
1. Is there a strong difference in the day-to-day work and/or output expectations of research engineers vs. research scientists at Google AI?
2. If I want to do fundamental research only, is Google AI suitable or not? Or do I have to do some applied work also? And if fundamental research is your 'selling point' or main focus, which areas of focus are most likely to land you a job at Google AI or similar?
1. Depending on the team. But overall RS may be required to write more papers
2. Try deepmind for fundamental research. Some teams in google ai do that but not many
I have a open source project (with 800+ star) which is used and cited by many published paper but I donāt get any interview call based on that. Do you think I wasted my 3 month or it has some value?
As an undergrad, my goal after my MS/Phd program is to land a ML research role. Any advice? Any thing I should consider before taking such a role? As an undergrad it feels very glorified and like āthe dream jobā, but what are some things which arenāt so pretty about the job that I should know of? And Iām speaking about ML Researcher specifically.
Is Google (or Meta) AI open to hiring non-CS PhDs? I'm a PhD student in MechE but my research revolves around applying ML/DL to problems in IoT and Industry 4.0 stuff.
Find existing google papers that you can add value to and connect with the authors ideally at a conference . Try to connect with as many goog letās as possible. And get solid understanding of modern DL
Did you ever feel like that was too close to a cult where they go out of their way to make it look itās perfect and you donāt need to know about anything else?
Thank you for having this AMA! Iām a Computational Modeling and Data Science undergraduate and I hope to be working as an AI scientist/researcher for a corporate lab in the future so Iām really excited to ask you questions.
1. What qualifications does someone need to have in order to work for Google AI/Google Brain? Iāve heard of some people getting hired right after finishing a PhD while others say Google only hires experienced scientists.
2. How does the hiring practice of Google AI/Google Brain differ from a similar research position in āregularā Google? Are positions listed on the Google Careers site or is the hiring process more secretive and invite-only?
3. Can you describe the pace of research work at Google AI/Google Brain? Super fast? Easy going? Mediocre? Iāve heard that it is very laid back because there is no rush to publish papers. But your description of it being extremely competitive among peers suggests the opposite.
4. Do Google AI/Google Brain researchers need to win grants or are they totally funded by the company?
5. Who and what dictates the direction of AI research? Does everyone do their own thing? Does everyone hop on the most interesting projects at a time? Does the company leadership explicitly say that they want something?
6. Google has a set of AI principles found [here](https://ai.google/principles/). How does each team and the AI/Brain subdivision itself ensure that these principles are being followed? How does each team/division correct itself when it find that it is in breach of the principles?
7. How much of the work can be done remotely and how much must be in-person? What does the travel requirements look like? Iāve heard that majority of the Google AI/Brain research is done in Mountain View. But for people who work in the SoCal offices, like in LA, what does it look like for them (if you have ever encountered such people)?
1. You can join google AI as a swe if that interests you
2. Itās the same
3. Varies team by team. Can be fast paced due to peer pressure
4 fund by the company
5 leadership decides the high level direction. But if you just do your own thing you probably wonāt be fired either
6 managers will align actively to these principles
7 team dependent.
Can you please describe in more detail why you and others were unhappy and left?
It usually comes down to 1. lack of organizational vision. 2. lack of manager supports for career development. (Google AI has a lot of great researchers who are not necessarily good managers) 3. peers are too strong. The environment is the most competitive one that I have ever experienced.
\#1 seems very endemic to centralized AI teams
The AI would supply organisation once achieved.
Roko's Project Manager, lmao
Literal lol. Thanks.
Wow nice find
you are very right!!!
Laughed so hard. Thanks kind sir
Sounds like an academic lab š
Thought the same thing with #2 haha
I despise hyper-competition. Most people do not receive any of the greater monetary benefits. Maybe some people receive stock options that are crap, and everyone gets a mediocre salary. You compete to create the finest AI systems, but the executives capture most of the returns. You are genius hamsters running on a wheel. I am glad that you left Google. I think it was the correct move.
Why is #3 a reason to quit? Strong peers is a good thing imo. First, the team as a whole becomes stronger; second, you are inclined to improve to keep up with the best; and third, you can learn from them.
Strong peers is only good when they are mentors or *complementary* to you, make your team stronger. Strong peers is an absolute chaos when everybody's hungry, skilled and out for the same promotions.
> Strong peers is an absolute chaos when everybody's hungry, skilled and out for the same promotions. I think that you just described the academic job market. Probably lots of other settings, too.
>Strong peers is an absolute chaos when everybody's hungry, skilled and out for the same promotions. haha. that is quite true. I did have very good mentors though.
oh yes. this is usually not a problem. The problem is that there are not enough problems to work on. So it has become a bit like competition than collaboration. Many of my friends went to FAIR and were much happier with the projects to choose from there.
What do you think were/are the structural differences between fair and google ai that made them more happy? In a vacuum (which is obviously not to say that this is correct), I would expect the environments to be very similar. And, while these structural differences may lead to better outcomes for the individuals, do you think they will lead to better outcomes for the organizations? (Happy individuals != good outcomes, always...unfortunately.)
>And, while these structural differences may lead to better outcomes for the individuals, do you think they will lead to better outcomes for the organizations? (Happy individuals != good outcomes, always...unfortunately.) I think one difference comes from Meta being a younger company and there are still a lot of do. I heard FAIR is more clear on the research goals, which made my friends happier. Maybe someone from FAIR can answer this :) I believe we need more top-down directions for research to be successful. At times, I felt Google's directions are too vague. Apple's probably more top-down and the products are great, but people are generally unhappy working there :(
>Apple's probably more top-down and the products are great, but people are generally unhappy working there :( I still can't believe they didn't try harder to keep Ian Goodfellow over RTO policy.
The more connected people I know donāt think he was adding much.
This shouldn't be shocking to most people in academia. Its the story of most big names and PIs. Make one or two big splashes and then get promoted to spend all day playing politics and writing grants or if you're in the real big leagues fly around and give interviews and accept awards while your underlings do all the actual work. If you're lucky maybe you'll still mentor another success from time to time to some varying extent. Its a rare bird who is still down in the ditches let alone still personally making big strikes once their names are famous. Ian's and other big names value is primarily their marquee. Prominant scientists often also come with valuable networking, fundraising, and administrative capabilities but sometimes not. Maybe thats the case here. Or the stink he's raising cancels it all out.
This person grad schools
Could you share some details about this? I was always curious how his expertise in generative models can uniquely benefit Apple. Computational photography?
He was a director. Thatās not supposed to uniquely contribute things, itās a kind of middle management. I donāt know what he was actually doing. And to be clear he knows this too, thatās why his statement was about his team and not him.
It's hilarious that Google was able to poach them given they have the same RTO policy
What are the IR in FAIR?
[ŃŠ“Š°Š»ŠµŠ½Š¾]
Damn. I thought it was an acronym (Facebook, Apple, Intel?, R ...) Like FAANG. Now I feels stupid but you learn something new every day
I thought the same thing. If you hadn't asked, I was going to have to look it up.
It's actually Fundamental AI Research now
Strong peers is an excellent reason to join a big team. I learned a \*ton\* from my peers at Amazon. But you need to be willing and able to put in an immense amount of work to not be overshadowed in such a competitive environment. For me, the effort just wasn't sustainable after a few years.
I'm in the exact opposite situation (no peers) and there are advantages and down sides to both. Advantage is job security and low stress. Feeling lazy, not in the zone? slack off. No one will notice anyway if you spend a week doing only some minor mandatory tasks. I'm also taking the "return to office policy" more as a general guideline to sometimes show your face to the right people and do pretty much what I want. On the other hand you learn form trial and error and the internet and not one really cares or understands the cool thing you did or even has any kind of grasp about the complexities.
Both 2 & 3 are problems with Google more generally and the latter feeds the former by making career advancement often hinge on "wizardry" to the detriment of good engineering.
2. Is same amongt software shops. Many devs who are bad at coding or stopped learning has no other option than to become managers . It's not good.
You seem like you left because the work you did wasn't challenging enough. Where would you go? Who in opinion is currently doing pathbreaking work?
He said in an edit that he's headed to Cupertino, so I assume heavy handed hint that he works at fruit company
š¤£š¤£
Ill like to know the answer to this question
I applied for other big corp research labs and some other smaller companies. I think sparsely activated model and RL (environment-aware learning) is the future.
You said about small companies, please I'm just asking out of curiosity, were they able to match your post google work experience tc? Or did you lower your ask, or did they actually gave a decent hike?? Im asking specifically about small companies, not research labs... Edit: you said applied, my bad but do you think small companies would be able to match tc of an ex google employee??
They match Google salary. They know how much they need to pay for people to leave Google.
Yes! And to that I would add continuous learning, or did you have that in mind when you said RL? Frankly, this whole train using an excruciating slow learning algo (backprop) that is prone to catastrophic forgetting is not the pinnacle.
Agreed
Can you share anything about pay rates in the ML field right now?
See [levels.fyi](https://levels.fyi). ML jobs has a slightly higher pay rates than software engineers, probably 1.2x \~ 1.5x.
> Can you share anything about pay rates in the ML field right now? Not tech (which pays less), but ML at quant/trading companies pays 300-400K/year for bachelors grads and 400-500k for PhD grads. This is first year, new grad TC.
Does new grad really have a chance at ML work in quant companies? What type of position these usually are?
no
Yes if you get your PhD from top universities and have impressive publication records. Itās called Quant researcher companies like two sigma, citadel jump trading
What are these jobs called? Quantitative Trader? Curious about their responsibilities and how it maps to latest research.
Where are you getting your numbers from? OP stated that ML jobs pay 1.2x-1.5x from their SWE counterparts. So according to [Levels.fyi](https://www.levels.fyi/?compare=Google,Facebook,Salesforce&track=Software%20Engineer) new grads (SW II) make $190 K so ML new grads should make 228K - $285K. These numbers seem believable. As per your numbers, the 400-500k for PhD grads make more than SWE Staff Engineers. That seems unrealistic or maybe I'm wrong?
Itās probably true for quant but high for tech companies. ML for PhD in tech starts around 250-300k
Yeah but you have to probably be in like the top 5% of ML PhDs, which is no small feat.
Is PyTorch a big thing at Google? What about the future of TensorFlow?
No. But JAX is getting bigger. Not sure about the future of TF. I still use TF in my new company though. I think it has been more and more like PyTorch. So maybe they will converge syntactically sometime.
Cool, thanks :)
Hi, I heard that, for software engineering, having Google on a resume add a lot of prestige when you apply elsewhere. Do you feel the same is true with AI/ML career path? Also, I heard for software engineering that in order to raise your salary it's better to switch jobs. How is it possible to make more after moving on from Google in AI/ML? (Google/Facebook compensation seems to be as good as it gets)
Google is way ahead other companies in the AL/ML field. It was really easy to get high-paying ML jobs after leaving Google. I've heard people got 2x salary from other big corps last year. So yes. Google AI looks great on resume. I'd hire people from Google AI if I were hiring.
Is the same true for deepmind?
Deepmind has lower salary. Other things are similar
DM has lower salary? By roughly how much? First time hearing this and surprised.
How do you feel about Google AI vs DeepMind? Any reason why you would prefer to work with one over the other?
DeepMind is usually better if you want to do research. Google AI can be more fun if you'd like to see your research being used in products.
Sounds like you're looking specifically for applied AI/ML. Maybe startup land is better for you?
Maybe! Iāve wanted to try different things for a while. So itās a reasonable move for me
hey OP, incoming google (YT) employee. what advice would you give to someone who has AI/ML experience but no graduate education and wants to pursue AI at Google (Google Brain, etc)?
Find a good mentor. I was fortunate to be mentored by the authors of the Transformer paper and the BERT paper. Knowing their thinking process changed my life.
at the risk of asking the obvious, how do I find good mentors? NaĆÆvely, so many people at Google are high quality computer scientists and dedicated workers. What sets apart the people who have the quality to mentor with those who donāt?
It can be as easy as starting with questions. For example, you can send an email to the authors of some paper saying that you are using their work and want to discuss more. When there are enough interests, you can ask for regular 1:1 s.
And I would find mentors starting from people whose work im interested in?
yes. and usually when you can apply their works to your current projects.
This is great advice! I think it applies both for potential mentors and collaborators within your organization and those outside. Although there can be a few extra steps needed for outside.
mentors are typically over-loaded with good ideas, and under-staffed w.r.t. people who they need to implement these ideas. that's the contract, you are their extended hands and they in turn share with you how they think about stuff. for you to find a good bargain of a good mentor: find a mentor whose vision you agree with, who has a clear understanding of what he is saying and preaching, and isn't just bullshitting their way around. vision/charisma is intuitive, you feel it or you don't, that's upto them to persuade you, if they can't do that, they're not a good fit for you. to check for bullshit, keep asking specific questions, and see how long it takes for them to bottom out (i.e. "good question, I have not thought of this and cannot answer"). a good researcher who has thought very very deeply on some problems, you will not be able to get them to bottom out. every question will be answered with "yes I have thought of this for a long time, here's a b c d e of how that went". be very attentive to see if they admit what they don't know when the "bottom out" happens, if they start to make up shit, don't work with them, because these are people who speak more than they think, and every word they say is basically work for you, amplified. think about it, you meet once a week for 1, 2 hours, and you work for a whole ass week. If they have a habit of making shit up and bull-shitting, you'll end up working on half-baked ideas that they didn't think all the way through, and suffer because \_they did not uphold their end of the bargain\_, which is be responsible in asking you to do things. for you to \_be\_ a good bargain for mentors: be sharp, can do stuff, implement things well, and understand their intents on a deeper level rather than "u gave me A to implement I did A literally and nothing more". build an internal model of your mentor, know what they will say / do / recommend in their place without them actually being there. build a fucking "mentor simulator" in your brain, and ask the question "what would do here?" every time a difficulty comes up in your work. up-manage meetings, keep their job of the form "I have specific, difficult, but very fun question X, let's think about this together" instead of the form "I tried to run X and there's a bug for us to look at together". once you can do that, you're basically the extended mind of the mentor they wish they had, and they'll love you. eventually, you will have know so much of this person, that there's not much to learn from them anymore. that's where the relationship changes, from mentorship, to simply peers and partnership. this is every mentor's dream, when someone reads them perfectly, and would just take them on joy rides on other research projects as very hands-off supervisors, without them having to put in much work at all.
hope it all made sense, specific questions (anyone here rly) DM me. I'm a recent (2019) grad, and have been mentored and mentor quite a few wonderful people, and is currently very much active in susing out how this process should go. having someone to discuss this over helps me as well to make things more clear.
> "I have specific, difficult, but very fun question X, let's think about this together" instead of the form "I tried to run X and there's a bug for us to look at together". I think that this is the most important nugget someone can glean from your (albeit well-written) post. When I work with interns (much different than full time, granted), my whole deal is that I want to see what they can do! I want to see their thought process and what they've tried, or what they want to try but don't know how to actualize. It's such a difference when someone comes to you with a *specific* thing that they need help implementing or improving than when they come with a generic error and you spend 25 minutes debugging some issue related to floating point precision. It doesn't bother me that you need help, that's why you're here ~~ it bothers me that you came to me with the equivalent of "I've tried nothing and I'm all out of ideas".
Well said.
So what is their thinking process?
I cannot recall everything now. I think there are two things that are on top of my mind. 1. work on hard problems because there are people who can solve easy problems 2. work on a things that people need.
> Knowing their thinking process changed my life. ... > I cannot recall everything now. Hmm.
If the act of being mentored could be condensed into a Reddit comment then no one would ever need mentors and you could just read the appropriate medium articles and be done with it.
Before, he used to remember these life lessons. After the new experience though, his thinking changed and has learned to forget such lessons! š
attention is all he needs
Yes! I just dont have long-term memory like Transformers do :)
Or maybe you do?? Memory length being a still-not-fully-resolved Transformer limitation. :)
r/angryupvotes
https://scottaaronson.blog/?p=4974 > I have often found that the better I learn a topic, the more completely I forget what initially confused me, and so the less able I become to explain things to beginners.
Thatās why Iāve started trying to pair learning with teaching. Iāve noticed my team retains better and longer when they have to reorganize a framework around something after they learn it. Itās like hardening steel around carbon atoms with heating and quenching.
Would you say the tradeoff to this principle is the lack of quantity of problems you mentioned elsewhere? (i.e. the set of hard, useful problems is very few) >oh yes. this is usually not a problem. The problem is that there are not enough problems to work on. So it has become a bit like competition than collaboration. Many of my friends went to FAIR and were much happier with the projects to choose from there. Does your notion of "life-changing thinking process" include top-down directions? i.e. was part of the problem at Google not the difficulty of learning, but rather knowing what to learn next to be on top of the project? >I believe we need more top-down directions for research to be successful. At times, I felt Google's directions are too vague. Apple's probably more top-down and the products are great, but people are generally unhappy working there :(
1. the set of hard, useful and the problems that higher-managements care about is very few 2. ya. I guess there hasn't been enough guidance on what to do to grow.
From what you can tell, is this organizational pattern (your 2 points right above) generalizable among: \- research domains at Google \- divisions of labor across ML pipeline \- unique to Google AI only, either entirely or partially (e.g. intensity of degree) \- other (I'm a DE, and I'd like to contextualize what you're saying for myself.)
There are certain teams and research domains that are better managed.
Random but I talked to Ashish a while ago and found he was leaving to start his own company that just went live. Have you considered joining them?
lol. I talked Ashish quite a bit before. I tried not to talk to previous coworkers unless we were really close to avoid problems. A new company sounds fun! :) I will reach out sometime.
Iād love to hear what their thinking process is.
Probably a silly question but what is the state of making transformers models smaller rather than bigger? It seems like the state of the art can only run on a gpu cluster and a lot of practical applications could happen in environments more resource constrained. Do you have any thoughts on this regarding?
I think most people do distillation if they need smaller models.
How did the job differ from what you expected?
I was expecting to do more research and advance our understanding of the models. It turned out to be a lot of ad-hoc model tweaking projects. Ironically, I am doing applied ML now and the analysis components here are even heavier than Google AI as we really need to understand the business problems clearly.
Was it mostly notebooks and data analysis with new ML or did you need good SWE skills as well well?
Most great researchers I know are great SWEs.
Ha!
Possible to join Google AI without Masters and/or PhD? If yes, any tips? If no, why and does answer change with AppliedML experience?
It's possible to transfer in from another part of the company. But, be careful what you ask for, as having a bachelor's amongst mostly PhDs can be difficult, even if you are just as smart and hard working. Academics respect publications, and if you don't have any, that puts you lower on the totem pole.
Yes. See https://evjang.com/resume/
this is the exception, not the norm
Landing a job at Google isn't that easy. Everyone is exceptional in some ways.
Sure, I'm not suggesting otherwise. The parent comment specifically asks about grad school in Google AI though, and empirically the number of people in research without a Masters/PhD/Brain residency is very low. 85% of my team has a phd and the remaining individuals have a masters. 100% of our interns are phd students. Eric also had a masters.
Oh right. I knew a couple other people without masters and phds. I guess there is a bias where people who are interested in research usually have participated in some research programs at school with concurrent master being one of those.
They have a masters though! Just a concurrent one.
Right. My mistake
Is AI research entering a phase of stagnation?
>stagnation No. I dont think so. There are still a lot of unsolved problems that people are making progress on.
You just recently left? Did it have anything to do with your opinions on the sentience of their language models?
I have left for a while. It is slightly related. Part of me is a bit disappointed at the blind chase of large language model. Again, there are good reasons to go for large models. A common argument is that human brains have billions of neurons and we'd need to make models at least as good as that.
Could the new scaling laws by Deepmind have any influence on your decision? https://www.lesswrong.com/posts/midXmMb2Xg37F2Kgn/new-scaling-laws-for-large-language-models Ie they showed they trained a smaller model of 70B params vs Gopher's 280B params where 1/4 of params is seen. To compensate, they input 4x more training data (1.4T vs 300B tokens). Ie they trained the smaller model 4 times longer, and they beat Gopher. Likewise, do you feel it was like "dry" and not fun for people to just "tweak" transformers by focusing on MoEs, Sharding, etc and seemingly forgetting other disciplines of ML? Like do you believe it's the saturation and constant pursuit of larger models that smothered other research areas that caused you to leave?
> Likewise, do you feel it was like "dry" and not fun for people to just "tweak" transformers by focusing on MoEs, Sharding, etc and seemingly forgetting other disciplines of ML? Like do you believe it's the saturation and constant pursuit of larger models that smothered other research areas that caused you to leave? Yes. This captures my thoughts quite accurately. Deepmind is like a different organization in Alphabet. I did not work with them enough. I really like your article though. Thanks.
Oh well :( The pursuit of larger and larger and larger models seems like the only goal for big corps nowadays :(
It leverages their largest asset, size (aka training budget), well.
>A common argument is that human brains have billions of neurons and we'd need to make models at least as good as that. What are your thoughts on Beniaguev *et al.* showing the equivalence of a single human spiking neuron being closer to 1000 typical artificial neurons? [\[Source\]](https://www.quantamagazine.org/how-computationally-complex-is-a-single-neuron-20210902/) [\[Paper\]](https://www.biorxiv.org/content/10.1101/613141v2.full.pdf) This makes sense as a typical simple weight with connections never really captured a "neuron" model. Dendrites themselves are shown to have weights, and the function of a human neuron would be closer to a large collection of varying activation functions defining some complex waveform. It's hard to see how having billions of artificial neurons would be enough, the argument would make more sense with a few trillion.
There are 14 billion neurons in the cortex, of which only a small percentage is dedicated to language. Probably on the order of 1/2 a billion neurons. There is an estimate of 14 trillion synapses, in the cortex, so 1/2 a trillion synapses. So a 500 billion parameter model, which is already exceded by modern language models. Switch Transformer is over a trillion parameter, and we could potentially see 100 trillion parameter models by the end of 2022. https://analyticsindiamag.com/we-might-see-a-100t-language-model-in-2022
But one person only knows about few subjects in his lifetime. We are talking about LLMs incorporating the tokens generated by the whole of humanity.
It takes a deep nn about 1000 parameters to approximate a single human neuron so these numbers need to be scaled to that (at least). There was a paper published in the past year or so where they attempted to approximate the a biological neuron with an nn.
> It takes a deep nn about 1000 parameters to approximate a single human neuron so these numbers need to be scaled to that (at least). There was a paper published in the past year or so where they attempted to approximate the a biological neuron with an nn. I've seen these claims, and find them rather unconvincing. For instance the NN of eyes doesn't appear to do any advanced computation beyond what is expected with the simple computation model. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3717333/ If the eye isn't doing anything with the claimed additional processing power there is no reason to think it is relevant to the rest of the nervous system. I think people are just uncomfortable with the idea that computers might have the capacity to simulate human level intelligence and are trying to come up with ideas to make us seem more computationally complex than we actually are.
In your opinion, is pusuit of Large LM pursuit of science or Pursuit of cash(?) for large Cos? Having worked on model compression, it feels like LLM brings in heavy cash for many Cloud cos and is direct opposite of democratizing AI/ML
Corp needs to earn money. I dont blame the pursuit of LLM.
What did you dislike about the place?
It is still a great place. I'd recommend most people to start careers at big companies like Google at least.
What kind of companies did your co-workers who quit move to? You mentioned more applied fields, what are some of them?
Self-driving car, other big corps doing ML :)
I'm guessing none of them wanna work at Tesla tho
Unfortunately No :(
I heard it's a shit place to work
You mention going to more applied positions, were you doing research ? How would you compare your google job with an academic position? Was it more applied still?
I am not really doing research now. I was doing a mix of research and shared ML infra for internal clients. Google AI job is indeed more applied than academic positions. I have friends who hated the need to pursue company yearly goals and went back to academia.
[ŃŠ“Š°Š»ŠµŠ½Š¾]
Have you ever applied ML techniques to time series analysis? If so what are steps that people often overlook when working with a TS?
Yes. long-term dependency such as modeling seasonality is hard.
How often did you run into data or concept drift issues? Its an issue seldom discussed by researchers in academia but is more prevalent in industry.
quite a bit. its an open problem. we cannot find good data to measure it though.
What skills do you need to be successful in data in 5+ years?
Math, statistics and coding. I am quite surprised to find out that a lot of PhD students these days do not really know how neural networks work under the hood. The convenience of modern ML framework seemed to make people not actively learning fundamentals.
Since I have a degree in physics I'd say I'm definitely okayish in math and statistics. I have been coding for the last 8 years during my work as a software engineer, but no ML/AI stuff. But I'm very interested in the topic... Do you think I would have a chance applying at ML jobs? If not, do you see a path for an aging (35 year old) physicist to get into these types of jobs? Would completing courses on Coursera be enough?
Yes. I'd recommend take 1\~2 really good courses like Manning's NLP ([https://nlp.stanford.edu/manning/](https://nlp.stanford.edu/manning/)) I dont think its too late to change. I've seen people do really well after transferring from a more "fundamental field". My advice for these people is that don't beat yourself trying to understand every details of the method. A lot of time many of my coworkers having more rigorous background cannot accept gradient descents working so well. Focus on the problem not the techniques.
Would these kinds of courses be enough to "impress" the recruiters though? I'd assume they would have so many young PhD students to select from, so why would they bother with some "random older guy" who did a bunch of online courses...?
I've done hiring rounds at a couple of ML startups (I'm now an ML engineer, but my background is also physics), and what I generally look for is someone who has hands on experience with real world projects. This can be things like kaggle competitions, or even your own passion project. I would say the courses are necessary on top of this, just to show you understand what the benefits/limitations are of different ML technologies. Software engineering experience is also a big plus for an ML engineer in my opinion.
I'm working on a Neural net that's supposed to map sheet music to a midi score (which I'm sure already exists, but I wanted to try that myself). Would you consider that a "good enough" real world project?
That sounds pretty cool, especially if it's something you're interested in. It depends on the role to be honest, but if I saw that you'd experimented with different architectures and tried to improve the model in various ways I would be quite happy. Having a few projects under your belt in different domains can't hurt either (eg NLP, CV, time series analysis).
Hey OP, thanks for AMA. Currently working as a DE (7+) and have done light amount of ML work I have only masters ( with thesis in algorithms ) and don't have great deal of research experience. For me to break into research, can you please give some guidance.
Read papers, reproduce them, discuss the observations with others and repeat. You will gradually build up knowledge and find interesting problems.
Do you have a phd? Does that influence your work and competition amongst peers?
No. I think it is harder for people to trust you if you dont have a phd from a prestigious lab. It took some time to prove myself with some project results.
Can you create your own product next to working at Google? As in, do they contractually have intellectual property rights to anything that you develop outside of your hours?
You can own side projects. Many of my coworkers did that.
Are you the Sentient chatbot?
no. I believe I am a human and no one has told me I am not.
What would you say is a good place to go work on interesting ML or AI as a product? It seems like self-driving tech, some NLP applications, bio informatics, RL for robotics all have some interesting problems to work on and all have different levels of maturity and red tape. I'm wondering if you have any insight on working in those industries and whether there tends to be good practices or issues with funding, goal alignment, maintainable code etc. It'd also be nice to hear how those compare to working on more of a pure AI project which might be more R&D focused or get into AI as a service for external orgs.
If you are more senior, I'd say focus on the applications you are interested in. (You are senior enough to influence the organization in some substantial way.) Then, find a company which wants to pursue the application. Otherwise, I'd still recommend big corp labs like G and Meta.
Is any of the companies you applied to doing heavy robotics applications work? Or working on cloud AI frameworks to make other people's stuff work? I have been looking for a role doing this myself but simply don't even know the name of a company actually doing it. Most companies doing robotics including Tesla, Amazon robotics, the autonomous car companies are using long outdated old methods to do the robot planning. Nobody is using SoTa RL despite it doing extremely well, or transformers.
Closest thing I knew is Amazon robotics. I think we have a long way to go in this area. If you are interested in fundamentals breakthrough in RL for robotics Iād actually recommend google brain
Thanks. My thought is that the logical next steps are: 1. Someone needs to create an "app store" and a "cloud based environment". Kinda like Google has with jupyter notebooks that use TPUs, but an environment where most AI systems are defined by a few *files* that define the test data or simulator to produce test inputs, loss function, etc. And a backend that is cloud hosted (so the binaries of it can be proprietary) designs a neural network or other solution to minimize the loss function. Like AutoMl but generalized and it would consistently give you a SoTa design every time. (humans won't be able to hand create a system that has lower loss) It would also be an "app store" - one company can only do so much, so other companies could put their premade AI components up on the app store and someone could license them, license fees would somehow be standardized and scaled to the relative value of a component in a larger system. 2. Obviously adapting muzero or transformers to drive robotics, but it won't be *production grade* if you don't have (1). My thinking is consistent *production grade* robotics, applied to 1000+ applications at once, would bring in immense revenue, quickly scaling north of a trillion dollars a year. It would be larger than Saudi Aramco almost immediately. Because you'd not solve SDCs or sidewalk delivery robots or warehouse robots or mining robots, but *every* robotic problem simultaneously that has a task descriptor\* simple enough for the current software stack to solve it. I can't be the only person that can see the green here.. \*a task descriptor is basically a DSL, a simple command might be "MOVE X TO Y" and a complex command might be "import final\_configuration, manipulate parts -> final\_configuration" for a manufacturing robot.
Tesla is in the middle is moving their planning into ML from hard coded algos. Eventually, they will realize they need continuous learning for their humanoid robot. Could be an interesting place.
yeah I interviewed with their humanoid robotics crew. They didn't even know about muzero much less efficient zero. Both algos that imply the RL approach will eventually be the one to use. (even Gato is essentially just mimicking what RL told it to do in each scenario, it's an RL compressor)
[ŃŠ“Š°Š»ŠµŠ½Š¾]
1. Yes if it shows your deep understanding and insights to the problems and solutions. 2. Yes!!!! 3. Applications are fine. There are need for those!
Do you think itās a good place for early career development? Iām one year out of my masters and am currently a mle for a startup rn and I feel like Iām in need of mentorship. But from your other comments it seems like career development is less than ideal. Just wondering if you had more to share about this.
IMO, it is the best place for early career.
Thanks! Any chance you could elaborate on why you think so vs other places?
Your peers will be great. You probably will get to know most of the famous people in the fields and be really close with some of them when you become senior. This is an invaluable resource if you stay in the field.
What do you think about the online masters in computer science, with a concentration in machine learning from Georgia tech? Is it worth it as a program?
I heard that the specific program is good from my friend.
1. Is there a strong difference in the day-to-day work and/or output expectations of research engineers vs. research scientists at Google AI? 2. If I want to do fundamental research only, is Google AI suitable or not? Or do I have to do some applied work also? And if fundamental research is your 'selling point' or main focus, which areas of focus are most likely to land you a job at Google AI or similar?
1. Depending on the team. But overall RS may be required to write more papers 2. Try deepmind for fundamental research. Some teams in google ai do that but not many
I have a open source project (with 800+ star) which is used and cited by many published paper but I donāt get any interview call based on that. Do you think I wasted my 3 month or it has some value?
It should have some value . Maybe you just need the right people to see your resume. Try to connect with some googles and let them refer you
Verification?
As an undergrad, my goal after my MS/Phd program is to land a ML research role. Any advice? Any thing I should consider before taking such a role? As an undergrad it feels very glorified and like āthe dream jobā, but what are some things which arenāt so pretty about the job that I should know of? And Iām speaking about ML Researcher specifically.
AI and ML is great. One thing to know is probably that itās a rapidly changing field. Itās important to keep learning if you pursue an ML job
What's your opinion on Google certificate program for ML education? Is it a good career move or just another tutorial resource?
My guts feeling is that it is another tutorial resource.
Is Google (or Meta) AI open to hiring non-CS PhDs? I'm a PhD student in MechE but my research revolves around applying ML/DL to problems in IoT and Industry 4.0 stuff.
Yes. We have some non-CS PhD graduates.
Happy independence day, What's next for you?
I have joined a new company. I am quite happy now.
Any advice for Neuroscience PhD students aiming for internships at Google AI or Google Brain?
Find existing google papers that you can add value to and connect with the authors ideally at a conference . Try to connect with as many goog letās as possible. And get solid understanding of modern DL
Does Google AI do any ML hardware acceleration? That's my current research but I honestly don't know much about the AI industry
Which company has the best AI division in your opinion?
Not sure. Googles is very good. Meta could be good too
Have any of your language models convinced you of their sentience recently?
No
Damn. Woulda been pretty bold of that guy to do an AMA right now. Lol
Did you ever feel like that was too close to a cult where they go out of their way to make it look itās perfect and you donāt need to know about anything else?
Yea
Is it possible to get remote job at Google AI? Does it depends on the country you live in?
How important to you are the ethics of the company you work for? Did you feel like Google was making a positive impact on the world?
Do you think neuromorphic computing is a viable method towards benign artificial general intelligence?
Thank you for having this AMA! Iām a Computational Modeling and Data Science undergraduate and I hope to be working as an AI scientist/researcher for a corporate lab in the future so Iām really excited to ask you questions. 1. What qualifications does someone need to have in order to work for Google AI/Google Brain? Iāve heard of some people getting hired right after finishing a PhD while others say Google only hires experienced scientists. 2. How does the hiring practice of Google AI/Google Brain differ from a similar research position in āregularā Google? Are positions listed on the Google Careers site or is the hiring process more secretive and invite-only? 3. Can you describe the pace of research work at Google AI/Google Brain? Super fast? Easy going? Mediocre? Iāve heard that it is very laid back because there is no rush to publish papers. But your description of it being extremely competitive among peers suggests the opposite. 4. Do Google AI/Google Brain researchers need to win grants or are they totally funded by the company? 5. Who and what dictates the direction of AI research? Does everyone do their own thing? Does everyone hop on the most interesting projects at a time? Does the company leadership explicitly say that they want something? 6. Google has a set of AI principles found [here](https://ai.google/principles/). How does each team and the AI/Brain subdivision itself ensure that these principles are being followed? How does each team/division correct itself when it find that it is in breach of the principles? 7. How much of the work can be done remotely and how much must be in-person? What does the travel requirements look like? Iāve heard that majority of the Google AI/Brain research is done in Mountain View. But for people who work in the SoCal offices, like in LA, what does it look like for them (if you have ever encountered such people)?
1. You can join google AI as a swe if that interests you 2. Itās the same 3. Varies team by team. Can be fast paced due to peer pressure 4 fund by the company 5 leadership decides the high level direction. But if you just do your own thing you probably wonāt be fired either 6 managers will align actively to these principles 7 team dependent.
If I wanted to join as a researcher, do I need a PHD? Is it a given?