18 ms·

The Machine Learning Job Market

265 points by sebg 5y ago

jstx1 5y ago
- This really isn't representative of the ML job market because the author is such an outlier. - The fact that it isn't representative is what makes the article an interesting read. - The fact that they claim to have a plan for solving AGI in 20 years really detracts from their credibility.
- shepardrtc 5y ago
  > - The fact that they claim to have a plan for solving AGI in 20 years really detracts from their credibility. I see they're going the cold fusion route.
- hervature 5y ago
  As someone who is in a somewhat similar position as the author (looking for senior ML roles), I found this part enjoyable: > I’m not like one of those kids that gets into all the Ivy League schools at once and gets to pick whatever they want. Followed by "FAANG + similar" and a deluge of options. Also, I feel like their message is pretty liberal with using future projections and implying it to be the present. For instance, the author has 6 years of experience with 2 at the senior level. This is pretty far from "staff level" (at 1M+ compensation, I think this is L8) which they imply is/was an option at a FAANG company. I don't doubt that in 5 years they would be at that level, but they almost certainly did not get offered a "staff" position at a FAANG.
  - jstx1 5y ago
    It's ambitious bordering on delusional. They're also doing the dirty trick of putting "2016 - 2022 Senior Research Scientist at Robotics at Google" on their resume even though they've been in the senior position only since 2020. Like, dude, you're doing great, your resume doesn't need any more artificial pumping up. Or I guess it does if you're aiming for those positions that are kind of out of reach.
    - colonelxc 5y ago
      I don't think that is unusual to list the latest level on a resume. I'm certainly not going to dedicate space on a resume to list time ranges for every promotion.
      - jstx1 5y ago
        So if this person had been promoted to staff level in 2022, they could have changed their resume to "Staff Research Scientist, 2016-present" and that would be okay with you? Because it seems deliberately misleading to me. It's different if the level isn't represented in the title - if they went from one band to another but the title was the same, I don't see a problem with putting down something like "Software Engineer, 2016-present" without wasting space on each promotion.
        colonelxc 5y ago
        In general, yes, I think that would be okay. I think it would be a mistake to create separate sections for each level. Overall your achievements within a single company should not be organized chronologically, but by what you want to show off. This may still be mostly chronological as you take on more responsibility/leadership. Now, I don't object to adding a line like: "promoted twice from Software Engineer 2 to Staff Software Engineer" or whatever, which I think is a good middle ground (and I would put this as the very last, least important entry for that company)
        uoaei 5y ago
        But it undeniably misleads the reader into thinking this person has held senior responsibilities since 2016, which is outright false, and may instill in the reader more confidence than is due.
        pbronez 5y ago
        As a hiring manager, I don't assume that. When there's a single title over a long range of time, I assume it's a terminal title. I look to the details for the position to see what kinds of work they've done. In the interview, I'll dig into trajectory and experience at various levels.
  - cletus 5y ago
    These outliers are rare but they do exist. I knew someone at Google who was hired as L3 straight out of college (as all non-PhDs are) and got promoted once a year to L6 (Staff) so 3 years. He got promoted to L7 2 years after that. It's a rare combination of talent and the right circumstances but it does happen.
    - hervature 5y ago
      I tried to hint at this by using quotes, I don't doubt that L6 is possible. But, please elucidate, are there L6s at Google making "low 7 figures"? From levels.fyi, there are no such reports. The average is about half and matches what I know from other companies. Those that are approaching 7 figures have at least a decade of experience. Anyway, the pay he describes is much closer to L8.
      - deep_etcetera 5y ago
        You can see some examples here https://aipaygrad.es/ https://aipaygrad.es/
        deleted 5y ago
        [deleted]
      - deleted 5y ago
        [deleted]
      - cletus 5y ago
        The only way L6s are making "low 7 figures" is because of large movements in the share price after they were given grants. Low 7 figures is realistic for L8s without the share price noticeably appreciating. With discretionary grants some L7s may squeak into that club. But L6? No way.
      - kyawzazaw 5y ago
        Are those SWE paybands or a Research Scientist pay band? That might matter.
  - toddm 5y ago
    Ironically, he graduated from Brown University, an Ivy League school.
    - ZephyrBlu 5y ago
      And he worked in one of the most exclusive teams, Google Brain.
      - toddm 5y ago
        Kids who graduate from Ivy League schools get a lot of job offers at once and get to pick whatever they want.
        mediumiqsad 5y ago
  - titanomachy 5y ago
    FAANG staff in 5-6 years out of school is not impossible. I know a couple. They are significant outliers in terms of focus, dedication (i.e. hours worked), and raw intelligence. If I had to guess, I'd say 1 in 30 from the population of Google-level engineers.
    - krat0sprakhar 5y ago
      > They are significant outliers in terms of focus, dedication (i.e. hours worked), and raw intelligence. As someone who has worked at FAANG for 5 years right out of school, getting to staff is less about raw intelligence and more about being lucky with working on projects that did not get canned and finding supportive managers. My friends much smarter than me have not had a good growth purely because they were unlucky with initial team assignment and PA / reorgs cancelling their projects.
      - davnn 5y ago
        Is there a large org where commitment/getting things done is more important/valued than social skills/network/luck? I.e. is there a fair model to measure an individual‘s contribution?
      - titanomachy 5y ago
        That is true, but one "young staff" I know was initially assigned to a dead-end project. She made it her life's mission to find something more interesting and promotable, and succeeded. But there's definitely luck involved.
- redredrobot 5y ago
  There are some groups defining AGI in a way where a 20 year timeline is aggressive but not impossible. The real question is how is AGI being defined by the author.
- evrydayhustling 5y ago
  On the plus side, the article accidentally produces the most useful definition I've seen of AGI. If you just define AGI as the the union and convergence of all hard problems, then you can just say "I'm working on AGI" to let people know you're doing the smartest, hardest thing, without sweating any details.
- queuebert 5y ago
  The problem with AGI is that it is people like this who are developing it.
spupe 5y ago
A lot of opinions and unverifiable statements (this and this company is X years ahead of everyone), and the whole piece is essentially about one person's job market. Skip
- melling 5y ago
  I’m interested in understanding the ML job market for traditional software developers. Does the opportunity exist to transition into any particular ML roles then grow from there?
  - claytonjy 5y ago
    I've been seeing a lot of Data Engineering/Platform roles that support ML without requiring past ML experience. How much future lateral movement would be available to you will vary widely, but this would be a fairly easy inroad.
    - visarga 5y ago
      You need 3-4 years of intense study to transition from software engineering to ML. It's different enough.
  - ccmonnett 5y ago
    Especially if you have Python experience, then yes the opportunity definitely exists. For example when I hire MLEs (which I am doing now if anyone wants to apply - supportlogic.io) I am willing to look at people who are solid Python/backend engineers and who have been "ML adjacent" or who we believe could learn the ropes of ML enough to contribute. The stronger an engineer, the more flexibility we have in ML knowledge. Some ML engineering is task-specific but a lot of it is automation, data engineering, and improving data scientist code (for which you do need ML experience I've found it's a lot easier to teach an engineer enough DS/ML fundamentals to do ML Engineering than it is to teach a data scientist engineering skills. A lot easier...
    - anonymousDan 5y ago
jowdones 5y ago
vmception 5y ago
You forgot to write that crypto startups compete with publicly traded FAANG on compensation on both cash and non-cash compensation, and there is no liquidity issue whatsoever on the non-cash they pay you with. Vesting schedules are more competitive than FAANG. And the publicly traded crypto companies compete with FAANG on compensation too. Non-crypto startups are the only ones sitting in the doldrums left out to dry right now.
- claytonjy 5y ago
  This doesn't sound quite right to me; Coinbase pays well, but looking at levels.fyi it's not FAANG money; is there someone else you have in mind? Would love to hear more about the startups; I tend to turn down such opportunities far before we talk non-cash comp.
  - vmception 5y ago
    Levels fyi shows its FAANG money at all levels I have it listed alongside Google and Amazon and Facebook right now, what did you compare that seemed different? (They’re not reporting consistent 7 figures for any of them) did you see something more granular? Outside of publicly traded crypto companies you need to talk to a third party recruiter in that space Solana Labs, for example, one of many, was paying engineers $650,000 back in 2019-2020 (and still is) to mostly write in Rust. Compensation was ~$200k cash and $1.6-$2 million in Solana tokens vesting 3-4 years with 1 year cliff. Solana tokens were $.10 cents back then, so those engineers are sitting on like $100 million+ as Solana trades at $100/sol now, down from $250/sol. For more typical results, companies that pay in crypto only have a few employees so giving them all a few million dollars in their much smaller less successful crypto still results in being able to liquidate close to the notional value they started with, derisking your time and coming out ahead in general. A “tiny” crypto is still like a $30 million marketcap. Even the $300 million marketcap ones are considered tiny. Market depth / liquidity is usually enough to support a few million dollars of periodic employee sell pressure.
    - djenendik 5y ago
      so you are saying there's a chance these solana engineers are sitting on a billion in crypto?
awinter-py 5y ago
> The most important deciding factor for me was whether the company has some kind of technological edge years ahead of its competitors. A friend on Google’s logging team tells me he’s not interested in smaller companies because they are so technologically far behind Google’s planetary-scale infra that they haven’t even begun to fathom the problems that Google is solving now, much less finish solving the problems that Google already worked on a decade ago. ^ this is increasingly a choice in your career -- 'where can you go to solve big problems', and 'big problems are increasingly complex' scale is real, and tools matter. you can spend your whole project burn at the wrong company building something that you could buy somewhere else, or which already exists at a competitor slight grain of salt here is that G's logging system, from my perspective as a gcp user, is slow as balls and the UX is the incarnation of scroll jank. and also this (very good) article led to the outcome of the author building soft hands happy-ending robots
- oneoff786 5y ago
  On the other hand, there’s a lot of real problems that real people actually deal with that just need a logistic regression to save million bucks here and there. I like that space more.
  - nerdponx 5y ago
    This space isn't sexy to write about, there's no fame and glory in it.
    - tomrod 5y ago
      Far sexier and larger than most care to admit.
    - deleted 5y ago
      [deleted]
    - Terry_Roll 5y ago
      The trappings are... https://www.youtube.com/watch?v=4VapnDFoR2U https://www.youtube.com/watch?v=4VapnDFoR2U
SCUSKU 5y ago
I was expecting something along the lines of how AI/ML was considered a sexy career path, but there are very few jobs available and high competition for those jobs. So, as a result, you end up with the only available jobs as data engineering/ML ops/backend that supports ML teams. I am happy for this author and their success, but they clearly are not representative of the majority of the people in the ML job market.
- PartiallyTyped 5y ago
  I think I have a decent CV, with quite a bit of experience for a master's student. I have been searching for a job in MLE, for a bit now with very little to show for it, as I am either getting no responses, or responses claiming that they are looking for more experienced people, and particularly those that had experience with a particular stack. In all honesty, after 6 years of studying, with 4 of those years studying ML, to be told that I lack experience with some particular stack as the ~~excuse~~ reason for rejection feels like a slap in the face. And all that ignoring everything that expects 3+ years experience for entry level positions.
  - benibela 5y ago
    That is why I am doing a Post Doc
    - hebrox 5y ago
      May I ask what you're working on?
      - benibela 5y ago
        On causality There is this interpretation of Bayesian networks that the parents are the true causes of a node and to predict what happens if you change a variable you need to remove the edges from the parents to that variable from the network. And then I study what you can do with that method
  - dunefox 5y ago
deleted 5y ago
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redsh 5y ago
See you in Rome
benibela 5y ago
Or you can go into academia to work on the really hard problems on a 5 figures salary.
oneoff786 5y ago
People are hopping on this guy for saying he’ll solve agi in 20 years, but I’m already laughing at him thinking he’ll be creating value with humanoid robots in 1 year.
cjalmeida 5y ago
"Crypto community has weird vibes" that's what I call an understatement.
- axg11 5y ago
  One person's weird vibes is another person's great vibes.
Havoc 5y ago
Interesting that Tesla gets its own row in the pro/con table while the faangs get lumped together.
- ackbar03 5y ago
  There's no t in faang
- gcheong 5y ago
  If Musk is to be believed their Teslabot is going to be in a class by itself.
  - bigbillheck 5y ago
    If Musk is to be believed, Flint would have clean water.
- nrmitchi 5y ago
  Why is this interesting? Tesla is... not a FAANG company? Not only is there no 'T' in FAANG, but the industry/product is completely different. Or maybe the author just wanted to make a joke about coffee. Who knows.
  - mediumiqsad 5y ago
  - kyawzazaw 5y ago
    Not an internet based company might be also factor
1024core 5y ago
"FAANG+similar : Low 7 figures compensation (staff level), technological lead on compute (~10 yr)" I don't know where OP is getting these figures from, but I doubt that FAANGs offer 7-figure comps to Staff-level people. It's probably more in the higher 6-figure level (400K - 600K).
- digitallyfree 5y ago
  I'd be interested in a credible source for this as well, even though ML work pays more. From what I know from people in the industry it's 6 not 7 figures.
  - ankeshanand 5y ago
    https://aipaygrad.es/ https://aipaygrad.es/
- greatpostman 5y ago
  600k is a senior engineers wage
  - opensrcken 5y ago
    No, it's not. If you get lucky with stock market movements, that might be your total annual comp, which is not your wage. I made close to what you're claiming at senior level, at the recent peak of stock market insanity, but I'd stick to the dictionary definition of "wage."
    - cellis 5y ago
      This all day. Sure if you joined FB in March 2020 your total comp today will be a multiple of that 700k, but FB is not giving out 600k packages today to e5s ( and e5 imo is staff+ at many lower tier companies ), more like 450k all in compensation.
- ren_engineer 5y ago
  looking at levels.fyi it seems there are at least several companies paying 7 figures to ML engineers with less than 10 years experience, although majority are at 15+ https://www.levels.fyi/Salaries/Software-Engineer/Machine-Learning/ https://www.levels.fyi/Salaries/Software-Engineer/Machine-Le...
blauditore 5y ago
> Low 7 figures compensation (staff level) Odd choice of level, since the author worked at one of these companies and was not at that level, certainly did not make 7 figures.
- bwy 5y ago
  They spent 2 years at the senior level at one FAANG. Why would they switch to another for anything less than the staff (senior + 1) level? (Not saying anyone "deserves" that or that's how it should be, but that's just how it is here in the valley.)
  - kyawzazaw 5y ago
    and not just generic senior SWE level.
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bjornlouser 5y ago
AGI, AGI, AGI, AGI, AGI, AGI... https://images.squarespace-cdn.com/content/v1/5de799b06bb59b7f6bbf27f0/e7f42c5d-87f2-45c9-bb3e-39027fdc9df7/B17R4708.jpg?format=750w https://images.squarespace-cdn.com/content/v1/5de799b06bb59b...
usgroup 5y ago
I appreciate the post in the sense that it is an insightful perspective that he didn’t have to share. If you are elite it is difficult to talk about your options, pay or way of thinking without it coming across as nothing but hubris to the rest of us.
ozten 5y ago
Author is going to Halodi Robotics. I always thought that human shaped robots are a terrible form factor. Why limit yourself to the awkward design that 3.77 billion years of evolution accidentally landed on?
- me_me_mu_mu 5y ago
  Why do you think it is an accident? I thought evolution is an adaptation mechanism. If anything I'd say we've got a pretty cool form factor (peak human form, not like me who is out of shape lmao).
  - ozten 5y ago
    I used the word accident to emphasize that there is no design or designer. I agree that evolution is adaptive and often creates optimal solutions.
  - pvalue005 5y ago
    The human body has been optimized for a very complex objective function, and in a very different environment to a robot. If you specify what the robots are doing, and the set of constraints like power source, size, weights, etc., the optimal design will unlikely be humanoid.
- sligor 5y ago
  Human shaped robot is not a technical solution, it is a feature
- riazrizvi 5y ago
  Perhaps because that form factor has heavily informed civilization's current challenges?
- tuckerman 5y ago
  There is a school of thought in robotics/AI that believe embodiment is necessary (or at least the fastest way) for us to learn abstract thought. Embodiment can really span the gamut of meanings, but there are definitely researchers that believe humanoid robots are the best path forward to that goal. If you have a more specific goal in mind, e.g. solving a small set of industrial/commercial use cases, that changes the calculus dramatically.
angarg12 5y ago
I don't want to derail the conversation, but OPs career path really stood out to me. He graduated in 2016, worked at Google in Bay Area, and now is joining a startup at a VP level. I graduated in 2008, obtained a PhD in 2014 in a no name EU university, worked in odd companies for a while and joined FAANG 4 years ago as a mid level developer, where I am still ATM. Looking at this disparity I wonder what could be possible explanations: * OP is a beast and has grown very quickly in a short time. * I'm particularly inept and I'm growing very slowly. * Working in the right conditions (e.g. Bay Area, Big Tech, right team) can greatly accelerate your growth. * Startups have a big title inflation.
- going_ham 5y ago
  Yeah, and the way the author presents themselves speaks volume too. There is a real pride in this essay. You can see how the author casually drops big names and insights like it is a fact. Why does valley culture makes it seem like everything is possible and anything innovative can happen soon? The innovation in AI really seems like it is being made on a thin line of engineering and compute. It doesn't happen overnight. It requires some people working through and through to pull it off. These days it requires collective contribution.
  - samhw 5y ago
    > The innovation in AI really seems like it is being made on a thin line of engineering and compute. This perfectly echoes my own thoughts. The advances being trumpeted in AI are functions of hardware advances that allow us to have massively overparameterised models, models which essentially 'make the map the size of the territory'[0], which is why they only succeed at a narrow class of interpolation problems. And even then nothing useful. That's why we're still being sold the "computer wins at board game" trope of the 90s, and yet somehow also being told that we're right on the verge of AGI. (OK, it's not only that. There's also a healthy amount of p-hacking, and a 'clever Hans effect' where the developer likely-unconsciously intervenes to assure the right answer via all the shadowy 'configuration' knobs ('oversampling', 'regularisation', 'feedforward', etc). I always say: if you develop a real AI, come show me a demo where it answers a hard question whose answer you - all of us - don't already know.) [0] Or far larger, actually. Google the 'lottery ticket hypothesis'.
going_ham 5y ago
This post feels like author is insecure about his position and wants to establish some validity. Having going through it all, it feels delusional at best. The glorified pattern matching can only take us so far. You know it's working as long as there is a pattern. I wouldn't call it a general intelligence per se. There is no "juice" in these algorithms. If we use these tools, we can immediately see where they fail and where they do not. These are just a new tools in software engineer's box.
- visarga 5y ago
  > The glorified pattern matching can only take us so far. This argument is becoming less and less convincing year by the year. We're amazed that things we were sure couldn't be done are actually done.
  - water-your-self 5y ago
    This argument has been on going, on and off, since the 1960s. Show me a research paper.
    - visarga 5y ago
      1960s were 60 years ago, we're talking about AI, a fast paced field in the last 15 years.
      - going_ham 5y ago
        Definitely! There was a time when compilers were part of AI research. Now they are just another tool. Same with DL, they are amazing tool. We need them and they provide value if used correctly. I just didn't want to call it as "intelligent" and use this as basis for defining "intelligence." We can call them something else. It's learning to do a specialized job as intended and in "intelligent" manner. But it is not intelligence. Even a small ant is intelligent than our current AI systems though they aren't sophisticated and can't perform human task, they are intelligent than AI system. I hope that made sense.
TigeriusKirk 5y ago
Off topic a bit - "Halodi Robotics (company he's joining) intends to produce thousands of humanoid robots by 2023" Are humanoid robots just around the corner? Musk claims Tesla will have a "prototype" humanoid robot this year. I dismissed that as Elon hype, but have I missed this coming?
- throwaway889900 5y ago
  The only thing you need for a humanoid robot is for it to be human shaped and have some electronics inside of it. Doesn't mean it has to even remotely be human in interaction.
  - captaincaveman 5y ago
    The robots on their site look pretty clunky to me, not to say they aren't doing smart things.
queuebert 5y ago
That's some godawful typography for such a smart guy.
thunkshift1 5y ago
This post reeks of someone who doesnt ‘need’ a job and has made enough money already. Good for op to go after ‘exciting problems’ rather than the mundane will I make rent and fees
- formerkrogemp 5y ago
  I don't think many machine learning folks working for FAANG are worrying about such things.
  - thunkshift1 5y ago
    Well, who knows.. maybe some overworked fellow probably is
minimaxir 5y ago
As a relatively unremarkable data scientist/machine learning engineer of about 5 years, I've been keeping an eye on DS/ML positions as they tend to give a sense on what is important to companies in that space, although I'm not actively looking for a new role. More and more positions seem to require Ph.D. credentials even for non-senior roles, even though modern DS/ML tooling doesn't require it. If I ever left my job I might have to quit DS/ML and do something else entirely.
- antupis 5y ago
  DE/ML ops/ Software engineer(data) is many ways new DS. Lots of greenfield projects and less competition.
- cwp 5y ago
  Nah, you wouldn't have to quit. If you've got 5 years experience, even on non-cutting edge projects, the PhD won't matter. Sure, you won't be able to get any job you want, but there are lots of ML jobs that list a PhD requirement that will nevertheless jump at the chance to hire someone with practical experience.
  - minimaxir 5y ago
    Hiring managers are more generous, but HR screening the resumes aren't.
    - cwp 5y ago
      That'll only be a problem if you're up against significant number of candidates that have both experience and a PhD. HR will certainly filter resumes based on something as clear-cut as a degree when they can. But they can't just filter everything out and tell the hiring manager he's SOL. So you don't have to check all the boxes as long as you check enough of them, and you're competitive with the other candidates. At the same time, nobody is going to do all that well if they just apply online and cross their fingers. You need some kind of human contact, either though an introduction to an insider through your network, or through a recruiter of some kind. It takes a bit of time to develop the relationships, but it's quite doable and worth it, even for introverts. Best to start before you're interested in changing jobs.
queuebert 5y ago
"Product impact is even slower than robotics due to regulatory capture by hospitals and insurance companies." The author apparently does not understand regulatory capture and is throwing around catch phrases to sound smart. Regulatory capture would imply that healthcare encounters less regulation than it should due to influence over the relevant government agencies. This should increase product impact and reduce time to market, the opposite of what he suggests.
- axg11 5y ago
  Doesn't the author's sentence mean: hospitals and insurance companies have coopted regulators for the benefits of their own businesses, at the detriment of medical device companies (developing AI)? I think the author's point still stands.
  - queuebert 5y ago
    Possibly that was the intended meaning, but the point is still invalid. Hospitals want more cool gadgets. They want to be able to treat more conditions and charge more for it. If anything, the FDA is a constant annoyance to a healthcare provider because it hamstrings them from providing care. This is why so many patients are enrolled in clinical trials, to get care ahead of the FDA approval time frame.
    - formerkrogemp 5y ago
      Right, the FDA is what's wrong with American healthcare. Not the insurance agencies or the for profit hospitals making as much money as possible. The lack of single payer healthcare is one of America's greatest failings on that end.
opensrcken 5y ago
The post is not only too braggadocious for my taste, but some of the figures quoted are highly unlikely. I would personally not work for someone with this kind of ego, but there are many such people in positions of power. This article is representative of an attitude I'm seeing around the tech industry, and if this is indeed the level of "confidence" in the Bay, I don't think that's a good sign.
- mysterEFrank 5y ago
  Eric Jang is top ML talent, these numbers are accurate. I work in ML and have followed his work for years
  - davnn 5y ago
    Still.. do you think being a top talent in ML guarantees success for your own company, for example? I think there are a lot of valuable skills to have, being an expert in X is just one of them.
  - water-your-self 5y ago
    He claims to be solving general intelligence in 20 years. Your advocacy is not enough to convince me.
    - marginalia_nu 5y ago
      General intelligence has been 20 years away since the 60s, along with fusion power and a bunch of other things. In marketing, they say 5 years when it's actually 20 years away.
      - 21723 5y ago
        In my view, AGI is farther away than fusion. We know how to do fusion. We know the physics behind it. We haven't yet figured out how to build profitable fusion plants, and we probably won't for a long time, if for no other reason than improvements in fission--modern fission plants are the best . When it comes to AGI, we have no clue. It's a constantly moving target, because our conceptions of intelligence evolve. Most things that were once "AI" became "non-AI" solved problems after we got good at them using a couple key insights, e.g. that image processing could be sped up with CNNs due to the existence of a topology on the inputs. We still have no idea what makes us tick, and moreover there is not a strong economic incentive to replicate all of our intelligence... although, of course, automation will continue and that itself will be disruptive enough.
sydthrowaway 5y ago
This guy is another Dan Luu. Insufferable posts.
- ericjang 5y ago
  Ouch
YossarianFrPrez 5y ago
Say what you will about the OP and the claims he makes in his career announcement... I was struck by how casually the author mentioned that Academia is behind various private companies. Call me romantic, for in an ideal world Academia is just as close to the cutting edge of knowledge as private R&D laboratories.
warner25 5y ago
Can someone with only 6 years of experience make credible predictions about things 20 years in the future? I'm around 15 years of experience, and my appreciation for my own lack of knowledge and ability to make predictions still grows with every year.
- xpe 5y ago
  I’ll bite on the above loaded question: Define experience. However you do, it should at least include work, education, and life in general, given that such experiences relate to the context or situation.
  - warner25 5y ago
    Yeah, it was a bit snarky, but still an honest question. In some domains, like astronomy or geology or climate change, I guess it seems reasonable to make predictions 100 or 1,000 years in the future based on available data rather than experience. In other domains, like politics or economics or finance, it seems like there would be much more value in having worked through a bunch of election and business cycles over the years. I'm not sure where computing and AI sits on that spectrum. I can imagine a young AI researcher failing to realize that some approach was tried and found to be a dead-end 30 years ago. On the definition of experience, I agree that education and life experience counts. I said "around 15" years for myself because the definition is fuzzy. I got some very specific career preparation and training in college, so that sort of counts, and I probably spend more personal time than many of my peers learning about relevant history and current events.
falsenine 5y ago
As someone whose intention is to go to Medical School and pick up programming (+ math skills) to potentially work at the intersection of ML + Healthcare, the knowledge of the regulatory hurdles expressed is discouraging. Not sure if it really is worth the effort to study tech on top of medicine. Are there any people with experience within ML + Healthcare/Medicine or know of startups that are making great strides within this realm?
- rakejake 5y ago
  I used to work in the healthcare vertical. While there are regulatory hurdles, they are there for good reason. Move fast and break things does not work in this industry and will get you fired. You will have better luck working with one of the larger companies who have a good history with the FDA, and more importantly, have good relationships with hospitals and physicians. They are aware of the time and resources it takes to push something out. Pay will not be FAANG level or anywhere close, but they usually have great work culture and WLB.
  - falsenine 5y ago
    Thanks for the comment. Do you have any feedback for a future physician interested within the intersection of ML + medicine (does not strictly need to be "healthcare")? Would learning ML and all the other things needed to know be helpful (huge time investment needed here)? Ultimately, would be interested in leveraging medical knowledge into a startup capacity.
    - rakejake 5y ago
      As a traditional physician, I imagine your value add would still be your domain expertise. The physicians I worked with were purely consumers of AI tech and had a say in the design of the software. But AI and more generally software is becoming an important part of medicine so I guess it couldn't hurt to learn some basic ML.
- max-hoffman 5y ago
  When I was an MD/PhD student in 2017-2018, there were only a handful of labs specializing in applied medical ML and computational biology. Since leaving to work as an ML eng/backend eng, I have been surprised by how the relationships between academics, practitioners, and investors in pure software contribute to a positively reinforcing loop. On the medicine side, the academics lean towards software skepticism, the investors make fewer and safer bets to compensate for historically lower margins/growth/return multiples compared to pure software, and the subset of practitioners get payed orders of magnitude less and have correspondingly less engineering development. The differences affect everything from managerial quality, skill and career development, location flexibility, upward mobility, product scope and impact.
Kalanos 5y ago
sounds like you are coming up with excuses not to start a company
akhmatova 5y ago
In the future, every successful tech company will use their data moats to build some variant of an Artificial General Intelligence. Is that what one gets paid 7 figures for - to go out in public and claim they "know" things like this with a straight face?
alfor 5y ago
Speed of iteration is all that matter. That is why all the big like Google no longer push things forward, even with the best engineer and Phds and with so much money. That’s why I think that Optimus at Tesla will crush all the other robotic platform. On the positive side success of Optimus will help startups to get funds or get acquired by corporations that want to get a slice of the newly proven market.
- sidibe 5y ago
  You are confusing speed of iteration with speed of announcements. There is a lot of stuff that happens, like research and process building, at a big "slow" company like Google that Tesla doesn't even realize is needed yet. Tesla makes a stream of wildly optimistic announcements that makes it feel like it's closing the gap with Waymo for example, but there's no evidence that it is
  - alfor 5y ago
    The iterations happen way faster too. Look at Munro teardown of Tesla, he has never see a tenth of that rate of change ever. Look at what SpaceX accomplished. Look where OpenAI is given the time it’s been operating. Look at Tesla rate of production increase. It’s going to be very hard to compete with Tesla at this point. So much ressources, so much bright engineers, all the knowledge in manufacturing, all the training on vision, etc.
    - sidibe 5y ago
      I guess the difference is you think they have more resources, more bright engineers, and I'm quite certain they don't. I'd guess Tesla has several times less engineers working on FSD than Waymo and making much lower salaries, and they are spread across large parts of the stack that Waymo can just tap into Google for.
      - alfor 5y ago
        No the main difference is the mindset of the place, the culture. Nokia, Blackberry didn’t overtake Apple, why? Culture. But before Tesla had more limited resources, now it’s almost a non issue.
zcw100 5y ago
After how many years of Facebook, instagram, youtubers, LinkedIn do people still not get it? This is the internet. How on earth can you verify the veracity of the authors statements? Everyone is discussing this like a blog post on themselves is an accurate portrayal of reality. It might be, but it might not. I'm not saying they're being dishonest, with they may be, but they are definitely not incentivized to give you an accurate portrayal of their life. This is instagram for IT nerds and there is no way they have a Badonkadonk that big.
- water-your-self 5y ago
  Ah but its much more fun when everyone hosts their own blog.
- fartcannon 5y ago
  To me, outside of this world, it feels like when one of the guys at a shop just like ours wins the lottery a few cities over. Everyone's just dreaming big for the fun of it.
water-your-self 5y ago
>In the future, every successful tech company will use their data moats to build some variant of an Artificial General Intelligence. Its rare that an article loses my faith in the first sentence.
sfriedr 5y ago
I work in theoretical machine learning and what I don't like about this piece is how casually the author assumes that what goes us to the present state (hacky approaches where you converge to something "good enough", unless the model is very simple, e.g. a feedforward net---and even here there are still open questions---and you have some theoretical guarantee) will also get us to "AGI". I believe that is unlikely. Here's metaphor for that, that I often like to use when I speak to engineers doing empirical ML work: In ancient times people build a lot of nice structures, such as pyramids, cathedrals etc. By trial and error many rules of thumb were devised and they more or less worked. But it's safe to say things like earthquake-resistant skyscrapes and modern bridge cannot be build without deep theoretical insights into structural mechanics. These are highly optimized, intricate structures. The same is probably necessary to build highly optimized, intricate models that deliver what we now consider to be "AGI" - but then again, the world of ML is full of surprises.
lysecret 5y ago
Not sure why this gets so much hate. I think he's a bit too optimistic about AGI prospects. But in his position these are all interesting and reasonable options. I'm a bit sceptical on this 10,5,1 whatever year ahead metric he pulled from wherever. Interesting read regardless. My opinion about the next few years is that most value will come from finding your niche, creating Datasets, iterating and building your ML model (a bit like he wrote but without this AGI...)
belowmedian 5y ago
I'm curious what the author's take on scale.ai is since he was considering building an ml-ops / data labeling company and they seem to be the most successful startup in that space.
sbmthakur 5y ago
"Technological Lead Time" - I have thought about this before. I wonder if anyone actively tracks it for different domains.
jasfi 5y ago
If you're interested in AGI take a look at my effort currently in development. It focuses on natural language understanding: https://lxagi.com https://lxagi.com

265 points by sebg 5y ago

- This really isn't representative of the ML job market because the author is such an outlier. - The fact that it isn't representative is what makes the article an interesting read. - The fact that they claim to have a plan for solving AGI in 20 years really detracts from their credibility.

shepardrtc 5y ago
> - The fact that they claim to have a plan for solving AGI in 20 years really detracts from their credibility. I see they're going the cold fusion route.
hervature 5y ago
As someone who is in a somewhat similar position as the author (looking for senior ML roles), I found this part enjoyable: > I’m not like one of those kids that gets into all the Ivy League schools at once and gets to pick whatever they want. Followed by "FAANG + similar" and a deluge of options. Also, I feel like their message is pretty liberal with using future projections and implying it to be the present. For instance, the author has 6 years of experience with 2 at the senior level. This is pretty far from "staff level" (at 1M+ compensation, I think this is L8) which they imply is/was an option at a FAANG company. I don't doubt that in 5 years they would be at that level, but they almost certainly did not get offered a "staff" position at a FAANG.
- jstx1 5y ago
  It's ambitious bordering on delusional. They're also doing the dirty trick of putting "2016 - 2022 Senior Research Scientist at Robotics at Google" on their resume even though they've been in the senior position only since 2020. Like, dude, you're doing great, your resume doesn't need any more artificial pumping up. Or I guess it does if you're aiming for those positions that are kind of out of reach.
  - colonelxc 5y ago
    I don't think that is unusual to list the latest level on a resume. I'm certainly not going to dedicate space on a resume to list time ranges for every promotion.
    - jstx1 5y ago
      So if this person had been promoted to staff level in 2022, they could have changed their resume to "Staff Research Scientist, 2016-present" and that would be okay with you? Because it seems deliberately misleading to me. It's different if the level isn't represented in the title - if they went from one band to another but the title was the same, I don't see a problem with putting down something like "Software Engineer, 2016-present" without wasting space on each promotion.
      - colonelxc 5y ago
        In general, yes, I think that would be okay. I think it would be a mistake to create separate sections for each level. Overall your achievements within a single company should not be organized chronologically, but by what you want to show off. This may still be mostly chronological as you take on more responsibility/leadership. Now, I don't object to adding a line like: "promoted twice from Software Engineer 2 to Staff Software Engineer" or whatever, which I think is a good middle ground (and I would put this as the very last, least important entry for that company)
        uoaei 5y ago
        But it undeniably misleads the reader into thinking this person has held senior responsibilities since 2016, which is outright false, and may instill in the reader more confidence than is due.
        pbronez 5y ago
        As a hiring manager, I don't assume that. When there's a single title over a long range of time, I assume it's a terminal title. I look to the details for the position to see what kinds of work they've done. In the interview, I'll dig into trajectory and experience at various levels.
- cletus 5y ago
  These outliers are rare but they do exist. I knew someone at Google who was hired as L3 straight out of college (as all non-PhDs are) and got promoted once a year to L6 (Staff) so 3 years. He got promoted to L7 2 years after that. It's a rare combination of talent and the right circumstances but it does happen.
  - hervature 5y ago
    I tried to hint at this by using quotes, I don't doubt that L6 is possible. But, please elucidate, are there L6s at Google making "low 7 figures"? From levels.fyi, there are no such reports. The average is about half and matches what I know from other companies. Those that are approaching 7 figures have at least a decade of experience. Anyway, the pay he describes is much closer to L8.
    - deep_etcetera 5y ago
      You can see some examples here https://aipaygrad.es/ https://aipaygrad.es/
      - deleted 5y ago
        [deleted]
    - deleted 5y ago
      [deleted]
    - cletus 5y ago
      The only way L6s are making "low 7 figures" is because of large movements in the share price after they were given grants. Low 7 figures is realistic for L8s without the share price noticeably appreciating. With discretionary grants some L7s may squeak into that club. But L6? No way.
    - kyawzazaw 5y ago
      Are those SWE paybands or a Research Scientist pay band? That might matter.
- toddm 5y ago
  Ironically, he graduated from Brown University, an Ivy League school.
  - ZephyrBlu 5y ago
    And he worked in one of the most exclusive teams, Google Brain.
    - toddm 5y ago
      Kids who graduate from Ivy League schools get a lot of job offers at once and get to pick whatever they want.
      - mediumiqsad 5y ago
- titanomachy 5y ago
  FAANG staff in 5-6 years out of school is not impossible. I know a couple. They are significant outliers in terms of focus, dedication (i.e. hours worked), and raw intelligence. If I had to guess, I'd say 1 in 30 from the population of Google-level engineers.
  - krat0sprakhar 5y ago
    > They are significant outliers in terms of focus, dedication (i.e. hours worked), and raw intelligence. As someone who has worked at FAANG for 5 years right out of school, getting to staff is less about raw intelligence and more about being lucky with working on projects that did not get canned and finding supportive managers. My friends much smarter than me have not had a good growth purely because they were unlucky with initial team assignment and PA / reorgs cancelling their projects.
    - davnn 5y ago
      Is there a large org where commitment/getting things done is more important/valued than social skills/network/luck? I.e. is there a fair model to measure an individual‘s contribution?
    - titanomachy 5y ago
      That is true, but one "young staff" I know was initially assigned to a dead-end project. She made it her life's mission to find something more interesting and promotable, and succeeded. But there's definitely luck involved.
redredrobot 5y ago
There are some groups defining AGI in a way where a 20 year timeline is aggressive but not impossible. The real question is how is AGI being defined by the author.
evrydayhustling 5y ago
On the plus side, the article accidentally produces the most useful definition I've seen of AGI. If you just define AGI as the the union and convergence of all hard problems, then you can just say "I'm working on AGI" to let people know you're doing the smartest, hardest thing, without sweating any details.
queuebert 5y ago
The problem with AGI is that it is people like this who are developing it.

spupe 5y ago

A lot of opinions and unverifiable statements (this and this company is X years ahead of everyone), and the whole piece is essentially about one person's job market. Skip

melling 5y ago
I’m interested in understanding the ML job market for traditional software developers. Does the opportunity exist to transition into any particular ML roles then grow from there?
- claytonjy 5y ago
  I've been seeing a lot of Data Engineering/Platform roles that support ML without requiring past ML experience. How much future lateral movement would be available to you will vary widely, but this would be a fairly easy inroad.
  - visarga 5y ago
    You need 3-4 years of intense study to transition from software engineering to ML. It's different enough.
- ccmonnett 5y ago
  Especially if you have Python experience, then yes the opportunity definitely exists. For example when I hire MLEs (which I am doing now if anyone wants to apply - supportlogic.io) I am willing to look at people who are solid Python/backend engineers and who have been "ML adjacent" or who we believe could learn the ropes of ML enough to contribute. The stronger an engineer, the more flexibility we have in ML knowledge. Some ML engineering is task-specific but a lot of it is automation, data engineering, and improving data scientist code (for which you do need ML experience I've found it's a lot easier to teach an engineer enough DS/ML fundamentals to do ML Engineering than it is to teach a data scientist engineering skills. A lot easier...
  - anonymousDan 5y ago

jowdones 5y ago

vmception 5y ago

You forgot to write that crypto startups compete with publicly traded FAANG on compensation on both cash and non-cash compensation, and there is no liquidity issue whatsoever on the non-cash they pay you with. Vesting schedules are more competitive than FAANG. And the publicly traded crypto companies compete with FAANG on compensation too. Non-crypto startups are the only ones sitting in the doldrums left out to dry right now.

claytonjy 5y ago
This doesn't sound quite right to me; Coinbase pays well, but looking at levels.fyi it's not FAANG money; is there someone else you have in mind? Would love to hear more about the startups; I tend to turn down such opportunities far before we talk non-cash comp.
- vmception 5y ago
  Levels fyi shows its FAANG money at all levels I have it listed alongside Google and Amazon and Facebook right now, what did you compare that seemed different? (They’re not reporting consistent 7 figures for any of them) did you see something more granular? Outside of publicly traded crypto companies you need to talk to a third party recruiter in that space Solana Labs, for example, one of many, was paying engineers $650,000 back in 2019-2020 (and still is) to mostly write in Rust. Compensation was ~$200k cash and $1.6-$2 million in Solana tokens vesting 3-4 years with 1 year cliff. Solana tokens were $.10 cents back then, so those engineers are sitting on like $100 million+ as Solana trades at $100/sol now, down from $250/sol. For more typical results, companies that pay in crypto only have a few employees so giving them all a few million dollars in their much smaller less successful crypto still results in being able to liquidate close to the notional value they started with, derisking your time and coming out ahead in general. A “tiny” crypto is still like a $30 million marketcap. Even the $300 million marketcap ones are considered tiny. Market depth / liquidity is usually enough to support a few million dollars of periodic employee sell pressure.
  - djenendik 5y ago
    so you are saying there's a chance these solana engineers are sitting on a billion in crypto?

awinter-py 5y ago

> The most important deciding factor for me was whether the company has some kind of technological edge years ahead of its competitors. A friend on Google’s logging team tells me he’s not interested in smaller companies because they are so technologically far behind Google’s planetary-scale infra that they haven’t even begun to fathom the problems that Google is solving now, much less finish solving the problems that Google already worked on a decade ago. ^ this is increasingly a choice in your career -- 'where can you go to solve big problems', and 'big problems are increasingly complex' scale is real, and tools matter. you can spend your whole project burn at the wrong company building something that you could buy somewhere else, or which already exists at a competitor slight grain of salt here is that G's logging system, from my perspective as a gcp user, is slow as balls and the UX is the incarnation of scroll jank. and also this (very good) article led to the outcome of the author building soft hands happy-ending robots

oneoff786 5y ago
On the other hand, there’s a lot of real problems that real people actually deal with that just need a logistic regression to save million bucks here and there. I like that space more.
- nerdponx 5y ago
  This space isn't sexy to write about, there's no fame and glory in it.
  - tomrod 5y ago
    Far sexier and larger than most care to admit.
  - deleted 5y ago
    [deleted]
  - Terry_Roll 5y ago
    The trappings are... https://www.youtube.com/watch?v=4VapnDFoR2U https://www.youtube.com/watch?v=4VapnDFoR2U

SCUSKU 5y ago

I was expecting something along the lines of how AI/ML was considered a sexy career path, but there are very few jobs available and high competition for those jobs. So, as a result, you end up with the only available jobs as data engineering/ML ops/backend that supports ML teams. I am happy for this author and their success, but they clearly are not representative of the majority of the people in the ML job market.

PartiallyTyped 5y ago
I think I have a decent CV, with quite a bit of experience for a master's student. I have been searching for a job in MLE, for a bit now with very little to show for it, as I am either getting no responses, or responses claiming that they are looking for more experienced people, and particularly those that had experience with a particular stack. In all honesty, after 6 years of studying, with 4 of those years studying ML, to be told that I lack experience with some particular stack as the ~~excuse~~ reason for rejection feels like a slap in the face. And all that ignoring everything that expects 3+ years experience for entry level positions.
- benibela 5y ago
  That is why I am doing a Post Doc
  - hebrox 5y ago
    May I ask what you're working on?
    - benibela 5y ago
      On causality There is this interpretation of Bayesian networks that the parents are the true causes of a node and to predict what happens if you change a variable you need to remove the edges from the parents to that variable from the network. And then I study what you can do with that method
- dunefox 5y ago

deleted 5y ago

[deleted]

redsh 5y ago

See you in Rome

benibela 5y ago

Or you can go into academia to work on the really hard problems on a 5 figures salary.

oneoff786 5y ago

People are hopping on this guy for saying he’ll solve agi in 20 years, but I’m already laughing at him thinking he’ll be creating value with humanoid robots in 1 year.

cjalmeida 5y ago

"Crypto community has weird vibes" that's what I call an understatement.

axg11 5y ago
One person's weird vibes is another person's great vibes.

Havoc 5y ago

Interesting that Tesla gets its own row in the pro/con table while the faangs get lumped together.

ackbar03 5y ago
There's no t in faang
gcheong 5y ago
If Musk is to be believed their Teslabot is going to be in a class by itself.
- bigbillheck 5y ago
  If Musk is to be believed, Flint would have clean water.
nrmitchi 5y ago
Why is this interesting? Tesla is... not a FAANG company? Not only is there no 'T' in FAANG, but the industry/product is completely different. Or maybe the author just wanted to make a joke about coffee. Who knows.
- mediumiqsad 5y ago
- kyawzazaw 5y ago
  Not an internet based company might be also factor

1024core 5y ago

"FAANG+similar : Low 7 figures compensation (staff level), technological lead on compute (~10 yr)" I don't know where OP is getting these figures from, but I doubt that FAANGs offer 7-figure comps to Staff-level people. It's probably more in the higher 6-figure level (400K - 600K).

digitallyfree 5y ago
I'd be interested in a credible source for this as well, even though ML work pays more. From what I know from people in the industry it's 6 not 7 figures.
- ankeshanand 5y ago
  https://aipaygrad.es/ https://aipaygrad.es/
greatpostman 5y ago
600k is a senior engineers wage
- opensrcken 5y ago
  No, it's not. If you get lucky with stock market movements, that might be your total annual comp, which is not your wage. I made close to what you're claiming at senior level, at the recent peak of stock market insanity, but I'd stick to the dictionary definition of "wage."
  - cellis 5y ago
    This all day. Sure if you joined FB in March 2020 your total comp today will be a multiple of that 700k, but FB is not giving out 600k packages today to e5s ( and e5 imo is staff+ at many lower tier companies ), more like 450k all in compensation.
ren_engineer 5y ago
looking at levels.fyi it seems there are at least several companies paying 7 figures to ML engineers with less than 10 years experience, although majority are at 15+ https://www.levels.fyi/Salaries/Software-Engineer/Machine-Learning/ https://www.levels.fyi/Salaries/Software-Engineer/Machine-Le...

blauditore 5y ago

> Low 7 figures compensation (staff level) Odd choice of level, since the author worked at one of these companies and was not at that level, certainly did not make 7 figures.

bwy 5y ago
They spent 2 years at the senior level at one FAANG. Why would they switch to another for anything less than the staff (senior + 1) level? (Not saying anyone "deserves" that or that's how it should be, but that's just how it is here in the valley.)
- kyawzazaw 5y ago
  and not just generic senior SWE level.

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bjornlouser 5y ago

AGI, AGI, AGI, AGI, AGI, AGI... https://images.squarespace-cdn.com/content/v1/5de799b06bb59b7f6bbf27f0/e7f42c5d-87f2-45c9-bb3e-39027fdc9df7/B17R4708.jpg?format=750w https://images.squarespace-cdn.com/content/v1/5de799b06bb59b...

usgroup 5y ago

I appreciate the post in the sense that it is an insightful perspective that he didn’t have to share. If you are elite it is difficult to talk about your options, pay or way of thinking without it coming across as nothing but hubris to the rest of us.

ozten 5y ago

Author is going to Halodi Robotics. I always thought that human shaped robots are a terrible form factor. Why limit yourself to the awkward design that 3.77 billion years of evolution accidentally landed on?

me_me_mu_mu 5y ago
Why do you think it is an accident? I thought evolution is an adaptation mechanism. If anything I'd say we've got a pretty cool form factor (peak human form, not like me who is out of shape lmao).
- ozten 5y ago
  I used the word accident to emphasize that there is no design or designer. I agree that evolution is adaptive and often creates optimal solutions.
- pvalue005 5y ago
  The human body has been optimized for a very complex objective function, and in a very different environment to a robot. If you specify what the robots are doing, and the set of constraints like power source, size, weights, etc., the optimal design will unlikely be humanoid.
sligor 5y ago
Human shaped robot is not a technical solution, it is a feature
riazrizvi 5y ago
Perhaps because that form factor has heavily informed civilization's current challenges?
tuckerman 5y ago
There is a school of thought in robotics/AI that believe embodiment is necessary (or at least the fastest way) for us to learn abstract thought. Embodiment can really span the gamut of meanings, but there are definitely researchers that believe humanoid robots are the best path forward to that goal. If you have a more specific goal in mind, e.g. solving a small set of industrial/commercial use cases, that changes the calculus dramatically.

angarg12 5y ago

I don't want to derail the conversation, but OPs career path really stood out to me. He graduated in 2016, worked at Google in Bay Area, and now is joining a startup at a VP level. I graduated in 2008, obtained a PhD in 2014 in a no name EU university, worked in odd companies for a while and joined FAANG 4 years ago as a mid level developer, where I am still ATM. Looking at this disparity I wonder what could be possible explanations: * OP is a beast and has grown very quickly in a short time. * I'm particularly inept and I'm growing very slowly. * Working in the right conditions (e.g. Bay Area, Big Tech, right team) can greatly accelerate your growth. * Startups have a big title inflation.

going_ham 5y ago
Yeah, and the way the author presents themselves speaks volume too. There is a real pride in this essay. You can see how the author casually drops big names and insights like it is a fact. Why does valley culture makes it seem like everything is possible and anything innovative can happen soon? The innovation in AI really seems like it is being made on a thin line of engineering and compute. It doesn't happen overnight. It requires some people working through and through to pull it off. These days it requires collective contribution.
- samhw 5y ago
  > The innovation in AI really seems like it is being made on a thin line of engineering and compute. This perfectly echoes my own thoughts. The advances being trumpeted in AI are functions of hardware advances that allow us to have massively overparameterised models, models which essentially 'make the map the size of the territory'[0], which is why they only succeed at a narrow class of interpolation problems. And even then nothing useful. That's why we're still being sold the "computer wins at board game" trope of the 90s, and yet somehow also being told that we're right on the verge of AGI. (OK, it's not only that. There's also a healthy amount of p-hacking, and a 'clever Hans effect' where the developer likely-unconsciously intervenes to assure the right answer via all the shadowy 'configuration' knobs ('oversampling', 'regularisation', 'feedforward', etc). I always say: if you develop a real AI, come show me a demo where it answers a hard question whose answer you - all of us - don't already know.) [0] Or far larger, actually. Google the 'lottery ticket hypothesis'.

going_ham 5y ago

This post feels like author is insecure about his position and wants to establish some validity. Having going through it all, it feels delusional at best. The glorified pattern matching can only take us so far. You know it's working as long as there is a pattern. I wouldn't call it a general intelligence per se. There is no "juice" in these algorithms. If we use these tools, we can immediately see where they fail and where they do not. These are just a new tools in software engineer's box.

visarga 5y ago
> The glorified pattern matching can only take us so far. This argument is becoming less and less convincing year by the year. We're amazed that things we were sure couldn't be done are actually done.
- water-your-self 5y ago
  This argument has been on going, on and off, since the 1960s. Show me a research paper.
  - visarga 5y ago
    1960s were 60 years ago, we're talking about AI, a fast paced field in the last 15 years.
    - going_ham 5y ago
      Definitely! There was a time when compilers were part of AI research. Now they are just another tool. Same with DL, they are amazing tool. We need them and they provide value if used correctly. I just didn't want to call it as "intelligent" and use this as basis for defining "intelligence." We can call them something else. It's learning to do a specialized job as intended and in "intelligent" manner. But it is not intelligence. Even a small ant is intelligent than our current AI systems though they aren't sophisticated and can't perform human task, they are intelligent than AI system. I hope that made sense.

TigeriusKirk 5y ago

Off topic a bit - "Halodi Robotics (company he's joining) intends to produce thousands of humanoid robots by 2023" Are humanoid robots just around the corner? Musk claims Tesla will have a "prototype" humanoid robot this year. I dismissed that as Elon hype, but have I missed this coming?

throwaway889900 5y ago
The only thing you need for a humanoid robot is for it to be human shaped and have some electronics inside of it. Doesn't mean it has to even remotely be human in interaction.
- captaincaveman 5y ago
  The robots on their site look pretty clunky to me, not to say they aren't doing smart things.

queuebert 5y ago

That's some godawful typography for such a smart guy.

thunkshift1 5y ago

This post reeks of someone who doesnt ‘need’ a job and has made enough money already. Good for op to go after ‘exciting problems’ rather than the mundane will I make rent and fees

formerkrogemp 5y ago
I don't think many machine learning folks working for FAANG are worrying about such things.
- thunkshift1 5y ago
  Well, who knows.. maybe some overworked fellow probably is

minimaxir 5y ago

As a relatively unremarkable data scientist/machine learning engineer of about 5 years, I've been keeping an eye on DS/ML positions as they tend to give a sense on what is important to companies in that space, although I'm not actively looking for a new role. More and more positions seem to require Ph.D. credentials even for non-senior roles, even though modern DS/ML tooling doesn't require it. If I ever left my job I might have to quit DS/ML and do something else entirely.

antupis 5y ago
DE/ML ops/ Software engineer(data) is many ways new DS. Lots of greenfield projects and less competition.
cwp 5y ago
Nah, you wouldn't have to quit. If you've got 5 years experience, even on non-cutting edge projects, the PhD won't matter. Sure, you won't be able to get any job you want, but there are lots of ML jobs that list a PhD requirement that will nevertheless jump at the chance to hire someone with practical experience.
- minimaxir 5y ago
  Hiring managers are more generous, but HR screening the resumes aren't.
  - cwp 5y ago
    That'll only be a problem if you're up against significant number of candidates that have both experience and a PhD. HR will certainly filter resumes based on something as clear-cut as a degree when they can. But they can't just filter everything out and tell the hiring manager he's SOL. So you don't have to check all the boxes as long as you check enough of them, and you're competitive with the other candidates. At the same time, nobody is going to do all that well if they just apply online and cross their fingers. You need some kind of human contact, either though an introduction to an insider through your network, or through a recruiter of some kind. It takes a bit of time to develop the relationships, but it's quite doable and worth it, even for introverts. Best to start before you're interested in changing jobs.

queuebert 5y ago

"Product impact is even slower than robotics due to regulatory capture by hospitals and insurance companies." The author apparently does not understand regulatory capture and is throwing around catch phrases to sound smart. Regulatory capture would imply that healthcare encounters less regulation than it should due to influence over the relevant government agencies. This should increase product impact and reduce time to market, the opposite of what he suggests.

axg11 5y ago
Doesn't the author's sentence mean: hospitals and insurance companies have coopted regulators for the benefits of their own businesses, at the detriment of medical device companies (developing AI)? I think the author's point still stands.
- queuebert 5y ago
  Possibly that was the intended meaning, but the point is still invalid. Hospitals want more cool gadgets. They want to be able to treat more conditions and charge more for it. If anything, the FDA is a constant annoyance to a healthcare provider because it hamstrings them from providing care. This is why so many patients are enrolled in clinical trials, to get care ahead of the FDA approval time frame.
  - formerkrogemp 5y ago
    Right, the FDA is what's wrong with American healthcare. Not the insurance agencies or the for profit hospitals making as much money as possible. The lack of single payer healthcare is one of America's greatest failings on that end.

opensrcken 5y ago

The post is not only too braggadocious for my taste, but some of the figures quoted are highly unlikely. I would personally not work for someone with this kind of ego, but there are many such people in positions of power. This article is representative of an attitude I'm seeing around the tech industry, and if this is indeed the level of "confidence" in the Bay, I don't think that's a good sign.

mysterEFrank 5y ago
Eric Jang is top ML talent, these numbers are accurate. I work in ML and have followed his work for years
- davnn 5y ago
  Still.. do you think being a top talent in ML guarantees success for your own company, for example? I think there are a lot of valuable skills to have, being an expert in X is just one of them.
- water-your-self 5y ago
  He claims to be solving general intelligence in 20 years. Your advocacy is not enough to convince me.
  - marginalia_nu 5y ago
    General intelligence has been 20 years away since the 60s, along with fusion power and a bunch of other things. In marketing, they say 5 years when it's actually 20 years away.
    - 21723 5y ago
      In my view, AGI is farther away than fusion. We know how to do fusion. We know the physics behind it. We haven't yet figured out how to build profitable fusion plants, and we probably won't for a long time, if for no other reason than improvements in fission--modern fission plants are the best . When it comes to AGI, we have no clue. It's a constantly moving target, because our conceptions of intelligence evolve. Most things that were once "AI" became "non-AI" solved problems after we got good at them using a couple key insights, e.g. that image processing could be sped up with CNNs due to the existence of a topology on the inputs. We still have no idea what makes us tick, and moreover there is not a strong economic incentive to replicate all of our intelligence... although, of course, automation will continue and that itself will be disruptive enough.

sydthrowaway 5y ago

This guy is another Dan Luu. Insufferable posts.

ericjang 5y ago
Ouch

YossarianFrPrez 5y ago

Say what you will about the OP and the claims he makes in his career announcement... I was struck by how casually the author mentioned that Academia is behind various private companies. Call me romantic, for in an ideal world Academia is just as close to the cutting edge of knowledge as private R&D laboratories.

warner25 5y ago

Can someone with only 6 years of experience make credible predictions about things 20 years in the future? I'm around 15 years of experience, and my appreciation for my own lack of knowledge and ability to make predictions still grows with every year.

xpe 5y ago
I’ll bite on the above loaded question: Define experience. However you do, it should at least include work, education, and life in general, given that such experiences relate to the context or situation.
- warner25 5y ago
  Yeah, it was a bit snarky, but still an honest question. In some domains, like astronomy or geology or climate change, I guess it seems reasonable to make predictions 100 or 1,000 years in the future based on available data rather than experience. In other domains, like politics or economics or finance, it seems like there would be much more value in having worked through a bunch of election and business cycles over the years. I'm not sure where computing and AI sits on that spectrum. I can imagine a young AI researcher failing to realize that some approach was tried and found to be a dead-end 30 years ago. On the definition of experience, I agree that education and life experience counts. I said "around 15" years for myself because the definition is fuzzy. I got some very specific career preparation and training in college, so that sort of counts, and I probably spend more personal time than many of my peers learning about relevant history and current events.

falsenine 5y ago

As someone whose intention is to go to Medical School and pick up programming (+ math skills) to potentially work at the intersection of ML + Healthcare, the knowledge of the regulatory hurdles expressed is discouraging. Not sure if it really is worth the effort to study tech on top of medicine. Are there any people with experience within ML + Healthcare/Medicine or know of startups that are making great strides within this realm?

rakejake 5y ago
I used to work in the healthcare vertical. While there are regulatory hurdles, they are there for good reason. Move fast and break things does not work in this industry and will get you fired. You will have better luck working with one of the larger companies who have a good history with the FDA, and more importantly, have good relationships with hospitals and physicians. They are aware of the time and resources it takes to push something out. Pay will not be FAANG level or anywhere close, but they usually have great work culture and WLB.
- falsenine 5y ago
  Thanks for the comment. Do you have any feedback for a future physician interested within the intersection of ML + medicine (does not strictly need to be "healthcare")? Would learning ML and all the other things needed to know be helpful (huge time investment needed here)? Ultimately, would be interested in leveraging medical knowledge into a startup capacity.
  - rakejake 5y ago
    As a traditional physician, I imagine your value add would still be your domain expertise. The physicians I worked with were purely consumers of AI tech and had a say in the design of the software. But AI and more generally software is becoming an important part of medicine so I guess it couldn't hurt to learn some basic ML.
max-hoffman 5y ago
When I was an MD/PhD student in 2017-2018, there were only a handful of labs specializing in applied medical ML and computational biology. Since leaving to work as an ML eng/backend eng, I have been surprised by how the relationships between academics, practitioners, and investors in pure software contribute to a positively reinforcing loop. On the medicine side, the academics lean towards software skepticism, the investors make fewer and safer bets to compensate for historically lower margins/growth/return multiples compared to pure software, and the subset of practitioners get payed orders of magnitude less and have correspondingly less engineering development. The differences affect everything from managerial quality, skill and career development, location flexibility, upward mobility, product scope and impact.

Kalanos 5y ago

sounds like you are coming up with excuses not to start a company

akhmatova 5y ago

In the future, every successful tech company will use their data moats to build some variant of an Artificial General Intelligence. Is that what one gets paid 7 figures for - to go out in public and claim they "know" things like this with a straight face?

alfor 5y ago

Speed of iteration is all that matter. That is why all the big like Google no longer push things forward, even with the best engineer and Phds and with so much money. That’s why I think that Optimus at Tesla will crush all the other robotic platform. On the positive side success of Optimus will help startups to get funds or get acquired by corporations that want to get a slice of the newly proven market.

sidibe 5y ago
You are confusing speed of iteration with speed of announcements. There is a lot of stuff that happens, like research and process building, at a big "slow" company like Google that Tesla doesn't even realize is needed yet. Tesla makes a stream of wildly optimistic announcements that makes it feel like it's closing the gap with Waymo for example, but there's no evidence that it is
- alfor 5y ago
  The iterations happen way faster too. Look at Munro teardown of Tesla, he has never see a tenth of that rate of change ever. Look at what SpaceX accomplished. Look where OpenAI is given the time it’s been operating. Look at Tesla rate of production increase. It’s going to be very hard to compete with Tesla at this point. So much ressources, so much bright engineers, all the knowledge in manufacturing, all the training on vision, etc.
  - sidibe 5y ago
    I guess the difference is you think they have more resources, more bright engineers, and I'm quite certain they don't. I'd guess Tesla has several times less engineers working on FSD than Waymo and making much lower salaries, and they are spread across large parts of the stack that Waymo can just tap into Google for.
    - alfor 5y ago
      No the main difference is the mindset of the place, the culture. Nokia, Blackberry didn’t overtake Apple, why? Culture. But before Tesla had more limited resources, now it’s almost a non issue.

zcw100 5y ago

After how many years of Facebook, instagram, youtubers, LinkedIn do people still not get it? This is the internet. How on earth can you verify the veracity of the authors statements? Everyone is discussing this like a blog post on themselves is an accurate portrayal of reality. It might be, but it might not. I'm not saying they're being dishonest, with they may be, but they are definitely not incentivized to give you an accurate portrayal of their life. This is instagram for IT nerds and there is no way they have a Badonkadonk that big.

water-your-self 5y ago
Ah but its much more fun when everyone hosts their own blog.
fartcannon 5y ago
To me, outside of this world, it feels like when one of the guys at a shop just like ours wins the lottery a few cities over. Everyone's just dreaming big for the fun of it.

water-your-self 5y ago

>In the future, every successful tech company will use their data moats to build some variant of an Artificial General Intelligence. Its rare that an article loses my faith in the first sentence.

sfriedr 5y ago

I work in theoretical machine learning and what I don't like about this piece is how casually the author assumes that what goes us to the present state (hacky approaches where you converge to something "good enough", unless the model is very simple, e.g. a feedforward net---and even here there are still open questions---and you have some theoretical guarantee) will also get us to "AGI". I believe that is unlikely. Here's metaphor for that, that I often like to use when I speak to engineers doing empirical ML work: In ancient times people build a lot of nice structures, such as pyramids, cathedrals etc. By trial and error many rules of thumb were devised and they more or less worked. But it's safe to say things like earthquake-resistant skyscrapes and modern bridge cannot be build without deep theoretical insights into structural mechanics. These are highly optimized, intricate structures. The same is probably necessary to build highly optimized, intricate models that deliver what we now consider to be "AGI" - but then again, the world of ML is full of surprises.

lysecret 5y ago

Not sure why this gets so much hate. I think he's a bit too optimistic about AGI prospects. But in his position these are all interesting and reasonable options. I'm a bit sceptical on this 10,5,1 whatever year ahead metric he pulled from wherever. Interesting read regardless. My opinion about the next few years is that most value will come from finding your niche, creating Datasets, iterating and building your ML model (a bit like he wrote but without this AGI...)

belowmedian 5y ago

I'm curious what the author's take on scale.ai is since he was considering building an ml-ops / data labeling company and they seem to be the most successful startup in that space.

sbmthakur 5y ago

"Technological Lead Time" - I have thought about this before. I wonder if anyone actively tracks it for different domains.

jasfi 5y ago

If you're interested in AGI take a look at my effort currently in development. It focuses on natural language understanding: https://lxagi.com https://lxagi.com