[HN Gopher] The AI research job market
___________________________________________________________________
The AI research job market
Author : sebg
Score : 155 points
Date : 2023-10-12 14:18 UTC (6 hours ago)
(HTM) web link (www.interconnects.ai)
(TXT) w3m dump (www.interconnects.ai)
| softwaredoug wrote:
| I just watched The Big Short
|
| I felt a bit eery how much the groupthink around mortgages
| matched todays AI hype. The unwillingness to listen to critical
| advice. To question the value. Data science depts who measure
| such things are often vilified.
|
| It's a kind of depressing job landscape in this way. You either
| go with the, often top down, groupthink against the face of
| measured evidence, or you're labeled a cynical naysayer and your
| career suffers.
| chasd00 wrote:
| just use your skills, i.e. mop, and soak of the cash.
| sillysaurusx wrote:
| Well, don't be cynical about it. One thing that I think a lot
| of us could benefit from is learning how to present a point in
| a way that people will listen. The key is to harness that hype
| -- use your point to unlock some pent up energy for a new
| direction, rather than merely try to say it can't work.
|
| A lot of endeavors can work, even if only a little. Find some
| aspect of it that can, and flip the problem around. Even if the
| whole thing is mostly bogus, is there a small part that isn't?
| Latch onto that.
|
| Or go elsewhere. The wonderful part about AI is that the whole
| world's problems are up for grabs. Part of why there's so much
| unfounded hype is because of how many real advances have
| recently become possible. This period in history will never
| come again.
|
| It's also a rare time in history that an individual can make
| lots of progress. Most of us need to be a part of big groups to
| do anything worthwhile, in most fields. But in this case lone
| wolves often have the upper hand over established
| organizations.
| x86x87 wrote:
| Yes. Don't tell the emperor that he has no clothes. Instead
| innovate and introduce a hybrid between his current clothes
| and old fashion textiles. Who know? In time they may end up
| covering their junk.
| softwaredoug wrote:
| To be clear, I'm mostly excited. But I also think its
| reasonable to be skeptical and try to educate stakeholders on
| the realities of the situation.
|
| My controversial take on AI is its actually a better time to
| take things slow, experiment, study, see what works. Not dump
| TONS of money and cash and get too distracted. Because nobody
| (besides big tech) has fully figured out how to make a
| product that makes a profit. Its not clear users want a
| chatbot (aside from ChatGPT)... But things could change.
| chung8123 wrote:
| I think the issue is, right now AI is a race. We have the
| Microsoft, Google, Meta, Apple, and Amazon's of the world
| with their massive compute and bankrolls racing to see who
| can build the biggest moat around an AI service. The
| massive upfront spending is hoping to hit the winning
| lottery ticket and spending slowly may leave you out of the
| drawing.
|
| As compute costs and requirements come down LLMs will be
| ubiquitous everywhere.
| akomtu wrote:
| It's the Manhattan Project 2.0. The AI, once created,
| won't be that hard to replicate, but those who fail to
| create it early, will be sidetracked later. The race
| among big tech is the american way of doing such
| projects: fund a few companies, let them compete and pick
| the winner.
| tayo42 wrote:
| > It's also a rare time in history that an individual can
| make lots of progress. Most of us need to be a part of big
| groups to do anything worthwhile, in most fields. But in this
| case lone wolves often have the upper hand over established
| organizations.
|
| I'm curious why you think this is true? My feeling as a broke
| individual trying to catch up on ml is that there are some
| simple demos to do. But scaling up requires a lot of compute
| and storage for an individual. Acquiring datasets and
| training are cost prohibitive. I'm only able to play around
| with some really small stuff because by dumb luck a few years
| ago I bought a gaming laptop with a nvidia gpu in it. The
| impressive models that are generating the hype are just a
| different league. Love to hear to how I am wrong though?
| sillysaurusx wrote:
| It's true that you need compute to do large experiments,
| but the large experiments grow out of small ones. If you
| can show promising work in a small way, it's easier to get
| compute. You can also apply to TRC to get a bunch of TPUs.
| They had capacity issues for a long time but I've heard
| it's improved.
|
| Don't focus on the hype models. Find a niche that you
| personally like, and do that. If you're chasing hype you'll
| always be skating towards the puck. My original AI interest
| was to use voice generation to make Dr Kleiner sing about
| being a modern major general. It went from there, to image
| gen, to text gen, and kaboom, the whole world blew up. I
| was the first to show that GPTs can be used for more than
| just language modeling -- in my case, playing chess.
|
| Wacky ideas like that are important to play around with,
| because they won't seem so wacky in a year.
| tayo42 wrote:
| Thats interesting, at a glance the TRC thing looks more
| altruistic and impactful than what I had in mind for
| learning or making money. I'll have to keep it in mind if
| I do, do something share worthy ever. Thanks!
| jebarker wrote:
| Another area where there's potential for an individual to
| make lots of progress is in theory and mechanistic
| interpretation. Although it's not where the money is, it's
| probably not rapid progress and it's really hard.
| satvikpendem wrote:
| There's API access for GPUs you can rent, as well as model
| specific APIs like for Stable Diffusion or GPT 4. You can
| do a lot as a solopreneur now.
|
| For example, Tony Dinh made a macOS GPT wrapper and makes
| like 40k a month from it, just utilizing OpenAI's APIs:
| https://news.ycombinator.com/item?id=37622702
| dmbche wrote:
| I'm not in ML - here a quick take
|
| In a gold rush, sell shovels! The ML pipeline has a lot of
| bottlenecks. Work on one, get _useful and novel_ expertise,
| and have a massive impact on the industry. Like maybe you
| could find a way to optimise your GPU usage? Is there a way
| to package what you feed it more efficiently?
|
| The point not being of competing with OpenAI, but to solve
| a problem that everyone in the field has.
| diogenes4 wrote:
| > The wonderful part about AI is that the whole world's
| problems are up for grabs.
|
| Most of the world's problems aren't technological,
| unfortunately for us in tech. There's little it can do
| against the momentum of capital tearing this globe apart.
| mrtksn wrote:
| IIRC, the movie ends with a joke on how those responsible were
| punished only to reveal that they walked away with the loot.
|
| You almost never see a group punishment, unless you lost as a
| group against another organization.
|
| So, if all the AI stuff goes bust in a year or two those who
| benefited from the bubble will keep their benefits. Also,
| there's a possibility that it doesn't go bust and user us into
| AGI era and win big.
| gymbeaux wrote:
| JS devs were/are paid the big bucks because !!"false" ==
| !!"true" and so on.
| mrtksn wrote:
| Decided to remove the JS part, lots of people made their
| fortune on battling its oddities.
| epups wrote:
| What is the measured evidence that AI is hype right now?
| 7thaccount wrote:
| Companies with massive valuations that have no product to
| sell or aren't making any profit. This is what I've heard
| anyway (not a stock guy). Some economic schools of thought
| say that when interest rates are low and money is practically
| free (historically speaking), you get a lot of bad/risky
| ideas (boom & bubble) and an inevitable correction of a bust.
| The bust is accelerated when all of a sudden you have to rise
| the interest rates to fight inflation and the money is no
| longer easy to get. All those companies being held afloat by
| the free money start collapsing.
|
| Again, we should make statements on data and I'll just be
| upfront that I haven't done much research in this area, but
| considering the sheer amount of AI startups and jobs and
| conferences and so on with very few transformative
| products...I would eventually expect a market correction in
| the form of another AI winter like what happened in the 80s
| when all the massive government acts defense research dollars
| dried up. The difference is it'll be far less severe. You'll
| still have plenty of AI research at the universities and
| large companies, but maybe not hundreds of questionable
| startups. This is all just conjecture on my part though.
| nightski wrote:
| That and even established companies with other
| products/services but who have seen massive market cap
| increases due to AI hype.
| andrewmutz wrote:
| > Companies with massive valuations that have no product to
| sell or aren't making any profit
|
| That's not enough to demonstrate that AI is just hype.
| Every technological breakthrough has opportunists trying to
| make a buck riding along the hype. In the 90s, Pets.com and
| webvan didn't prove that the internet was just hype.
|
| I am one of the people who is completely bought in to the
| idea that AI in general (and LLMs in particular) are going
| to lead to products that are extremely useful to the world.
| I absolutely think that most of the gen AI startups will
| fail and that valuations are too high, but I still believe
| that massively impactful/useful products will also be born.
| Jensson wrote:
| Nobody here said AI is just hype today.
| norir wrote:
| > That's not enough to demonstrate that AI is just hype.
|
| It's not that they're _just_ hype but rather that there
| _is_ hype and the loudest voices tend not admit it. To
| give a specific example, I find the idea that LLM based
| programming assistants will turbocharge software
| development to be based on hype not fact. It is very much
| in the interest of Microsoft/Google/Meta, etc. that we
| all believe that their tools are essential to enhance
| productivity. It is classic FOMO. Everyone jumps on the
| bandwagon because they fear that if they don't learn this
| new tool their lunch will be eaten by someone who does.
| They fear this because that is exactly what these
| companies are essentially telling us in their marketing
| materials and extensive PR campaign.
|
| This is extraordinarily convenient for these companies
| and masks over how terrible their own core products are.
| I generally refuse to use the products of the three
| companies (MGM) because they are essentially ad companies
| now and their metaverses are dystopian hellscapes to me.
| Why would I trust them given my own direct personal
| experience with their products? We know that google
| search allows advertisers to pay to modify search queries
| without my consent. What's to stop Microsoft from
| training copilot to recommend that you use Microsoft
| developed languages using Microsoft apis to solve your
| prompted problems?
|
| > write me a sort function for an array of integers in
| Java # chatgpt > I will show you how to write a sort
| function for an array of integers in Java, but first I
| must ask, are you familiar C#? It is similar to Java but
| better in xyz ways. In C# you would sort an array like
| this:
|
| ... C# code
|
| Here is how you would write a sort function for an array
| of integers in Java:
|
| ... Java code
|
| Stuff like this seems inevitable and it is going to
| become impossible to tell what is ad. Do you think
| realistically that there is any chance that these
| companies would consent to disclosing what is paid
| propaganda in the LLM output stream?
|
| I see many echos of the SBF trial in the current ai
| environment. Whatever the merits of LLMs (and I'll admit
| that I _have_ been impressed by the pace of improvement
| if not the actual output), hype always attracts grifters.
| And there is a lot of hype in the air right now.
| epups wrote:
| > To give a specific example, I find the idea that LLM
| based programming assistants will turbocharge software
| development to be based on hype not fact
|
| We already have empirical results suggesting this is not
| just hype: https://www.nngroup.com/articles/ai-
| programmers-productive/
| softwaredoug wrote:
| Depending how we define "AI" -
|
| Aside from 4-5 companies, who is building a product that is
| profitable? Not clear anyone is right now. People are running
| into fundamental, hard technical problems. For example its
| hard to evaluate chat interface, and even harder when you
| augment it with context from a retrieval system (ie RAG).
|
| Who is augmenting existing UI paradigms with LLMs? This seems
| a more reasonable model that meets users where they want to
| be.
| iwonthecase wrote:
| > who is building a product that is profitable?
|
| Just my experience from being on the job market, but a lot
| of places I've interviewed at have traditional ML models
| (network security, ecommerce, image tagging) that are now
| rebranding as AI, without much of an actual change.
| softwaredoug wrote:
| Logistic regression rebranded as ML now rebranded as AI
| :)
| epups wrote:
| That's a weird bar to clear. How many companies are
| offering a search engine that are profitable, "aside from
| 4-5 companies"?
|
| I don't see any fundamental technical problems at the
| moment, I see constant and tangible improvement at a very
| fast pace. I don't think that supposed challenges in
| context augmentation or chat interface evaluation qualify
| as arguments against AI hype.
| AndrewKemendo wrote:
| Very similar to my experience- modulo my role more in the AI in
| production, operations side, aka MLOps
|
| MLOps leads/lags research depending on your application patterns
| so it's an extremely dynamic place to be to see what's happening
|
| I'd argue based on what I'm seeing with implementations, and
| importantly how FLEXIBLE transformers seem to be, this is the
| most true part of this article:
|
| "we're going to get way further with the Transformer architecture
| than most ideas in the past"
| tayo42 wrote:
| > AI in production, operations side, aka MLOps
|
| How did you get into this? Seems like a lot of places are stuck
| on the idea if you didn't do it in the past you cant do it now
| rg111 wrote:
| This is closer to traditional SWE than AI research.
|
| Take some courses and get some certifications. And also make
| some serious projects where you demonstrate your capabilities
| with cutting edge tools.
|
| This is more focused on tools and use of said tools.
|
| Take some trained models, and demonstrate how well you can
| use them.
|
| Some ideas:
|
| 1. Take a cats vs. dogs model, deploy it online. Design an
| API around it. Document the API well. Create a mechanism to
| show confidence score, and store low confidence score
| examples in a database that you can later manually label and
| retrain the model with.
|
| 2. Take a smallish LLM, design a VS code extension that
| documents your functions based on docstring.
|
| Just demonstrate your basic knowledge in ML, and really good
| software engineering skills, learn the vocabulary well, and
| then start applying for jobs. It's much better if you have a
| CS/EE degree.
| alfalfasprout wrote:
| As someone in this space I could not disagree more.
|
| Certifications will do nothing for you. The harsh reality
| is only real world experience doing this stuff at scale
| will help you understand all the complexity involved. There
| are tons of people trying to hop onto this train after
| taking a few online courses and it's making it hard to
| filter down candidate pools.
| AndrewKemendo wrote:
| rg111 below does a good job explaining how to go from scratch
|
| For me, my work in software and AI specifically predates 2012
| - blood sweat and tears of going from non-big data
| statistical forecasting programs (Bayes nets) to big data
| forecasting (R, Python stat packages) to geometric vision
| (SURF, HOG etc) to big data CNN & MDP image processing for
| CNNs (tensorflow) etc...
|
| Like I said, blood sweat and tears
| FrustratedMonky wrote:
| Hype doesn't mean it isn't real.
|
| There was hype with the Internet, and lots of scams, and
| naysayer, and bogus money, and real money.
|
| I remember reading a Java 1.0 Book, and someone just casually
| saying "why learn that, that internet thing isn't going to last,
| it's all hype."
| JohnFen wrote:
| I think the internet is a poor analogy because by the time it
| started to enter the awareness of the general public, it had
| already been around and subjected to refinement for years. Its
| value and usefulness was proved before the average joe had
| access to it at all.
| ska wrote:
| > awareness of the general public, it had already been around
| and subjected to refinement for years.
|
| And how is that different than "AI" ? It's not like these
| techniques sprang out of the ether in the 2000s.
| __rito__ wrote:
| AI research essentially started in 1940s, and not the 2010s
| like you might think.
|
| I read a ton of AI research papers written in the 80s and
| 90s.
|
| Lack of AI hype is was because the lack of data and compute.
| The actual field is here for a very long time.
| Xcelerate wrote:
| Unlike previous hype cycles, the potential value of this one is
| extraordinary if it's actually unlocked (I mean, what was the
| theoretical upper limit on the benefit of cryptocurrency for the
| world? Probably not that much.) Previous attempts at AI/AGI have
| been constrained by computational resources. It's quite possible
| that we already have sufficient computational power and the
| necessary data for AGI--all we need are the right algorithms.
|
| Even if for some bizarre reason we've already tapped the maximum
| potential of transformer architectures and all of this money goes
| nowhere, compared to all the other ways that society wastes
| money, I would be fine with calling this a big bet for humanity
| that didn't pay off. It doesn't mean that it wasn't worth the
| attempt though.
| brucethemoose2 wrote:
| Any correlation between current models and AGI is, IMO, hype.
|
| GenAI is a remarkably useful tool, but its not one step away
| from an AGI.
| anonyfox wrote:
| depends on how you define "step". Engineer a 10x/100x version
| of what we have in terms of LLM (either by being more
| efficient and/or more/specialized hardware) and let this
| thing build novel attempts for AGI algorithms 24/7 in a
| evolutionary setting.
|
| I guess the challenge is more to agree on a fitness function
| to measure the "AGI"-progress" against, but thats a different
| topic. But in general scaling up the current GenAI tech and
| parallelize/specialize the models in a multi-generational way
| _should_ be a safe ticket to AGI, but the time scale is
| inknown of course (since we can't even agree on the goal
| definition)
| staticman2 wrote:
| _" depends on how you define "step". Engineer a 10x/100x
| version of what we have in terms of LLM (either by being
| more efficient and/or more/specialized hardware) and let
| this thing build novel attempts for AGI algorithms 24/7 in
| a evolutionary setting."_
|
| The current LLM's get stuck in loops when a problem is too
| hard for it. They just keep doing the wrong thing over and
| over. It's not obvious this sort of ai can "build novel
| attempts" at hard problems.
| somestag wrote:
| I like this comment because I think it highlights the exact
| difference between AI optimists and AI cynics.
|
| I think you'll find that AGI cynics do not agree at all
| that "engineering a 10x/100x version" of what we have and
| making it attempt "AGI algorithms 24/7 in an evolutionary
| setting" is a "safe ticket" to AGI.
| Jensson wrote:
| > I mean, what was the theoretical upper limit on the benefit
| of cryptocurrency for the world
|
| The value of potential bank scams that are otherwise illegal
| was enormous to investors though. Lots of people got extremely
| wealthy thanks to crypto scams. Then when the legal holes were
| covered crypto was forgotten extremely quickly since the hype
| was mostly kept alive by scams.
|
| AI doesn't have nearly as lucrative scams, so I doubt you will
| see the same investor frenzy.
| dragonwriter wrote:
| > Unlike previous hype cycles, the potential value of this one
| is extraordinary if it's actually unlocked
|
| That was also true, in AI, of the expert-system hype cycle. And
| the actual value unlocked was extraordinary, just not at the
| scale people saw as the potential.
|
| Actually, it was seen as true of all of the hype cycles
| _during_ the hype cycle, that 's what makes it a hype cycle.
|
| > (I mean, what was the theoretical upper limit on the benefit
| of cryptocurrency for the world? Probably not that much.)
|
| If you believed the people that were as breathless about it as
| you are about the current AI hype cycle, basically infinite,
| unlocking ways human potential and interactions, economic and
| otherwise, are held back by centralized and/or authoritarian
| systems.
|
| That's what made it a hype cycle.
|
| > It's quite possible that we already have sufficient
| computational power and the necessary data for AGI--all we need
| are the right algorithms.
|
| Yeah, but that's always been true. If software-only AGI is
| possible, we've always had the data in the natural world, and
| with no strong theoretical model for the necessary
| computational power, its always been possible we had enough.
| What we clearly lacked were the right algorithms (oh, and any
| reason to believe software-only AGI was possible.)
| somestag wrote:
| I think I agree with basically your whole comment but I'm
| wondering if you could explain what you mean by "software-
| only AGI". Obviously all software runs on hardware, and
| creating specialized hardware to run certain types of
| software is something the computing industry is already very
| familiar with.
|
| In the far far future, if we did crack AGI, it's not
| impossible to believe that specialized hardware modules would
| be built to enable AGI to interface with a "normal" home
| computer, much like we already add modules to our computers
| for specialized applications. Would this still count as
| software-only AI to you?
|
| I've held for a long time that sensory input and real-world
| agency might be necessary to grow intelligence, so maybe you
| mean something like that, but even then that's something not
| incredibly outside the realm of what regular computers could
| do with some expansion.
| dragonwriter wrote:
| There's some discussion of embodiment as an important
| factor in intelligence such that it would defy pure
| software implementation. I'm personally of the opinion that
| even to the extent this is true, it probably just means the
| compute capacity required for software is higher than we
| might otherwise think, to _simulate_ the other parts,
| alternatively, with the right interfaces and hardware, we
| don 't need that cheat. But "everything involved can be
| simulated in software at the required level", while I
| believe it, somewhat speculative.
| dmbche wrote:
| https://en.m.wikipedia.org/wiki/Portia_(spider)
|
| This spider could be evidence of "software based
| intelligence" in biological brains - it exhibits much
| more complex behaviors than other animals it's size, more
| comparable to cats and dogs.
|
| What I mean is that some believe that their brain is
| "emulating" all parts of the larger "brain", but one at a
| time, and passing the "data" that comes out of one into
| the next.
|
| Just a cool thing.
| mjr00 wrote:
| > (I mean, what was the theoretical upper limit on the benefit
| of cryptocurrency for the world? Probably not that much.)
|
| According to the crypto-faithful at the time: solving
| territorial disputes (Gaza Strip? blockchain solves this!),
| identity management, bank transfers, payments over the internet
| with no transaction fees, "the supply chain" (whatever that
| means), etc. Not as interesting to a layperson as AGI, but if
| all those (or ANY of those) ended up panning out, crypto would
| have been a multi-trillion dollar industry and fundamentally
| transformed vast swathes of modern society.
|
| I do think LLMs are _far_ more useful than blockchain, but
| claiming "the potential value of this one is extraordinary" is
| _exactly_ what people said in previous hype cycles.
| yieldcrv wrote:
| > crypto would have been a multi-trillion dollar industry
|
| what metric would you like to use, specifically? double check
| that its a metric that matches other industries
|
| the market cap of the digital spot commodities? the marketcap
| of the businesses that use the digital spot commodities? the
| revenue of all participants and service providers? the volume
| of all shares and futures and spot trades when sliced down to
| a submetric that represents 'real' trades? all of the above?
|
| > and fundamentally transformed vast swathes of modern
| society
|
| thats ... _a_... goal post. I 'm not sure if that's a goal
| post I would have, its market microstructure plumbing. At
| best, it modifies capital formation, letting different
| ventures get funding, which it already has.
|
| and then, what time frame? its a pretty good S-curve from
| 2009. there is a pretty clear chronology of what delays what,
| everything that has resulted in a seasonal bubble in crypto
| comes from a software proposal being ratified that allows it
| to touch another industry that it previously didn't. Many
| overlapping similarities to IETF proposals for WWW, but I
| understand this level of discussion might not reach your
| circles, the point stands that there are _plenty_ of people
| in the tech space that had the exact same observation and you
| and chose to contribute to the proposals that make crypto now
| more accessible to the next group.
|
| There are plenty of proposals now in many different crypto
| communities, even ones to make ratification more egalitarian
| and collaborative.
|
| some turn out to be hits for adoption.
|
| I think it is interesting for people to then use that reality
| to say crypto hasnt fulfilled any lofty idea they overheard
| an enthusiast say, because it took too long.
|
| Prior proposals and their ratification were necessary for the
| reported market cap to reach $1bn, but I know I know "market
| cap!? you cant sell it all at once!" Holding crypto assets
| and industry to a separate higher standard than all other
| industries on the planet.
| lawlessone wrote:
| > crypto would have been a multi-trillion dollar industry
|
| The same people saying this are often the same ones betting
| on sovereign currencies crashing.
| ska wrote:
| > Unlike previous hype cycles, the potential value of this one
| is extraordinary if it's actually unlocked
|
| That sounds exactly like most hype cycles, it's almost a
| tautology that the perceived potential value is immense (at
| least to enough people).
|
| Consider e.g. the hype around "the internet" in early mid
| nineties, which led to the dot.com collapse. Today the internet
| has undeniably had a massive impact globally, so the naysayers
| have been comprehensively proven wrong. On the other hand, the
| most optimistic views have not begun to come to pass yet
| (ever?) either. Lots of ideas that were floated in the 90s
| didn't really work until 10, 15, 20 years later. Some things
| that are now ubiquitous weren't really conceived of then, etc.
| etc. As usual, it turned out the technology wasn't the really
| hard part.
|
| So far the current AI cycle seems to be following the usual
| playbook.
| 0xDEAFBEAD wrote:
| The potential downside is extraordinary too
| Xcelerate wrote:
| Touche
| Aurornis wrote:
| From the other side of the table, the machine learning candidate
| pool is also a clown show right now.
|
| I did some hiring for a very real machine learning (AI if you
| want to call it that) initiative that started even before the LLM
| explosion. The number of candidates applying with claimed ML/AI
| experience who haven't done anything more than follow online
| tutorials is wild. This was at a company that had a good
| reputation among tech people and paid above average, so we got a
| lot of candidates hoping to talk their way into ML jobs after
| completing some basic courses online.
|
| The weirdest trend was all of the people who had done large AI
| projects on things that didn't need AI at all. We had people
| bragging about spending a year or more trying to get an AI model
| to do simple tasks that were easily solved deterministically with
| simple math, for example. There was a lot of AI-ification for the
| sake of using AI.
|
| It feels similar to when everyone with a Raspberry Pi started
| claiming embedded expertise or when people who worked with
| analytics started branding themselves as Big Data experts.
| jvalencia wrote:
| 100% this. ML has been very hyped for a while now and having it
| was seen as a badge for the company. To be fair, ML is not also
| something that was historically central to a degree, so many
| people wanting to get into AI, even good engineers, did not
| have the background in it. This too is changing though, but the
| hype and the lack of an experienced pool doesn't help.
| mikrl wrote:
| I don't think the background is really that important tbh.
|
| From physics I have a good theoretical grounding in how ML
| works (optimizing a cost function over a high dimensional
| manifold to reconstruct a probability distribution, then
| using the distribution for some task) but I personally find
| actually 'doing ML' to be rather dull.
| hashtag-til wrote:
| > The weirdest trend was all of the people who had done large
| AI projects on things that didn't need AI at all.
|
| I can relate to this a lot. In my company many things you can
| sell as "AI" can really be solved with traditional data
| processing.
| MaxBarraclough wrote:
| > We had people bragging about spending a year or more trying
| to get an AI model to do simple tasks that were easily solved
| deterministically with simple math, for example.
|
| Fad-chasing often leads to silly technical decisions. Same
| thing happened with blockchains when they were at the peak of
| the famous hype cycle. [0]
|
| [0] https://en.wikipedia.org/wiki/Gartner_hype_cycle
| sillysaurusx wrote:
| > The weirdest trend was all of the people who had done large
| AI projects on things that didn't need AI at all.
|
| This is how people get experience with ML though. I don't think
| that's a bad thing.
|
| It sounds like you're looking for a candidate with current ML
| experience. But I've seen so many people go from zero knowledge
| to capable devs that this seems like a mistake. You'll end up
| overpaying.
|
| Just try to find someone with a burning ambition to learn. That
| seems like the key to get someone capable in the long run. If
| they point out something beyond Kaggle that makes you think,
| pay attention to that feeling -- it means they're in it for
| more than the money.
| discmonkey wrote:
| I have to agree. Especially given the very real possibility
| that your ML project won't be cutting edge research grade. At
| that point someone who doesn't have bias and is willing to
| search for a reasonable looking approximation to the problem
| and try a canned solution may actually be an optimal
| candidate.
| 0cf8612b2e1e wrote:
| Considering the number of problems that could be plugged
| into a random forest with good results, data proficiency
| seems more important than strong ML experience.
| uoaei wrote:
| Depends heavily on the application once you get to more
| specialized domains.
|
| I wish there was an easier way to label roles differently
| based on when you just need to throw X or Y model at some
| chunk of data and when more specialized modeling is
| required. Previously it was roughly delineated by "data
| science" vs "ML" roles but the recent AI thing has really
| messed with this.
| htrp wrote:
| >Just try to find someone with a burning ambition to learn.
| That seems like the key to get someone capable in the long
| run. If they point out something beyond Kaggle that makes you
| think, pay attention to that feeling -- it means they're in
| it for more than the money.
|
| If you're teaching them, you shouldn't be paying them at the
| AI expert rate.
| outside1234 wrote:
| But we do this in software engineering all of the time, why
| is AI different?
| __loam wrote:
| Corporations love people with experience but they don't
| want to actually invest in creating those people. If
| nobody is supposed to hire people who have only taken
| classes or done tutorials, how do you actually get people
| who have that experience? Or are these guys expecting us
| to bootstrap our own PhD before they deign to speak to
| us?
| chinchilla2020 wrote:
| This reminds me of when I started learning spark (back in the
| dinosaur days). It was considered this cutting edge
| 'advanced' technology that only the top tier of 10x engineers
| knew how to implement. The documentation was crap and there
| were not many tutorials so it took forever to learn.
|
| These days people can get an excellent introductory class to
| spark and be just as good as I've ever been at it. I wouldn't
| call them 'charlatans' like the poster above did. It's just
| that the libraries used to implement spark have been
| abstracted and people learn it faster.
|
| That's just how it goes in tech. Anyone who wants to learn is
| a treated like a poser. We over-index on academic credentials
| which are really not indicative of actual hands-on
| engineering ability.
|
| PS. There are no AI/ML experts. There are LLM experts,
| prediction model experts, regression experts, image
| recognition experts.... If you are hiring a 'AI/ML expert',
| you have no idea what you are hiring.
| swatcoder wrote:
| If you can make do with generalist techies who can ramp up in
| a few weeks, you probably don't need to be paying them
| $500k-$1M TCO. They're just a new technician.
|
| But that doesn't mean that having people with actual
| research/depthful expertise aren't essential and hard to find
| amongst the noise.
|
| The person you responded to is talking about would-be
| technicians applying for researcher roles. That happens in
| tech booms and opens amazing doors for lucky smart people,
| but it's also a huge PITA for hiring managers to deal with.
| garba_dlm wrote:
| > it means they're in it for more than the money.
|
| but isn't ALL of 'private' industry (i.e. not academia) in
| anything at all ONLY for the money?
| softwaredoug wrote:
| Reminds me of the software hiring market during the dotcom
| boom.
|
| I think the hype on the field and the shitty candidate pool go
| hand in hand. The shitty candidate pool will groupthink / cargo
| cult the space without much critical thinking. The groupthink /
| hype will cause people to jump into the field who don't have
| any business being in the field.
| TrackerFF wrote:
| "The weirdest trend was all of the people who had done large AI
| projects on things that didn't need AI at all. We had people
| bragging about spending a year or more trying to get an AI
| model to do simple tasks that were easily solved
| deterministically with simple math, for example. There was a
| lot of AI-ification for the sake of using AI."
|
| I've seen two variants of this
|
| 1) People that have worked for traditional (as in non-tech)
| companies, where there's been a huge push for digitalization
| and "AI". These things come from the very top, and you don't
| really have much say. I've been there myself.
|
| The upper echelon wants "AI" so that they can tick off boxes to
| the board of directors. With these folks, its all about
| managing expectations - but frankly, they don't care if you
| implement a simple regression model, or spend a fortune on
| overkill models. The most important part is that you've brought
| "AI" to the company.
|
| 2) The people that want to pad their resumes. There's no need,
| no push, but no-one is stopping you. You can add "designed and
| implemented AI products to the business operation blablabla" to
| your CV.
|
| These days, I've seen and experienced 1) an awful lot. It's all
| about keeping up with the joneses.
| haltist wrote:
| What's hard about AI that requires special expertise? In many
| ways it is much simpler than regular software engineering
| because the conceptual landscape in AI is much simpler. Every
| AI framework offers the same conceptual primitives and even
| deployment targets whereas most web frameworks have entirely
| different conceptions of something as simple as MVC so knowing
| one framework isn't very useful for learning and understanding
| another one but if you know how to use PyTorch then you can
| very easily transfer that knowledge to another framework like
| Tensorflow or Jax.
|
| It should be possible for a competent software engineer to get
| up to speed in AI in less than 6 months and much of that time
| can be on the job itself.
| Jensson wrote:
| AI is much harder if you need competitive results, and if you
| don't need competitive results you don't need to hire a
| dedicated AI person. Just feed data into some library which
| is typical software engineering and doesn't have anything to
| do with AI.
| haltist wrote:
| The only metric that matters for a business is whatever
| helps their bottom line. No one really cares about
| competitive results if they can just fine tune some open
| source model on their own data set and get good business
| outcomes. So if there is good data and access to compute
| infrastructure to train and fine tune some open source
| model then the only obstruction to figuring out if AI works
| for the business or not is just a matter of setting up the
| training and deployment pipeline. That requires some
| expertise but that can be learned on the job as well or
| from any number of freely available tutorials.
|
| I don't think AI is hard to learn. The fundamentals are
| extremely simple and a competent software engineer can
| learn all the required concepts in a few months. It's
| easier if you already have a background in mathematics but
| not required. If you can write software then you can learn
| how to write differentiable tensor programs with any of the
| AI frameworks.
| Jensson wrote:
| Yes, and those businesses don't need to hire an AI
| person. This topic is AI research jobs, not for people
| who sometimes has to call an ML library once in a while
| in their normal software job.
|
| Edit: You asked what it is about these jobs that requires
| expertise. I answered: it requires expertise to create
| competitive models. So companies that need competitive
| models requires expertise.
| haltist wrote:
| Do you build competitive AI models?
| Jensson wrote:
| I worked on AI at Google, some would say Google isn't
| competitive in the space but at least they try to be and
| their business model depends on it.
|
| Edit: Why do you ask? I don't see why it is relevant for
| the discussion.
| haltist wrote:
| HN is often full of abstract argumentation so it helps to
| know if someone has actual experience doing something
| instead of just pontificating about it on an internet
| forum.
| Jensson wrote:
| I thought what I said was common knowledge on HN, it was
| last time I was in one of these discussions a few years
| ago. But something seems to have changed, I guess the
| "use ml library" jobs drowned out the others by now and
| that colored these discussions.
| haltist wrote:
| People come and go so I don't know how much can be
| assumed to be common knowledge but what changed is that
| big enterprises figured out that ML/AI can now be applied
| in their business contexts with low enough cost to
| justify the investment to shareholders without anyone
| getting fired if things don't work out as expected. Every
| business has data that can be turned into profits and
| investing in AI is perceived to be a good way to do that
| now.
| Jensson wrote:
| Those jobs has been on the rise for over a decade now, it
| was the majority of people talking a few years ago as
| well, but at least there was more awareness of the
| different kinds of jobs out there.
| belval wrote:
| > What's hard about AI that requires special expertise?
|
| AI is ill-defined so the premise of your comment makes it
| difficult to answer. For small well-known tasks (image
| classification, object detection, sentiment detection) that
| is train-once on a single dataset and deploy-once what you
| are saying is true, but for more complex products there is a
| lot of arcane knowledge that can go in
| training/deploying/maintaining a model.
|
| On the training side, you need to be able to define the
| correct metrics, identify bottlenecks in your dataloader,
| scale to multiple nodes (which is itself a sub-field because
| distributing a model is not simple) and run evaluation.
| Throughout the whole thing you have to implement proper
| dataset versioning (otherwise your evaluation results won't
| be comparable) and store it in a way that has enough
| throughput to not bottleneck your training without
| bankrupting the company (images and videos are not small).
|
| Finally you have a trained model that needs to be deployed,
| GPU time is expensive so you need to know about compilation
| techniques/operator fusing, quantization and you need to be
| able to scale. The requirements to do that are complex
| because the input data is not always just text.
|
| So yes all the above (and a lot more) require specific
| expertise.
| haltist wrote:
| How long would it take for someone to learn all that?
| skirmish wrote:
| IMO you can only learn al that by doing a few successful
| ML projects end-to-end. So, a few years?
| haltist wrote:
| Not that long then, especially if someone was motivated
| enough to complete the projects as quickly as possible.
| iaw wrote:
| Is this a catch-22 then or is there a rational course to
| self-study into the field for those that are competent?
| skirmish wrote:
| Well, these were senior level skills, a person who can
| drive and complete a project. I don't know how you could
| become senior via self-study and without practical hands-
| on experience on a project (working with and learning
| from somebody with experience).
| belval wrote:
| As with most topics in software engineering I'd say you
| will be have to keep learning as you go. They keep coming
| out with larger models that require fancier parallelism
| and faster data pipelines. Nvidia comes out with a new
| thing to accelerate inference every year. Want to use
| something else than Nvidia? Now you need to learn TPU,
| Trainium, Meta Accelerator (whatever its name is).
| rg111 wrote:
| 2-3 years of full time or near full time study.
|
| I know cause I did it.
|
| And I knew the math beforehand. I was a Physics major in
| college with a CS minor.
| kriro wrote:
| I'd at least debate if it's much harder to learn a new web
| framework and it's concepts or whatever is required to solve
| the ML tasks at a company. If you know how
| database+frontend+backend work (and are already used to
| HTML/CSS/SQL//JS+another language), you can also on the job
| learn a new framework.
|
| Knowing the library is the least hard part about ML work just
| like knowing the web framework is the least hard part about
| webdev (both imo). It's much more important to understand the
| actual problem domain and data and get a smooth data pipeline
| up and running.
|
| Scaling, optimizing inference, squeezing out better
| performance and annoying labeling. There's a pretty solid gap
| from applying some framework to a preexisting and never
| changing dataset vs. curating said dataset in a changing
| environment. And if we're talking about RL and not just
| supervised/unsupervised then building a suitable training
| environment etc. also become quite interesting.
|
| If someone asked me "what's so hard about webdev" my answer
| would be similar btw...it's fairly easy to set up a
| reasonably complicated "hello world" project in any given
| framework but it gets a lot harder when real world issues
| like different auth worklflows, security, scaling and
| handling database migrations etc. enter the picture.
| haltist wrote:
| These are good points to consider.
| avn2109 wrote:
| > "What's hard about AI that requires special expertise?"
|
| Several years ago on HN there was a blog post which
| (attempted to) answer this question in detail, and I have
| been unsuccessfully trying to find it for a long time. The
| extra facts I can remember about it are:
|
| * It was by a fairly well known academic or industry
| researcher
|
| * It had reddish graphics showing slices of the problem
| domain stacking up like slices of bread
|
| * It was on HN, either as a submission or in the comments,
| between 2016 and 2018.
|
| If anybody knows the URL to this post, I would be stoked!
| screye wrote:
| Becoming an ML engineer is about 6 months of work for a
| competent backend engineer.
|
| But becoming an X-Scientist (Data/Applied/Applied Research)
| is a whole different skill set. Now, this kind of role only
| exists in a proper ML company. But, just acquiring the
| Statistics & Linear Algebra 201 level intuition is about 6
| months of fulltime study in its own right. You also need to
| have deep skills in one of the Tabular/Vision/NLP/Robotics
| areas and get hired into a role accordingly. Usually 1 year
| intensive masters level is good enough to get your foot in
| the door, with the more prestigious roles needing about 2
| years of intensive work with some track record of State-of-
| the-art results on 1 occasion.
|
| Then you have proper researchers, and that might be the most
| impossible to get in field right now. I know kids who have
| only done hardcore ML since high school, who are entering the
| industry after their masters or PhD. I would not want to be
| an entry level researcher right now. You need to have
| undergrad math-CS dual major level skills just to get
| started. They're expected to have delivered state-of-the-art
| results a few times just to be called for an interview. I'd
| say you need at least 3 years of fulltime effort if you want
| to pivot into this field from SWE.
| haltist wrote:
| Good to know.
| rg111 wrote:
| If your job is only calling the APIs' .fit() method, then
| that is not a job at all.
|
| If something is already done, i.e. a model is available for
| your exact use case (which is never), then for using and
| deploying that can be done by a good SWE and any ML/AI
| specialist is not needed at all.
|
| To solve any real problem that is novel, you need to know a
| lot of things. You need to be on top the progress made by
| reading papers and be a good enough engineer to implement the
| ideas that you are going to have iff you are creative/a good
| problem solver.
|
| And to read those papers you need to have solid college level
| Calculus and Stats.
|
| If this is so easy, then why don't you do it, and get a job
| at OpenAI/Tesla/etc?
| haltist wrote:
| It's a matter of opportunity cost. I don't think working in
| AI would be the best use of my time so I don't work at
| OpenAI/Tesla/etc.
| light_hue_1 wrote:
| There's no way even the smartest hard working expert engineer
| will be competent in AI in 6 months.
|
| I've been in industry and now I do research at a top
| university. I hand pick the best people from all over the
| world to be part of my group. They need years under expert
| guidance, with a lot of reading that's largely unproductive,
| while being surrounded by others doing the same, in order to
| become competent.
|
| Writing code is easy. You can learn to use any API in a
| weekend. That's not what is hard.
|
| What's hard is, what do you do when things don't work. Fine,
| you tried the top 5 models. They're all ok, but your business
| requirements need much higher reliability. What do you do
| now?
|
| This isn't research. But you need a huge amount of experience
| to understand what you can and cannot do, how to define a
| problem in a way that is tractable, what problems to avoid
| and how to avoid them, what approaches cannot possibly work,
| how to tweak and endless list of parameters, how to know if
| your model could work if you spent another 100k of compute on
| it or 100k of data collection, etc.
|
| This is like saying you can learn to give people medical
| advice in 6 months. Sure, when things are going well, you
| could handle many patients with a Google search. But the
| problem is what happens when things go badly.
| MattGaiser wrote:
| Hasn't that just been the tech market ever since software dev
| appeared on the lists of best paid low stress jobs?
| notsurenymore wrote:
| I think it's not even about low stress, but low barrier to
| entry. There are plenty of things I'd rather be doing than
| software development (in fact I never planned on going into
| this field professionally), but I just can't.
|
| I'm also not surprised by the " _The number of candidates
| applying with claimed ML /AI experience who haven't done
| anything more than follow online tutorials is wild_". Just go
| look at any Ask HN thread about "how do I get into ML/AI".
| This is pretty typical advice. Hell it's pretty typical
| advice given to people asking how to get into any domain. Now
| sure we'll how it works outside of bog standard web
| development though.
| UncleOxidant wrote:
| > The number of candidates applying with claimed ML/AI
| experience who haven't done anything more than follow online
| tutorials is wild.
|
| Sure, I get this, but I suspect that the number of people who
| have actual ML/AI experience is pretty small given that the
| field is nascent. If you really want to hire people to do this
| kind of work you're going to need to go with people who have
| done the online tutorials, read the papers, have an interest,
| etc. Yes, once in a while you're going to find someone who has
| actual solid ML experience, but you're also going to have to
| pay them a lot. That's just how things work in a field like
| this that's growing rapidly.
| archero wrote:
| I'm genuinely curious, what is your expectation of candidates
| looking to get into ML at the entry level?
|
| You seem to look down on those who have
|
| 1) learned from online courses
|
| or
|
| 2) used AI on tasks that don't require it
|
| Isn't this a bit contradictory? Or you expect candidates to
| have found a completely novel usecase for AI on their own?
|
| I understand that most ML roles prefer a master's degree or
| PhD, but from my experience most of the master's degrees in ML
| being offered right now were spawned from all the "AI hype".
| That is to say, they may not include a lot of core ML courses
| and probably are not a significantly better signal of a
| candidate's qualifications than some of the good online courses
| out there.
|
| So what does that leave, only those with a PhD? I think it's
| unreasonable that someone should need that many years of formal
| education to get an entry level position. Maybe I'm missing
| something, but I'm really wondering, what do you expect from
| candidates? I think a few years of professional software
| engineering experience with some demonstrated interest in AI
| via online courses and personal projects should be enough.
| michaelt wrote:
| It sounds like Aurornis was not, in fact, trying to hire
| people at the entry level.
|
| Most companies doing regular, non-ML development hire a mix
| of junior and experienced engineers, with the latter
| providing code reviews, mentorship and architectural advice
| alongside normal programming duties.
|
| It's understandable that someone kicking off a new ML project
| would _hope_ to get the experienced hires on board first.
|
| But there are a lot more junior people on the market than
| senior people right now - as is the nature of a fast growing
| market.
| archero wrote:
| Ok, that makes sense.
|
| I agree, it's problematic that there are so many more
| juniors than seniors in the industry right now. I feel like
| many juniors are being left without mentorship, and then it
| becomes much harder for them to grow and eventually become
| qualified for senior roles. So that could help explain why
| many candidates seem so weak, alongside with all the recent
| hype.
|
| I guess eventually the market will cool off and the hype
| will die down since this stuff seems to be cyclical, and
| the junior engineers who are determined enough to stick it
| out and seek out mentorship will be able to grow and become
| seniors.
|
| But it definitely seems like the number of seniors is a
| bottleneck for talent across the industry.
| lawlessone wrote:
| >We had people bragging about spending a year or more trying to
| get an AI model to do simple tasks that were easily solved
| deterministically with simple math, for example.
|
| TBF theres whole companies doing this. It's a good way to learn
| too, as you have existing solutions to compare yourself too.
| BeetleB wrote:
| A former colleague of mine (SW guy) took Andrew's Coursera
| course, downloaded some Kaggle sets, fiddled with them, and put
| his Jupyter notebooks online. He learned the lingo of deep
| learning (no experience in them, though). Then he hit the
| interview circuit.
|
| Got a senior ML position in a well known Fortune 500 company.
| Senior enough that he sets his goals - no one gives him work to
| do. He just goes around asking for data and does analyses. When
| he left our team he told me "Now that I have this opportunity,
| I can actually _really_ learn ML instead of faking it. "
|
| If you think that's bad, you should hear the stories he tells
| at that company. Since senior leadership knows nothing about ML
| practices, practices are sloppy to get impressive numbers.
| Things like reporting quality based on performance on
| _training_ data. And when going from a 3% to a 6% prediction
| success rate, they boast about "doubling the performance".
|
| He eventually left for another company because it was harder to
| compete against bigger charlatans than he was.
| rg111 wrote:
| > _" took Andrew's Coursera course"_
|
| If he really did take those and did all the assignments
| himself and understood all the concepts, that still puts him
| at least in the 95th percentile among ML job seekers.
| CamperBob2 wrote:
| _If you think that 's bad_
|
| (Shrug) I don't. Hustle gets rewarded, as usual. Sounds like
| he contributed at least as much value as he captured.
| confidantlake wrote:
| I don't have any ML experience but I don't see what is wrong
| with it. To me it seems like the equivalent of someone self
| teaching software development. As long as they are interested
| and doing a good job there background shouldn't matter much.
| i1856511 wrote:
| I have a background in computational linguistics from a good
| university, and then I got sidetracked by life for the last
| decade. What real experience did you look for that was a good
| signal?
| klyrs wrote:
| > The weirdest trend was all of the people who had done large
| AI projects on things that didn't need AI at all.
|
| Yeah this is a major phenomenon. Everybody's putting "ai"
| stickers on everything. So the job market screams "we need ai
| experts!" in numbers far exceeding the supply of ai experts,
| because it was a tiny niche until a couple years ago. Industry
| asks for garbage, industry gets garbage.
| paxys wrote:
| This isn't unique to AI. Post any programming job and something
| like 50-80% of applicants with seemingly perfect resumes won't
| be able to pass a FizzBuzz test.
| screye wrote:
| It is getting doubly weird with the LLM/Diffusion explosion
| over the last year.
|
| The applied research ML role has evolved from being a
| computational math role to a Pytorch role to a 'informed throw
| things at the wall' role.
|
| I went from reading textbooks (Murphy, Ian Goodfellow, Bishop)
| to watching curated NIPS talks to reading Axriv papers to
| literally trawling random discord channels and subreddits to
| get a few month leg up on anyone in research. Recently, a paper
| formally cited /r/localllama for their core idea.
|
| > follow online tutorials
|
| The Open Source movement moves so quickly, that running
| someone's collab notebook is the way to be at the cutting edge
| of research. The entire agents, task planning and meta-
| prompting field was invented in random forums.
|
| ________________
|
| This is mostly relevant to the NLP/Vision world......but take a
| break for 1-2 years, and your entire skill set is obsolete.
| breakds wrote:
| I had two successful hires who just graduate from college, with
| no machine learning experience (major in accounting and civil
| engineering). With 3 months training by working on real world
| projects, they become quite fine machine learning engineers.
|
| You probably do not need AI experts if you just need good
| machine learning engineer to build models to solve problems.
| akomtu wrote:
| What motivates you and other ML researchers to do this work?
| What's the end goal and why do you want it?
| jsight wrote:
| So having no experience is bad, but also going out of their way
| to get experience is also bad? Isn't this presenting a bit of a
| no-win scenarios?
|
| Then again, that seems to be common with the job market.
| benreesman wrote:
| I've done heavy infra, serious model and feature engineering,
| or both on all of FB Ads and IG organic before 2019, did a
| startup on attention/transformer models at the beginning of
| 2019, and worked in extreme multi-class settings in
| bioinformatics this year.
|
| And out of all the nightmare "we have so many qualified
| candidates we can't even do price discovery" conversations in
| 2023, the ML ones have been the worst.
|
| If you're running a serious shop that isn't screwing around and
| you're having trouble finding tenured pros who aren't screwing
| around, email me! :)
| dwroberts wrote:
| > All of Google DeepMind's headcount is in the first two
| categories, with most of it being in the first.
|
| Does that mean that DM now does _no_ fundamental research - or
| does it still happen and it has simply been rebranded /hidden
| away?
| aabhay wrote:
| In the last year, they have developed what they call a
| "product" focus. But they still do more basic research, good
| luck getting a GPU allocation though.
| agomez314 wrote:
| Jeez it's kind of amazing to hear the kind of treatment you get
| if you're lucky enough to be an AI researcher. Being in the right
| industry in the right place seems to trump everything else.
|
| I'd be happy just being a cog in the machine, work 9-5, and get
| to have an upper middle class lifestyle with my family the rest
| of the time. That's probs better than what 95% of people (in the
| US) get to experience.
| npalli wrote:
| Good chance AI has winner-take-all dynamics. So while the field
| itself might be very hot and valuable, only a few will make
| massive bank and rest will get nothing. Like trying to be a
| basketball or soccer star, much demand and prestige but the
| average joe is not making millions or on TV.
| aabhay wrote:
| Hard disagree. My guess is that AI is actually a race-to-the-
| bottom dynamic. Given the competition across all/every FAANG
| and tons of startups, my guess is we'll have a wide range of
| options for APIs across clouds and providers. On the consumer
| side we'll have a range of options for chatbots, API
| integrations, and more.
|
| For most use cases of AI, there is a ceiling to how intelligent
| it needs to be. I am guessing we'll be selecting from dozens of
| models based on various sizes, context lengths, etc. Just like
| we right-size VMs in the cloud.
| vagab0nd wrote:
| "Race-to-the-bottom" might lead to "winner-take-all"?
| Eventually the profit margin is so thin that only the biggest
| companies can survive.
|
| In other words, once you have a "race-to-the-bottom"
| situation, it's hard for newcomers to get in the game.
| waveBidder wrote:
| ml is entirely dependent on your access to relevant data,
| which itself has strong network effects.
| aabhay wrote:
| Data access has weaker network effects than you would
| expect. Generated chats / outputs are rarely good enough as
| training data, the "best"/"cleanest" data is still expert-
| created.
| lawlessone wrote:
| The below writing is just my opinion ,anecdotal and sour grapes.
|
| Have been interested in this stuff for years. Did my CS project
| with NN just before everyone started using GPU's ,and a short DS
| course more recently. Seeing all the marketing people move into
| space with their prompt cheat sheets on LinkedIn while many tech
| people are ,ironically, locked out by blackbox recruitment
| algorithms is maddening.(This particular problem goes far beyond
| tech jobs though).
|
| Some also seem to be mixing up DS and DE roles a bit, one of the
| few times I got an interview I had end it and apologize as what
| they were looking for was a data engineer.
|
| Another was listed as a machine learning role ,when I got the
| offer it was travelling tech support and paid less. With the
| promise of undefined ML work later.
|
| Some companies are just tacking irrelevant ML and AI stuff onto
| job descriptions.
|
| Also so many live coding tests , and that one weird recruiter
| asking about "skeletons in closets"
| rg111 wrote:
| > I got an interview I had end it and apologize as what they
| were looking for was a data engineer.
|
| 100% happened to me once. Wasted hours of my time.
|
| > Some companies are just tacking irrelevant ML and AI stuff
| onto job descriptions.
|
| Some of them do this deliberately. I have seen this practice in
| companies targeting junior roles and fresh out of college
| grads. They hire them with shit pay and promise them ML
| experience, and then make them do non ML stuff.
| lawlessone wrote:
| Luckily in my case it was very short. The first tech question
| they asked me was about the how to move all a companies data
| from old gov systems to a data lake. We both got a quick
| lesson really. All polite.
|
| The second bait and switch example though went on a lot
| longer. I had an off feeling about it from the first call.
|
| One guy on call was stifling a laugh the whole time.
|
| They made sure to emphasize they we're offering me a lot of
| experience, doing me favor essentially.
|
| When they gave me the offer they also requested I send them
| over a professional photograph of myself. Maybe that's normal
| in some countries but to me it was the red flag that finally
| made me notice all the other red flags.
| VirusNewbie wrote:
| I had the opposite happen at a FAANG company. Did multiple
| rounds of coding and data architecture interviews only for the
| final round to be an ML round with me being quite surprised and
| having to tell them "well, i'll do my best, but I actually
| have...0 experience with AI/ML/DL"...
| spatalo wrote:
| Would you mind sharing your CV? As an AI researcher with PhD in
| Europe, I am applying to multiple post-grad positions but can't
| even get interviews. And I'm not even talking about the big
| companies.
| natolambert wrote:
| Author here, you can find more on my website:
| https://natolambert.com/cv Have been building RLHF systems at
| HuggingFace since ChatGPT, with some other experience before.
| sad_robot wrote:
| Is it a good idea to pivot into ML/AI?
|
| Would the bubble have burst before one can finish a PhD?
| zffr wrote:
| How do you personally define "good idea"?
|
| If this is about your financial outcome, make sure to factor in
| the opportunity cost of a PhD. It will require 5-7yrs where you
| will make very little money.
| dsign wrote:
| It must be hard to get those experts out of the bushes. First,
| there is the fact that not every expert out there is after the
| money or working 60 hours a week, or even 40, or work at the
| office. Second, there is this thing about how much of a shit-show
| any hiring process is...
| slowhadoken wrote:
| The definition for "AI" is being blurred, it's a black box
| buzzword lately. Bros will talk my ear off about AI and none of
| them know basic graph theory.
| constantly wrote:
| These concepts are orthogonal so that's probably expected.
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