[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. ___________________________________________________________________ (page generated 2023-10-12 21:00 UTC)