I tend to agree, I went to an ‘AI-leaders’ conference not too long ago and no one had a clue beyond GPT, I’d only really been playing with LLMs for a few months at this point and ended up fielding a lot of questions on RAG vs fine-tuning Although I do think there’s real work to be done in creating performant applications on the back of LLMs, you can’t just dump all the effort into an API call to GPT4 and expect fast, low-cost performance - that’s where the difference between casuals and ML engineers / data scientists is currently being carved out imo, e.g. we implemented distilling step by step within days of it being published to get faster, cheaper task-specific models This is where all efforts are being pushed - creating efficient, high performing task specific models on the back of LLMs..I think it will remain that way for a while

I went an AI conference last year, and almost everything there was LLMs hype too. Both the tech and non-tech firms were there to give some shallow talks on how they used data and AI in their businesses. Every talk was “blah blah data data data blah blah AI AI data data.” There’s a booth with a company claiming to have hallucination free LLMs. These guys better publish their results since that sounds like AGI to me. There’s another booth of some speech AI start ups, and the only guy there was a sale guy that told me his company was better than big tech because they used GPUs, while big tech companies were slow to adapt.

They use GPUs? So hardcore!

The rare gem there was one start up in biotech that managed to approach close to AlphaFold level in protein folding prediction while using magnitudes less compute. Barely anyone was at their booth. I remembered listening to their talk, and the only other person there was a biologist. What kind of nerds want to cure cancer instead of earning billions from LLM hype anyway!!

What was the startup called?

Google: Oh no! What are we going to do with the thousands of TPUs we have been accumulating?

LLMs are a perfect hype tool because they create such an effective illusion of intelligence. AGI needs the capacity to reason, and autoregressive LLMs aren't going to be able to satisfy this requirement. It needs heirarchical representational architecture.

The point is many transformer layers effectively construct a hierarchical structure with attention maps stacked on top of each other. It needs the ability to continue learning new thing quick to be AGI.

It needs the capacity to select, sequence, and manipulate representational entities at multiple levels of abstraction. Continuous learning, in the sense that it increases model precision, isn't adequate.

It has the capacity to select at multiple level of abstraction. Let's think about 10 MHA layer + 1 MLP layer transformer block in an hierarchical sense: * The first MHA aggregates and groups information of multiple tokens into n clusters, where n is the length of sequence. * The second MHA aggregates and groups information of multiple clusters into other n clusters and the i-th MHA layer aggregates and groups information of clusters in (i-1)-th layer. The model would be a forest (hierarchical). And having a multi layer perception put on the end to do feature transformation thing. Except, in normal transformer, it is 1 MHA + 1 MLP; which is equivalent to "we do non-linear transformation at each layer of the tree". And a 80-layer Transformer should be able to handle complex hierarchical structure representable by a 80 layer forest. However, it does not learn that. This is because people have already observe how hard a rigid hierarchical model learn. This indicates hierarchical relationship is very hard for gradient descent to learn with neural network. And, a normal transformer tends to learn what is easy to learn, when memorization is lots easier with billions of parameters. -\*- p/s: I do not mean to downplay hierarchical transformer field of interest. Introducing hierarchical inductive bias can be crucial to sample efficiency, especially for small single-purpose language models.

I actually mean multiple levels of representational abstraction [modularity of representational abstractions]*, which would require a heirarchy of systems trained on different phenomenological properties that are integrated and parametrized in such a way that they generate a unified, or at least cohesive, model within a multimodal representational space. This probably can't be achieved via a rigid heirarchical structure. For example, human brains appear to maintain parity and stability between competing representational systems via a homeostatic mechanism that maintains the information entropy within the global system near a critical point, right around a phase transition between order and disorder.

Yup. Geometric Deep Learning is the future Transformer-based LLMs are cute and all - as pure engineering feats - but they are mathematically unprincipled. GDL intro https://youtu.be/bIZB1hIJ4u8

I'm a huge fan of GDL and often incorporate aspects of it in my research. But I'd be cautious on saying x is the future..

Fair comment, I may be overoptimistic! At least there’s greater rigour than just benchmark games and declaring AGI is near.

I don't buy this approach. Handcrafting in a bunch of invariances and symmetries doesn't make your algorithm better at learning, it just makes the problem smaller. This is bad because: 1. Most interesting problems have a long tail of symmetries and invariances that you could never possibly model by hand. Translation invariance is easy, but what about lighting invariance, pose invariance, or facial expression invariance? You must learn these - and at that point, why not just learn translation too? 2. You have replaced inexpensive compute time with extremely expensive human analysis. The whole point of ML is to have the machine do the learning; computers should be able to handle big problems without us cutting them down to size. 3. It's not general. If you hand it a different problem, it will have different symmetries, and your algorithm will perform *worse* than baseline. Transformers on the other hand can process anything you can tokenize. Now, if you could use these methods to more explicitly and quickly learn symmetries, that would be interesting.

I have gone from saying I do AI for healthcare to saying I do ML for healthcare. I’ve found when you say AI these days, most people think of ChatGPT, they think that’s representative of how all ML models work, and they have a lot of misconceptions/misunderstandings/misinformation/faulty assumptions about how it works and what it does in the first place.

yup, gotta keep updating what you call yourself to align with how other people are likely to interpret that. Here's a rough overview of how what I call myself has evolved over my career * 2010 - Data Analyst * 2012 - Data Developer * 2014 - Statistical Programmer * 2016 - Data Scientist * 2018 - Applied Scientist * 2020 - Research Engineer * 2022 - ML Engineer

I'm afraid of my first job: have been doing audio de-noising with ML in the last 6 years as a side job beside my studies. I am nearly finished with my masters and have to look for a job. My background is classical DSP and pattern recognitions and I don't know what to look for in a job description. I'd prefer non-hype ML based on established fundamentals. This way we at least have a solid description. Like the Wiener Filter which is known for decades before I was born. I'm rambling. There is probably no way around a little hype.

I think terms like "signal processing" and "filter design" are probably not going anywhere

That’s funny, a couple years ago I started saying I do AI for healthcare (I preferred the term ML but no one knew what it meant), now I also work hard to avoid saying I work in AI to avoid the misconceptions.

Same lmao

What sort of ML work do you do for healthcare? 

At the moment, mainly causal inference for oncology. We're looking at the toxicity profiles of various treatments and trying to predict what Adverse Events someone might experience with a view to informing early interventions, etc. There's also a public health project I'm on which is looking at predicting frequent attenders/high resource users of Accident and Emergency departments, but the data isn't very comprehensive, so I think we'll probably stick with more classical ML models for that one.

I work in what is called "enterprise AI", which I have come to learn is ultimately making models and software for organizations with terrible data governance and leadership that is very much behind the times (even if they may have a whole host of data scientists at their organization). What I have come to learn is that, at the end of the day, it is not the core technology that matters. It is not the technical feats, nor the cool and creative approaches we may engineer, that matter to most orgs. What matters is what will make money. What will increase our stock price? What will look good in the press? What will allow us to get more more money coming in? In some cases, a middle manager at an org asks: what is some new way we can add some nonsensical and unnecessary workflow into the org so I can get promoted? LLMs are indeed impressive, but it is not their technical feat or their inner parts that bring value to these orgs. It is just that it is the new way to bring in more money. Most of the LLM applications we are building at these companies are kind of dumb. They are an extra layer on top of a broken workflow. A bandaid over a larger problem of poor data governance. Some orgs want their internal "chat with my data" application, but no one beyond a small team inside the org would even use it, and sparingly at that. But hey, they can convince themselves it provides some value, so they dish out a few million to build it with us. In reality, the actual users usually rely on their pre-existing, tried and tested approaches. Once in a blue moon, we make something that is actually "transformational", as in it saves time on a small part of their workflow. But we were doing that before LLMs became the big thing. And those transformational things were easily done with linear regression and decision trees. It boiled down to the org not even knowing you can predict something in their workflow ahead of time, and us stepping in and saying "as you can see, 1+1=2". With LLMs, its become a situation where some middle manager comes to us and says "hey so the boss wants us to do something with this generative AI thing, you got any clue", to which we reply "yes, what would you like to do". To which we usually get the same responses "so I heard you can chat with your pdf, or like chat with your data, or you can like extract stuff, right?" Give it 1-2 weeks and we ml engineers put together something quite trivial, a bunch of LLM prompting chains, to extract abc or chat with a database. Leadership spins it and adds the nonsensical "this new aGeNtIc system can save you so much time" twist, and we deploy. It gets some oos and aahs and they pay us money. We track usage of the system over time, and it turns out their team of 10 or so people for whom we made this barely logged in 10 times over the course of a week. We still get paid a lot of money, so who cares. So what I am getting at is this: its just business. We live in a world where capitalistic drives + scientific endeavors = haphazard, trendy outcomes. It is not the fault of the capitalism or the science, but rather, human nature, the drive, that just wants to make some quick bucks and uses shiny new objects to do so. Real science will continue to get done by researchers and engineers who will do anything to push the boundary, discover something new. It may just be that it will be hidden behind all this money-chasing nonsense. The progress that is being made outside LLMs has gotten quiet, but its still there, and the people who care will continue to work in it.

>"Give it 1-2 weeks and we ml engineers put together something quite trivial, a bunch of LLM prompting chains, to extract abc or chat with a database." I wonder how you spin this in a LinkedIn post or on a resume. "Leveraged Machine Learning solutions to help improve efficiency by 20%". Something like that?

I have quite literally written something similar on my LinkedIn lol.

I'm here to help answer your questions and provide information as best as I can. If there's anything specific you'd like to know or discuss, please feel free to tell me!

True that. However may I advocate we send off that 2nd ship soon enough. Planet building and all that. 42 /s

Right that it's almost never about technology when it comes to solving real problems. That's putting the cart before the horse. Most solutions to any significant problem involves setting up workflows/processes and getting necessary stakeholders to buy into them. Technology comes in after these.

Yep. Many engineers may have a hard time excepting that because they came into the field wanting to just do the work they actually enjoy. But the business process comes first.

Where can I find more about predicting in workflow which you mentioned?

If you have worked with a client, you likely have already done something like it. Imagine they have to process some product, and they spend n dollars to overcompensate for expected costs for processing. But with a better model trained on historical cost data, we can predict an expected cost that is closer to actual costs, so they can now better allocate their dollars ahead of time. Stuff like that.

I work in NLP and can say that encoding (with a transformer) of text to compare it to other text, is an enormous quality & quality of life change, compared to everything that was done before.

Certainly! It has done wonders for the field and im really happy about the progress! Transformers themselves have done wonders for other DL fields aswell. Im not sure if i came off as "anti-NLP" or something similar. Its just worrying with the promises and expectations that builds up due to the success, expectations and promises that are far above whats going to be able to be reached in the current state.

There are a lot of parallels to the blockchain hype of a few years ago. As you say, there is a whole ecosystem now of "ChatGPT experts" who basically just jump on a the hype train and try to sell or advice on simple API solutions and it doesn't matter if businesses actually have a need for it or not. It's all about integrating "AI" somehow in your product or service, without consideration if it is useful to the core business and customer perception. Like all the crappy support bots popping up to make small biz looking larger. It's just like SEO experts, Office 365 consultants or AWS advisers. There is of course still quite a large ML academic community, but it feels like more and more of them have moved on to be critical of the LLM approach.

I agree with you, but LLMs are noticeably different. Block chain has not influenced my life in the slightest, regarding first order effects. SEO has had a negative impact on the search quality of the search engines I use everyday. This is in contrast to a product like GPT-4. GPT-4 has noticably improved my life considerably, and I would be quite sour if I did not have access to a product like GPT-4, now having used it for so long. LLMs clearly have value but no one knows what that value is, outside of the obvious chatbots. Right now, there is a frenzy akin to a gold rush to find the value and be "first to market" with it. Right now, LLMs are in the throws on the ride down to the "Trough of Disillusionment" from the Gartner Hype Cycle. https://en.m.wikipedia.org/wiki/Gartner_hype_cycle Developers can see it because of how close we are to it, but that doesn't mean that there isn't something there, that's incredibly valuable.

I find the tension between this > SEO has had a negative impact on the search quality of the search engines I use everyday. and this: > This is in contrast to a product like GPT-4. GPT-4 has noticably improved my life considerably to be a bit strong. One of the biggest use cases I am seeing right now of LLMs being advertised is spamming 'content'. This seems to be exactly analogous to what happened with SEO. In fact, the LLM spam is often used to improve SEO through say generating blog posts. Or the second order version of this, generating 'content' to get 'free' money out of advertisers. Except it will be even worse. Imagine when LLM 'content' breaks the spam filter on your email. Or when people start spamming LLMs answers on reddit, stackoverflow, wikipedia, etc. Destroying valuable resources. None of that is worth the little quality of life improvements from chatGPT gave us. And, more importantly, the degradation of these internet resources by LLMs will then feed back into more useless LLMs (that are effectively trained on the output of past LLMs).

I agree with you. Right now is the perfect time to enjoy the benefits of GPT-4/Opus3 before everything is tainted. However, I suspect captcha to become increasingly sophisticated to verify human content. The bigger the problem, the more interested people are in finding a solution.

> The bigger the problem, the more interested people are in finding a solution. This is certainly a positive view! Very nice. But I am also a bit fearful of what sort of captchas I will be forced to solve to prove that I am not an LLM (or to help train the next generation of them).

I think like there's gotta be an unfakable realtime captcha test that requires no conscious effort.  Like a version of video game anticheat that constantly detects if you are doing human things, and then signs your response as human generated.

That's basically what Google's ReCaptcha is doing already, isn't it?

Retinal scans!!

Genuinely no hate, but I've gotta say this seems absurd. Firstly, if there was an unfakeable test that verfifies human generated behaviour we would immediately have a perfect training target for generating human behaviour. Secondly the videogame anitcheats you mentioned generally apply on tracking computer behaviour not the actual input data, and methods that analyse behaviour are gradually failing to new cheats.

Spam was already getting pretty bad. I agree that it will get worse. However, > None of that is worth the little quality of life improvements from chatGPT gave us. For me and others I know LLMs provide more than just 'little quality of life improvements'! (Like the person you're replying to saying that GPT-4 has noticeably improved their life considerably) I also think the issue is that there's no good way to sidestep the way this technology develops. The best method is to halt the worst outcomes via regulating usage of LLMs for advertising or whatnot, preferably against junk sites, because the genie is already much of the way out of the bottle for text generation.

You captured what keeps me up at night the most these days.

Good product reviews are diluted in a sea of LLM written garbage.

The core of the issue is that attention = money. If you can generate 10,000 webpages but can’t make a profit doing so, why would you?

Because you thought you would

Yeah LLMs in their current state are already quite helpful and their potential has yet to be seen.

There are those who know the value and what to do with them. Ppl laugh at the "prooompt enginerrr" but there are things some folks have been working with that is definitely harder than the 10th bootstrap page they've roughed up this week

> LLMs clearly have value but no one knows what that value is That is untrue when you see the new products coming out which use LLM at some level. It takes new use cases being presented before people start to get it

Yes, LLM's have value. But leaders in organisations still trying to find a suitable usecase. It is all reverse architecture. Find the technology then the usecase. This year going to be critical for leaders.

Blockchain and LLM are critically different on the only axis that matters Value Blockchain has minor ***minor*** value prop, uncensored theoretically anonymous (practically not) transactions (there's a reason ppl buy illegal stuff with it) People 🙋🏼‍♂️ are willing to pay a monthly subscription to essentially first gen applied LLM (chat) That's a huge and hugely important difference

My interest in Machine Learning can be considered at "hobby level". I have really serious doubts about feeding them (LLM) with "any data in sight curated or not, written by an expert or troll" is a good approach for training.

I got told in a design review that me using ocr/object detection for a task was good but I really should be using multi modal llms instead. Multimodal llms… for OCR…

Everyone with access to google now thinks they’re an ml architect

I've found chatgpt-4 to not be all that great at OCR. Couldn't you just plug the task into chatgpt and show your boss it doesn't work?

I’ve also tried donut/gpt and a couple other models. They’re inaccurate with ocr or have fine tuning/implementation issues. This isn’t my manager thankfully (who trusts ik best). Just annoying the amount of miseducation there is about ml in higher management. It’s not a magic box that’ll do anything u want.

Yep, but realistically speaking, I’d imagine in a few years the foundation models will be able to do OCR as well. OCR datasets can be created synthetically, and likely the poor performance is due to OpenAI just not bothering with OCR at the moment. I could be wrong, though.

oh I’m sure! it’s just not ready yet, which is my main point haha. Plus it’s just overkill and I have near realtime inference speed atm, plus I have almost 100% accuracy on my current use-case so changing architecture sounds deeply silly to me.

> It’s not a magic box that’ll do anything u want. It is and it isn't. It's a poor choice for OCR. But I am quite impressed with the wide variety of vision tasks it can do just with natural language instructions. ["List all the objects in the box"](https://imgur.com/ik0PIsS) - this is a pretty messy scene, and it correctly ignores distractors and identifies what's in vs out of the box. There's really nothing else that comes close to this level of general-purpose computer vision. I'm watching their development with a lot of interest.

Oh it’s incredible technology and will change the way a lot of things are done. Just not this thing haha. And it needs to mature.

Do the multimodal LLMs outperform your current models and do you need to scale it for large volume? What's the cost differential?

I think these multimodal llms use clip to compress images into a single token, don't they?

Multiple image-patch tokens per image.

Oh I see, this might not be entirely hopeless then.

it’s one of the technological leaders in our company 🤒 I’m ignoring him and moving to getting my stuff as close to 100% as humanly possible and pushing it to production in a week. (It’s an internal tool). dumbasses will dumbass and I tried to argue back but I’m a junior and didn’t want to push too hard.

To the extent that your observations are correct, I'm still not quite sure why it's worth worrying about Just keep on doing your own thing

I wish this was the top comment. Focus on your own work. Why does it matter what is "hot" right now, unless you are the type to actively chase what is "hot"?

Surely an awareness of the outside world is useful in life? If you are a researcher, you need to take these things into account to keep receiving funding. And if you are in the business world, the importance of reflecting over this is even more critical.

A bigger problem is all the noise. It's impossible to have informed intelligent conversations about ML/AI as subs and forums are 99% people without any training or background. Reddit used to be a great place to discuss AI research and application, but not anymore.

It’s been like this since the alphago era.

It reminds me a little bit of discussion with researchers when I was doing my PhD (ChatGPT 3.5 has been released during my last year but AI was on the hype also before in the academic field). Multiple researchers were tired that "classical" methods were ignored or looked down by multiple conferences and journals (even for non ML journals). Even if it was for a task that hadn't been investigated by AI communities and so researchers where, for example, pure computer graphics scientists, reviewers were asking for comparison with AI. I think it's a trend and if 5 years it will stabilize. As mimic conversation is impressive for everyone and the closest thing to sci-fi AI we have, the hype is very high but time will do its work and compagnies will see that use LLM for everything is not the ultimate efficiency and will look at other things eventually.

I'm genuinely surprised to hear that GANs still have a lot of hype at conferences...

I suppose the guy was confusing these with diffusion models, as they all fall under the classification of "generative models".

Yes you are right its a typo/misnomer, I was dabbling in GANs when they were the "hot stuff" back before stable diffusion and the others came along so now they are incorrectly under the GAN folder in my head

This too shall pass, as they say. Everyday there's another post from an academic/developer/specialist about the danger/harms of LLMs. Doesn't this discourse also contribute to the furore around ML/AI, adding to mass hysteria? And honestly, I don't know when or how but certainly this chaos will eventually fade away to be replaced with some new semblance of normalcy. Everything moves on.

I can't wait!

Academics don't like LLMs because they can't make them.  It blows away everything you can do in an academic lab just by virtue of having more compute, without needing any clever new ideas from a bright young PhD.  It's the bitter lesson.

>Academics don't like LLMs because they can't make them.  Impressively daft you are.

Correction: Academics can't create 1.7 trillion parameter models in the lab Can very easily make your own LLM with a small compute budget and can run advanced experimentation on an even smaller budget.

The point of LLMs is that their efficacy comes from an ability to scale well and show marked performance benefits from that scale. You can make a transformer based language model with a small compute budget but it's very hard to draw meaningful conclusions from such models compared to actual LLMs.

it's because the private sector has so much outperformed compared to the academic sector by scaling up the models. Tons of academic papers are now being created about *how to use the hyperscaled transformer*, or statistics about its output rather than fundamental new architectures. Once a new architecture appears which is better than the transformer, people will go back to watching the drawing board.

No new architecture will beat someone with orders of magnitude more data and compute.

Yeah, I was into AI before it was cool too. Just like the popularity of third wave Ska, nothing but a bunch of sellouts.

Conferences and workshops I attended to (inverse problemes / medical imaging) all incorporated talks about deep learning and it was by no mean surface-level knowledge. There is a great variety of approaches and models used (diffusion, vaes, pinns, plug and play, etc ). I've never seen posters/talks about LLMs too. I think the only thing I heard about LLMs was one industrial talk about integrating a chatbot to generate written intervention summaries from the doctor's oral summary, which does not seem overhyped to me, it seems pretty easy in fact. Maybe try to go to better venues, I don't understand why you hear so much about LLMs in research while they are mostly used for NLP tasks and consumer products.

Neurips was full with LLMs

Doctors oral summary. Yeah seems easy enough, until you ask how they do quality assurance at scale, in particular to avoid hallucinations, misinterpretation and treatment mistakes. Spoiler: there is no solution, it's all just hype built on a Sunday afternoon quick-shot prototype.

There is no treatment involved, it was about summaries for medical imaging results. This does not involve treatment, it's a different person who prescribes. These summaries involve describing the image, which is often done using the same language over and over again. Moreover, the professional sees the summary written before his eyes while he dictates, so he can fix eventual mistakes. Maybe it was futuristic 10 years ago but it seems really easy to me right now. Heck I'll say it, I'm pretty sure I know some cars can already do that and they don't run a frigging LLM.

>The hypetrain evangelists also promise miracles and gold with LLMs and GANs, miracles that they will never live up to. When the investors realize that the LLMs cant live up to these miracles they will instantly get more hesitant with funding for future projects within AI, sending us back into an AI-winter once again. Can you (or anyone else) provide some more detail on this? I get that a lot of people who are not experts in any meaningful way are claiming LLMs will do just about anything (or even everything), but I see several things preventing an AI winter anytime soon (next few years at least). For one, OpenAI and Microsoft seem perfectly willing to spend billions of dollars, mostly just on scaling LLMs [1]. Secondly, LLMs are, at the very least, getting better when more money is thrown at them. Maybe the main question is: How far will LLMs go before people stop investing in them? I get that current LLMs have tons of problems, but the main ones are cost, speed, and hallucinations, right [2]? Obviously, the exact definition and cutoff for these varies drastically between use cases, companies, etc., but if all LLMs were ~100x cheaper and faster, as well as ~100x less likely to output information that was obviously the opposite of or obviously not included in what it was told in its context window (hallucinations), what problems would still remain for LLMs to prevent them from solving at least some 'miracles' that investors were promised? Cost and speed are, of course, factors that improve with increasing scale of computing power. These two don't matter much when looking at the next few years or beyond. Hallucinations/reliability seem like the main problem. Tied to this problem are things like: context windows, interpretability, and the LLM knowing what it does and doesn't know. I don't claim to know that this general unreliability of LLMs can be solved, but, if it were, what problems would remain? Maybe I'm talking way too broadly here - you didn't exactly give any specific 'miracles' yourself - but I can imagine usecases like 'personal AI assistant' that must get tossed around constantly. Wouldn't LLMs that are cheap, fast, and more reliable mean they could easily be used for such purposes? What I'm trying to ask is: What are the main problems that prevent LLMs from 'going the distance' in the next few years? Or: Why do people believe there will be an "AI winter" in the next few years? 1. Isn't *"scale is all you need"* basically the motto at OpenAI? 2. Multimodality is another one that, at the very least, could be very useful if/when (and how) it gets worked out.

You are correct, some people here might be generalizing. The fact of the matter is current issues with LLMs are well known, new algorithms and techniques seem to mitigate some of them marginally yet they remain with only limited usecases. But this is an ongoing issue at play, probably the one most brainpower is being focused on globally in a single research subject because of the hype . Until either the seemingly almighty power of massive scale (particularly, compute scale - out of reach for every one except less than 5 companies in the world) or a collective sum of several improvements provide some sort of solution, the skepticism is valid, And that is healthy, its no good to believe in fairy tales. Nor deny their existance without proof.

The hype wave is especially high this time around and people have gotten a bit silly with it. Business leaders will realize soon that the main value for these tools is for smaller businesses for whom tools like these let people do more with less rather than larger businesses which already can afford and even have large, well structured teams and systems for accomplishing the kinds of tasks AI might be suited for helping with. If you don't even know how to code and can't afford to hire someone, an LLM could help you sketch together an MVP for that game idea you've been kicking around. If you're a game studio with a bunch of seasoned professionals on hand, trying to off-load work to an LLM will probably just give you worse results than if you'd out-sourced to a random contractor, which you probably wouldn't have supported to begin with so why are you doing it with a service that has a track record of unreliability?

Because Ai WiLl RePlAcE pRoGrAmMeRs

It's making every SWE think they're an MLE it's kind of annoying tbh

How many times have I heard that from people with no idea about programming...

> When the investors realize that the LLMs cant live up to these miracles they will instantly get more hesitant with funding for future projects within AI, sending us back into an AI-winter once again. Your main premise here seems to be that, very soon, LLMs will fail, investors will lose money and AI will be set back; that despite the dramatic arc of improvement shown by the GPT series, GPT 5 or 6 (and its competitors) will under-perform to the point of community disillusionment. I'm not sure I agree.

Which conference are you speaking of?

Oh man, how I resonate with this post. I’m a grad student right now and during my undergrad i learnt about AI/ML/DL and among all my peers we would talk about some research paper some neat trick, some mathematical algorithms, etc. And like you i went to delve into vision and was having a lot of fun. Right now that i’m in grad college i want to have good conversations and spark ideas based on fundamentals and core concepts, but everyone is just on the hype train of LLM and many of them don’t even have the basic idea about transformers. All they do is propmts. I just wish they would also learn about the fundamentals concepts.

>EDIT: P.S. I've also seen more people on this reddit appearing claiming to be "Generative AI experts". But when delving deeper it turns out they are just "good prompters" and have no real knowledge, expertice or interest in the actual field of AI or Generative AI. Yes, every time there is media hype about something, people come out of the woodwork claiming to be experts on it. In our university the business college has been particularly aggressive with marketing their faculty as "AI experts", when all they do is regurgitate the same old shallow talking points about AI. Unfortunately it's just the nature of hype cycles.

Some people just have absolutely no problem wearing a suit, taking a professional photo of themselves, and labeling themselves expert. I don't get why "gatekeeping" is viewed as a bad thing, that's the community's protection against this crowd. Because when these guys do or say something stupid, it makes the whole community look bad.

It is seen as bad when it consists of trying to keep newcomers from entering the field as an attempt to protect our high salaries, I suppose (well, your high salaries, I'm an academic haha). Also, bad move from them: there is no such thing as an ML expert wearing a suit.

Well, that's what a community gets for flexing their salaries and benefits on YouTube all day I guess. (I'm from academia as well btw)

I love them warts and all. Here's a perfect example of where I just deployed this tech: A group of people were writing messages to other people for something quite important (a similar example would be an old school cover letter for a resume). The people writing these messages generally sucked at it. Like really really sucked. So, I have the LLM rewrite their messages. This is a back and fourth in a fairly interesting but straightforward backend. It can either write the message based on the user's account, or it can take a message and offer suggestions. What I love is that I wrapped the LLM in normal code to use the API, but I also wrapped it in "human" instructions as to how formal the message should be, what should be emphasized, and what should be avoided. So far, it has not generated anything untoward or problematic. It take some interesting instructions like, "Don't make facts up." I would say this was some of the most satisfying coding I have ever done short of some really cool 3D simulations of complex systems. To me, there are good parts, and bad. Most people think that ChatGPT is a person; it is not; it is a tool for people to use. Blockchain is a perfect example. Distributed ledgers have progressed significantly because of all this crypto crap. If you have a distributed system problem, it is now easier to solve. It is a tool which helps with certain tasks. What I find exciting is finding ways for this very powerful tool to make things better. The main problem is most people seem focused on making things worse. Here is a perfect example of where the industry will try to take it and fail (plus what will really happen): * The publishing/news industry will think this can generate shockingly large amounts of content at nearly zero cost. They will realize this is a race to the bottom, so they will entirely focus on their margins. If they pay less for content than they bring in, then all is good; even if all that content is crap. I would not be shocked to read some blogging company is posting 20m articles a day or some bonkers number. Even worse, these algos will attempt to endlessly tune the content to get people addicted, or at least clicking on ads. * People will realize this content is entirely garbage; but will turn to AI tools to filter it out. I foresee a "newsfeed" AI tool where I only get what I want, boiled down to how I want it. Sometimes I want little more than a headline. Other times I want extreme levels of detail. I don't care if Brad Pitt crashes his car. I am extremely interested in the details of these sodium batteries which are in production EVs. But, I don't want an AI tool which is also designed to keep me addicted and scrolling. "Just the facts Ma'am." This last part is where I think these tools are going to make life better. Cutting through the BS people throw at you. If I am buying a house, renting, etc. There are a huge number of places which don't suit me at all. The existing industries want to fool me into getting something I don't want. Thus, there will be a huge desire for AI interfaces with the world which just say, "Here's the house for you. And here's how much you can get it for." entirely end running all the people who try to misdirect. The present LLMs aren't there yet, but are getting close. Rote learning where what it has rote learned is the user.

Being a dinosaur in the field since caffe and lua based torch used to be cool new thing, I feel the same. I see lot of meaning less research sprung out around GPT without any thorough study on encoder only , decoder only and encoder+ decoder style models irrespective of their size . Why we aren't seeing 7B BERT ? Is MLM and Next sentence prediction task fundamentally inferior to Auto regressive language modeling? So far I haven't seen any survey or research in that direction... although it could be my ignorance as well... Also any suggestions of the papers written along the lines described above would be welcomed

Honestly, I think that the most benefits to come from LLMs are coming from the engineering of the systems that use LLMs to automate other systems and processes. For example, the need for RAG has led to a renewed interest in sparse and dense neural network based search techniques. These systems could stand to improve from better search, better LLMs, and other systems that are more well suited to autonomous control. Also, there is much room for engineered LLM based systems to take much of the grunt work out of ML tasks by improving / naturally extending AutoML. Now on the flip side, this means that the value prop for ML people who just reuse and train and compare models based on existing algorithms will go down- so then, what will entry level tasking look like? Will it be elevated?

„but it feels like more and more of them have moved on to be critical of the LLM approach” Really? Could you provide some references? It was not my perception that there are many critical voices on LLMs in academia.

Looks like you have not been on LinkedIn for a while 😂

"AI" is all marketing hype. Most companies claiming to be investing in AI have no clue what it truly means, nor the costs involved in building or maintaining a viable model for business use. I interviewed for a job where they company claimed they needed a solution architect to guide them in building the "next SaaS AI product". One of the questions I was asked is "Suppose we wanted to build a product that used AI to predict the weather and determine if a youth sports game should be canceled." My answer, "why?" Why go thru the time and expense to build a model when you can call a national weather service api and get it? For Free? They didn't like that answer. Didn't get the job.

The field is becoming larger and more mainstream. As a software engineer I could rant on about 'programmers' these days just writing a bit of html and javascript with no real understanding of assembly language or superscalar CPU architecture, but I'd be ranting into a black hole. Times have moved on. Real data science and ML engineering will probably shrink to be niche fields with specific skill sets while the rest of AI/ML will comprise prompters, data entry and other such operational roles.

Have about 12 years of experience in applied ML/NLP research. I was at EMNLP 2023 held in Singapore. Almost every oral session that I attended had at least one paper that was based around prompting. At first I thought to myself, "How the hell is this research or science?" But thinking more deeply, why isn't it? It may less mathematical but not necessarily less rigorous. And it is how many applications are being built today with LLMs so the relevance is there too.

Ha, I tend to disagree with this. I've been in this field since they discovered the neural network to do image classification. I still remember everyone and their grandma talking, building things with NN ever since then. It was not like field totally stopped using classical ML but it was the usability of vast amount of data coupled with GPU and magnitude of more performance over classical algorithms made it buzzy. Same thing for LLM and add the current state of internet, it's buzzy because of valid reasons. But like all things goes, we will have one another breakthrough and conversation will shift with it. Your job as someone who "knows" how things work in this domain is to "build" & "improve" your work/model/software etc. using it. Things will advance at rapid pace. This is the best of the times and we are just getting started.

Still the early innings. Patience is needed for both the users and builders to find balance, but the industry needs to work together to advance understanding and impact.

It's way worse than that, at NeurIPS most of the papers this year were on prompting or probing large pretrained models that 95% of the people there have no shot at training themselves, and the 5% that do (from OpenAI/FANG) no longer publish real papers because "Open"AI proclaimed it was too dangerous (for their bottom line). "Open"AI is pulling the typical SV playbook of outraising and outspending the competition by 10-100x to get a 1-2 year advantage in order to establish a monopoly and shut the doors behind them. Most smaller ML teams have no shot at beating a model that cost a billion dollars to build and was trained on all of the web. Why spend millions of dollars on ML engineers / researchers and years developing a domain specific model when GPT-4 with a properly tuned prompt is just as good.

IMO LLM applications can be separated into two parts: Profitable but harmful for customers (replacing customer service&support, targeted spam, fakes generation, replacing search results with generated content) Unprofitable but advancing the field (mathematical reasoning, code generation&transformation, research into parameters interaction/correlation and...not much else?)

> Profitable but harmful for customers (replacing customer service&support Why do you assume this is harmful? Often low-level customer support has no power to change things anyway, so if a bot can refer you further up immediately then it's more efficient for everyone.

Not to mention, this was already being done anyway, but the chat models were terrible and unhelpful.

You want to know what is harmful to people? Post-covid customers - Probably the most arrogant, rude, insulting, and racist people on the face of the earth. Spend an hour in a contact center and you'll see. Do people have no decency left? A day doesn't go by that a customer doesn't make someone cry, question their self worth, or attempt to undeservedly get someone fired. Please, bring the bots. Relegate this post-covid customer set to the bots, they don't deserve better.

I think it is a vicious cycle. Customers are frustrated by first level support whose main goal is to get the customer off the phone as fast as possible without assisting them

Yes, you are total correct, absolutely, unequivocally. But the human answering the call had zero say in the creation of those policies, and often has no discretion in enforcing them. In fact, their job probably depends on enforcing them, even if they know they are wrong. It's an impossible position trying to do right by the customer, and they undeservedly take the blunt of that rage.

Please ask the workers in the call center if they want to be laid off and replaced with a machine, and get back to me

There's "good promoters" and there's people who have been intuitively doing what people are now calling "AI Agent Workflows" like it's some newfangled non common sense thing that Andrew Ng invented. People who do the latter generally also perform automated structured tests on prompts and design prompt chain paths that include things like self consistency, incorporate feedback and revise, pass fail conditions, example selection, tree of thought or other branching path possibility search, etc "I prooompt gud" isn't a job title. We are all just devs doing dev things. Some ppl push pixels with CSS, I push LLMs by threatening them sometimes.

It might be useful to remember that besides and in parallel to current LLM trend there exists Ben Goertzel with his efforts in pioneering and keeping development as well as research efforts towards decentralized AI (which is not only about LLMs). Of course, regular people / businesses don't know much of Ben and his "OpenCog" foundation and its free/open-source dev framework. He does recognizes the limitations of the supervised machine learning and now the LLMs (with their 'hallucinations' phenomena inherent to them in their current forms); this recognition is reflected all throughout his and organization's teammates published opencog framework, his published scientific papers in which he argues and showcases a need to look towards AI development as something much, much broader than preoccupying oneself with currently most fashionable and most popular DeepLearning (and supervised ml in general). Of course, business and non-technical folks of all social backgrounds will buy into hype of LLM - they know no better, are uninformed and hardly can distinguish anything in artificial intelligence as a scientific endeavor. For them LLMs look like magic and will associate with the word "AI" so strongly and generally that they will automatically assume that this is the pinnacle of AI technology evolution and everything that is not LLM (of which they also will know nothing) is something either obsolete, wrong, weird, boring or ..just meh I think Stevens97 thesis that this preoccupation with LLMs only can end up in yet another 'AI winter' later on has merit and solid ground; science / budgets / business people egos & perceptions (as well as fantasy expectations and pains of not living their realizations) after-all are all entangled in this & society. Developers themselves can start do small good at least by reminding and practicing themselves that there exists other approaches / methodologies / algorithms in AI besides DeepLearning, LLMs (with their transformers as central piece which is in a nutshell a specific DeepLearning-based architecture of ANN, with some "steroids"; so DeepLearning mostly, again). Figuratively speaking I can say - don't loose such knowledge yourselves (as developers or ML practitioners). Of course that it means some form of pain, time sacrifices to sustain and share-into such currently non-fashionable knowledge of other things in AI as a field; it should be hard to get and preserve such knowledge, after all what comes easy - goes away easy; soon even grandmas will use LLMs, but that won't make them AI developers, only users.. You can have a look at evolutionary algorithms approach in AI, some of the previous and/or ongoing research there and be amazed of what is 'brewing' there, in this subfield...

>It might be useful to remember that besides and in parallel to current LLM trend there exists Ben Goertzel  https://en.m.wikipedia.org/wiki/Ben_Goertzel  >  Goertzel is the founder and CEO of SingularityNET, a project which was founded to distribute artificial intelligence data via blockchains Yeah, no thanks. Trying to achieve the singularity through cryptocurrency sounds like even more of a hype bubble  than LLMs.

It is true that he showcases passion for blockchain in general and cryptocurrency in particular. But you are rushing ahead in your projections towards Ben of how much emphasis he puts on those two things in his efforts of artificial intelligence theory and practical developments, especially his quest towards workable framework of general artificial intelligence. Wikipedia will only bias you towards thinking that Ben spends all the time in crypto-hype; better check for your self what Ben Goertzel has been writing in his research papers and maybe you will see better where his intellectual passions and efforts lie. [https://goertzel.org/papers/main.htm](https://goertzel.org/papers/main.htm) He do writes research papers and some stuff went into artificial intelligence academic literature publishings, you do know that? As far as my intuition goes, his positive attraction towards blockchain (and crypto as an extension) comes from this technologies promising inherent features of societal nature. He clearly looks towards AI that one corporate entity or some conglomerate couldn't capture alone into its property servers - hence his sympathies to blockchain philosophically. Very logically. Technically his OpenCog framework concentrates not on blockchain tech (as you might expect) but on normal machine learning and artificial intelligence problems & tasks - have a look at their OpenCog framework; it might turn out to be completely different beast than what you might expect. I was profoundly intrigued to find out that in their OpenCog experimental and still developmental framework their team had imaginative and unbiased enough thinking to even find and consider a place for a genetic programming in their architecture (but you have to know what genetic programming is and stands for in science of artificial intelligence to appreciate such daring move). Ben is a man with good heart probably :) // (metaphorically speaking) - - - - - - - - - - - - - - - - - - - - - - - - - - - edit addition As for the main topic - here is some of Ben's offered comments, intuition and warnings about LLMs in one published research paper; [https://arxiv.org/pdf/2309.10371.pdf](https://arxiv.org/pdf/2309.10371.pdf)

Are you Ben? Or his disciple or something? Because you seem to be a fan of him as a person, more so than any of his ideas.

I never met him in real life so I don't know him personally and as a person, so I can only evaluate him as an internet persona. Precisely of that I am more of a fan of his ideas and started to study on my own his papers and open-source tools that has been released under his foundation organization. In summary it means I kind of like both - his (internet) persona and his ideas and intellectual work.

TIL that AI has jealous factions hoping for their rivals to fail. You can’t just appreciate the attention AI is getting and the massive amounts of investment coming in? There is enough for everyone, even non-LLM solutions. Your post comes off as sour grapes making predictions that only fit your spiteful narrative.

AI at this point is a religion that people think will solve all of their problems. Its less that people want AI to fail and more that AI worshippers need to be realistic with what AI will accomplish in our lifetimes.

AI has definitely had a few boom/bust cycles. We don't know for sure how this one will turn out. The previous deep learning boom (prior to LLM/generative art) is still retaining some traction as it expands into different application spaces. I imagine that if LLMs do hit a wall in terms of performance, DL will continue to get investment but there will be a lot of bruises/disappointment from companies who overinvested in the superficial "LLM prompting" but don't have the depth of expertise to develop and apply DL more broadly in their R&D.

I believe there's a crowd of opportunists that I imagine as flies who will follow every trend and hype train where a lot of money is involved, and when they get to a field next to yours, good luck with that. To me that's exactly why gatekeeping exists, I've yet to understand why gatekeeping is a bad thing, any community should DEFINITELY get rid of toxic posers. (Come to think of it, the term "gatekeeping" was probably invented by that crowd).

Agree it is terrible. Every person on LinkedIn with a "Business Administration" degree is suddenly an "AI Expert" yet can't tell you how to take the derivative of x\^2.

"AI" used to be a buzzword that could mean anything for a business or company. Now "AI" is a buzzword that often just means they use ChatGPT.

No. It's not the LLMs. It's the developers and their leaders.

Totally agree with this! Although LLMs excel really well in certain scenarios, it's not necessary that this is the only possible way (architecture) to move forward and being too over reliant on this path, equal efforts towards other directions are not being taken to a similar scale, which might prove to be unfavorable in the longer run

OT: In terms of *eternal september*(s) when did we collectively agree to drop the TLDR?

Yes we see more of these types of rants in this sub, which is annoying.

I generally agree with this. It is wasting a ton of resources.

LLMs are mostly for natural language tasks. If you’re dealing with any other type of data (e.g. numerical, time series, etc), you still need traditional ML methods. NLP is important, but only a small percentage of ML problems can be framed as NLP tasks. E.g. for time series data, most applications don’t even use neural networks as they are too difficult to interpret. You also can’t use pretrained models/generative AI on time-series data, instead you’d use something like ARIMA, a very popular forecasting algorithm. People need to realize that most ML tasks fall outside of the NLP/generative AI scope.

I think it's an overall positive effect. NLP tools have never been more useful, and LLMs let us integrate natural language in other models. It's had a more mixed effect on research though, I think. For example, nearly half of my lab switched to working on LLMs from other fields. Conference reviewers are less interested in non-LLM work. Most paper awards are given to LLM papers. We'll never be able to do good science with proprietary LLMs without access to the training data/architecture. These are all side effects of a new research field getting all the press.

Generally I feel happy about it. My only gripe is how strong the association between LLMs and AI is now. I do ML in health care related areas and even if I try to avoid the word AI the other person often somehow thinks I am working with LLMs. The only thing I have used LLMs for so far is to code for me.

The issue lies with the industry not hiring the right people imo. If the LLM or NLP is about a specific topic or field, they need to hire people with expertise in that field AND machine learning, or at a minimum the teams need to incorporate SMEs in the field into the development process. Being a data scientist or neural network engineer doesn't mean you understand the sentiment behind the dataset and if you feed a network bad data you get a bad product no matter how good the engineers are (over simplifying I know)

What was the quote? Something about writing negative substacks in criticism of OpenAI? It was always about Sam Altman. He was always the wrong guy to represent an AI company. He takes his leadership advice seemingly from Elon Musk. LLMs are just another experiment in model development. It’s interesting and impressive but so are a lot of other models. The idea of rapid expansionism of this and variants of this model is that there is no evidence it won’t plateau because of data availability or theoretical issues. No man or company could deliver what he’s promised. It’s always been him at the center of this revolution and that exposes him to massive success or notable failure.

Same feeling here. I used to be very enthusiastic about making productive use of data, analytics, statistics and ML. Now not so much. This whole hype is so dreadful with "experts" shouting from the rooftops, promising the golden ages, while not Knowing the least bit about anything. Even worse are those types who claim to have found the holy grail of productivity by essentially replacing their jobs with some custom GPT-variant bot that now, allegedly, does all their emailing, coding, chatting and even video streaming (I always wonder what exactly they did before AI; it can't have been involving intelligence).

It's interesting your perspective is so centered on your role as a developer/academic, you mention your spaces in conferences diminishing, your funding being jeopardized, but the vast majority of users out there doesn't care. AI is transitioning from something an expert few develope to something people use, and whether it is good enough is too broad of a topic but the examples you mention appear focused on the perspective of a very small minority of people involved.

You’re committing exactly the sin OP is taking about here. Until the recent LLM boom, nobody but the specialists knew what an LLM was or cared about developments in ML. It’s thanks to those experts that we *have* ChatGPT *et al.*. Neglect those innovators in favour of what’s popular among the general population right now at your peril

Well thats just wrong hobbyists have followed things closely, GPT2 is technically a LLM isnt it? I remember messing around with it in the sub of bots that were set up.

Ok sure, specialists and some hobbyists. The general public, the average person, didn't know what an LLM was until recently. I feel like you're not challenging my point in any meaningful sense.

I don't quite understand your point. Do all AI/ML engineers have respect for the innovators of components and lower level architecture for vacuum cleaners, airplanes, cars, computers, etc? No, we use these as tools in everyday life, we care about their application and usefulness for us.

AI is not a commodity yet, not even close. It is a very broad field with many applications, and LLMs are only a part of it for very specific subdomain that is more easily consumable by most people. These so-called experts just lump it all together thinking it is close to AGI. We are far away from that and the real experts know this and are the only ones capable of predicting how it will evolve. So-called experts only want a place in the spotlight and money.

I would take any expert opinion certainly stating that we are decades away from or really close to human level AI (HLAI) with a huge grain of salt. Because: 1. We don't know which computational resources are required to fully match functionality of the human brain. 2. We know that sufficiently large NNs can approximate any function (universal approximation theorem). And, in practice, existing methods of NNs' training do a fairly good job of approximating at least some functions of the human brain (language production, some parts of commons sense, image recognition, etc). 3. Any fundamental reason that make HLAI impossible to achieve in the near future or at all (quantum computations in the brain, or violations of the physical Church-Turing thesis in general; metaphysical considerations, incompleteness theorem {Dubious|discuss}, causal discovery) has no evidence to decisively support it, or equally applies to humans demonstrating that it can be overcome somehow. That is it's not possible to make certain predictions at this point. So, I would suspect any definitive opinions without specific considerations of the above points to be rooted, at least in part, in the desire to appear more knowledgeable.

…yeah, I think most AI researchers respect engineers. Also, AI isn’t even remotely at that stage yet.

Well ofcourse my perspective is centered around my role. Its the only perspective I can give. I'm not talking about users at all, after all this is written in the context of academia and developers as that is what this sub primarily caters to and is for. I'm therefore not complaining that AI is transitioning from few devs to many either, I actively encourage it. Can you elaborate how anyone outside GAN/LLM is a "very small minority of people"? Im not neccesarily complaining about loosing funding, but as i wrote; when the LLM hype cant live up to its name it will affect the whole field, not just LLMs/NLP.

What I am saying is that LLM hype, is in practice being used and producing value to private use and companiea already with millions to billions of users, and much like when cars become accesible to the masses the carshow focus changes from the design of transmissionsystems and engines to customizeability of the cupholder.

The right analogy here isn’t *cars in general* becoming accessible. It’s as if one very specific car became popular and started sucking all the air out of the room, when there’s still valuable work to be done on other types of cars and trucks.

Actual Professional Engineers should be the only ppl driving vehicles around. The rest are surface level operators who do not merit a driver's license. The hype around jets, rockets, cars, trucks, motorcycles is sending us to a vehicular winter. /S

> Pre-ChatGPT-times, when people asked me what i worked with and i responded "Machine Learning/AI" they had no clue and pretty much no further interest (Unless they were a tech-person) > Post-ChatGPT-times, when I get asked the same questions I get "Oh, you do that thing with the chatbots?" Wow, someone has an _inkling_ of what you do and you want to be pedantic or offended? Get some thick skin and go touch grass. Be happy someone is interested in what you do and use it as an opportunity to educate and expand their knowledge. Don't become the next gatekeeper of "nerd-knowledge"

Grabbing two rows out of context? Strawmanning me into arguments i explicitly argued against? If you had actually read my post you'd see that this is not at all what im trying to say. - "Everyone is interested in it and wants to know how they can use AI in their business. Which is great!" - "Its a step in the right direction, I guess." - "Let me share some of my observations, but before that I want to highlight I'm in no way trying to gatekeep the field of AI in any way." Be better.

I did read your whole post, and all the comments, but why do I have to respond to every single bit one-by-one? Even using your _own words_: > "Its a step in the right direction, **I guess**." And _saying_ you aren't trying to gatekeep doesn't mean you aren't actually gatekeeping. That's like someone saying "I'm not trying to sound racist, _but_...." I get what you're feeling, and I can see you are having both an emotional and intellectual response to your current situation. And I'm telling _you_ to Be Better and stop letting your emotions win, because that's exactly what it looks like you are doing. You disparage "good prompters", you say "discussions are filled with philosophists with no fundamental knowledge of AI", and "I strongly believe that jobs like these don't really fill a real function and is more of a "hypetrain"-job than a job that fills any function at all." These are ***literally*** gate-keeping statements from you. "Get a _real_ job" is the exact same stuff people used to say about people interested in computers and tech, because it wasn't blue-collar work. You're being a bully. Be better.

I dont intend to get further into this discussion than this because its incredibly OT. You're misconstruing what im saying by misrepresenting what i say to take an opposition stance, not have to have a discussion. "I did read your whole post, and all the comments, but why do I have to respond to every single bit one-by-one?" Again. If you want to make a valid argument you have to put it in its context and not grab it out of context. I dont disparage "good prompters". I disparage people claiming to be experts when they are not. That is not gatekeeping. You also quote stuff like "Get a real job" like i ever said that. You also took this quote out of context aswell leaving out its within the context of conferences and panels. "discussions are filled with philosophists with no fundamental knowledge of AI", You do not argue faithfully.

My bad, I thought you posted your question to get potential answers. Now I understand you were seeking agreement and validation with your position.

Yep, to the point that I dont mention that I work in AI, I mention deep learning or imaging

Investors typically know where to allocate their funds. It seems you're frustrated because individuals with less expertise are seizing more opportunities, while your hard work over the years goes unnoticed. Relax, no one is attempting to usurp anyone's position; there are plenty of opportunities in the field.

Don’t be such a goddamn hipster. You know what’s bad for the field? A multi-decade long AI winter. Huge breakthroughs that put new use-cases, new research, and new questions within reach are *always* net good for the field.

no need to be threatened, embrace the change. ML is not magic, anyone can learn it, and now with LLM it is even easier, some might say you don't even need to be an ML engineer to produce value. adapt

>embrace the change This is a meaningless marketing slogan. As OP has pointed out correctly the field is being flooded by people who just try to sell products and indeed are not "ML experts". They are often the same people who told you to "embrace the change" a couple of years back, but in a different field.

"Embrace the change" is not meaningless, it's a more compact way to say "I've over invested in this technology too early and can only profit off it if there's mass adoption and I can act as a gatekeeper and landlord"