[HN Gopher] The Rise of the AI Engineer
___________________________________________________________________
The Rise of the AI Engineer
Author : swyx
Score : 159 points
Date : 2023-06-30 17:01 UTC (5 hours ago)
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(TXT) w3m dump (www.latent.space)
| janalsncm wrote:
| I still feel a bit strange about calling someone an "AI engineer"
| and I think there are a few reasons.
|
| 1. _AI is poorly defined_. This is a fundamental problem behind
| almost every conversation on the "topic". Depending on the
| context, AI can mean anything from a decision tree to deep neural
| networks to science fiction.
|
| 2. Engineering implies a deeper level of understanding. If you
| want to engineer a system, you need a deeper understanding of how
| each of the components work. Using a tool does not make one a
| tool engineer. It makes them a 21st century blacksmith. Does
| calling an LLM API make me an AI engineer? If so, calling a
| weather API makes me a weatherman.
|
| 3. This is a relatively new area, and make no mistake, it is a
| new area. 2 years ago most of the tooling around ML work meant
| you had to get your hands dirty. To use BERT, you pretty much
| needed to learn about tokenization and attention masks and CUDA.
| Not so much with GPT3. So it seems premature to even circumscribe
| at the moment.
|
| What I am not saying: I don't think one needs to take a class in
| linear algebra to work with this stuff. I also never believed in
| calculus for CS students, which may be a minority opinion too.
| [deleted]
| preommr wrote:
| Oh God, this is going to be like DevOps all over again isn't it?
| Where there's people constantly gluing together things from
| different companies.
|
| I foresee layers and layers of abstraction dependent not on
| technology spec but on some company's poorly maintained docs and
| apis that you get by calling some service rep.
| sanderjd wrote:
| I have found data engineering to feel that way as well. Just
| throwing darts at a board of vendor solutions, trying to figure
| out how to cobble them together into something useful. I am
| worried this is going to be that as well.
|
| I'd like to learn more about how to self-host systems that are
| large enough to be useful, rather than doing this cobbling
| together proprietary APIs thing.
| __rito__ wrote:
| > _" the fundamental gatekeeping that still persists in the
| market"_
|
| This person just called me a gatekeeper when just our definitions
| of what an AI Engineer is different... all while discussing the
| ambiguities of the term "AI Engineer".
|
| Well, that's the internet for you!
|
| My intentions were never of gatekeeping, our definitions of the
| term are wildly different. I laid the beginning part of a roadmap
| for a person who wants to solve problems that exist in the real
| world with Deep Learning- by choosing how to structure data,
| training models with existing architectures (and optimizers, loss
| functions, activation functions, and so on) and creating new ones
| from scratch- and to deploy them to solve the problem in the
| physical space.
|
| Although, I have not spent hours on what the term "AI Engineer"
| should mean, it is not prompt engineering to me. If there is a
| consensus in future on the term meaning prompt engineering, then
| I will not give any advice on that, because I don't know much.
| Neither will I fight the definition.
|
| My definition, still is the person who develops new kinds of
| microwaves as opposed to the author's whose definition is that of
| a cook who uses the microwave, by creating new recipes and
| dishes. That's fine, but didn't liked being called a gatekeeper.
| boredumb wrote:
| I think it's all just being more accessible for people like me to
| integrate into applications via AIs. With that said - the barrier
| for entry also lowered substantially in the last few years to a
| point where implementing your own neural network from scratch
| with Pytorch is something you can do in a few (took me a weekend)
| hours coming in blind, but reading the docs and scrolling some
| existing repositories.
|
| I remember a decade ago taking a stab at it and being very much
| over my head. Not sure if i'm getting older and there's less
| magic going on around it or if it's the
| ecosystem/docs/examples/pytorch/jax/hours of youtube content/etc
| but somewhere in there, at least for me personally, it's gotten
| much more accessible.
| amilios wrote:
| Personally I think it's a combination of
|
| a) much better resources being available than in the past,
| which explain concepts clearly and in relatively simple terms,
| and
|
| b) much better tooling existing now than ever before, so many
| things that you used to need to do "by hand" are now taken care
| of by relatively standardized tooling. Even just automatic
| differentiation engines are a huge deal, not having to backprop
| "by hand" (first release of TensorFlow was 7 years ago, so if
| you looked at it a decade ago I'm assuming you had to implement
| backprop yourself). Beyond that another jump was from
| Tensorflow's delayed execution/compilation model (it really was
| a headache to work around its APIs/set up the computation
| graph) as well as just having a generally ugly API, to
| PyTorch's "literal" setup, where it feels like you're just
| writing regular Python code and performing operations
| "normally"
| paxys wrote:
| I'm not sure I agree with their reasoning. There are a lot of
| generalist backend/infra engineers who are working in the AI
| space. What is the special skillset that distinguishes them from
| all others? If you say that they are "AI engineers" because they
| are working on AI as a product, then should we also have
| "advertising engineers", "billing engineers", "API engineers"?
|
| The reality is that tons of people just build a trivial app
| calling an OpenAI API and put "AI engineer" on their resume to
| capitalize on the hype.
| m3kw9 wrote:
| AI engineer is a hype term for someone that can incorporate LLM
| to solve an engineering problem. It isn't that hard to use OpenAI
| apis, LLM are like super abstractions. You tell it in natural
| language, as opposed to a specific programming language. It's
| still a very narrow field. The hype is real but it's getting a
| bit frothy
| charles_f wrote:
| There's a bit of snake oil in all of that.
|
| By any means ML is a very specialized subfield, where you need
| solid math basis and a deep understanding of the science behind
| it all.
|
| But I struggle to see the same thing for AI. If by "AI engineers"
| you mean someone who builds an LLM*, then it's very much just ML.
| If you mean someone who integrates with the LLM someone else
| built, then it's very much just backend work. Sure, you might
| need a few days to understand a few concepts, I've integrated
| with Paypal in the past and that doesn't make me a payment for
| engineer.
|
| * wont even get into the argument of labelling LLMs with AI
| godelski wrote:
| > where you need solid math basis and a deep understanding of
| the science behind it all.
|
| I __REALLY__ really wish this were true. But I'll be honest, I
| know quite a number of researchers at high level institutions
| (FAANG and top 10 unis) that don't understand things like
| probability distributions or the difference between likelihood
| and probability. There's a lot of "interpretability" left on
| the table simply through not understanding some basic
| mathematics, let along advanced (high dimensional statistics,
| differential geometry, set theory, etc). The AI engineering
| often "needs" less of an understanding.
|
| But I don't think this is a good thing. I specifically have
| been vocal about how this is going to cause real world harm.
| Forget the AGI, just look at how people are using models today
| without any understanding. How people think you can synthesize
| new data without considering diversity of that data[0], can
| create "self healing code" that will generate high quality and
| good code[1,2], how people think LLMs understands
| causality[3,4], or just how fucking hard evaluation really
| is[5] (I really cannot stress this last one enough). There is a
| serious crisis in ML right now, and it is also the thing that
| made it explode in funding: hype. I don't think this is a
| bubble in the sense that AI will go away, but I think if we
| aren't careful with how we deal with this then it isn't
| unlikely to see heavy governmental restrictions placed on these
| things. Plus, a lot of us are pretty confident that just
| learning through data is not enough to get to AGI. It just
| isn't a high enough level of abstraction, besides being a pain
| (see the semantic deduplication comments about generation). But
| academia is even railroaded into SOTA chasing because that's
| what conferences like. NLP as an entire field right now is
| almost entirely composed of people just tuning big models
| instead of developing novel architectures (if you don't win,
| you struggle to get published despite differing factors). We
| let big labs spend massive amounts of compute to compare to
| little labs who can get similar performance with a hundredth,
| but don't publish those works. It is the curse of benchmarkism
| and it is maddening. Honestly, a lot of times I feel like a
| crazy person for bringing this up. Because when I say "ML needs
| a solid math basis and deep understanding of the science behind
| it" everyone agrees, but when the rubber hits the road and I
| suggest mathematical solutions to resolve these, I'm laughed at
| or told it is unnecessary.
|
| [0] https://news.ycombinator.com/item?id=36509816
|
| [1] https://news.ycombinator.com/item?id=36297867
|
| [2] https://news.ycombinator.com/item?id=35806152
|
| [3] https://news.ycombinator.com/item?id=36036859
|
| [4] https://www.cs.helsinki.fi/u/ahyvarin/papers/NN99.pdf
|
| [5] https://news.ycombinator.com/item?id=36116939
| fragmede wrote:
| Well, and frontend work. ChatGPT wouldn't been anywhere near
| the product it is without the webterface.
|
| As far as PayPal integration, no but it makes you the subject
| matter expert (SME) in the room above a room full of people who
| aren't, and maybe aren't even developers.
| senko wrote:
| If you have React engineers, why couldn't you have AI
| engineers?
|
| I think the post is right to try to delineate the roles of
| "builds LLMs or other ML models" and "uses those models", as
| (as you noted) the skills required are very different (vs.
| "creates a JS framework" vs "uses a JS framework" are the same
| skills, just on another level).
|
| "AI engineer" is, as you say, inaccurate, but as swyx says,
| it's least cringy of the alternatives.
|
| > Sure, you might need a few days to understand a few concepts,
| I've integrated with Paypal in the past and that doesn't make
| me a payment for engineer.
|
| No, but the post explicitly calls out the breadth of knowledge
| that the future AI engineers will have to have. It's not
| something you can pick up in a few days, even now, if you want
| to be on top of things.
|
| I can look up some docs and hook up static file hosting on S3
| and that doesn't make an AWS engineer. But there are people who
| full time work on providing solutions using AWS-everything and
| have built their entire careers (and companies) on top of that
| specialization.
|
| The difference in scale _is_ the difference in job description.
| ignoramous wrote:
| > _If you have React engineers, why couldn 't you have AI
| engineers?_
|
| We most definitely could. That's one way to prop up a cottage
| industry, stay relevant, command hefty salaries, and maintain
| that charade of importance through complexity by creating
| Frankenstein features in search of problems and promotions.
| bedobi wrote:
| like the canned messages that are increasingly finding
| themselves into any app where you can send messages to
| people, be it email, whatsapp, tinder, whatever
|
| "Hey John! I see you like traveling, I love traveling too!
| What are some of the most memorable trips you've had?"
|
| like, who would want to receive such an obviously canned
| message lol?
|
| soon, it will just be the apps talking to each other, no
| humans required
| 29athrowaway wrote:
| Because AI as terminology is vague and can mean anything.
|
| Graph search is AI, pathfinding is AI, decision trees are AI,
| fuzzy logic is AI, expert systems are AI, machine learning is
| AI.
|
| Just say LLM analyst or whatever.
| selalipop wrote:
| Maybe the term "engineer" did the concept a disservice, but
| prompt engineering has a lot of parallels to the field of UX.
|
| Currently there's a lot intuition involved so it can come
| across as made up, but there are novel concepts which
| meaningfully affect the end quality of what you make, and it
| takes time to learn and/or discover them.
|
| As time goes I expect our understanding of what underlies "good
| prompts" will start to bridge the gap from intuition to science
| much like how UX bridged into neuroscience and psychology. If
| you understand things like attention and logits that's already
| kind of happening: you can use that knowledge to identify gaps
| in the abilities of LLMs and start to bridge those gaps.
|
| -
|
| People are convinced that future LLMs will obsolete prompt
| engineering. To me that'd be like people from the 90s thinking
| computers are going to obsolete UX because more powerful
| computers will be better at making user interfaces, and in turn
| anyone will be able to do it.
|
| In some ways they'd be right: Today you don't need a UX expert
| at PARC to integrate a WYSIWYG interface into your product.
| Computers got so powerful that in milliseconds we can download
| libraries that implement the interface and render it across any
| form factor you can imagine. So now a WYSIWYG on your contact
| form is nothing.
|
| But as computers got more powerful they could do new things, so
| UX advanced onto improving how we interface with those new
| things. Things like the Vision Pro will unlock new areas of UX
| based on novel capabilities they posses.
|
| I think people are making a similar mistake with LLMs: they're
| focused on this idea that we'll just do the current things but
| better with more powerful models. But the more powerful models
| will be something we can "prompt engineer" into usecases we
| haven't even considered yet. (I also built notionsmith.ai and
| I'd argue it fits into that bucket a bit)
| vjust wrote:
| recently saw a demo app built on top of GPT3. Used a rest API for
| prompts. The backend was hooked to a corpus of PDFs and a SQL
| Database with financial data.
|
| What changed was the queries were English/natural language. The
| query language has changed. That's the R in CRUD. I wonder if the
| C, U, and D will also change.
|
| While this is one of many types of AI, it means commands will be
| in natural English. This might be a big deal for UI builders
| because how we ask questions has changed to NL.
| wizofaus wrote:
| I'm curious about this sort of use case - how long does it take
| for a GPT-based system to process a bunch of documents it's
| never seen before so that you can perform NL searches on them?
| Assuming something ike a million total pages of text are we
| talking minutes? Hours? Days?
|
| And is it at all feasible to ensure whatever factual
| information is returned is only sourced from said documents, vs
| being "hallucinated" by virtue of whatever weights exist based
| on the core training corpus?
| rufius wrote:
| AI/ML Engineers will be our modern day variant of the Mystic.
| They'll whisper sweet nothings into the ears of their models,
| looking at the goat entrails of what their models output and tell
| us of their predictions.
|
| It's going to be a shit show.
| amelius wrote:
| This stuff is going to be so vague that an AI will be able to
| do it.
| swyx wrote:
| hi HN! am writing this up as a recap of the enhanced role of code
| in LLM applications, and the emerging professionalization that
| will happen as a result. would welcome any and all feedback!
|
| I'm also soft launching the conference I am planning for Oct.
| Join us if you are in SF (will be streamed) https://ai.engineer/
| Xen9 wrote:
| I predict Cognitive Engineer / Cognitive Architect will become a
| thing.
| forrestbrazeal wrote:
| Hey Shawn! Always enjoy your writing.
|
| I think you've done well laying out what a lot of people want "AI
| Engineer" to mean at this moment in time. My concern (coming
| fresh out of the absolute semantic nightmare that was the
| "serverless" community) is that the term AI is so hopelessly
| overloaded, and has been such a moving target over the years,
| that it's unlikely that a plurality of people will ever share
| your mental model of what an "AI Engineer" is/ does / knows / is
| paid.
|
| Personally I've been using Generative Engineering / GenEng [0] to
| describe the professional practice of building stuff with AI as
| your pair programmer. I recognize some are pulling away from the
| term "generative", but to me it feels like a better anchor into
| the specific flavor of AI we're talking about.
|
| [0] https://cloud.google.com/blog/products/ai-machine-
| learning/t...
| swyx wrote:
| hey Forrest! thanks so much!
|
| agree with the semantic overload risk, but i think at this
| point Worse is Better is applying here. like I said in the
| piece I'm not starting the trend, just calling out that it's
| already under way.
| sanderjd wrote:
| Hmm, I think "the professional practice of building stuff with
| AI as your pair programmer" is just "software engineering".
| That is, it's just one more tool to use to do the existing
| work. We never had "Search Engineering" or "StackOverflow
| Engineering" to describe the practice of building stuff using
| web search and stack overflow as tools...
| giovannibonetti wrote:
| Maybe Text Processing Engineer or Text Engineer for short could
| express well that this person handles both text analysis and
| synthesis. It is analogous to the term Data Engineer, which
| describes someone that handles data processing and pipelines in
| general.
| ehnto wrote:
| Generative isn't perfect for similar reasons perhaps, I
| consider procedural generarion adjacent to machine learning
| based generation so it would need to encompass both to make
| sense to me.
| cbm-vic-20 wrote:
| I'm a natural skeptic, and I believe we're still on the rising
| edge of the "AI" hype cycle. Five years ago, it was "blockchain",
| and everyone was trying to ram blockchain into everything,
| attracting lots of VC and media attention, etc. It seems that
| blockchain is beyond the honeymoon phase: I haven't seen an NFT
| or even a Bitcoin headline in HN for a while.
|
| So I'm trying to wrap my head around what an "AI Engineer" is. As
| I see it, it's all about calling a function that takes some text
| as an input, and getting some text as output. That function, of
| course, is being run on some big hardware that in most cases, you
| don't own. So is the "engineering" part of this finessing the
| input and massaging the output? Do most "AI Engineers" actually
| understand what's going on in that function, beyond what they
| learned in the "LLM 101" videos and articles that have been
| flooding the web over the past year?
|
| While an application developer doesn't need to know all of the
| ins-and-outs of the underlying operating systems they run on,
| those developers that _do_ have a deep understanding of the OS
| are the ones who write more performant code, and can really get
| to the bottom of issues that arise do to how their code uses the
| OS. Can the same be said of "AI Engineers"?
|
| All of a sudden, everyone's an AI Engineer. Where where these
| experts hiding five years ago?
|
| https://trends.google.com/trends/explore?date=today%205-y&ge...
| Xenoamorphous wrote:
| I think comparing AI to blockchain is not fair.
|
| Sure, it was a hyped technology a few years back, but only in
| tech environments.
|
| My mom has never heard of blockchain, I bet. AI is in the
| mainstream media all the time.
|
| But ultimately it's a matter of scope. AI has the potential of,
| at the very least, transforming lots of jobs. Blockchain never
| had that potential.
|
| The company I work for, a non-tech one, never ever mentioned
| blockchain. But they're trying to get AI everywhere.
| PheonixPharts wrote:
| AI Engineer here.
|
| To start with, I share some of your skepticism about AI and
| hype (but I love these problems, so I'm happy to take the risk
| of overhyping to try to solve these challenges). But there is a
| lot of real work going on in this space. Though most of these
| are basically the same answers as you'd get in the article.
|
| > it's all about calling a function that takes some text as an
| input, and getting some text as output.
|
| Real world AI applications involve non-trivial prompts that are
| often composed of many different components and dynamically
| changed based on user interaction with the environment. So it's
| not quite as simple in practice as just calling an API.
|
| > So is the "engineering" part of this finessing the input and
| massaging the output?
|
| You could make this claim about _all_ software engineering at
| the end of the day.
|
| If you want to understand whether or not any of the billion
| companies shipping "AI" products right now are _really_ doing
| AI, the big term to ask about is "evaluations". It is not
| trivial to evaluate the performance of LLM output across a
| broad range of tasks. However if you're not doing this, then
| you can't possibly know how your efforts are doing. The
| companies that are slapping "AI" stickers on old products are
| largely ignoring this issue.
|
| The next challenge is "how do you improve bad outputs?" Prompt
| engineering is one solution, but there are potentially may
| other engineering solutions to recovering from a bad state.
| None of these are trivial.
|
| A rapidly growing part of this space is working with "agents",
| that is you have multiple LLMs that are capable of interacting
| with each other. This area is changing rapidly.
|
| Vector databases are also becoming very important of the work
| as not all LLM/AI work is just throwing around prompts, but
| often working with embeddings.
|
| > All of a sudden, everyone's an AI Engineer. Where where these
| experts hiding five years ago?
|
| It's not that mysterious. Everyone I know working in this space
| right now was either a very engineering focused data scientist
| in their last role, or an ML engineer working near this space.
| In either case they're people that have been interested in this
| space before that have all the skills necessary to change
| roles.
|
| > Can the same be said of "AI Engineers"?
|
| At least in my circle, everyone doing this work right now has a
| long history of working in machine learning and quantitative
| problem solving. Of course that used to be true of ML engineers
| as well (and I've not far to many MLEs that don't understand
| gradient descent).
| chefandy wrote:
| > Where where these experts hiding five years ago?
|
| I'll bet that many of them were trying to ram blockchain into
| everything!
|
| Personally, I'm curiously watching emerging prompt pen testing
| scene. It brings a tear of cyberpunk joy to my eye to consider
| that we've built important, powerful systems advanced enough to
| be vulnerable to social engineering attacks... and pretty dumb
| ones, too.
| ryandrake wrote:
| What we're seeing now is a frantic attempt by companies to
| ram "AI" into everything. Some force (Wall Street?) is
| expecting companies to say "We're using AI." What do you need
| it for? "We don't know, but damn we've got to use it for
| something!"
|
| A few years ago, few were talking about AI. Today, if it's
| not somehow crammed into your product roadmap, you might as
| well start looking for another job.
| badrequest wrote:
| > Do most "AI Engineers" actually understand what's going on in
| that function, beyond what they learned in the "LLM 101" videos
| and articles that have been flooding the web over the past
| year?
|
| Do they need to, to be effective at their jobs?
| visarga wrote:
| When your task is too nuanced to be described in a prompt and a
| few demonstrations you need to use one of the fine-tuning
| scripts to bake into the model much more supervised data.
| That's still doable for AI Engineers, prepare data, call LoRA
| fine tuning script, deploy model.
|
| The problem is having a good supervised training set. But
| recently it can be generated with LLMs + plugins and language
| chains, basically amplified LLMs. In other cases you need to
| collect human preference data and apply a different kind of
| fine-tuning. Still mostly dataset curation and iterative model
| building, something and AI Engineer should be able to do.
|
| Working with LLMs is very different from building neural nets
| like in 2017. Tons of those old skills are not needed anymore,
| so many of the old tasks are basically solved at human level.
| And a whole new set of problems appeared.
| pcthrowaway wrote:
| It really wasn't blockchain 5 years ago.
|
| It was blockchain 6 years ago (though to a much lesser degree,
| and focused on different iterations of the distributed ledger
| idea). Then it was _really_ blockchain 2 years ago (but focused
| on protocols built on smart-contract-enabled blockchains).
|
| Interestingly, those two targets are completely different. 6
| years ago the application was the ledger, and the language was
| usually C, 2 years ago the application was "a financial product
| built to run on a resource-constrained virtual machine that
| executes a custom language (or Rust)".
|
| The demand follows the money, so if the whole space experiences
| another "bull run" there will be lots of demand for
| "blockchain" again, and I'm sure the actual associated skills
| expected will be different as well
| dr_dshiv wrote:
| ChatGPT is useful in a way that block chain never was. There
| may be inflated expectations, but we are already on "the
| plateau of productivity." I don't think LLMs are overhyped
| relative to the normal background levels of tech hype.
| fuddle wrote:
| Bitcoin is probably a better comparison -
| https://trends.google.com/trends/explore?date=today%205-y&ge...
| voz_ wrote:
| Imagine comparing blockchain and AI.
| minimaxir wrote:
| Unfortunately, web3 bros pivoted to AI, which just adds even
| more noise to the space.
| wpietri wrote:
| For me that's absolutely the strongest ground for
| comparison.
|
| In recent decades, we've had two big waves of tech advance:
| the Web and mobile. A lot of people have lived through them
| both, giving them an expectation that another such wave
| should be along soon. You could see that in the decade of
| blockchain/ICO/DAO/NFT/web3 hype, where people, many with
| shaky credentials, touted the transformation soon to come,
| taking in a lot of cash.
|
| In retrospect, from Mt Gox to FTX we can see that it was
| all horseshit. The main real advance was decentralizing not
| finance or property or computing, but the Ponzi scheme.
|
| Despite this failure, and echoing The Great Disappointment
| [1], we see a lot of the same hype and even the same
| people. Is it possible that this is different, that there's
| more substance here? Or is this going to be another Groupon
| or Metaverse? That _does not matter_ to the hypesters. They
| 're going to run the same routine that worked before.
| They're going to take in a lot of money, which is the
| primary goal. It's possible that some of them will, by luck
| or accident, latch on to something that isn't a total
| fraud. Surely most of them won't.
|
| But we should never forget that is basically irrelevant to
| a lot of people in the early stages of a cycle. And not
| just for the fraudsters, but for anybody who makes their
| living on the upswing of the the hype cycle, including a
| notable fraction of investors, "experts", and journalists.
| how ever it turns out, they'll get paid just fine.
|
| [1] https://en.wikipedia.org/wiki/Great_Disappointment
| janalsncm wrote:
| Fraudsters will do what fraudsters do. If it wasn't
| crypto it would be scamming retirees or hoarding PS5s.
| They're a largely insignificant part of the economy. At a
| macro scale private capital has a much larger impact.
|
| The issue is there's a lot of dumb VC money floating
| around looking for a quick billion instead of investing
| long term fundamental research (boring!) that may produce
| results later on. It's a fundamental issue with the
| economy because what Capital wants is not to do what's
| best for humanity or even to build a sustainable widget
| factory. Capital wants a money printer. It's a big
| inefficiency in the economy because ideally they'd prefer
| sustainable growth instead of 1000x unicorns.
| minimaxir wrote:
| Web3 / Metaverse was a solution in search of a problem.
|
| AI is solving problems today.
| wpietri wrote:
| You're responding as if I said they were the same, but I
| was pretty careful to say the opposite. My point is
| explicitly setting the utility of "AI" aside.
| jsight wrote:
| It'll be fun to look back on these comments in a few years.
| It will be like looking back on the internet skeptics of the
| 90s. Most people have forgotten about those.
|
| Of course, there was a big boom and bust cycle back then too,
| but just like then, this cycle is nowhere near its peak.
| aerhardt wrote:
| LLMs and GenAI are already useful at scale but the current
| hype that they will lead to infinitely generalizable models,
| myriad groundbreaking applications in all fields and
| industries, or even AGI could be overblown - let's at least
| admit that as a possibility.
| sanderjd wrote:
| I would submit that we don't yet have enough evidence to say
| whether the comparison is apt or inapt. I personally think
| what's going on with these "generative" models seems like a
| bigger deal than blockchain, but it's fiendishly difficult to
| know what is or isn't hype while embedded within a hype
| cycle.
| janalsncm wrote:
| Seriously? The only comparison you can make is that both
| were hyped. Digging even a millimeter under the surface
| reveals they're completely different.
|
| Blockchain was and still is rife with scams and "you just
| don't understand the technology bro" hype men. Just check
| out Dirty Bubble Media. Blockchain was rarely if ever a
| product that solved a problem, the whole point is The Line
| Goes Up. That's why no one uses blockchain in industry, and
| crypto bros were constantly finding themselves proposing
| silly use cases like ticket sales and property deeds. These
| are people who have apparently never heard of a relational
| database.
|
| The hype around AI is due to increased attention on things
| that have already existed and have already been studied,
| used, and improved for decades now. There was never much
| R&D into blockchain tech because the tech isn't the point.
| For ML, there are researchers who have worked on these
| problems for decades. It doesn't need to justify its own
| existence, the justification is that it can solve real
| problems.
| sanderjd wrote:
| Again, I do tend to agree that there is a lot more
| "there" there with generative AI. But I think it's also
| true that it's too early to be sure.
|
| You're comparing the two technologies at totally
| different points in their hype cycles. The comparison
| point to where AI is right now is to Bitcoin / very early
| Ethereum in the late 2000s to early 2010s. Nobody knew
| where it was all going, some people saw endless
| potential, other people saw nonsense and scams. The
| explosion of bitcoin into mainstream consciousness in the
| early 2010s is akin to the explosion of ChatGPT over the
| past 6 to 9 months.
|
| But what's next? That's what matters. The early 2010s
| bitcoin boom now pretty clearly looks like a fad, in
| hindsight. Was ChatGPT also mostly a fad, or is it going
| to be a lasting fixture of productivity and/or
| entertainment moving forward? I think it's the latter -
| it has already changed my habits at work in ways that I
| think will be permanent - but I just think it's too early
| to say for sure.
|
| (And to be clear, I'm not talking about machine learning
| as an academic discipline; I totally agree with you that
| there is definitely enough evidence to say there is a lot
| more "there" there than research into chained hashing to
| solve double-spend-like problems.)
| janalsncm wrote:
| Well if you narrowly define AI to be ChatGPT and other
| generative LLMs, I think I agree so some extent. Unlike
| blockchain they do have use cases but it remains to be
| seen if those use cases can justify the money being
| thrown at them. How much is code completion really worth?
|
| However, I disagree insofar as the outcome truly depends
| on an unknown technology. Blockchain was never going to
| revolutionize finance or any of its other grand claims.
| At best (and that's if it worked), it would be a new
| database type that all of the existing financial systems
| would plug into. It was a libertarian pipe dream, naive
| about how the world actually works.
|
| For any AI application, the world is different. If we
| simply replace AI with "automated system" we can see why.
| Pretty much every company would like to replace their
| workers with machines. And maybe machines can do things
| that humans would never be able to do (for example,
| search the entire internet for a very specific topic).
| sanderjd wrote:
| Yes that's what I'm talking about because that's what the
| article is talking about! The article is _explicitly_ not
| about the ML / AI academic research. I agree that's well
| established.
|
| What the article is about is the current hype cycle of
| people trying to take the newest generation of "AI"
| tools, of which GPT-4 is the leading edge and most widely
| known, and make useful products with them. And whether
| that is going to be a big deal or a fad is, as yet,
| unproven.
|
| It is super easy to say, in 2023, that "blockchain was
| never going to revolutionize finance". But in 2013, that
| was an unknown. For what it's worth, you could go back to
| my commenting history in that period of time to find _me_
| saying "bitcoin is never going to revolutionize
| finance"; I was a skeptic then. But that doesn't mean I
| was definitely going to be right, I was just educated-
| guessing, just like the people on the other side of the
| conversation. That guess looks to have been prescient
| with the benefit of hindsight, but I've been wrong about
| lots of stuff too - I thought the iPad was stupid, I
| hated "Web 2.0", I thought the Facebook IPO was doomed,
| the list goes on and on.
|
| My best guess is that building products on top of
| "generative AI" is going to prove to be a big deal, but I
| don't _know_ that, and it 's hard not to be influenced by
| an ongoing hype cycle, is all I'm saying.
|
| > For any AI application, the world is different. If we
| simply replace AI with "automated system" we can see why.
| Pretty much every company would like to replace their
| workers with machines. And maybe machines can do things
| that humans would never be able to do (for example,
| search the entire internet for a very specific topic).
|
| Sure, but again, we just don't know yet if the "AI
| Engineering" thing this article is talking about is going
| to, in any way, turn into any of that, or if it's going
| to be more of a bust.
| spmurrayzzz wrote:
| > So is the "engineering" part of this finessing the input and
| massaging the output?
|
| I don't know if I'll ever use the phrase "AI Engineer" myself,
| but there's plenty of meaningful engineering work in that space
| that strays pretty far from just calling some provider's APIs.
| A few that come to mind just for LLMs:
|
| - Custom fine-tuning of foundational models both in the classic
| sense and with more modern strategies like PEFT/QLoRA
|
| - Data preprocessing pipelines to help automate fine-tuning,
| vectorization, etc
|
| - Continuous integration suites to evaluate models on standard
| benchmarks as they change over time
|
| - Vector db / semantic search engineering to help decorate
| context windows effectively
|
| - Architecting ensemble models infrastructure to accommodate
| more complex task processing
|
| I think many of those probably going into what folks are
| calling the "MLOps" bucket, but I think its a more broadly a
| combination of research, application engineering, and
| operations engineering.
|
| Edit: for clarity, my position is that the line in the article
| between AI Engineer and ML Engineer need not be that bright.
| Just like software engineers today that write/operate their own
| devops tooling to deploy and manage the apps they build.
| buffalobuffalo wrote:
| I'm also a skeptic, but for a slightly different reason. There
| are currently two types of business use cases that seem to be
| the focal point of this generation of AI.
|
| 1) Tooling. This one I think will probably bear fruit. It will
| likely result in huge productivity gains (I mean, it already
| has for me). But i don't know if it will result in a paradigm
| shift.
|
| 2) Agents. This is where most of the hype is focused. The idea
| is that you can cut humans out of the loop. If doable, this
| would be revolutionary. But from what I've seen, the currently
| technology is not likely to do this. The reason is that agents
| that are required to perform a long series of independent
| actions without human intervention can fall victim to an
| accumulation of small errors that occur in each step. This kind
| of "snowball effect" will often result in complete garbage
| being produced.
|
| As a result of point two, I think a lot of the new AI products
| are likely to fall flat. And as this happens, the hype/funding
| will dry up pretty quickly. All the same, I think this will
| have more positive economic outcome than all the crypto stuff
| did.
| phillipcarter wrote:
| > Where where these experts hiding five years ago?
|
| Prompt engineering has been around for a few years already, and
| there are a lot of existing ML engineers who have been able to
| quickly learn how to adapt their skills.
| minimaxir wrote:
| > Where where these experts hiding five years ago?
|
| You could use GPT-3 in 2020 but it was expensive and difficult
| to make it behave. Iterations of GPT-3 starting in 2021-2022
| allowed it to obey commands (InstructGPT) and made it more
| feasible to "engineer" with it.
|
| The true inflection point was due to _free, accessible and
| good-enough-quality AI generation_ in the form of Midjourney
| and ChatGPT.
| Der_Einzige wrote:
| If you are someone who was working in LLMs/Generative AI before
| it got "cool", The market has fundamentally changed and changed
| extremely in your favor.
|
| Talent is extremely scarce and expensive right now in this space.
| Ask for well above FAANG compensation. Try to shop around for
| offers. I had 4 offers and a bidding war for my talent (with no
| leetcode - but I have an extensive github and publications about
| LLMs), when previously these same companies would have brutally
| leetcoded me.
|
| I thought that there would be a ton of people getting really good
| really fast with Generative AI. It turns out that most people are
| terrible at using the tools, and there's even research about this
| right now - https://dl.acm.org/doi/10.1145/3544548.3581388
| sanderjd wrote:
| I'm someone who would like to get "really good really fast",
| but have found that the on-ramps remain pretty weak. For
| instance, I have yet to find a good book on the subject. There
| are a ton of tutorials and articles, but it's maddening to try
| to cobble together any depth of understanding from those little
| nuggets. And there are tons of good papers on how the systems
| actually work, but these are not very useful for people who are
| new to this set of tools to figure out how to use them to
| create useful things.
|
| But I think this will hole will close up incredibly quickly
| over the next six months. (And it's yet another really good
| opportunity for people like you to be involved in that gold
| rush!)
|
| Edit to add: For instance, the article lightly lambasts
| peoples' recommendations to read up on AI / ML fundamentals,
| and contains this:
|
| > _n the near future, nobody will recommend starting in AI
| Engineering by reading Attention is All You Need, just like you
| do not start driving by reading the schematics for the Ford
| Model T.
|
| But, amazingly, _it doesn't actually suggest an alternative
| starting point*. There is this call to action about the
| conference, but presumably that will mostly be people who have
| already figured this out to some degree. But (to carry on the
| author's analogy) what is the recommendation for driver's ed?
| swyx wrote:
| we haven't launched it widely yet but if you peek at the top
| level nav you'll find the course we are working on :)
| https://www.latent.space/s/university
| sanderjd wrote:
| How about that! :)
|
| One interesting thing is that there do seem to be _courses_
| available for this, but I still haven 't come across any
| _books_. Maybe this is just because I 'm a dinosaur, but I
| really feel like what I'm missing is a book about this,
| with a good Introduction and Chapter 1 motivating the
| subject and giving a lay of the land. I'm sure every techie
| publisher will have one of these by the end of the year,
| but so far I really haven't seen what I think I'm looking
| for in this space.
|
| (But having said that, you can bet I'll check out your
| course.)
| swyx wrote:
| thank you! yeah i guess its easier to iterate on a course
| than a book, but ofc we are also effecitvely writing and
| market testing the book contents. having written my own
| book before i'm not particularly keen on doing that again
| but am trying to work with partners to do this :)
| sanderjd wrote:
| Ha, totally get it.
| Animats wrote:
| Has "robopsychologist" shown up in job ads yet?
| minimaxir wrote:
| I would never be able to be hired as an "AI Engineer" or "Prompt
| Engineer" despite my extensive AI portfolio because my job title
| is Data Scientist and the discrepancy would confuse most hiring
| managers.
|
| Job titles are moving faster than career ladders.
| amelius wrote:
| AI engineer is the new webdeveloper.
| 29athrowaway wrote:
| When you see the taxonomy of AI and ML you will learn that saying
| "AI engineer" is vague and essentially useless.
|
| Do you use depth first search at work? Congratulations, you are
| an AI engineer.
|
| Do you use any form of search or information retrieval at work?
| Congratulations again.
|
| Do you have a system that makes decisions based on a decision
| tree? Again, congratulations.
|
| In fact, stop using "AI" at all. Your washing machine is an AI
| agent, it uses fuzzy logic.
|
| Are you using LLMs? then just say you work with LLMs.
|
| Search engineers don't call themselves AI engineers.
| ilaksh wrote:
| I mean, I consider myself an effective "AI Integration Engineer"
| but I _have_ done an Andrew Ng ML Coursera course and also built
| a MLP in C++ from scratch. But none of that really matters when
| it comes to applying something like GPT or Stable Diffusion to a
| particular application. You just send appropriate text to a model
| via an API call.
|
| I think "AI Integration Engineer" is a bit more of an accurate
| title because usually it's about integrating AI into existing
| products or domains. And "AI Engineer" by itself sounds a little
| bit like you might be claiming to be a PhD. But just a bit. I
| think it's fair enough to shorten it to AI Engineer though so we
| don't all have to type out "integration" over and over.
| supportengineer wrote:
| There are a couple types of roles that sounds really interesting
| to me. One would be taking some proprietary data and training LLM
| in a format it could use. One company I know has a database of
| cars. They want to train their LLM with some inventory facts like
| "We have a Ford Mustang on the lot whose vin is ABC123 and it has
| the following features...". And then another role would be
| writing the prompts for API calls to the LLM. "Write a report on
| all cars currently in the lot which are available for sale and
| have the following features...."
| ilaksh wrote:
| What I have done for a similar use case is to have ChatGPT via
| the OpenAI API generate SQL (or KQL) based on the user request
| and then run that query and display the results (with some
| prose if appropriate). Works fairly well even with
| GPT-3.5-turbo. With GPT-4 can handle more complex requests
| (slower). It could even create a custom Chart.js chart on the
| fly if requested.
|
| To me this demonstrates that there is a specific job here, even
| if you don't want to call it "engineering". Which I would argue
| is the correct category of job at least.
|
| The above project was presented as "let's put a table of data
| in a vector database and then search it using the embedding of
| the user query". Here you were suggesting fine-tuning an LLM
| with the structured data. Again, it makes more sense to just
| generate the SQL and leave it in the relational database.
|
| So there are a few basic things about how this stuff works that
| are not obvious and require some specialization. Even for
| programmers.
|
| Right now I think it's fair enough to put it in its own job
| category since there are plenty of software engineers that just
| don't have any experience with generative AI. But within a few
| years, I think knowing how to integrate generative AI into a
| product will be considered core knowledge for a software
| engineer. So using LLMs or Stable Diffusion will become bullet
| points on a job requirements list.
| Jtsummers wrote:
| > They want to train their LLM with some inventory facts
| like...
|
| So are they actually intending to retrain their LLM every time
| the inventory changes? Because, otherwise, how is it going to
| "know" the current state of the inventory? This is useless
| after a single sale or a single new delivery without
| retraining. (And it's likely useless before that anyways.)
|
| And if they already have a database of inventory data with all
| this then they could just generate a report the "old fashioned"
| way that's worked for decades.
| wizofaus wrote:
| I would expect the solution is to take the NL question and
| get GPT to transform it into a SQL (or similar) statement to
| extract the data. Then another call (or set of calls) to
| generate "reports" summarizing the data returned by the DB
| query.
| Jtsummers wrote:
| That's a _very_ generous take. But that application would
| be far more useful than just car inventories (the limited
| application described) and not trained in the manner
| described (on inventory data). It would be trained on
| transforming natural language to SQL (or other) query
| languages, and the application of that is exactly what we
| 're seeing with code generation applications of LLMs (to
| the extent they're presently useful).
| wizofaus wrote:
| Existing LLMs are already pretty good at this, no? The
| tricky part is mapping however the NL question refers to
| the various types of data to the actual column names,
| which is where I'd imagine some prompt engineering (or
| pretraining) would be necessary.
| wizofaus wrote:
| BTW I tried it with ChatGPT 3.5 - with a prompt that
| roughly described the database schema and a question "I
| need to know the manufacturer for the vehicle with VIN
| X7820-A and to confirm whether it has the feature 'rear
| camera' installed", it came back with
| SELECT TVehicles.Make, CASE
| WHEN TVehFeatures.FName = 'rear camera' THEN 'Installed'
| ELSE 'Not Installed' END AS RearCameraStatus
| FROM TVehicles JOIN TVehFeatures ON
| TVehicles.TV_ID = TVehFeatures.TV_ID WHERE
| TVehicles.VIN = 'X7820-A';
|
| One interesting thing to note - I didn't tell it that
| "Make" and "Manufacturer" are the same thing.
|
| I even went the next level and asked it to write me code
| to execute the query and generate appropriate HTML output
| from the results. It didn't quite manage it to handle any
| possible SQL query (remembering that the query itself has
| been dynamically generated), but wasn't far off. My
| description of how the output should look was simply
| "sleek and modern", and it came up with CSS that could be
| reasonably said to fill that brief.
| [deleted]
| [deleted]
| john2x wrote:
| At which point, maybe a "GUI-interface" will be cheaper to
| build and maintain in the long run.
|
| Now if AI could automagically update inventory data with
| what's actually physically happening on the lot, that would
| be cool.
| wredue wrote:
| This doesn't seem like a thing any business should need AI for.
|
| Filtering a known list for a report takes a few seconds at
| most, and is much more reliable than LLMs.
| vsareto wrote:
| For this conference, I think it would also be great to have a
| presentation about job requirements at various levels, then
| continue to cite that presentation often.
|
| It's important to have an on-ramp for people interested in this
| space with well-known requirements to aim for.
|
| This also makes it easy for companies to know what they're
| getting with this title. The ambiguity of full stack engineers
| meant that it was a dumping ground for responsibilities.
| simonw wrote:
| It feels far too early to me for there to be any clarity on job
| requirements.
|
| If I was hiring for an "AI engineer", I'd want to see examples
| of things they had built.
| swyx wrote:
| (author here) one of my ideas of research for this blogpost was
| to actually ask a few employer friends who want to hire AI
| Engineers for a job description, and then put out a generic JD
| that people can use and clone (kind of a SAFE for AI Engineers
| if you think about it). however I feel like it is too early
| still to prescribe something. I'll maybe put up a strawman for
| the conference and would love feedback then.
| spaintech wrote:
| My thought on what an AI engineer is; an individual who uses AI
| and machine learning techniques to develop applications and
| systems that can help organizations increase efficiency, reduce
| costs, increase profits, and make better business decisions 1 .
|
| AI engineers play a crucial role in helping enterprises leverage
| the capabilities of large language models (LLMs) like GPT-3 and
| beyond, this means that they will,
|
| "Develop Domain-Specific Models" AI engineers can fine-tune LLMs
| to create domain-specific models to ensure that the data and the
| business process align to provide more context. For example, a
| model fine-tuned on medical literature can assist doctors in
| diagnosing diseases or answering patient queries, ect.
|
| "Data Preparation and Management" By this, I presume they will be
| involved in cleaning the data, dealing with missing or
| inconsistent data, and ensuring the data is representative of the
| task the model will be performing.
|
| "Integration with Existing Systems" they will help integrate
| these AI models into the existing IT infrastructure of an
| enterprise. This can involve developing APIs, designing user
| interfaces, and ensuring the model's outputs can be used by other
| systems or processes.
|
| Etc... I believe that they enable organizations transform data
| into knowledge and ultimately, into wisdom - the highest level of
| data maturity. This is particularly true when dealing with
| domain-specific models, which can provide highly targeted and
| context-specific insights. When you can reach a level of data
| maturity that enbles actions on data, this is where AI will drive
| the change that is just starting. It's very exciting to watch it
| unravel, not because of the possibility of generating a sentient
| machine, but with the possibility that will drive new fields of
| discovery that has been under our noses, and these tools will
| help us make sence of it all, IMHO.
| cj wrote:
| I can see a world where "ML Engineer" (or similar) is someone
| that's hired to solve a known problem (whether it be with
| classifiers, LLMs, neural nets, etc), whereas a "AI Engineer" (or
| whatever the title) is hired to figure out how the hell to
| capitalize on the AI hype, without a specific problem to solve.
|
| IMO right now we're entering the "Peak of Inflated Expectations"
| in Gartner's hype cycle model.
| https://en.wikipedia.org/wiki/Gartner_hype_cycle#/media/File...
|
| Lots of companies want to jump on the AI bandwagon, but they
| don't really know what to do or how to leverage it.
|
| What the LLM community needs now is for companies to leverage and
| productize these LLM models for truly game changing use cases. If
| that doesn't happen soon, the hype will start to fade and lose
| momentum ("trough of disillusionment").
|
| I'd really love to see some killer use cases emerge soon.
| swyx wrote:
| (author here) There are at least 4 killer apps ($100m/yr
| revenue potential) so far:
|
| 1. Generative Text for writing - Jasper AI going 0 to $75m ARR
| in 2 years
|
| 2. Generative Art for non-artists - Midjourney has by some
| accounts $80m ARR
|
| 3. Copilot for knowledge workers - GitHub's Copilot has roughly
| 50-80m ARR as well
|
| 4. Conversational AI UX - ChatGPT probably has >$100m ARR by
| now, Bing Chat has brought $m's worth of attention to Bing
|
| (More on agents as the immature #5:
| https://www.latent.space/p/agents)
|
| what else do you need to see to believe? (geniune question)
| onion2k wrote:
| 75+80+80+100 is PS335m per year in revenue. For a point of
| comparison, that's approximately 11 hours of Google's
| revenue.
| dist-epoch wrote:
| I find myself using Bing Chat more and more instead of
| googling specific questions. And no, hallucinations are not
| a problem, because the questions are concrete and the
| answers immediately verifiable.
| pclmulqdq wrote:
| The innovator's dilemma on display.
| staunton wrote:
| Consider the possibility that may be a point in a
| technology's evolution where you can "look at what is being
| done with it" and conclude that it's useful _from that_ ,
| rather than just comparing revenue figures across domains.
| ImaCake wrote:
| Yeah the dollar values here are a distraction. I use
| copilot everyday at my Data Science job. Its useful!
|
| What we are not considering is integrating a custom
| trained LLM into our work because the tech just isn't
| there yet.
| riku_iki wrote:
| but rate of growth is likely 100-1000 times higher
| ducharmdev wrote:
| On a smaller time scale, is rate of growth that
| meaningful?
|
| I guess I'm just thinking, until we see the how this all
| pans out over the next decade, we don't really know if
| the current rate of growth will hold, or if it'll
| plateau/slow down.
| 0xEFF wrote:
| Yes, it's meaningful. If Google doesn't fix search in the
| next decade then Bing (or some other winner) will be the
| service earning Google's ARR in 11 hours.
| riku_iki wrote:
| > On a smaller time scale, is rate of growth that
| meaningful?
|
| revenue in initial stage of extreme growth is much less
| meaningful.
| cj wrote:
| > what else do you need to see to believe? (geniune question)
|
| It's not that I don't believe in LLMs.
|
| It's that the level of media attention AI is getting isn't
| backed by the same level of real world use cases, yet. (All
| of the use cases you listed are awesome, no denying that, but
| I don't think those alone justify the amount of hype in
| mainstream media)
| swyx wrote:
| i mean, theres nothing i can do about mainstream media
| hype, but i guess my main point is this is a growing field
| with real money and utility behind it, and so will
| professionalize. if I am correct on that then AI Engineer
| will be a thing (because it is Least Bad title for the
| thing)
| skepticATX wrote:
| AI Engineers are just software engineers who use specific
| tools. If we want to use a fancy title then that's fine,
| I guess. But let's not pretend that a typical dev can't
| easily learn vector DBs, data pre-processing, fine
| tuning, etc.
|
| None of these things require specialized knowledge in the
| way that say being an AI researcher would.
| jsight wrote:
| You could say the same about things like SREs or Devops.
| In fact, many of them transitioned from regular "software
| engineer" to these roles simply by learning closely
| related skills.
|
| That won't stop the industry from inventing new, useful
| titles.
| echelon wrote:
| The longer HN continues to doubt in AI's deliverables, the
| bigger our revenues and our moats can grow. Don't tell them.
|
| (It's not that hard to hit $1M ARR with a good AI product. So
| many classes of new products and solutions have opened up.)
| constantly wrote:
| I'm sure I could find a similar comment about
| cryptocurrency or NFTs. There's always snake oil salesmen.
| jsight wrote:
| I remember thinking this about Tesla too. They built a
| fairly massive automaker while everyone kept saying their
| whole approach was fundamentally doomed.
| mschuster91 wrote:
| I'd add these use cases:
|
| 5. automated processing of unstructured paperwork and
| ingestion into ERP systems. Basically, upload _any_ kind of
| bill and get all of the information into the system, not just
| "find out the total amount". That can save so much in
| accounting it's not even funny any more.
|
| 6. related to this, something that sorts incoming emails.
| Classify stuff into "look into it _now_ " vs "look into it
| later" vs "yet another bullshit marketing email".
| oblio wrote:
| My problem with this is that AI at the moment is a kind of
| 80% thing.
|
| And you want to plug that into ERPs and accounting?!?
|
| We don't even really understand its failure modes.
| moffkalast wrote:
| The tech problems will eventually get sorted out I'm sure,
| the most uncertain problems are legal. The world's copyright
| law is not even up to date enough to deal with the internet
| without crappy patchwork workarounds (like Youtube's
| contentID nonsense), much less LLMs.
|
| Steam just banned AI art and text assets and built in
| generators because they don't want the liability of hosting
| it. Midjourney is getting sued by Getty, Copilot breaks GPL
| licenses and was even facing class action lawsuits, OpenAI
| trained using copyrighted and even pirated content, LLama
| models are unavailable for commercial use, AGI alarmists want
| to ban the whole thing altogether, etc.
|
| A lot of end usage depends on how all of this plays out in
| courts over the next few years. Why would people dump capital
| into something that will be declared illegal?
| staunton wrote:
| If people get used to LLMs governing their lives, those
| copyright laws will "just" be changed. People will not
| accept a legal challenge taking away their toys. Granted,
| we're not at that point yet. But the window for the legal
| stuff _really_ making a difference is slowly closing.
| Meanwhile, the legal system is pretty slow.
| jsight wrote:
| Because if they don't, their competitors will, even if
| those competitors are overseas in a more lax environment.
|
| Either IP laws will catch up, or the countries with better
| IP laws will become more competitively successful.
| rvz wrote:
| We are indeed at the "Peak of Inflated Expectations" in the
| Garter hype cycle. Everyone is screaming that we are out of the
| AI winter and throwing LLMs at every problem.
|
| > What the LLM community needs now is for companies to leverage
| and productize these LLM models for truly game changing use
| cases.
|
| The killer serious use-case has always been summarization of
| existing text. That's it. Everything else is a constant flow of
| creative bull-shitting from a black box AI requiring triple
| checking everything before using the output, meaning that the
| output can't be trusted.
|
| > If that doesn't happen soon, the hype will start to fade and
| lose momentum ("trough of disillusionment").
|
| I think they will realize that there is more to "AI" than LLMs,
| just like the hype with CNNs and the like.
| visarga wrote:
| Not just summarisation, information extraction in general.
| LLMs are great data normalisers.
| z3c0 wrote:
| No. No they are not. Do not keep proliferating this idea,
| as there are real consequences. Your input only informs the
| likelihood of the output. There is no model actually
| extrapolating rules from the information, so the premise of
| LLMs being extractive is 100% verifiably false. They
| summarize well due to being handed a roughly correct
| arrangement of tokens to mimic the order of. This should
| not be confused with extraction.
|
| To put it simply, keep falling for that, and it'll bite you
| in the ass.
| yacine_ wrote:
| The right way to leverage AI is to use it to do ye olde
| engineering, faster. Actually, most AI use cases are in fact
| that! Ye olde engineering - data pipelines, automation. Stuff
| we've been doing since before I was born.
|
| Everything else - transformative tech, will hit the open domain
| fairly quickly, is my bet. Similar to databases.
| richardw wrote:
| I've been thinking the leading edge is already in the trough of
| disillusionment! At least on HN etc.
|
| We see lots of reports of limitations on HN. Those in the know
| don't trust it nearly as much as the public and all the CEO's
| of businesses drooling to install AI and have the money go up.
| Going to have to dig our way through those issues to get real
| value.
| moffkalast wrote:
| Well you have to consider the inherent bias on HN, in that
| the trough of disillusionment is HN's default state of mind
| regarding literally every topic. It's really rare to see a
| thread where most of the comments aren't negative.
| intellectronica wrote:
| In my experience people who make dismissive comments about AI (in
| its current form) and AI Engineering as a discipline tend to have
| very little experience with and superficial understanding of AI.
| Once you start using it seriously as part of your engineering
| stack it quickly becomes clear that there's a lot of detail and
| complexity that more than justify specialisation.
| z3c0 wrote:
| Heavily disagree, and this just seems like posturing from the
| other side of things. I understand AI rather heavily, and have
| been a part of the scene far before the explosion of even the
| first GPT model.
|
| Frankly, I've been quite irked by all the people who claim
| themselves "ML engineer" when most of what they do is glue
| huggingface models together. They're doing none of the work
| required to make models, which is quite extensive. They
| understand very little about the models they wield.
|
| This new wave, I find even more irksome. "Prompt engineer" and
| "AI engineer" and aren't much more than pseudoscientists who
| fanangle API inputs until they get what they want. This is in
| stark contrast to all the maths I perform to understand a
| model's performance.
| intellectronica wrote:
| I think you've just proven my point.
| z3c0 wrote:
| On reddit, this might seem like a burn, but here, you need
| to explain that claim and not just hope somebody rides with
| it. How is tinkering with an API an actual understanding of
| AI? Please actually explain
| Zetobal wrote:
| It's not the AI Engineer it's the AI Operator that everyone is
| looking for.
| sanderjd wrote:
| "Operator" feels right to me as well here.
| john2x wrote:
| Nice. One step closer to having Smooth Operator be a real
| title
| avereveard wrote:
| The rise of the monkey king of infinite monkeys
|
| I'm using LLM for many things, but let's not pretend it's
| engineering
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