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.
The Rise of the AI Engineer
71–80 of 159 posts
Re: The Rise of the AI Engineer
#72Earlier quoted context omitted.
(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…
75+80+80+100 is £335m per year in revenue. For a point of comparison, that's approximately 11 hours of Google's revenue.
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.
Re: The Rise of the AI Engineer
#73Earlier quoted context omitted.
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 p…
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
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.)
Re: The Rise of the AI Engineer
#74I'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" i…
> 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?
Re: The Rise of the AI Engineer
#75Earlier quoted context omitted.
(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…
75+80+80+100 is £335m per year in revenue. For a point of comparison, that's approximately 11 hours of Google's revenue.
The innovator's dilemma on display.
Re: The Rise of the AI Engineer
#76recently 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 b…
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?
Re: The Rise of the AI Engineer
#77There 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 t…
> 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…
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.
Re: The Rise of the AI Engineer
#78Earlier quoted context omitted.
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, o…
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…
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.
Re: The Rise of the AI Engineer
#79I 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.wiki…
(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…
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".
Re: The Rise of the AI Engineer
#80Earlier quoted context omitted.
> 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…
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.
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