“Replacing Talent” is not what AI is meant for, yet, it seems to be every penny-pinching, bean counting studio’s long term goal with it.
Yep AI at best can supplement talent, not replace it.
I’m not a developer, but I use AI tools at work (mostly LLMs).
You need to treat AI like a junior intern… You give it a task, but you still need to check the output and use critical thinking. You cant just take some work from an intern, blindly incorporate it into your presentation, and then blame the intern if the work is shoddy…
AI should be a time saver for certain tasks. It cannot (currently) replace a good worker.
As a developer I use it mainly for learning.
What used to be a Google followed by skimming a few articles or docs pages is now a question.
It pulls the specific info I need, sources it and allows follow up questions.
I’ve noticed the new juniors can get up to speed on new tech very quickly nowadays.
As for code I don’t trust it beyond snippets I can use as a base.
It’s clutch for boring emails with several tedious document summaries. Sometimes I get a day’s work done in 4 hours.
Automation can be great, when it comes from the bottom-up.
I am a developer and that’s exactly how I see it too. I think AI will be able to write PRs for simple stories but it will need a human to review those stories to give approval or feedback for it to fix it, or manually intervene to tweak the output.
I do think given time, AI can improve to the level that it can do nearly all of the same things junior level people in many different sectors can.
The problem and unfortunate thing for companies I forsee is that it can’t turn juniors into seniors if the AI “replaces” juniors, which means that company will run out of seniors with retirement or will have to pay piles and piles of cash for people just to hire the few non-AI people left with industry knowledge to babysit the AIs.
It’s very short sighted, but capitalism doesn’t reward long term thinking.
The problem is the crazy valuations of AI companies is based on it replacing talent and soon. Supplementing talent is far less exciting and far less profitable.
Current AI*
I don’t see any reason to expect this to be the case indefinitely. It has been getting better all the time and lately been doing so at a quite rapid pace. In my view it’s just a matter of time untill it surpasses human capabilities. It can already do so in specific narrow fields. Once we reach AGI all bets are off.
Maybe this comment will age poorly, but I think AGI is a long way off. LLMs are a dead-end, IMO. They are easy to improve with the tech we have today and they can be very useful, so there’s a ton of hype around them. They’re also easy to build tools around, so everyone in tech is trying to get their piece of AI now.
However, LLMs are chat interfaces to searching a large dataset, and that’s about it. Even the image generators are doing this, the dataset just happens to be visual. All of the results you get from a prompt are just queries into that data, even when you get a result that makes it seem intelligent. The model is finding a best-fit response based on billions of parameters, like a hyperdimensional regression analysis. In other words, it’s pattern-matching.
A lot of people will say that’s intelligence, but it’s different; the LLM isn’t capable of understanding anything new, it can only generate a response from something in its training set. More parameters, better training, and larger context windows just refine the search results, they don’t make the LLM smarter.
AGI needs something new, we aren’t going to get there with any of the approaches used today. RemindMe! 5 years to see if this aged like wine or milk.
Yeah LLMs might very well be a dead-end when it comes to AGI but just like chatGPT seemingly came out of nowhere and took the world by surprise, this might just aswell be the case with an actual AGI aswell. My comment doesn’t really make any claims about the timescale of it but rather just tires to point out the inevitability of it.
How does this amazing prediction engine discovery that basically works like our brain does not fit in a larger solution?
The way emergent world simulation can be found in the larger models definitely point to this being a cornerstone, as it provides functional value in both image and text recall.
Nevermid that tools like memgpt doesn’t satisfy long term memory and context windows doesn’t satisfy attention functions properly, I need a much harder sell on LLM technology not proving an important piece of agi
Not even that, it’s a tool. Like the same way Photoshop, or 3ds max are tools . You still need the talent to use the tools.
https://www.cognition-labs.com/introducing-devin There are people out there deliberately working to make that vision a reality. Replacing software engineers is the entire point of Devin AI.
I saw this the other day and I’m like well fuck might as well go to trade school before it gets saturated like what happened with tech in the last couple years.
Yeah, the sad thing about Devin AI is that they’re clearly doing it for the money, they have absolutely no intentions on bettering humanity, they just want to build this up and sell it off for that fat entrepreneur paycheck. If they really cared about bettering humanity they would open it up to everyone, but they’re only accepting inquiries from businesses.
One single comment when I posted this on the technology community:
sed “s/studio’s/tech industry c-suite’s/“As an engineer, the amount of non-engineering idiots in tech corporate leadership trying to apply inappropriate technical solutions to something because it became a buzzword is just absurdly high.
But that’s pretty much why AI is developed.
It was more like a scientific discovery
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The article doesn’t say much. So I checked the source for more information. It doesn’t say much more, but IMO in a much better way, in two concise paragraphs. In the sourced financial report, it is in the intro, two paragraphs:
An example R&D initiative, sponsored by the Innovation team was Project Ava, where a team, initially from Electric Square Malta, attempted to create a 2D game solely using Gen AI. Over the six-month process, the team shared their findings across the Group, highlighting where Gen AI has the potential to augment the game development process, and where it lags behind. Whilst the project team started small, it identified over 400 tools, evaluating and utilising those with the best potential. Despite this, we ultimately utilised bench resource from seven different game development studios as part of the project, as the tooling was unable to replace talent.
One of the key learnings was that whilst Gen AI may simplify or accelerate certain processes, the best results and quality needed can only be achieved by experts in their field utilising Gen AI as a new, powerful tool in their creative process. As a research project, the game will not be released to the public, but has been an excellent initiative to rapidly spread tangible learnings across the Group, provide insights to clients and it demonstrates the power and level of cross-studio collaboration that currently exists. Alongside Project Ava, the team is undertaking a range of Gen AI R&D projects, including around 3D assets, to ensure that we are able to provide current insights in an ever- evolving part of the market
The central quote and conclusion being:
One of the key learnings was that whilst Gen AI may simplify or accelerate certain processes, the best results and quality needed can only be achieved by experts in their field utilising Gen AI as a new, powerful tool in their creative process.
Which is obvious and expected for anyone familiar with the technology. Of course, experiments and confirming expectations has value too. And I’m certain actually using tools and finding out which ones they can use where is very useful to them specifically.
The overall point may be relatively obvious, but the details are not.
Which steps of which processes is it good at, and which not? What can be easily integrated into existing tooling? Where is is best completely skipped?
Yes, exactly. That’s what I was referring to in my last sentence.
Folks really didn’t understand how AI will work. It’s not going to be some big we’re dropping 1000 people.
It’s going to reduce demand over time.
And in that regard it’s no different than any other productivity tool or automation, I have seen software being bought that immediately Eliminated 80 odd jobs.
I’ve heard it as “No one is losing their job to AI, but they will lose their jobs to someone who is using AI.”
Case in point: I’m using ChatGPT to help me write cover letters. I make sure to proofread them and sometimes it hallucinates my expertise, but it makes it a lot faster.
Also not going to happen. It’s massively overrated.
I mean that’s already happening at some big companies now.
Will it last? My guess is no, but they’ll enjoy saving the money that they would pay human beings in the mean time.
My hope is just that they’ll suffer losses due to a drop in product quality and start struggling, but let’s face it, the big tech companies are almost never the ones’ that are actually hurt by their decisions.
It will start with going from 5 writers to 3, or going from 10 animators to 6.
Then 10 years from now as it gets more advanced we will be down to maybe 1 writer and 2 animators.
going from 10 animators to 6
It’s still crazy to me that like half of Across the Spider-Verse was AI generated
Folks really don’t understand how AI will work. It’s not going to be
somebigwe’re dropping 1000 people.
ai automates the behavior of an average agent, not a talented one
Unless you specify that you want a talented output. A lot of people don’t realize that you need to tell AIs what kind of output you want them to give you, if you don’t then they’ll default to something average. That’s the cause of a lot of disappointment with tools like ChatGPT.
Ahhh so the secret to using ChatGPT successfully is to tell it to give you good output?
Like “make sure the code actually works” and “don’t repeat yourself like a fucking idiot” and “don’t hallucinate false information”!
Unironically yes, sometimes. A lot of the best works which its training samples are based on cites the original poster’s qualifications, and this filters into the model where asking for the right qualifications directly can influence it to rely more on high quality input samples when generating its response.
But it’s still not perfect, obviously. It doesn’t make it stop hallucinating.
Yeah, you still need to give an AI’s output an editing and review pass, especially if factual accuracy is important. But though some may mock the term “prompt engineering” there really are a bunch of tactics you can use when talking to an AI to get it to do a much better job. The most amusing one I’ve come across is that some AIs will produce better results if you offer to tip them $100 for a good output, even though there’s no way to physically fulfill such a promise. The theory is that the AI’s training data tended to have better stuff associated with situations where people paid for it, so when you tell the AI you’re willing to pay it’ll effectively go “ah, the user is expecting good quality.”
You shouldn’t have to worry about the really quirky stuff like that unless you’re an AI power-user, but a simple request for high-quality output can go a long way. Assuming you want high quality output. You could also ask an AI for a “cheesy low-quality high-school essay riddled with malapropisms” on a subject, for example, and that would be a different sort of deviation from “average.”
Absolutely, it’s one of the first curious things you discover when using them, such as stable diffusion “masterpiece” or the famous system prompt leaks from proprietary llms
It makes sense in how it works but in proprietary use it is mostly handled for you
Finding the right words and amount is a hilarious exercise that provides pretty good insight in the attention mechanics
Consider the “let’s work step by step”
This proved a revolutionary way to system the coders as they then will structure the output better, there’s then more research that happened around why this is so amazingly effective at making the model proof check itself
Predictions are obviously closely related to the action part of our brains as well, so it makes sense that it would help when you think about it
So I need to praise it and call it a good boy?
Interestingly enough, I’ve started adding something similar to my AI prompts because a Lemmy user had it in theirs and I liked their images.
No shit.
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Duh.
That’s a great image.
RIP Norm.
The game will not be released to the public as it was just a research project, and Keywords didn’t provide any additional information about what type of 2D game it created.
So we just have to trust them on this? Yeah, no.
“House made entirely of cement is a failure because you still need doors and windows and stuff.”
I am astonished by an established, commercial website having good structure.
It looks like a documentation website. Sidebar with clear categories and navigation. I really like it.
Huh, would you look at that.
Well, I’m glad somebody did the experiment at least.
Add “, yet” to the headline and come back in a year or two.
Currently AI may fail to produce a video game, but so was the case for images, videos, and texts only a few years ago.
Failure is a good thing because it’s preceded by attempt.
Yeah. Just because it can’t do it now, doesn’t mean it won’t ever. And also refer to my other comment for how this is a bad study as they didn’t even provide any details on the game itself, let alone release the game. But anyone can do a similar study for themselves at home, since AI is free to use!
give it a few more years.
Just like self driving! In 2010 it was almost there, just needed a few more years…
We’ll have cold fusion in 10 more years‽
I really don’t think there’s more examples of optimistic predictions than there are pessimistic ones.
The discoveries made in recent years definitely point to an emergent incredibly useful set of tools that it would be amiss to pretend wouldn’t eventually replace junior developers in different disciplines. It’s just that without juniors there will never be any seniors. And someone needs to babysit those juniors. So what we get is not something that can replace an entire workforce in a long long while even if top brass would love that
Yes actually (except more than a few years).
Waymo is already operating a robotaxi service in 3 cities, now they just need to expand and find a way to make it not lose money.
Sounds like they just need a few more years…
Until what? 100% replacement of human-driven cars? Being rolled out for areas covering 50% of the population? Where is the goal line here?
We are already at the stage of commercial operation, with rides available to the general public - even though only in a few locations.
Sure, it’s far from being everywhere, but why pretend that progress has stalled, when it clearly hasn’t?
My point is that the ‘give it a few more years’ mantra gets repeated for decades
Go see videos of how well FSD V12 performs and you’re up for a surprise. Full self driving sucks untill it doesn’t. AIDRIVR puts up good content if you want recommendations.
Not yet able to replace talent
They forgot to import the talent module.
