“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.
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.
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.
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.
Honestly, that’s been my favorite - bringing in automation tech to help me in low-tech industries (almost all corporate-type office jobs). When I started my current role, I was working consistently 50 hours a week. I slowly automated almost all the processes and now usually work about 2-3 hours a day with the same outputs. The trick is to not increase outputs or that becomes the new baseline expectation.
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.
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.
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
I didn’t say it wasn’t amazing nor that it couldn’t be a component in a larger solution but I don’t think LLMs work like our brains and I think the current trend of more tokens/parameters/training LLMs is a dead-end. They’re simulating the language area of human brains, sure, but there’s no reasoning or understanding in an LLM.
In most cases, the responses from well-trained models are great, but you can pretty easily see the cracks when you spend extended time with them on a topic. You’ll start to get oddly inconsistent answers the longer the conversation goes and the more branches you take. The best fit line (it’s a crude metaphor, but I don’t think it’s wrong) starts fitting less and less well until the conversation completely falls apart. That’s generally called “hallucination” but I’m not a fan of that because it implies a lot about the model that isn’t really true. Y
You may have already read this, but if you haven’t: Steven Wolfram wrote a great overview of how GPT works that isn’t too technical. There’s also a great sci-fi novel from 2006 called Blindsight that explores the way facsimiles of intelligence can be had without consciousness or even understanding and I’ve found it to be a really interesting way to think about LLMs.
It’s possible to build a really good Chinese room that can pass the Turing test, and I think LLMs are exactly that. More tokens/parameters/training aren’t going to change that, they’ll just make them better Chinese rooms.
Thanks, I’ll check those out. The entire point of your comment was that llm is a dead end. The branching as you call it is just more parameters which approach, in lower token models a collapse. Which is why more tokens and larger context does improve accuracy and why it does make sense to increase them. LLMs have also proven to in some cases have what you call reason and what many call reason but which is not a good word for the error. Larger models provide a way to stimulate the world which in turn gives us access to the sensing mechanism of our brain, which is to stimulate and then attend to disparages between the simulation and actual. This in turn gives access to action which unfortunately is not very well understood. Simulation, or prediction, is what our brains constantly do to be able to react and adapt to the world without massive timing failure and massive energy cost, for instance consider driving where you focus on unusual sensing and let action be an extension of purpose by just allowing constant prediction to happen where your muscles have already prepared to commit even precise movements due to enough practice with your “model” of how wheel and foot apply to the vehicle.
*Simulate, not stimulate lol
But that’s pretty much why AI is developed.
It was more like a scientific discovery
Not really, no, all of the current models built to intended scale are selling it as a product, especially OpenAI, Microsoft, and Google. It was built with a purpose and that purpose was to potentially replace expensive human assets.
Yes, it was. Like all scientific discoveries several corporations started building proprietary products. You are wrong that it was built with that purpose.
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Using AI to automate super tedious and repetitive tasks is great and everybody should start doing it
Yeah, there’s many times I type “class for:” followed by a a dump of SQL, JSON, XML or whatever and it’ll make a class with properties named correctly with the right types. I still have to figure out tricky data relationships and that sort of thing, but the boring tasks of creating interfaces to databases and objects for serializing stuff goes a lot faster now.
So a much larger percentage of my time is devoted to solving problems rather than doing all the boring grunt work usually involved with getting data in and out of the app.
Yeah current gen AI is still very much a human tool - an assistant - maybe a companion if you stretch it to it’s edge. I for one welcome a personal AI buddy
Too many people see AI doing work as an either or thing. AI won’t replace people outright, it’ll just reduce the amount of people you need.
Which in turn replaces people. What happens if a person is 50 percent more productive with AI? Is the company going to let them simply have 50% of the workload they would before, or will they lay off the other unneeded employees?
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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.
Honestly it sounds extremely generous by saying the best results can be achieved by experts with GenAI. In my opinion the best results can be achieved without it entirely.
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?
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.
ai automates the behavior of an average agent, not a talented one
This is a quote that should end in ‘yet’. I am very confident in saying there will be an AAA game released that will be designed and implemented 95%+ by a machine. I am less confident in providing a timeline. If you consider the history of machine learning is ~70 years old (in one sense, one can argue other dates) and you plot the advances from tic-tac-toe to what machines can do today (chess being a prime example), it doesn’t take much vision to see that it won’t be but a matter of time before this is a real thing.
Sure it may produce a game but much of what makes a game good is making it fun and memorable. If we can eventually create a general AI then absolutely I think such a thing is possible. Otherwise it will be a copypasta mishmash and having a cohesive and fluent design is a huge if.
lmfao
This kid is not going places.
you’re right, you won’t.
I look forward to the day it can make a fully functioning game. The best games will mostly be AI created eventually
I respect your opinion, but it’s one of the stupidest I’ve ever heard
You are confidently incorrect and I can respect that.
