Ah, well so long as it's discouraged... I'm sure no one's critical systems would depend on it, right?
2,662 karma · joined October 11, 2022
Those who are obsessed with their reputation above morality have usually had a lot of practice punching down, and they don't really look as bad as someone being dunked on who gets confused.
I think this is like a cornerstone of bullying. It seems to work in social situations. I'm sure everyone reading this comment can picture it.
Under them - an organization in partnership with Microsoft, together filled with exceptional software engineers and scientists - experts in their field. All under management by kindergarteners.
I wonder if this is what the staff are thinking right now. It must feel awful if they are.
There’s a whole genre of press releases and videos for apologies, so I’m not sure it’s such a reputational risk to admit one is wrong. It might be a bigger risk not to, it would seem.
But what you say sounds interesting.
1. Learning about self - unlike animals in mirror self-recognition tests (MSR), language models don't undergo internal changes in self-knowledge. Their data is static post-training, and no real learning can occur. Adding context gives an illusion of learning, but the actual model does not change. Specifically, deep learning does not happen.
2. Recognizing self - merely describing oneself isn't self-recognition. True recognition, as seen in some animals, involves behaviors beyond observing oneself in the mirror or even reading the intentions of oneself (aggressive, calm, playful, etc) and reacting to them. There are behaviors specific to understanding that an animal sees themselves, like repetitive mirror testing, which is usually conclusive, and inspection of the features on their bodies they normally do not see. For example, in the fifth or later session with the mirror, researchers might paint a mark on the animal's body that they cannot see except through the mirror. Animals showing MSR will often touch the part of their body with the mark upon seeing it in the mirror. This is strong evidence they understand they are seeing themselves.
Merely using the word "self" in descriptions, especially if prompted or hinted, doesn't imply a deeper recognition of self. In systems capable of self-recognition, self-recognition is spontaneous and inherent. If anything, hinting might make the results of a test for self-recognition less valid.
3. Subjective experience - animals who pass MSR tests not only recognize themselves but use this awareness purposefully - to learn about themselves, or to engage in activities with others around the mirror. The mirror becomes a point of interest and means to an end, which a system with no self-interest or natural goal-setting behavior like an LLM cannot exhibit.
4. Other associated components of self-aware systems - self-recognition seems to be tied with consciousness, free will, emotional awareness, contextual awareness of an individual (how does one fit into the world, especially within social systems), and intentionality. We cannot observe signs of this in LLMs. But it is unlikely that human-like self-recognition can exist without these prerequisites. Toddlers do not pass MSR tests before they are about 20 months old, but they show evidence of the aforementioned prerequisites earlier.
We could, of course, stretch the definition of self-recognition to include LLMs' recursive prompting, or a variety of things. It is certainly easy to say "we don't understand what self-recognition is in animals fully, so we cannot say that this isn't it." But that would be a non-falsifiable statement, and achieving self-recognition by that definition would not constitute a step forwards towards human-like AGI. It would just be playing with semantics.
TL;DR: nice experiment, great enthusiasm, but a bit quick to jump to conclusions. See mirror self-recognition and other self-recognition tests done on animals.
Way to pollute the planet so much the bible becomes an instruction manual lol (no offense to believers, just a joke)
But where do you fit other adult responsibilities, chores, social life, spiritual life, self-care, entertainment, learning, hobbies, intellectual pursuits, and other things? Where do you fit your commute, shopping, and kids - the non-negotiables? So something has to give. Sleep or exercise, right? Perhaps this is why we're all so unhealthy?
You have exceptionally strong opinions about USB A and C.
Injustices are made to executives all the time. But airing dirty laundry is not sagacious.
So what was there to gain from the company speaking ill of their past employee? What was even left to say? Nothing. No one wants to work in an organization that vilifies its own people. It was prudent.
I will emphasize again that the morality of these situations is a separate matter from tact. It is very well possible that doing what is good for business does not always align with what is moral. But does this come as a surprise to anyone?
We can recognize that the situation is not one dimensional and not reduce it to such. The same applies to the press release from Open AI - it is graceless, that much can be observed. But we do not yet know whether it is reprehensible, exemplary, or somewhere in between in the sense of morality and justice. It will come out, in other channels rather than official press releases, like in Bobby's case.
Certainly, this is very immature. It wouldn't be out of context in HBO's Succession.
Whether what happened is right or just in some sense is a different conversation. We could speculate on what is going on in the company and why, but the tactlessness is evident.
Convincing everyone that content online is free while taking payment for it is one of the greatest sleights of hand of the century. That kind of thing used to be called a con.
I think that’s the case with every discipline for it, not only programming. Even when everyone was amazed it could make poetry out of everything, if you asked for a specific type of poem and specific imagery in it, it would generally fail.
Not really. In AAA game programming, you mostly own the same systems you specialize in throughout the production process.
For example, someone in Rockstar North might work on the minimap for the entire production of a game.
In smaller AAA companies, a person might own vehicles or horses, or even the entire progression system. But still, programmers are rarely working on disconnected things.
You rarely step out of your expertise zone. And you are usually expected to perform much better than GPT would in that zone.
Documents and code are confidential in the AAA games industry as they are the money makers. Developers are not free to hand them over to third parties, that would be known as a leak. With textbooks, that would be a pretty grey area use case. So I’ve not experimented with that.
I think it could help, but because it’s so infeasible practically, there’s no incentive to try this with synthetic data, too.
GPT code in a lot of critical path systems wouldn’t pass code review, not probably integrate well enough into any bespoke realtime system. It seems to be more useful in providing second opinions on high level decisions to me, but still not useful enough to use.
Maybe it could help with some light Lua or C# gameplay scripting, although I think co-pilot works much better. But all that doesn’t matter as due to licensing, the AAA industry still generally can’t use any of these generative AIs for code. Owning and being able to copyright all code and assets in a game is normally a requirement set by large publishers.
To conclude, my experience is indeed very different from yours.
No one said regularly or intentionally. Note that this thread stems from a comment talking about producing a one-off business report.
Unfortunately, it hasn’t been helpful once, and often due to the same reason - when the question gets specific enough, it hallucinates because it doesn’t know, just like in the early days.
Moreover, I am a domain expert in my area, so I only ask for help when the problem is really difficult. For example, when it would take me several days to come up with an answer and a few more weeks to refine it.
Game development has a lot of enthusiasts online sharing material, but most of this material is at junior to intermediate level. You very quickly run out of resources for questions at a principal level, even if you know the problems you have have been solved in other AAA companies.
You have to rely on your industry friends, paid support from middleware providers, rare textbooks, conferences, and, on the off-chance that anything useful got scooped up into the training data set - GPT. But GPT has been more like wishful thinking for me.
When I’m stumped, it’s usually on a complex and very multi-faceted problem where the full scope doesn’t fit into the human brain very well. And for these problems, GPT will produce some borderline unworkable solutions. It’s like a jack of all trades and master of none in code. It’s knowledge seems a mile wide and an inch deep.
Granted, it could be different for junior to mid programmers.
It takes a bit of effort even to get an app on public TestFlight on Apple’s side. Plus the fee, which is reasonable for a thorough review.
Anyways, I edited my comment to not misrepresent the Play Store process, thanks.
What if this is a one-off to produce a business report? Would it make sense to use programmer time to create an O(n) structure in memory, or just loop through the files line by line and let the CPU take a minute or five, or thirty? What is the programming language - something that has a library for this or something very low level where we’d read the file byte by byte?
If we’re dealing with the latter, a small amount of data, and a one off report, I don’t care at all in my work whether an engineer I’m managing somehow writes it in O(n^3).
It’s interesting how quick to judge the author is - ask this question for points, don’t even think about that, don’t mention arrays because they’re fixed size (despite implementations for dynamically allocated arrays totally existing and the candidate might be coming from that), and so on. Some humility would be nice.
Although I think what they wrote is very valuable, as this is how many interviews go. And I have to at least appreciate the author’s approach for trying to start a conversation, even if he still takes a rather reductive approach to evaluating candidates.