I came across this article in another Lemmy community that dislikes AI. I’m reposting instead of cross posting so that we could have a conversation about how “work” might be changing with advancements in technology.
The headline is clickbaity because Altman was referring to how farmers who lived decades ago might perceive that the work “you and I do today” (including Altman himself), doesn’t look like work.
The fact is that most of us work far abstracted from human survival by many levels. Very few of us are farming, building shelters, protecting our families from wildlife, or doing the back breaking labor jobs that humans were forced to do generations ago.
In my first job, which was IT support, the concept was not lost on me that all day long I pushed buttons to make computers beep in more friendly ways. There was no physical result to see, no produce to harvest, no pile of wood being transitioned from a natural to a chopped state, nothing tangible to step back and enjoy at the end of the day.
Bankers, fashion designers, artists, video game testers, software developers and countless other professions experience something quite similar. Yet, all of these jobs do in some way add value to the human experience.
As humanity’s core needs have been met with technology requiring fewer human inputs, our focus has been able to shift to creating value in less tangible, but perhaps not less meaningful ways. This has created a more dynamic and rich life experience than any of those previous farming generations could have imagined. So while it doesn’t seem like the work those farmers were accustomed to, humanity has been able to shift its attention to other types of work for the benefit of many.
I postulate that AI - as we know it now - is merely another technological tool that will allow new layers of abstraction. At one time bookkeepers had to write in books, now software automatically encodes accounting transactions as they’re made. At one time software developers might spend days setting up the framework of a new project, and now an LLM can do the bulk of the work in minutes.
These days we have fewer bookkeepers - most companies don’t need armies of clerks anymore. But now we have more data analysts who work to understand the information and make important decisions. In the future we may need fewer software coders, and in turn, there will be many more software projects that seek to solve new problems in new ways.
How do I know this? I think history shows us that innovations in technology always bring new problems to be solved. There is an endless reservoir of challenges to be worked on that previous generations didn’t have time to think about. We are going to free minds from tasks that can be automated, and many of those minds will move on to the next level of abstraction.
At the end of the day, I suspect we humans are biologically wired with a deep desire to output rewarding and meaningful work, and much of the results of our abstracted work is hard to see and touch. Perhaps this is why I enjoy mowing my lawn so much, no matter how advanced robotic lawn mowing machines become.
Talking psychology, please stop calling it AI. This raises unrealistic expectations. They are Large Language Models.
Raising unrealistic expectations is what companies like OpenAI are all about
In computer science machine learning and LLMs are part of AI. Before that other algorithms were considered part of AI. You may disagree, probably because all the hype around LLMs, but they are AI
Granting them AI status, we should recognize that they “gained their abilities” by training on the rando junk that people post on the internet.
I have been working with AI for computer programming, semi-seriously for 3 months, pretty intensively for the last two weeks. I have also been working with humans for computer programming for 35 years. AI’s “failings” are people’s failings. They don’t follow directions reliably, and if you don’t manage them they’ll go down rabbit holes of little to no value. With management, working with AI is like an accelerated experience with an average person, so the need for management becomes even more intense - where you might let a person work independently for a week then see what needs correcting, you really need to stay on top of AI’s “thought process” on more of a 15-30 minute basis. It comes down to the “hallucination rate” which is a very fuzzy metric, but it works pretty well - at a hallucination rate of 5% (95% successful responses) AI is just about on par with human workers - but faster for complex tasks, and slower for simple answers.
Interestingly, for the past two weeks, I have been having some success with applying human management systems to AI: controlled documents, tiered requirements-specification-details documents, etc.
I have no idea what you’re doing, but based on my own experience, your error/hallucination rate is like 1/10th of what I’d expect.
I’ve been using an AI assistant for the better part of a year, and I’d laugh at the idea that they’re right even 60% of the time without CONSTANTLY reinforcing fucking BASIC directives or telling it to provide sources for every method it suggests. Like, I can’t even keep the damned thing reliably in the language framework I’m working on without it falling back to the raw vendor CLI in project conversations. I’m correcting the exact same mistakes week after week because the thing is braindead and doesn’t understand that you cannot use reserved keywords for your variable names. It just makes up parameters to core functions based on the question I ask it, regardless of documentation until I call it’s bullshit and it gets super conciliatory and then actually double checks it’s own work instead of authoritatively lying to me.
You’re not wrong that AI makes human style mistakes, but a human can learn, or at least generally doesn’t have to be taught the same fucking lesson at least once a week for a year (or gets fired well before then). AI is artificial, but there absolutely isn’t any intelligence behind it, it’s just a stochastic parrot that somehow comes to plausible answers that the algorithm expects that you want to hear.
This is the point nobody seems to get. Especially people that haven’t worked with the technology.
It just does not have the ability to learn in any meaningful way. A human can learn a new technique and move to master simple new techniques in a couple of hours. AI just keeps falling back on its training data no matter how many times you tell it to stop. It has no other option. It would need to be re-trained with better material in order to consistently do what you want it to do, but nobody is really re-training these things…they’re using the “foundational” models and at most “fine-tuning” them…and fine-tuning only provides a quickly punctured facade…it eventually falls back to the bulk of its learning material.
I’m having AI write computer programs, and when I tried it a year ago I laughed and walked away - it was useless. It has improved substantially in the past 3 months.
Yes, that is the “limited context window” - in my experience people have it too.
I have given my AIs basic workflows to follow for certain operations, simple 5 to 8 step processes, and they do them correctly about 19 times out of 20, but that 5% they’ll be executing the same process and just skip a step - like many people tend to as well.
In the past week I have been having my AIs “teach themselves” these workflows and priorities. Prioritizing correctness over speed, respecting document hierarchies when deciding which side of a conflict needs to be edited, etc. It seems to be helping somewhat. I had it research current best practices on context window management and apply it to my projects, and that seems to have helped a little too. But, while I type this, my AI just ran off and started implementing code based on old downstream specs that should have been updated to reflect top level changes we just made, I interrupted it and told it to go back and do it the right way, like its work instructions already tell it to. After the reminder it did it right : limited context window.
The main problem I have with computer programming AIs is: when you have a human work on a problem for a month, you drop by every day or two to see how it’s going, clarify, course correct. The AI does the equivalent work in an hour and I just don’t have the bandwidth to keep up at that speed, so it gets just as far off in the weeds as a junior programmer locked in a room and fed Jolt cola and Cheetos through a slot in the door would after a month alone.
An interesting response I got from my AI recently regarding this phenomenon was: it provided “training seminar” materials for our development team telling them how to proceed incrementally with the AI work and carefully review intermediate steps. I already do that with my “work side” AI project, it didn’t suggest it. My home side project where I normally approve changes without review is the one that suggested the training seminar.
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You missed the psychology part?
No, I saw it, but I was replying to the “please stop calling it AI” part. This is a computer science term, not a psychology term. Psychologists have no business discussing what computer scientists call these systems
What do i even answer here…
Who talks even about computer scientists? It’s the public and especially company bosses who get wrong expectations about “intelligence”. It’s about psychology, not about scientifically correct names.
The solution to the public misusing technical terms isn’t to change the technical terms, but to educate the public. All of the following fall under AI:
AI is a broad field, and you probably interact with non-LLM variants every day, whether you notice or not. Here’s a Wikipedia article that goes through a lot of it. LLMs/GPT are merely one small subfield in the larger field of AI.
I don’t understand how people went from calling the computer player in their game “AI” (or even older, “CPU”), which nobody mistook for actual intelligence, to now people believing AI means something is sentient. Maybe it’s because LLMs are more convincing since they do a much better job at languages, idk, but it’s the same category of thing under the hood. ChatGPT isn’t “thinking,” and when it claims to “think,” it’s basically turning a prompt into a set of things to “think” about (basically generates and answers related prompts), and then uses that set of things in its context to provide an answer. It’s not actually “thinking” as people do, it’s merely following a set of statistically-motivated steps based on your prompt to generate a relevant answer. It’s a lot more complex than that Warcraft 2 bot you played against as a kid, but it’s still following steps a human designed, along with some statistical methods to adapt to things the developer didn’t encounter.
Ah, I see. We in the software industry are no longer allowed to use our own terms because outsiders co-opted them.
Noted.
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Dude, what age are you? 13? Log off and go play with your friends.