501–510 of 604 posts
Wow, this is incredible news! Congratulations to the team behind Devin, the first AI engineer! This is a monumental leap forward in technology and innovation. I'm absolutely thrilled to see how Devin will revolutionize the field of engineering. As someone passionate about the potential of AI in tech, I can't wait to see what amazing feats Devin will accomplish. And who knows, maybe one day, companies like Munesoft Te…
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Don't forget the 20/80 rule. They haven't even gotten to 15% yet. Our jobs are safe. I would even expect more "beginners" to try something with AI and then need an actual programmer to help them ( At least, if they are unwilling to invest the time in development and debugging themselves Ps. Probably all the given examples are in top 3 most popular programming languages.
Machines currently are at an amateur level, but amateurs across the board on the knowledge base. Amateurs at Python, Fortran, C, C++ and all programming languages. Amateurs at car engineering, airplane engineering, submarine engineering etc. Amateurs at human biology, animal biology, insect biology and so on. I don't know anyone who is an amateur at everything.
No, and that is a one of their limitations. LLMs are human-level or above on some tasks - basically on what they were trained to do - generating text, and (at least at some level) grokking what is necessary to do a good job of that. But, they are at idiot level on many other tasks (not to overuse the example, but I just beat GPT-4 at tic-tac-toe since it failed to block my 2/3-complete winning line).
Things like translation and summarization are tasks that LLMs are well suited to, but these also expose the danger of their extremely patchy areas of competence (not just me saying this - Anthropic CEO recently acknowledged it too). How do you know that the translation is correct and not impacted by some of these areas of incompetence? How do you know that the plausible-looking summary is accurate and not similarly impacted?
LLMs are essentially by design ("predict next word" objective - they are statistical language models, not AI) cargo-cult technology - built to create stuff that looks like it was created by someone who actually understands it. Like (origin of term cargo-cult) the primitive tribe that builds a wooden airplane that looks to them close enough to the cargo plane that brings them gifts from the sky. Looking the same isn't always good enough.
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At least in theory we can achieve incremental learning by training from scratch every time we got some new training data. There are drawbacks with this approach such as inconsistent performance for different training runs and significantly higher training cost but it's achievable. Now the problem is if there exist methods more efficient than gradient descent? I think it's very clear now that there are no other algori…
The obvious alternative to gradient descent here would be Bayes Formula (probabalistic Bayesian belief updates), since this addresses the exact problem that our brains evolved to optimize - how to utilize prediction failure (sensory feedback vs prediction) to make better predictions - better prediction of where the food is, what the predator will do, how to attract a mate, etc. Predict next word too (learn language),…
If you know a little about the math behind gradient descent you can see that an embedding layer followed by a softmax layer gives you exactly the best Bayes estimate. If you want a bit of structure, like every word depends on previous n words, you get a convolutional RNN which is also well studied. These ideas are natural and elegant but maybe a better idea is to comprehend the research already done to avoid diving into dead ends too much.
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I recently had an introspective dream revealed to be based on a literal prompt at the end: "Game to learn to talk about It and its player." When I asked GPT to craft a plot from this prompt's title (and the fact it is revealed at the end), it reproduced the dream's outline, down to the final scene: GPT reconstruction: The dream reaches its peak when you meet the "final boss" of the game: an entity that embodies the u…
Look I don't mean to downplay. Or maybe I do. But we're talking about LLM replacing professional problem solvers, software architects, not generating great sounding probability modeled token distributions.
In less time that it would take me to figure out how dictionary comprehension work in python. But hey, I guess you already know about these use cases.
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Those optimizations increase the value of labor by greatly increasing the amount of output per unit of labor. Do you understand how much wealthier the modern worker is than the pre Industrial Revolution peasant?
Pre-industrial societies may not have had flat screen tvs, but they had time. The only thing you can't buy.
That said if you are satisfied with a pre industrial quality of life no one is stopping you from doing zero hours of work and just becoming homeless.
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This is objectively accurate, and I think you mean to be nothing but. However, the mechanistic focus on efficiency and profit at the direct expense of human quality of life is downright goulish. We need political changes that ensure people who are “outmoded” by this tech can still have good lives.
The mechanism for this is charity. I understand people like to see social welfare programs, but I believe they are inferior to private sector charity because they ignore moral hazard, tend to produce worse outcomes, and are less economically efficient. At the core, welfare steals from all to provide provide for some. This introduces deadweight loss. I see a future where those displaced by automation are able to retra…
"they ignore moral hazard"
Sometimes economic risks are negative, like spending your paycheck on lottery tickets, but other times they're positive, like starting a risky but ambitious startup or getting an education. If social welfare causes a significant change in the average person's risk tolerance, a claim that definitely requires substantiation, then I still might argue this isn't a net negative for the economy or society at large. Even if the claim is true, is using homelessness and privation as a threat (a threat often realized upon the unfortunate) a worthwhile ethical sacrifice? How much does it cost us not to do this, because I think it's worth some money.
"tend to produce worse outcomes"
If this is true, which I also wouldn't take for granted, then you'd still need to factor in the asymmetry in access that private charities have vs a social welfare system. Perhaps a person who gets the aid of charity benefits more than one who gets welfare, but does the average person?
"are less economically efficient"
This is likely to be true simply because government tends to be inefficient, but can be worth it for the above reasons and can be assessed objectively. I'd be fine with spending some margin more than should be spent, say 50%, if it means that 95% rather than 10% of people have access to food or housing or whatever else.
Overall, I don't see how charity can even serve as a mechanism for what we're talking about. If we see unprecedented unemployment due to AI, how exactly do we expect voluntary charity to expand to meet demand? What can we do other than some form of wealth redistribution if we don't want lots of people to starve? Furthermore, why can't we do both? If charity will expand to meet demand, then let's use social welfare to fill in the gaps and prevent destitution.
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I often ask a similar question of what happens when we, as humans, have offloaded everything to technology, to AI to Robots? What kind of a society will we have then? When you no longer have to think about how to do something, or how to build or repair something, or create something original from your imagination. I shudder to think the direction this is all leading to.
Raising kids, social stuff, exercise, travel, any leisure stuff like racing, horse ridding, acrobatics. entertainment, cooking, art, gardening etc. Just check what rich girls do and you will see they aren’t bored though they don’t have to work.
Exactly zero of that unlocked time/value will come back to the workers.
It simply can't work within capitalism. We've seen it before so many times that it's a given. It's a natural conclusion to this.
Textile workers don't get happy when a machine can replace them despite it meaning they won't get injured anymore and this brutal job will get easier. It's objectively a good thing, but because our society ties all our livelihoods to work, they have to hate the machine.
Given how low standards are, executives will layoff swaths of people for a crappy AI very soon. I'm genuinely scared of how our world will cope with that.