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The end of classical Computer Science is coming

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Re: The end of classical Computer Science is coming

#11

Post by q-big »

Rochus wrote: post #5

> Fast forward to today, and I am willing to bet good money that 99% of people who are writing software have almost no clue how a CPU actually works The fact that many people do not know exactly enough what they are doing can be seen in the result. The people whose goal is to write as robust and efficient software as possible still do have to know and control the details. It's like driving a car; you do not have to b…

> It's like driving a car; you do not have to be a engineer to drive one; but the more you want to push the limits of performance, the more you need to know about the details.

This is a potential argument why users don't have to know the arcane details of how the internals of a CPU works, but on the other hand a good argument why programmers should better have a quite good knowledge about that.

Re: The end of classical Computer Science is coming

#12

Post by chowells »

jmfldn wrote: post #2

This keeps me awake at night I must admit. How do I best future proof my career? I was sceptical about this until I started playing with GPT 3 and has it not only writen code for me, but also "explained" code to me. Sure, it's kind of limited right now, but it can only be a matter of time now before this all radically improves. Maybe I should focus on system design and translating the messy real world into systems. T…

Just take a look at the generated code and explanations. A surprising amount of it is subtly but fundamentally wrong because gpt is just a regurgitation engine. The issues may look superficial, but when you start looking at why they happen, you realize the truth. The ml tools are usually great at writing boilerplate that's the same every time. The instant you do anything else, they fall over. They're statistical autocomplete, not any kind of important change to the process of programming.

I don't see any reason to believe the current approaches can extend to something that actually changes programming. They're not based on understanding code, they're based on generating text that matches what they would expect to see given the context. They have no model of what code means, so they can't model why sometimes code is subtly different if there are no local contextual cues. And when there are your prompt would need to reproduce those contextual cues for it to key off of. In other words, you as the programmer still are directing the generation of the code. You're just doing it via an undocumented and somewhat unpredictable autocomplete.

This doesn't remove the need to have someone who knows what they're doing in the loop. Best case is that it reduces the amount of time you spend typing by a little bit. As long as your job is to know what you're doing rather than to generate text, the current systems are no threat to it.

Re: The end of classical Computer Science is coming

#13

Post by shakow »

dwohnitmok wrote: post #9
Rochus wrote: post #5

> Fast forward to today, and I am willing to bet good money that 99% of people who are writing software have almost no clue how a CPU actually works The fact that many people do not know exactly enough what they are doing can be seen in the result. The people whose goal is to write as robust and efficient software as possible still do have to know and control the details. It's like driving a car; you do not have to b…

> we are obviously still a long way from replacing humans as drivers. This is only because we as a society have an extremely low tolerance for errors in automated driving and essentially require by default superhuman performance (a self-driving car with an error rate of the median human would never be allowed to be set loose by itself). In scenarios where a 0.1% error rate, 1% error rate, or maybe even 10% error rate…

AI is making stride in comparatively easy environments, i.e. highways and squared suburbs – when they don't crash into white trucks.

Now, what tends to be forgotten is that there AI-average vs. human average is that humans can also drive e.g. in Turin or Paris at rush hour, on mountain roads under the snow or in the Cornwalls roads while under tempest rains.

It's not that I believe that self-driving AI will never progress to this level, but let us be honest when comparing; they still drive themselves into fully-visible plots by daylight or run over cyclists at night.

Re: The end of classical Computer Science is coming

#16

Post by dwohnitmok »

shakow wrote: post #13
dwohnitmok wrote: post #9

Earlier quoted context omitted.

> we are obviously still a long way from replacing humans as drivers. This is only because we as a society have an extremely low tolerance for errors in automated driving and essentially require by default superhuman performance (a self-driving car with an error rate of the median human would never be allowed to be set loose by itself). In scenarios where a 0.1% error rate, 1% error rate, or maybe even 10% error rate…

AI is making stride in comparatively easy environments, i.e. highways and squared suburbs – when they don't crash into white trucks. Now, what tends to be forgotten is that there AI-average vs. human average is that humans can also drive e.g. in Turin or Paris at rush hour, on mountain roads under the snow or in the Cornwalls roads while under tempest rains. It's not that I believe that self-driving AI will never pro…

> humans can also drive e.g. in Turin or Paris at rush hour, on mountain roads under the snow or in the Cornwalls roads while under tempest rains.

I'm not sure the median human driver can do all that.

Re: The end of classical Computer Science is coming

#17

Post by bigbillheck »

> A time traveller from even 20 years ago would have a hard time making sense of the three sentences in the (75-page-long!) GPT-3 paper that describe the actual software that was built for the model

First off, the only one of those three sentences that a 2002 researcher would be stumped by is the first, and that solely due to the unfamiliar nouns. The other two sentences are perfectly classical, and the only difficulty one of the ancients would have is putting their eyes back in after they popped out on seeing the model sizes.

Second, isn't that good? It means the field has advanced, and there are new concepts being used, which I'd have thought is exactly what we want.

Third, how different is this than the past? Would a time traveller from 1982 be equally stymied by a paper from 2002? How about 1962 to 1982?

Re: The end of classical Computer Science is coming

#18

Post by angry_octet »

bigbillheck wrote: post #8

> 99% of people who are writing software have almost no clue how a CPU actually works, let alone the physics underlying transistor design. My undergraduate education was in the early 90s, and at no point in my life have I ever had much of a clue regarding the physics underlying transistor design. EDIT: also at one time I probably did have a reasonably solid grasp on how CPUs work, there's been an awful lot of advance…

For a CS person transistor design would be useless, but for EE it’s still core knowledge. But CPU design constraints are still the same now as they were in the 90s: speed of light, cache and coherency architecture, Amdahl’s law, pipelining. You’re way ahead of most coders if you understand even the basics of memory hierarchy.

Re: The end of classical Computer Science is coming

#19

Post by SrslyJosh »

chatterhead wrote: post #10

There is a reason we are getting robot dogs with guns on their backs at the same time AI is advancing; because once AI crosses a certain line it is going to be powerful enough to nullify most jobs. It's not science fiction. What happens when you have a NN that understands how to integrate new physical input and render usable actions for creating outputs without human intervention? That's where we get machines buildin…

> What happens when you have a NN that understands how to integrate new physical input and render usable actions for creating outputs without human intervention?

You're going to have to define "understands" and explain how to get there from where the technology is right now, because a model is a statistical artifact and doesn't "understand" anything, including its inputs and outputs.

> How about recreational drugs designed for specific kind of Overdoses - like crumple zones on a car?

Why would anyone want an overdose?

> Or using AI to find the best cocktail of psychedelics that allow us all to work stoned and to maximum benefit all day long without diminishing returns?

Who benefits from this? Because it doesn't sound like it would benefit the people doing the work.

> We really are very close now.

Just a few more puffs and I'm sure you'll have the solution.

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