Even in good times, CS was poorly matched with the jobs people got the degree for. People put up with the BS because there was gold at the end of the rainbow.
This is true. I loved my time doing Compsci in university and I feel I was probably more suited to doing that for career, but software jobs were plentiful and I still don't know where I would find a computer science job aside from a university
Is this a terminology difference? Where I am, most computer science people are getting software jobs, and they're considered to be computer science jobs. So, I find this remark confusing.
That's one of those decades-long debates that continues. Is CS actually "science"? Are developers actually "engineers"? Is a person in charge of a Kanban board an "architect"?
There are absolutely computer science jobs, but in general, a casual distinction would be what side of the research/implement line you fall (though in truth, it's probably more of a 2D graph than a hard line).
Building software is just one application of computer science
It's a bit like the difference between someone who is a psychologist helping people in therapy, and a psychologist who studies psychology in a lab. They require similar educations but they are very different professions
Some CS departments are known to be "theory" heavy whereas others are more "applied" heavy. In theory heavy departments many students do not get exposed to the day-to-day details of writing perfomant code and they have the feel of being an offshoot of the math department as opposed to engineering.
I don't think that it's primarily about jobs. At this rate, CS is going to be the first field to be "solved." By 2030 we'll probably know whether P=NP (like many others, I'm starting to suspect that it does,) and just about every other open problem shall fall, so everything that seemed important to us from first principles will be known.
> CS is going to be the first field to be "solved."
It seems to me that such a prediction can only be made based on a fundamental misunderstanding of the purpose and meaning of the field. It's not only about "computers" or "science". And "solving" may not even be the most important aspect of it. Same with mathematics.
It's like saying, we will solve algorithmic thinking, or we will figure out the machine to end all machines. OK, getting closer..
Or that we will make the ultimate tele/microscope that sees everything, the entirety the universe, all 93 billion light-years of it down to the Planck scale. Or that we will fully understand the human mind, all 1.3 kilograms of grey matter. The mystery is not only about the "brain", although it likes to think of itself as the quintessence of existence. And logical thinking may not even be the most important aspect of it. Same with art, biology, music, or theology.
Finally, it's like saying, science will "solve" solving in the next few years. As we get closer, we will find that there will be more and more questions, a seemingly infinite well-spring of answers. Perhaps in the end, the universe will understand itself, and God will awaken from the nightmare of history. What room then, for humanity?
Im 40 years old and in my time on earth theres only a few things Im still certain of - and one of them is every time a new bit of knowledge is discovered it creates 2 more questions.
> CS is going to be the first field to be "solved."
With due respect, your statement reflects a lack of experience building production software. In real-world software systems, code is only one aspect. Automation helps, but "what needs to be built" is a harder problem and remains a human endeavor.
To be pedantic about it, CS =/= software engineering. CS is a scientific field that's more closely related to math or analytic philosophy. It concerns itself with what is computationally possible, with what is tractable, with algorithm design principles, and broadly with information theory.
The scientific field doesn't have that many open problems -- whether P=NP is the greatest and most famous -- and they're set to fall like dominoes. A condition where CS is "solved" and has no major open problems is a very real near-term possibility, and probably a medium-term inevitability.
Though software engineering is a fundamentally different discipline, none of this implies that software development isn't in trouble. Human planners and software architects might remain, but very soon LLMs are going to write virtually all code.
The same will happen in biotech, pharma and many fields where data is easier to process with ai. Its already in progress. The only fields immune are where the cost of robotics exceeds the cost of humans. Say aircraft maintenance, ,mechanics, etc.. fields where robotics are still way more expensive
The problem with biotech/pharma is that data processing doesn't work if you lack the data to begin with. There are too many holes in science's understanding of how the cell works, how the organism ages, etc., and LLMs can't easily fill these in. Hypotheses can and will be generated, but it will take great lengths of time and comparatively heroic efforts before the picture becomes much clearer.
The contrast with math and CS couldn't be starker, really. Everything that hinges on formal logic or logical relations is done for.
Hm. I think it depends on two things: (1) how sample-efficient AI can become if CS and mathematics becomes "solved", (2) if AI still sucks on this metric compared to biological brains despite that, can it at least self-play its way to solutions?
Given what AI could do with protein folding, I wouldn't bet on biology being a "no" for AI. It might be, but I wouldn't bet on it.
Likewise, if AI can do mathematics so well it solves "just" all of computer science, let alone other branches of mathematics, if this somehow doesn't lead to AI becoming similarly sample-efficient as biological brains, that's very interesting and will say something nice and surprising about the nature of minds and possibly the nature of being alive.
I think Ai understands biology better than humans because it can correlate everything where humans can never know enough to correlate all. But Ai will not just focus on a solution till it is given a runway to solve and deliver.
There is a lot more data than one individual can process. Is there something that humans can know and it wouldn't, I pretty much doubt it. But can it do experiments that are biological? Yes it can order it I think; it probably knows where its knowledge is ambiguous.
Even in good times, CS was poorly matched with the jobs people got the degree for. People put up with the BS because there was gold at the end of the rainbow.
This is true. I loved my time doing Compsci in university and I feel I was probably more suited to doing that for career, but software jobs were plentiful and I still don't know where I would find a computer science job aside from a university
Is this a terminology difference? Where I am, most computer science people are getting software jobs, and they're considered to be computer science jobs. So, I find this remark confusing.
That's one of those decades-long debates that continues. Is CS actually "science"? Are developers actually "engineers"? Is a person in charge of a Kanban board an "architect"?
There are absolutely computer science jobs, but in general, a casual distinction would be what side of the research/implement line you fall (though in truth, it's probably more of a 2D graph than a hard line).
Computer Science is the study of computing
Building software is just one application of computer science
It's a bit like the difference between someone who is a psychologist helping people in therapy, and a psychologist who studies psychology in a lab. They require similar educations but they are very different professions
Some CS departments are known to be "theory" heavy whereas others are more "applied" heavy. In theory heavy departments many students do not get exposed to the day-to-day details of writing perfomant code and they have the feel of being an offshoot of the math department as opposed to engineering.
https://wallhop.io/https://www.bloomberg.com/news/articles/2...
I don't think that it's primarily about jobs. At this rate, CS is going to be the first field to be "solved." By 2030 we'll probably know whether P=NP (like many others, I'm starting to suspect that it does,) and just about every other open problem shall fall, so everything that seemed important to us from first principles will be known.
> https://en.wikipedia.org/wiki/List_of_unsolved_problems_in_c...
Then advances in algorithmic efficacy will be the province of machines, who will act in ways and at speeds that are beyond human.
What room, then, for CS students?
I wonder if biology and theology are picking up students. Those fields, I'd bet, won't be solved within our lifetimes.
> CS is going to be the first field to be "solved."
It seems to me that such a prediction can only be made based on a fundamental misunderstanding of the purpose and meaning of the field. It's not only about "computers" or "science". And "solving" may not even be the most important aspect of it. Same with mathematics.
It's like saying, we will solve algorithmic thinking, or we will figure out the machine to end all machines. OK, getting closer..
Or that we will make the ultimate tele/microscope that sees everything, the entirety the universe, all 93 billion light-years of it down to the Planck scale. Or that we will fully understand the human mind, all 1.3 kilograms of grey matter. The mystery is not only about the "brain", although it likes to think of itself as the quintessence of existence. And logical thinking may not even be the most important aspect of it. Same with art, biology, music, or theology.
Finally, it's like saying, science will "solve" solving in the next few years. As we get closer, we will find that there will be more and more questions, a seemingly infinite well-spring of answers. Perhaps in the end, the universe will understand itself, and God will awaken from the nightmare of history. What room then, for humanity?
Im 40 years old and in my time on earth theres only a few things Im still certain of - and one of them is every time a new bit of knowledge is discovered it creates 2 more questions.
> CS is going to be the first field to be "solved."
With due respect, your statement reflects a lack of experience building production software. In real-world software systems, code is only one aspect. Automation helps, but "what needs to be built" is a harder problem and remains a human endeavor.
To be pedantic about it, CS =/= software engineering. CS is a scientific field that's more closely related to math or analytic philosophy. It concerns itself with what is computationally possible, with what is tractable, with algorithm design principles, and broadly with information theory.
The scientific field doesn't have that many open problems -- whether P=NP is the greatest and most famous -- and they're set to fall like dominoes. A condition where CS is "solved" and has no major open problems is a very real near-term possibility, and probably a medium-term inevitability.
Though software engineering is a fundamentally different discipline, none of this implies that software development isn't in trouble. Human planners and software architects might remain, but very soon LLMs are going to write virtually all code.
The same will happen in biotech, pharma and many fields where data is easier to process with ai. Its already in progress. The only fields immune are where the cost of robotics exceeds the cost of humans. Say aircraft maintenance, ,mechanics, etc.. fields where robotics are still way more expensive
The problem with biotech/pharma is that data processing doesn't work if you lack the data to begin with. There are too many holes in science's understanding of how the cell works, how the organism ages, etc., and LLMs can't easily fill these in. Hypotheses can and will be generated, but it will take great lengths of time and comparatively heroic efforts before the picture becomes much clearer.
The contrast with math and CS couldn't be starker, really. Everything that hinges on formal logic or logical relations is done for.
Hm. I think it depends on two things: (1) how sample-efficient AI can become if CS and mathematics becomes "solved", (2) if AI still sucks on this metric compared to biological brains despite that, can it at least self-play its way to solutions?
Given what AI could do with protein folding, I wouldn't bet on biology being a "no" for AI. It might be, but I wouldn't bet on it.
Likewise, if AI can do mathematics so well it solves "just" all of computer science, let alone other branches of mathematics, if this somehow doesn't lead to AI becoming similarly sample-efficient as biological brains, that's very interesting and will say something nice and surprising about the nature of minds and possibly the nature of being alive.
I think Ai understands biology better than humans because it can correlate everything where humans can never know enough to correlate all. But Ai will not just focus on a solution till it is given a runway to solve and deliver.
There is a lot more data than one individual can process. Is there something that humans can know and it wouldn't, I pretty much doubt it. But can it do experiments that are biological? Yes it can order it I think; it probably knows where its knowledge is ambiguous.
https://archive.ph/bhUVm
1793283976 | US computer science enrollment drops 18% in two years | https://nscresearchcenter.org/prelim-fall-enrollment-trends/ | https://news.ycombinator.com/item?id=50289646
more inane, hyperbolic bullshit from the vendors of non-deterministic black boxes of bullshit. so much hype, so little results.