11–20 of 64 posts
Earlier quoted context omitted.
Yes, now we can do thinking without thinking. Good job.
Computers can not think & that is why they are useful. Thinking computers would become very problematic very quickly.
See also Bill Thurston’s classic Math Overflow answer to a student wondering where they fit compared to a Gauss or Euler: https://mathoverflow.net/questions/43690/whats-a-mathematici... “The product of mathematics is clarity and understanding. Not theorems, by themselves. [Their importance is not just in their specific statements], but their role in challenging our understanding, presenting challenges that led to mat…
> The product of software engineering (or computer science) is clarity and understanding. Not programs, by themselves. Their importance is not just in their specific statements (lines of code in a specific language), but their role in challenging our understanding, presenting challenges that led to computational (?) developments that increased our understanding.
> ..In short, software only exists in a living community of developers that spreads understanding and breaths life into ideas both old and new. The real satisfaction from computers is in learning from others and sharing with others.
That seems to work. What about other areas of human activity that are currently being consumed by automation and "AI"? Like writing, the arts, or the sciences.
I see many people reacting with fear towards IA and many of them do not feel the same level of danger in other places which are clear to me (and I think that what they fear most is the unknown, as it has to be bad, for sure). I like the quote of the other message from Whitehead !! Let's enjoy the ride. It might be last one!
"Civilization advances by extending the number of important operations which we can perform without thinking about them." - A. N. Whitehead
What worries me isn’t AI replacing experts, it’s that we may stop producing people who know enough to notice when AI is confidently wrong.
The running-joke is that a LinkedIn-lunatic AI booster, with a Nano Banana-generated profile-pic, will immediately slide into the chat to tell you that that this is already a solved problem: just spin up another agent to do the work to verify the first agent. Token-cost-be-damned. And we laugh and downvote them to oblivion and carry on with our day.
But today I had some exposure to a SotA agentic team coding loop thingie which had been running almost hands-off for a few weeks on a (pretty serious) Win32+Direct3D-to-Emscripten+WebGL porting project - and I'm genuinely spooked at how well it all works; I mention this example because all the agents' processes involved a decently rigorous verification step: any time any agent confidently asserts something then it has to provide associated evidence, such as a unit test report, or build artefact, or external citation, and the system will spawn a new agent (perhaps using a different backing LLM) to verify the claim. I know a unit-test pass/fail isn't quite the same thing as, say, a medical AI agent confidently wrong about me having/not-having terminal spleen cancer, but the capability for a team-of-agents to be self-checking is definitely there.
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Also, the past 3 years of AI/LLM/etc developments have taught me to never cling to any shortcoming or weakness they have because plenty of them do seem to have been solved or mitigated, either directly or indirectly.
"Civilization advances by extending the number of important operations which we can perform without thinking about them." - A. N. Whitehead
AIs should be forced to show their work. Every tool they use, every program they generate and run in the background, and every logical inference. They should be forced to produce Lean or Rocq proofs or execution traces for all the computation they use. For facts, they should be able to produce sources. For any abstract reasoning, they should be able to break it down into explainable steps. Then, on top of that, they…
What worries me isn’t AI replacing experts, it’s that we may stop producing people who know enough to notice when AI is confidently wrong.
> it’s that we may stop producing people who know enough to notice when AI is confidently wrong. The running-joke is that a LinkedIn-lunatic AI booster, with a Nano Banana-generated profile-pic, will immediately slide into the chat to tell you that that this is already a solved problem: just spin up another agent to do the work to verify the first agent. Token-cost-be-damned. And we laugh and downvote them to oblivio…
See also Bill Thurston’s classic Math Overflow answer to a student wondering where they fit compared to a Gauss or Euler: https://mathoverflow.net/questions/43690/whats-a-mathematici... “The product of mathematics is clarity and understanding. Not theorems, by themselves. [Their importance is not just in their specific statements], but their role in challenging our understanding, presenting challenges that led to mat…
Curious to see if that can map to what's happening in the software industry/community. > The product of software engineering (or computer science) is clarity and understanding. Not programs, by themselves. Their importance is not just in their specific statements (lines of code in a specific language), but their role in challenging our understanding, presenting challenges that led to computational (?) developments th…