> "I believe it's not in serious doubt that an intelligent computer is possible" - Yes, of course it is possible, if we change the meani...
> "I believe it's not in serious doubt that an intelligent computer is possible" - Yes, of course it is possible, if we change the meaning of "intelligent" to fit what a computer does. :) Generally, I think the problem here - as in many other situations - starts with the lack of rigor that computer science has when "naming" fields or, most exactly, appropriating concepts from other fields. * Computer Vision: "vision" has a defined meaning, even "organic-related". Signal processing and a bunch of algorithms aren't exactly what vision is and what we define as "vision"; * Machine learning: does the computer really learns, as understood by the common definition of "learning"? * Context awareness: if we consider common definitions, neither a few sensor readings are "context", nor acting according to said readings is "awareness". This might sound like nitpicking, but I truly think it's a real problem. Talking about vision, learning or intelligence put's the stake at what people or the general scientific community commonly perceive as those concepts. What's most frustrating to me is the contrast. Computer science research is thorough; why are we not thorough on naming our fields of study? Why do we appropriate concepts by "analogy", knowingly that analogies aren't truly exact?
Posted by paulojreis on Jul 10, 2015, 2:38 PM
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- I mean that's the problem though.
- I've always believed the opposite: get conditionals deep in your code so that the higher level control flow is regular.
- The biggest problem with AI isn't being able to do things.
- I am a 3D printing maker type nerd (I make my own tools for other creative tasks, rather than as their own outcomes) and I saw an incredi...
- Of course not, those are clearly scams.
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Raise real-world problems, find people facing the same issue, and discuss solutions.5,500 issues0 comments
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I mean that's the problem though.
I've always believed the opposite: get conditionals deep in your code so that the higher level control flow is regular.
The biggest problem with AI isn't being able to do things.
I am a 3D printing maker type nerd (I make my own tools for other creative tasks, rather than as their own outcomes) and I saw an incredi...
Of course not, those are clearly scams.
Claude makes more detailed plans that seem better if you just skim them, but when analyzed, has a lot of errors, usually.
Effectively everyone is building the same tools with zero quantitative benchmarks or evidence behind the why / ideas … this entire s...
> creating and operating a small company in Germany is a nightmare To be fair, I think the problem operating in Germany is its federated...
> "I believe it's not in serious doubt that an intelligent computer is possible" - Yes, of course it is possible, if we change the meani...
> "I believe it's not in serious doubt that an intelligent computer is possible" - Yes, of course it is possible, if we change the meaning of "intelligent" to fit what a computer does. :) Generally, I think the problem here - as in many other situations - starts with the lack of rigor that computer science has when "naming" fields or, most exactly, appropriating concepts from other fields. * Computer Vision: "vision" has a defined meaning, even "organic-related". Signal processing and a bunch of algorithms aren't exactly what vision is and what we define as "vision"; * Machine learning: does the computer really learns, as understood by the common definition of "learning"? * Context awareness: if we consider common definitions, neither a few sensor readings are "context", nor acting according to said readings is "awareness". This might sound like nitpicking, but I truly think it's a real problem. Talking about vision, learning or intelligence put's the stake at what people or the general scientific community commonly perceive as those concepts. What's most frustrating to me is the contrast. Computer science research is thorough; why are we not thorough on naming our fields of study? Why do we appropriate concepts by "analogy", knowingly that analogies aren't truly exact?
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