Computer Science: "an Alzheimer's patient who has forgotten more than he knows."

While I'm not sure I can disagree with the conclusion, I disagree with the arguments leading up to it. Some of the most interesting progress I see today is occurring in the field of computer vision, and the reason why it is occurring today and not 15 years ago is because 15 years ago, you had a Pentium/MMX 133 with 16 MB of RAM. All of the algorithms that today run in just-barely-real time, or run in batch mode on a timeframe that a single grad student can do many compile-run-analyze-try something else cycles on per day, would have taken weeks on that hardware.

I think general AI will see some stuff like this too, algorithms that could have been conceived of 20, 30, 40 years ago but couldn't be feasibly run on even a supercomputer of the time, that run fairly comfortably in a modern <$1000 laptop at Best Buy. (And of course runs even better in a dedicated server performance box; my point is that even low-grade consumer hardware is pretty damn amazing nowadays.)

When I took a grad-level evolutionary computation course about eight years ago, one of the two professors related a story about one of his earliest runs of an evolutionary algorithm. He literally carried around a stack of punchcards as he moved between several schools, and set up the computation so that he could run it for a few hours as there was time, then suspend it again. It literally took him years. This same computation of course runs in a few seconds on something as powerful as an iPhone. (Which isn't too dissimilar in spec from the laptop I used in that class, an 800 MHz Duron PoC (even at the time, admittedly) which was proud to have 256MB of RAM, IIRC.)

It's not just the raw speed of the algorithms, it's the speed of the analysis loop. Where computation time once dominated the research of even the best minds, soon the best minds of our generation will actually be able to get feedback about their AI explorations on reasonable time scales. The fat lady has not sung yet.

(This still explains a lot in the non-AI cases too. It took a while before garbage collection was not a big performance hit, then it took a while longer for popular languages to pick it up. It was never forgotten, it's just that there is often a larger gap between "a good idea" and "a good implementation" than ideas-people realize. Clearly, UNIX is old and out-of-date, but creating an entire ecosystem that is clearly superior and also ready to go is not something you hand to a grad student to write a thesis on. And so on.)

> Really, whatever can be done now with a computer was in principle doable in 1965

Why are people so attached to this line of reasoning... it's like saying whatever can be done now with an automobile was in principle doable in 1920 because the laws of mechanical engineering haven't changed. Cars now are "just" faster and have a few more doohickeys on them.

It's sort of a tautology once you make the paradigm in question all-encompassing enough. I would love to see mind-erasing advances in CS, too... but the lack of them does not mean that smaller and continually useful advances aren't happening and aren't worth discussing/praising.

> Many such authors think "object oriented" or other fancy chinese words like polymorphism mean something which it doesn't, really.

I don't know that 'polymorphism' is a 'fancy chinese word' or really just an attempt to bridge a definition which had already existed for a hundred years which had a common contextual meaning.

The number of fallacies and blind ignorance in this post is absolutely astonishing.

As a student working in AI, I have to say that I both agree and disagree with this writer. I agree with his assertion that AI, in its current state, is not nearly as generalizable as we hope it to be, but simply because it is not now doesn't mean we should give up on it entirely and wait for some breakthrough to come in from another field. Better hardware has been a great boon to AI researchers, but it's in times like these that we need not only to verify or dismiss the mathematical theories put forth 50 odd years ago but enhance them with experimentation.

For example, perhaps descriptors are truly the wrong way to go about object recognition in images, or perhaps segmentation is the wrong way to look at a scene, but they are ideas and they are worth testing. We won't find out unless we try, because unlike the equations proven decades before, there are no constraints we can simply apply.

Does anyone know what the 'Ford paradox' is? He refers to it in regards to the physical implications of real number representation.

>despite their inability to write decent high level >compilers (like, say, natural language coding), or come >up with a methodology which prevents common software >bugs, they think they can reproduce the functions of the >human brain with a tin can.

Horrible premise to start with. Just because one hasn't achieved perfection in a particular field, he/she should not try to aim high in other related fields?

Who's to say focusing on a broader spectrum won't be of help in regards to the overall picture or help find solutions to challenges that haven't yet been solved.

Little substance is right.

Who is lupoleboucher.livejournal.com and why are their trolls showing up on HN now?

this is fantastic trolling but there doesn't really seem to be much substance to it.