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Comment by Terr_ | Hacker News Reader

Good interview, there are a bunch of bits I feel like I ought to be Quoting For Truth but then I'd end up with a pretty bloated reply.

> I want to emphasize that historically, from the very first moment somebody thought of computers, there has been a notion of: “Oh, can the computer talk to me, can it learn to love?” And somebody, some yahoo, will be like, “Oh absolutely!” And then a bunch of people will put money into it, and then they'll be disappointed.

Reminds me of a pre-transistor computing quote from Charles Babbage, about some overeager British politicians:

> On two occasions I have been asked, — "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" In one case a member of the Upper, and in the other a member of the Lower, House put this question. I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

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For some devils advocacy...

I remember hearing from some old salty in the oil business that geologists are the wrong people to ask about peak oil. They always understimated future discoveries. The ones that tended to get it right were finaciers and investors.

The idea is that geologists have their noses down in the details of practical, useful knowledge that they have or can get. Financiers don't really know anything, just that wells have been found in the past. They just model things like exploration money, the rate and quality of new finds, oil prices, production costs...

There could be somethng similar here. The real technology people see mostly problems. All the stuff that would need to be solved, that they have no idea how to solve. The fact that we don't even know what intelligence is. The frauds making audacious claims.

Outsiders see drones, self driving cars, spam filters, google search, chess, face recognition, translation, chatbots^. They see that voice recognition now works. I reckon medical diagnosis might do something soon. In any case, it seems that pone way or another, these add up to something. ...just as a hunch.

Obviously I don't know the answer and this whole comment is based on an anecdote that may not even be true. Still, I don't discount the possibility that the unwashed masses are right.

^just kidding

This reminds me of the inspiration for the name of Taleb's "green lumber fallacy". From Wikipedia:

The term green lumber refers to a story by authors Jim Paul and Brendan Moynihan in their book What I Learned Losing A Million Dollars, where a trader made a fortune trading lumber he thought was literally "green" rather than fresh cut.[26] "This gets at the idea that a supposed understanding of an investment rationale, a narrative or a theoretical model is unhelpful in practical trading."[27]

The protagonist makes a big discovery. He remarks that a fellow named Joe Siegel, one of the most successful traders in a commodity called "green lumber," actually thought that it was lumber painted green (rather than freshly cut lumber, called green because it had not been dried). And he made it his profession to trade the stuff! Meanwhile the narrator was into grand intellectual theories and narratives of what caused the price of commodities to move, and went bust. It is not just that the successful expert on lumber was ignorant of central matters like the designation "green." He also knew things about lumber that nonexperts think are unimportant. People we call ignorant might not be ignorant. The fact is that predicting the order flow in lumber and the usual narrative had little to do with the details one would assume from the outside are important. People who do things in the field are not subjected to a set exam; they are selected in the most nonnarrative manner—nice arguments don’t make much difference.[25]

That is a very impressive anecdote in how powerfully it expresses the dichotomy in thinking styles between two different types of people. Babbage the engineer presumed the most literal interpretation of the query and came to the most logical conclusion. But arguably the politicians, having a keener understanding and appreciation of human fallibility, had the more profound and shockingly prognosticative insight.

What is this supposed to mean?

I believe what the politicians really wanted to ask was: “how do we account for the tendency for human operators to make mistakes?” If you stop to think about it, what seems at first like a dumb question in fact presages difficulties that will have to be addressed by everything from unit testing to spell checkers to entire classes of non-deterministic algorithms.

I've always wondered if those questions came after a statement like "this eliminates the possibility of errors", and were less of a question than a statement.

Speaking as a 'loon', his AI history is wrong in several places:

1. the Fifth Generation Project (https://en.wikipedia.org/wiki/Fifth_generation_computer) was 1980s officially ending in 1992, not 'late 1990s' (during the Dot-com bubble?!); 2. the Lisp bubble didn't pop because of a failed DoD piloting project, it popped because of the first AI Winter + commodity SPARC/x86 pressure + recession (https://en.wikipedia.org/wiki/Lisp_machine) (and I don't recall DARPA instituting any policy like 'no AI', just stopping subsidizing Symbolics and later Connection Machine); 3. the Club of Rome report couldn't've killed its modeling language because it only really acquired its present ill repute by the 1990s, the implementation language Modelica (https://en.wikipedia.org/wiki/Modelica) didn't die (last release: April 2017) and is still in industrial use which is more than almost all languages from the 1960s-1970s can say, and even the World3 model (https://en.wikipedia.org/wiki/World3) analyzed in the report continued development for decades; 4. the Oxford paper (https://www.fhi.ox.ac.uk/wp-content/uploads/The-Future-of-Em...) doesn't make precise forecasts for when any automation may happen (merely saying "associated occupations are potentially automatable over some unspecified number of years, perhaps a decade or two"); 5. the GPU server comparison is really weird as computers have almost always cost more than humans and only relatively recently do any computers' hourly costs fall below minimum wage; and 6. the Dartmouth description is wrong, the conference merely proposed (http://www-formal.stanford.edu/jmc/history/dartmouth/dartmou...) that meaningful progress could be made by 10 researchers, not grad students ("We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College...We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.")

Also, come on dude, Keras isn't hard to use - it's not even comparable to Tensorflow. But at least he didn't tell the tank story.

Here's another factual error: Data science is from the 1960s, and was used first in a paper published by Peter Naur in 1974: https://en.wikipedia.org/wiki/Data_science

Data science is actually statistics, which goes quite a bit further than the 1960s. In fact, today's data scientists love to quote Box and Fischer.

Data science and data mining are victories of marketing over common sense.

Sorry, I meant that in the sense of the origin of the term. But yes, DS is mostly just another word for statistics. About as pointless as the term AI has become.

And there's more where he's plain wrong, like Aluminium.

Despite all that a great antidote to the overhype that I see most days.

I did notice that one, but aluminum is kind of a complex topic (https://en.wikipedia.org/wiki/Aluminium#Synthesis_of_metal): the early cost was both the chemical processing and the low ore content, and one could charitably read him as referring to discovering bauxite and the electrolysis method, and then he's certainly right about the cost of electricity coming down drastically and making aluminum even cheaper. So not clearly wrong IMO, given that it's an extemporaneous interview.

Luckily math has developed methods such as error-detecting/error-correcting codes (to insure against small typos/transmission errors), constructive results on continuity and robustness of functions (i.e. we can prove that if the error in the input data is less than some concretely computable delta, the solution will have an error less than epsilon; or we can ensure that the error in the solution is less than some computable epsilon if we can ensure that the error in the input data "is not too large" (i.e. bounded by some computable epsilon) etc.

In this sense I don't consider the question as that absurd.

From what I know, error correcting codes wrap around information (in a manner of speaking) so as to provide a measure of consistency, which then enables error correction properties. If the information itself is riddled with errors then the error correcting code can't do anything here.

People using Babbage's machine would have entered raw information into that thing. No error correcting code would correct the human induced flaws in that. So the question was absurd at the time.

And yet the Schiaparelli lander crashed because the machine couldn't give the right answer to a question that was wrong.

All these solutions are good for a noisy input, but have no use when the input is incorrect (ie. doesn't match reality).

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