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Thanks for reading. But I think a key take-away is that even with infinite computing power, you only increase the chance of being right a little. It's still a g

Thanks for reading. But I think a key take-away is that even with infinite computing power, you only increase the chance of being right a little. It's still a guess. That's the real surprise. It's not a question of computing power.

dmfdmf 16y ago
Yes, infinite computing power with current methodologies will never work. The article mentions that the number of possible sentences is of the order 10^570 and current methods attempt to crack that (or sub) solution domains (primarily) using probabilities of association. This method will never work, except on small sub-domains (e.g. numbers or medical field transcription) as the article mentioned. However, if P=NP then there are better algorithms that can solve the problem. Does P=NP? Nobody knows but to me it seems to be true because when anyone interprets speech they definitely are not searching a 10^570 domain to solve the problem.
ahk 16y ago
Your articles seem to follow a theme of "no new ideas/breakthroughs". The anecdote of the NASA head imploring people to come up with ideas was striking. Any guesses as to why this is happening now? After all, we _were_ having critical breakthroughs through much of the last century.
- robertfortner 16y ago
  To some degree, I'm critiquing Ray Kurzweil who generalizes about progress. So, for better or worse, I take a lesson from that which is to be empiricist: look closely at each field to see whether it's vaulting forward or not. It's not happening in space (although hypersonics seem to be getting more real). It's not happening in AI or speech recognition/understanding. It's not happening in medicine. So the net empirical situation looks like the opposite of what Kurzweil says. Only IT and Moore's Law are going totally nuts. One can conjecture about why, but I think that's conjecture and there aren't deep fundamental principles about scientific and technological progress, at least that we've found so far.
mturmon 16y ago
Nice article with a lot of information. Honest question here: you make some strong claims, have you been involved in speech recognition research, or are you an informed outsider? It seems like the latter? Poking fun at AI optimists like Kurzweil is fun, but not insightful at this time. The field has taken a different direction since then and I'm not sure that people know where corpus based statistical methods are going to go. Again, honest question.
- robertfortner 16y ago
  Correct surmise: I'm not a speech recognition researcher. I'm a science and technology writer. I was very surprised to find that recognition accuracy stopped getting better a long time ago. I do poke fun, but I also think Kurzweil (as I said in the piece) very reasonably believed that Moore's Law would get us a long way to AI. And surprisingly, it hasn't. I like to think I don't have a stake in the argument and just let what's actual decide whether or not we're going to get to computer understanding of language and/or AI.
Tichy 16y ago
Obviously, if you run the wrong algorithm, even infinite computing power won't help. Hence I don't see how you arrive at that conclusion.

dmfdmf 16y ago

Yes, infinite computing power with current methodologies will never work. The article mentions that the number of possible sentences is of the order 10^570 and current methods attempt to crack that (or sub) solution domains (primarily) using probabilities of association. This method will never work, except on small sub-domains (e.g. numbers or medical field transcription) as the article mentioned. However, if P=NP then there are better algorithms that can solve the problem. Does P=NP? Nobody knows but to me it seems to be true because when anyone interprets speech they definitely are not searching a 10^570 domain to solve the problem.

ahk 16y ago

Your articles seem to follow a theme of "no new ideas/breakthroughs". The anecdote of the NASA head imploring people to come up with ideas was striking. Any guesses as to why this is happening now? After all, we _were_ having critical breakthroughs through much of the last century.

robertfortner 16y ago
To some degree, I'm critiquing Ray Kurzweil who generalizes about progress. So, for better or worse, I take a lesson from that which is to be empiricist: look closely at each field to see whether it's vaulting forward or not. It's not happening in space (although hypersonics seem to be getting more real). It's not happening in AI or speech recognition/understanding. It's not happening in medicine. So the net empirical situation looks like the opposite of what Kurzweil says. Only IT and Moore's Law are going totally nuts. One can conjecture about why, but I think that's conjecture and there aren't deep fundamental principles about scientific and technological progress, at least that we've found so far.

mturmon 16y ago

Nice article with a lot of information. Honest question here: you make some strong claims, have you been involved in speech recognition research, or are you an informed outsider? It seems like the latter? Poking fun at AI optimists like Kurzweil is fun, but not insightful at this time. The field has taken a different direction since then and I'm not sure that people know where corpus based statistical methods are going to go. Again, honest question.

robertfortner 16y ago
Correct surmise: I'm not a speech recognition researcher. I'm a science and technology writer. I was very surprised to find that recognition accuracy stopped getting better a long time ago. I do poke fun, but I also think Kurzweil (as I said in the piece) very reasonably believed that Moore's Law would get us a long way to AI. And surprisingly, it hasn't. I like to think I don't have a stake in the argument and just let what's actual decide whether or not we're going to get to computer understanding of language and/or AI.

Tichy 16y ago

Obviously, if you run the wrong algorithm, even infinite computing power won't help. Hence I don't see how you arrive at that conclusion.