[virgilp]

No, it did not work adequately, not even close, not even in English. Maybe if you gave it good conditions (god forbid any background noise!) and spoke clearly you could get some decent results. But that's not "adequate". How many people did you know using speech to text 20 years ago?

It's exactly the difference you suggested with Haber Bosch vs precursors - "we know how to do it" is a far cry from "it's cheap & repeatable enough to be actually available in practice, to everyone".

[scottlocklin]

Lol, OK dude. Rabiner is still a classic and useful textbook on this topic, and it was published in 1978.

[virgilp]

So are all the stats books. What exactly is that supposed to prove? Basic things seldom change, and remain useful.

[scottlocklin]

I guess I'm always shocked and amazed how hypnotized people are by their ipotatoes, and how little technological perspective people actually have.

I was there back in 1990s using speech recognition software which worked just like it does today, with vastly more than 50% accuracy. Dunno where you were. Romania I guess.

[virgilp]

Yes. Romania. What you think software didn't run in Romania?

What did you use, Dragon Speech? Maybe it worked better for you? I can assure you for non-native speakers it was pure crap. Also, what was the usage in western world? I know they had _some_ sales, but I don't remember it exactly taking off.

Lastly - you're obviously willfully ignoring progress, like translation for instance works so much better with RNNs and attention. The basics of RNN were indeed there (LSTM was just published - though e.g. GRU is more recent), but attention was not. And attention is critical. That's just one of the things that's not "basically the same" and "worked just as well 20 years ago".

[scottlocklin]

Yeah, we had a guy at LBNL who was a big OS2 fan. Worked fine for me, and I do have a fairly heavy coastal accent. Mind you it worked in a big, loud industrial facility.

I thought the previous KNN based approaches worked better than the RNN based ones. Which is more or less my point here. The main piece of "progress" we got was the result of a giant corporation having a large corpus of data. The actual technology hasn't improved or changed a whole hell of a lot.

I'm eventually going to write a history of technological progress documenting this. Progress is very obviously slowing down other than the improvements in lithography which are now fading out. Throw away anything with a screen in your household and life is basically identical to what it was 20, even 40 years ago. That wasn't true within my lifetime: there were huge strides made in technology in decades gone by -in my lifetime even. We're at the point where a lot of "progress" is just changes in fashion; KNN versus neural approaches. People buying the latest iphone (which is really no more capable than the early iphones); wondering at the facial recognition IR thingee. Oh sure, DL can do some tricks that were not possible before, but their actual real world impact is marginal, and sometimes, it's not even an improvement over other techniques.

[virgilp]

Knn works better with little data, rnn with lots of data. But we do have lots of data - that, too, is part of the progress. Also progress is non-linear; and the real world impact is huge, though we may not always like it (social media & content algorithms have literally changed the world, influenced elections and sparked literal revolutions). Driverless trucks would be a huge shift, and it’s not unconcievable that we’ll have them. RPA is threatening a lot of repetitive white collar jobs; sure it’s boring technology, but that’s exactly what makes it disruptive in my mind. Image understanding _did_ get a lot bettter (face recognition along with it) and we still haven’t fully processed the implications of that.

You have grounds to be skeptical about the future and to believe it is dark; but IMO you don’t have grounds to claim that there was no progress.