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Comment by bsaul | Hacker News Reader

Siri still isn’t able to understand « do NOT set the alarm to 3pm », and many image classifier produces aberrations that no human would ever commit.

Many people feel that ML has so far only produced « tricks », but still doesn’t show any sign of « understanding » anything. As in, provide meaning. It may be unfair, but i think that at this point people would be more impressed by a program « smiling » at a good joke than by something able to process millions of a positions per second, or « learn » good moves by playing billions of games against itself.

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Sure, ML is not at human-level intelligence - it is very much a tool-AI where we give a task to the machine and let it get very good at that.

Nonetheless, it seems like progress on that front has been made incredibly quickly. Sure, Siri might not always "understand" what you're asking but the ML is able to very accurately transcribe your voice to text, something not really possible a few decades ago. Image classifiers produce misclassifications, but so do humans - it is a very alien technology to us, so we view some of the machine's misclassifications as absurd, but at the same time the machine might view some of our misclassifications as obviously off-base as well.

None of what you said seems to explain the consistent negativity on HN to what has really been a transformative technology in a lot of ways.

Progress in speech recognition has been slow and incremental, not fast and impressive. Hidden Markov Models did a decent job in the 90s. Now we have more data and more computing power.

In the past three years we have dropped error rates by a factor of 3, I don’t really think that claim holds water.

We have seen huge progress in a number of other fields as well. Anecdotally, voice recognition has definitely gotten way better as well.

Both voice recognition had first working research machines in the 1950s. Of course present models are way better than these, but fundamentally these are the same as those in the '50s, "just" with tremendously better hardware and algorithms. But there is zero intelligence in these, the models have no internal concept of language or the world around them.

This will certainly produce many great specific solutions for specific problems, but generic problems like driving a car are most probably not solvable without an internal world model.

I'm one of the naysayers and have a background in educational science, I'm constantly baffled why AI research seemingly never looks at theories of human learning (though it may very well be that I haven't looked closely enough!). What essentially all AI approaches do is modelling associative learning (https://en.wikipedia.org/wiki/Learning#Associative_learning), but employing more and more processing power for training. This is akin to having a fruit fly brain and copy-pasting that over and over again, in the hope somehow a higher order will organize from that by itself. But most things humans reason about are not learned this way, but rather by infering meaning from things and situations, i.e. one learns how a steering wheel works not through hundreds and hundreds of trial and error cases, but through single Aha! moments in which ad-hoc generated mental models (concepts) are validated in the environment. And GPT-3 has no knowledge organized in a hierarchical system of concepts, just as ELIZA didn't have one.

There are tons of research projects that do exactly what you're talking about, initializing an agent with zero knowledge in an environment which just provide rules of discovery and reward. The agent then takes actions in that space and learns from its own experiences. It's just that it is hard to compare with humans, who have had the benefit of evolution over millions of years. A basic example is that of AlphaGo Zero[1], which learns to play a top level game by being given enough time and just the rules of the game. This is similar to how a child learns to walk, it is just that we only have the capability to model toy situations (a board game in this instance) right now, and access to harder instances (movement in the real world) will slowly come about. There are cases of robots being programmed to poke / move / pick objects to try and learn about their shapes[2], in case you are interested in another such example.

[1] - https://en.wikipedia.org/wiki/AlphaGo_Zero [2] - https://bair.berkeley.edu/blog/2019/03/21/tactile/

AlphaGo Zero definitely falls in the same category as GPT-3 - yes, this is unsupervised learning, but it still is fundamentally the same approach, exactly because of the way discovery and reward work on the model.

This isn't how a child learns to walk at all: A child a priori has the concept of walking, the concept of self, the concept of movement in space, the concept of willing to walk etc. - it just doesn't have the motoric control. The small part of training motoric control through repeated trial and error is indeed similar to what unsupervised learning models, but the important part is missing.

>In the past three years we have dropped error rates by a factor of 3

What are you referring to here?

While I can't speak for Siri, Google's voice recognition continues to surprise and even frighten me. And if you go online and look up deepfakes or AI generated voice of famous people, it also is freakishly accurate.

Sure, there are some issues and most of them still fall somewhere in the uncanny valley.. but we are just starting to fully exploit this technology. AI that people don't tangibly see but make a giant impact in their lives is for example the Facebook algorithms that decide what shows up on your feed.

When a large subset of the population gets majority of their news from Facbeook, Facebook has effectively created a mechanical system that decides what a large chunk of the population is aware of. I don't think people fully realize what effect that has had on our entire society, including elections. The future will only see more of this type of thing.

I'm always disappointed with Google Asistant. When I say "navigate back home" it tries to route me to a company called "Home". Great job. I've included a hint "back". I've never been to a company called "Home". I'm after work. This is a fun example, but I never get good results. I say "weather" and it shows me the weather, just in a different city. FFS. Maybe in the next 5 years I'll be finally able to do something using voice commands. Right now I just want to smash my phone.

Fair enough. Usually when I use "Navigate to the nearest gas station" or "navigate home" or "set an alarm for 8am on Tuesday" it all works perfectly for me, but I concede I don't use the feature that often so perhaps I've just been using it for well-adjusted commands.

That's the convolution of two problems:

a) Good general-purpose automatic speech recognition, which was an unattainable holy grail for DECADES. Previously, you would have to record a lot of your own voice to train the system for you in particular before it was at all reliable. Now it basically just works.

b) Making good use of the results of (a). Your examples are clearly in this category. This is ALSO a very hard problem; voice interfaces are basically brand new creatures, and I expect we'll be seeing 'best practices' form up for a while yet.

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