[Animats]

That's kind of broad. Also, the robots shown have very little "AI".

Maybe machine learning has reached a peak. It's routine now to make classifiers that are about 90% accurate, and really hard to get much beyond that. What we really have are systems which extract lots of signals from an input set and construct a statistical model that maps signals to results. This works moderately well with enough data, but hits a limit at some point. It's great for the class of problems where that's good enough. Like ad targeting and search. Not so great where a wrong result is a serious problem. Like self-driving cars and medical diagnosis.

I wonder what the next idea will be. I'd like to see progress on "common sense", defined as being able to predict the consequences of real-world actions as a guide to what to do next.

[V-2]

Humans (doctors) aren't perfect at medical diagnosis either, so any improvement over human performance is still a net gain, even if it's still below 100% accuracy.

[choppaface]

Classifiers don’t need to be 100% accurate. They might need 100% precision, or 100% recall, and likely just for a controlled set of data thrown at them. The spam filter in your email client doesn’t need high accuracy, just high precision. The “recommendation system” in your favorite product doesn’t need high accuracy, it needs good content to surface to users.

And there’s a spectrum of what constitutes a classifier. It might be a fancy deep model, or more likely just be some threshold functions (perhaps crafted based upon the results from some deep model that’s not too accurate and not yet ready for production).

The hype is that these classifiers are all 90% accurate now and thus all software is going to turn into AI. That’s garbage and can disenfranchise people who don’t understand AI. That storyline only benefits investors.

What’s different today versus 2012 is we have some deep research models with impressive results. But more importantly, software has compounded itself. There are tools to store and mine data. Mobile compute is also now closer to where laptops were years ago. Competition requires taking hold of these developments in order to innovate. Products will inevitably become more intelligent, AI hype or not.

[TulliusCicero]

> Also, the robots shown have very little "AI".

That's what happens when you change what "AI" means every time there's a breakthrough.

[unishark]

The bar has been getting raised overall though, as more computer tasks get taken for granted. Unless your requirement is nothing short of scifi machine consciousness.

[2sk21]

Even the best attempts at task-oriented dialog currently using purely ML techniques are pathetically bad. Having worked in the field for a while, one thing that I have observed is that chatbots simply discard the bulk of the useful information that user provide them. They then fall back to asking users direct questions. Apart from this, they can only handle limited information retrieval tasks for which training data exists.

The next challenge in my opinion is to create a task-oriented chatbot that can help users to actually solve real problems which may not be directly related to previously seen problems. Related to this, consider the problem of creating a chatbot to automate support for an entirely new product.

I have played around a little in this space and feel certain that hybrid approaches will be necessary. For example, I created a car diagnosis/remediation chatbot driven by a Bayes net model of a car's subsystems. This actually showed signs of working - sadly got distracted with other projects.

[Animats]

I know; I've been playing around with "Rasa", a chatbot system based on Tensorflow. All the ML part does is match up canned answers with incoming questions. Someone has to provide all the answers and a few questions for each answer, then look at errors from user input and manually classify them for retraining. The rest of the system is just a template system for implementing phone trees.

Interestingly, MIT's START questioning-answering system is pretty good.[1] That project started in 1993, before machine learning, and it's more "traditional AI". Try it and comment.

[1] http://start.csail.mit.edu/index.php

[unishark]

Don't know anything about the amazon robots specifically, but manufacturing robots commonly use computer vision methods which perform a lot better than 90 percent. I don't really agree with the claims of 99.9% accuracy and being generally superior to humans in this area. But in practical terms a vast number of new vision tasks are solved problems now, a really huge achievement.

Though on the "curve fitting" versus "general intelligence" argument, I agree with you. The robots still can only do exactly what we tell to do, we just don't need to be quite so exact in telling them how to do it. But for the most part, this is only gained by having to show them very very thoroughly how to do it by using tons of data.