Applications involving computer vision and speech recognition are now buildable by small companies, which will hopefully yield a proliferation of really interesting novel applications.

Sure, we don't have terminator-style AI, but honestly people using the term AI need to shut up, a lot. There is no AI, these days, just massively creaky giant ML systems with a host of ph.d's being thrown onto the fire to keep them running. But the ML applications are super cool.

Machine learning-derived applications are impressive and give a good show until one winds up in a situation where they are expected to work reliably. Sure, it's nice that the insurance company's phone-based, voice-recognition-driven, registration/etc system can understand 99% of the choices people give them - except total failure in that 1% is actually going to leave a large population unserved and angry. Of course the company has keypad backup - except they don't 'cause that would cost the money they claimed voice recognition could save, etc.

Machine learning apps are great for situations where 1) You don't expect 100% reliably and the degree of non-reliably doesn't have to even be quantified. 2) Either you are accept that they'll degrade over time and have an army of tuners and massive data collect to keep that from happening OR you are dealing with an environment you completely control.

This is kind of the conditions for regular automation - except even more so.

There is a common expression that "AI is whatever computers can't do yet." At one time computers couldn't play board games or do vision, so those things were called AI.

This leads to both overuse and trivialization of the word, but also moving goal posts for the field. And actual progress isn't taken seriously, because nothing feels like intelligence when you understand it.

It's all a Mysterious Answer (http://lesswrong.com/lw/iu/mysterious_answers_to_mysterious_...), a codeword for magic (http://lesswrong.com/lw/ix/say_not_complexity/).

The search does seem able to understand simple sentences but there's much less utility in that than one might imagine. Just consider that even an intelligent human who somehow had all of the web in their brain couldn't unambiguously answer X simple sentence from Y person whose location, background and desires were unknown to them. Serious search, like computer programming, actually benefits from a tool that does what you say, not what it thinks you mean. Which altogether means they're a bit behind what Alta Vista could give in the Nineties but are easier to use, maybe.

Part of the situation is the web itself has become more spam and SEO ridden and Google needs their AI just to keep up with the arms race here. So "Two cheers" or something, for AI.

https://www.google.com/search?q=recursion

Typical Google :)

I became interested in natural language processing in the early 2000s, more as a hobby and as part of my personal projects, but even so, I remember that back then most of the AI-related discussions on things like forums and mailing lists were mentioning the AI winter as the big bad wolf that had killed an entire industry. It also killed LISP, they were saying.

Interesting to see that that memory seems to have faded away to the distant past.

So while you may be right that many AI articles are smoke-and-mirrors from journalists who don't get the tech, I think you picked the wrong article to make that point about.

That is not understanding.

The difference today is the scale at which it operates in our daily lives, and the accelerated rate at which it is growing.

You're not. So far I have not seen/heard anything remotely resembling AI. Neural nets are just weighted graphs.

How is Google going to "organise the world's information" unless it has a model of how all the facts in the world line up? That model is the Knowledge Graph. How does Google intend to map queries that it has never seen before to pages in its vast index? With the help of the Knowledge Graph and natural language processing and machine learning.

I'm going to try to articulate something here that I've not fully worked out but that I'm sort of intuiting so cut me some slack for the next paragraph :)

People keep banging on about machine learning and the impact that it is having. This is undeniable. But we can see even from AlphaGo that a hybrid approach that combines artificial neural nets with some sort of symbolic system outperforms neural nets on their own. For AlphGo that symbolic system is tied to the mechanics of the game of Go. For internet search that symbolic system is a generalised knowledge graph.

Do you get what I mean? I'd love to hear what others think …

> But for a time, some say, he [Singhal] represented a steadfast resistance to the use of machine learning inside Google Search. In the past, Google relied mostly on algorithms that followed a strict set of rules set by humans.

I know because I spoke with Metz at length and was quoted in the article.

The Knowledge Graph was, by definition, a rules engine. It was GOFAI in the tradition of Minsky, the semantic web and all the brittleness and human intervention that entailed.

What he's saying here is that Google has relied on machine learning in the form of RankBrain to figure out which results to serve when it's never seen a query before. And the news, in this case, is that statistical methods like RankBrain will take a larger and larger role, and symbolic scaffolding like the Knowledge Graph will take a smaller one.

You are right that the most powerful, recent demonstrations of AI combine neural nets with other algorithms. In the case of AlphaGo, NNs were combined with reinforcement learning and Monte Carlo Tree Search. I don't think a rules engine (the symbolic system you refer to) was involved at all there. Nor is it necessary, if by studying the world our algorithms can intuit its statistical structure and correlations without having them hard coded by humans before hand. It turns they do OK learning from scratch, given enough data.

So in many cases we don't need the massive data entry of a rules engine created painstakingly by humans, which is great, because those are brittle and adapt poorly to the world if left to themselves.

The Knowledge Graph is just a way of encoding the world's structure. The world may reveal its structures to our neural networks, given enough time, data and processing power.

"The Knowledge Graph is just a way of encoding the world's structure." Precisely. Very well said. "The world may reveal its structures to our neural networks, given enough time, data and processing power." But that's the point, NNs don't have to perform this uncovering because we do the hard work for them in the form of Wikidata and Freebase and what have you. I don't get what you think is brittle about this.

I was referring to the very recent article[1] by Gary Marcus, I need to quote a good chunk:

"""To anyone who knows their history of cognitive science, two people ought to be really pleased by this result: Steven Pinker, and myself. Pinker and I spent the 1990’s lobbying — against enormous hostility from the field — for hybrid systems, modular systems that combined associative networks (forerunners of today’s deep learning) with classical symbolic systems. This was the central thesis of Pinker’s book Words and Rules and the work that was at the core of my 1993 dissertation. Dozens of academics bitterly contested our claims, arguing that single, undifferentiated neural networks would suffice. Two of the leading advocates of neural networks famously argued that the classical symbol-manipulating systems that Pinker and I lobbied for were not “of the essence of human computation.”""

For Marcus the symbolic system in AlphaGo _is_ Monte Carlo Tree Search. I'm saying that for the so-called Semantic Web the symbolic system is the Knowledge Graph. This Steven Levy article[2] from Jan. 2015 put the queries that evoke it at 25% back then. I figure it's more now and growing slowly, alongside the ML of RankBrain.

[1] https://backchannel.com/has-deepmind-really-passed-go-adc85e...

[2] https://backchannel.com/how-google-search-dealt-with-mobile-...

* we still don't know if they are

* we still don't know how they effectively work

* the scale of the things is far from the computational capabilities of my phone (10^11 neurons and 10^15 synapses for a human brain)

(But of course AI means a lot of things, not just "thinking androids")