[payne92]

This article is off in so many dimensions.

Fundamental research is not less active, but it's happening in different places (e.g. the Google Brain team). Find the most profitable companies and you'll find the research.

And to suggest a computer Go player, taught in a few days, is a "marginal improvement" over decades of "AI research": as my kids say, "wut?"

If anything, today's deep learning driven AI is a prime example of how fundamental research can work (neural networks were considered research "fringy" by many until about 10 years ago).

[nkurz]

And to suggest a computer Go player, taught in a few days, is a "marginal improvement" over decades of "AI research": as my kids say, "wut?"

Maybe I'm the one that has it backward, but I'm pretty sure that Harford would not agree with the statement that Alpha Go Zero is only a "marginal improvement".

To the contrary, he says that Alpha Go is an "outlier", and uses it as an example of the sort of "speculative research" we should be doing more of: "Productivity and technological progress are lacklustre because the research behind AlphaGo Zero is not typical of the way we try to produce new ideas."

Apparently he should have been clearer, but I took the article as a call for more real research of the type that produced Alpha Go, and fewer of the "pragmatic shortcuts" and "brute-force approaches that taught us little but played strong chess"

[mannykannot]

There seems to be a mismatch between the headline and the article itself. I see this quite often, and I think it is often due to headlines being written by editors, or even editorial assistants.

The author's choice of examples, featuring a counter-example prominently, seems odd - perhaps it is to capitalize on the interest in AlphaGo Zero. The article is something of an anachronism, in that it would have worked better immediately after Deep Blue (or even after Watson/Jeopardy).

[jamesrcole]

> Fundamental research is not less active, but it's happening in different places (e.g. the Google Brain team). Find the most profitable companies and you'll find the research.

Aren't you contradicting yourself a bit there? If it's only/mainly the most profitable companies and the topics of interest to them, then that hardly indicates fundamental research is very active. There can't be that many fundamental research positions across those companies.

(caveat: the article is currently unavailable "Error establishing a database connection" so i'm not entirely clear if it's just about fundamental research in AI areas or not).

[0xfffafaCrash]

The articles wasn't saying that AlphaGo Zero was a marginal improvement. On the contrary, I think it was saying that the type of improvement AlphaGo offered is an outlier because it approached the task in a fundamentally different way (especially with AlphaGo Zero where it didn't use training data).

I think the primary argument is that much of the industry has generally not been doing that sort of research for ground-breaking moon-shots. Instead most AI researchers focus on optimizing for metrics which lead to small and safer short-term improvements in a particular niche application as opposed to pursuing large and riskier long-term ones.

I've seen this same line of criticism before of the way in which AI systems have been generally designed for decades and to me it rings true. Things like the Turing award arguably lead researchers astray. Essentially, the crux of the argument is that most people focus on climbing trees to achieve success when the real goal is reaching the moon or they build better springs when the real goal is achieving flight. [1] Both show some short-term improvements on certain heuristics, but they are obviously the wrong approach if you want serious breakthroughs.

I think it's a valid criticism that most of us are still focusing too much on the wrong metrics to make many serious advances. If you look at AI, there haven't really been that many major breakthroughs. We just haven't been all that creative and most advances are ultimately tweaks on existing technology or involve throwing more data through deeper neural nets. Back-propagation (popularized by Geoffrey Hinton) for deep-learning with neural nets was a big deal and an important idea. Since then, there hasn't been much that's earth-shattering. Generative adversarial networks are arguably a big idea. LSTM as well. The work by Naftali Tishby's group on understanding what's going on with information in neural nets is a significant development. Hinton's capsule networks also seem like they may be a big idea. A few people have recently started publishing some work on building AI that builds better AI. However for the most part, it seems like the vast, vast majority people in AI aren't aiming to do any fundamentally ground-breaking things. They mostly look around at the existing body of research and slightly tweak the tools that seem most suitable for the problem domain they are working on. (This isn't entirely surprising for a number of reasons involving the incentives in the industry, but it is something worth discussing.)

Personally, I don't think that it's necessarily a bad thing that we've been sluggish with AI advances. You don't necessarily want to hand power tools to children. I think our society is unfortunately full of unwise and unkind people with already more power than they should have. Our social institutions for distributing power wisely and responding to abuse of power are far less mature than our technology and I suspect the risks of abusing advanced technology dwarf the enormous benefits they can bring.

[1] https://www.eecs.harvard.edu/shieber/Biblio/Papers/loebner-r...

[eli_gottlieb]

>I've seen this same line of criticism before of the way in which AI systems have been generally designed for decades and to me it rings true. Things like the Turing award arguably lead researchers astray. Essentially, the crux of the argument is that most people focus on climbing trees to achieve success when the real goal is reaching the moon or they build better springs when the real goal is achieving flight. [1] Both show some short-term improvements on certain heuristics, but they are obviously the wrong approach if you want serious breakthroughs.

Basically, we incentivize researchers to work on the most near-term solvable problems, rather than the most difficult problems where we understand how to check for a solution -- let alone to work on developing the solution-properties we can check, to get past wholesale conceptual confusions.