[xamdam]

You're underestimating the field. The goal was always intelligence, and many leading researchers are pretty openly aiming for that. Stupid Mathematical Tricks are components of intelligence, and while it's true SVM with a cool new kernel is not going to take over the world, good prediction ability is something you can build on (as for example deep nets do to some extent). In the limit people should be thinking of implications of intelligent machines, not Stupid Mathematical Tricks. Whether it's an important topic at this point in the fields development is a debatable topic; timelines differ drastically among top researchers.

[gradstudent]

I have attended on many occasions top AI conferences and have spoken to more researchers than I care or could count. I can tell you there is a very little interest in "intelligent machines"[1]. Rather, everyone I have ever met works on different types of problem solving techniques. What constitutes a "problem" and what makes a successful "technique" differ wildly. Taking a step back however and analysing the field as a whole you will find one commonality: almost all the research can be described as searching and sorting: i.e. stupid mathematical tricks.

The AI of today is not so drastically different from the AI of our academic grandfathers; what's changed is our ability to scale up to larger and larger versions of the same searching and sorting problems. Certainly there are worrying implications in this; machines that are able to parse and sift through very large data sets present all kinds of headaches for privacy and safety but let's not kid oursevles: there's nothing intelligent here. Tomorrow's AI is almost certainly going to be just a better version of today's AI; i.e very fast and dumb as a bag of hammers.

[1] The exception is when researchers need to sell their wares to funding bodies and the media. It is much easier to impress upon the lay person an idea involving "intelligent machines" than it is to explain what we actually do.

[xamdam]

I would expect to get this kind of impression from an average researcher, because that's what average researchers do (even at AI conferences). What do the top researchers think? Google paid 10M per head for DeepMind guys, explicitly working on AGI. Top researchers at FB works on specifing "Artificial tasks for artificial intelligence" (http://www.iclr.cc/doku.php?id=iclr2015:main#antoine_bordes), basically a better Turing metric. Schmidhuber, one of the original Deep Learning folks, http://people.idsia.ch/~juergen/ has always been very open about pursuing AGI. There is a lot of work on combining graphical models with logic, for example Pedro Domingos' work, the goal is clearly machine reasoning.

Also, I'm sympathetic to Roberto's point about how the brain works; I definitely agree that there is no magic; it might just be a few stupid mathematical tricks layered all the way down.

[gradstudent]

> I would expect to get this kind of impression from an average researcher

Not even out of the gate and already reaching for an ad-hominem. You must be fun at parties.

> What do the top researchers think?

The same thing. Except when they're in front of a camera. Then they get all stupid and start talking about machines being on the cusp of taking over. This phenomenon can be observed all the way back to the origins of AI. After the interview is over these same researchers go back into the lab and are once again searching and sorting.

> There is a lot of work on combining graphical models with logic, for example Pedro Domingos' work, the goal is clearly machine reasoning.

I feel there is a difference between automated reasoning and intelligence. All current AI is just machines imbued with human insight and (often, especially in the most effective cases) domain-specific knowledge. These efforts manifest as search and sort techniques that allow said machines to analyse facts and propagate information in order to select from myriad possible actions. There is no intelligence here except that which we provide. It's all smoke and mirrors. We don't even know what intelligence is; how can we aspire to replicate it? AI researchers are, by-and-large, just Computer Scientists. Not biologists, not psychologists; just guys and gals working with ever more elaborate Turing Machines. The algorithms they come up with are without exception dumb dumb dumb.

> Google paid 10M per head for DeepMind guys, explicitly working on AGI

Please. DeepMind is just a startup based on (among other things) David Silver's work into reinforcement learning. Google is not interested in these guys because they want intelligent machines; they just want automatons to better sift through reams of data in order to make recommendations and better sell you crap you do not need.

[xamdam]

> Not even out of the gate and already reaching for an ad-hominem. You must be fun at parties.

I think you misunderstood; I did not mean you're an average researcher - I have no idea - but unless you hang our with hotshots at AI/ML conferences (which is a bit of a club) you're hanging out with average researchers.

[gradstudent]

Do I really need to start mentioning names and h-indexes for you to take my point seriously?

[RobertoG]

"but let's not kid oursevles: there's nothing intelligent here."

There are good indications now that brains work repeating a simple probabilistic algorithm in a multilevel fashion. Intelligence is not a magic property, but a kind of "searching and sorting".

" It is much easier to impress upon the lay person an idea involving "intelligent machines" than it is to explain what we actually do."

In my opinion, the opposite is actually true. No serious researcher want to be associated with those freaks that are waiting for the end of the world. They have a reputation that they need for making a living.