
Ask HN: So what's new in the world of A.I.? - dstein
AI research and related topics and startups rarely make news.  Is it really such a dead field?  There's never any breakthroughs announced, and there's no grandiose research projects underway that I know of.  I read every day how mobile social network platforms are being funded en masse in Silicon Valley while an entire branch of computing appears to be whithering away in the grips of academia.<p>Are there any websites that try to track the different AI projects going on?  Which books should someone fascinated in this field be reading to try stay up to date?
======
jacquesm
Whenever there is a 'breakthrough' in AI two years onwards we call it by some
different name and AI gets pushed just a little bit further in to the future
again.

Plenty of stuff that is 'normal' in machine learning today would have been
advanced AI twenty years ago. I don't expect this to change much, until the
time that we reach the stage where you can click together a functional agent
from a bunch of pre-made units that can go out in to the world and do useful
work for you. And quite possibly by that time the definition of AI will have
shifted to 'a machine intelligence so advanced that it can solve problems that
humand can't solve' or something to that effect.

AI simply is a moving target, nobody (except for Alan Turing maybe) ever sat
down and wrote a description of the border between regular programming and
artificial intelligence (self improving systems) with enough clarity that we
will know when we've crossed. By plenty of standards we crossed it a decade or
more ago, by many standards we may never cross it at all.

~~~
alextp
Also, a lot of today's robotics (like anything from anybots, or from Andrew
Ng's group at stanford) would be scary AI a few years ago.

In this sense AI can be seen to be similar to philosophy.

------
iamelgringo
I'm flummoxed by this question. From what I see, AI is at the core of much of
what's happening in Silicon Valley and in tech in general.

But then, I'm also using semantic web tools in building a search engine for
financial news at <http://Newsley.com>. Right now we're indexing and
automatically categorizing 500 financial news articles a day. And, I'm really
looking forward to adding on recommender systems and use some machine learning
with the scads of data we're collecting.

 _AI research and related topics and startups rarely make news. Is it really
such a dead field? There's never any breakthroughs announced, and there's no
grandiose research projects underway that I know of._

AI companies are all over the place. Any decent online ad platform is
essentially a huge recommender system. Search and search quality are
essentially problems that traditionally fit into a "AI" category. Have you
shopped on Amazon recently? Where would they be without their recommendation
system. Same with Netflix.

I just bought a $40 HD webcam that comes with software that can track points
on my face in real time. It then drives a 3D puppet in sync with my facial
expressions and movements. When I was studying animation 8 years ago, it took
$500K worth of equipement to do motion capture. Gazehawk launched a few months
ago, and are already profitable selling eye tracking tools:
<http://www.gazehawk.com/>.

You can now buy a friggin robot to vacuum your living room for $150. The US
airforce now considers commanding a wing of drones essential experience for
Colonels interested in promotion. Most drone pilots in the Iraq and
Afghanistan wars a pilot their aircraft from offices in Nevada. Robots are
essential members of bomb and IED disposal teams. One of DailyBooth's founders
was stuck in the UK because of visa problems, so last year he was attended
invetsor meetings virtually by using an Anybot.

You can play virtual golf by swinging a controller in your living room if you
own a Wii. The last RPG that I played was Oblivion and I was blown away by the
character AI. That was years ago. I'm afraid to buy any new games because
they'll suck up so much of my time.

Far from whithering away, just the opposite has happened. What used to be
considered AI has now become so commonplace that we don't think twice about
using it. If you don't believe me, just Google around for a while. You'll find
the answer.

~~~
krschultz
I think the military drones are proof of the failure of AI research. Every
single one of those devices lack AI, they must be controlled by a person. They
are large remote control weapons, but not intelligent robots. The military
funds plenty of research on AI but haven't actually fielded autonomous systems
yet.

~~~
another
Unfortunately untrue.

Many military robots are purely remote controlled, but many others integrate
often substantial levels of autonomy. Most UAVs provide autonomous navigation
and stationkeeping, for example, and often much more; other military robots
operate with no human in the loop whatsoever (eg, AUVs).

It's also interesting to consider the autonomy of fast-react weapons-in-the-
loop systems such as AA/SAM sites and ABM installations (eg, Patriot), and the
contribution of that autonomy to associated friendly-fire incidents. Methods
normally considered AI---probabilistic filters, reasoning, and data fusion, in
particular---are fundamental to such systems.

~~~
krschultz
Forgive me, I failed to make this clear, but I work on such systems and I
don't think they are all that smart. The code for station keeping is less
interesting than the code for directions on Google Maps. (Connecting GPS
points in space is pretty damn easy). Landing a UAV is pretty sweet, but I
still wouldn't call it AI. The complexity is on the control side, but if you
call that AI then is your cruise control in your car AI?

------
physcab
Well I think for one, the field of AI is massive and spans enormous amounts of
research over 60-70 years. You wouldn't know about any breakthroughs unless
you studied in the field.

From my brief exposure, here are some big algorithms and some applications in
roughly chronological order. Definitely not exhaustive. Those in the
know...feel free to correct me:

\- Least squares regression (prediction- used everywhere)

\- Fishers Discriminant (classification tasks)

\- Perceptron networks (classification tasks)

\- Markov Models/Hidden Markov Models (Speech/Handwriting recognition)

 __* Machine Learning community develops out from AI __*

\- Support Vector Machines (Image recognition/ classification)

\- Expectation Maximization (prediction)

\- Relevance vector machines (classification / prediction)

\- Gaussian processes

\- Predictive sampling

My prediction (heh pun intended) is that you see enormous changes in the field
when processing by GPU's becomes much more available. There are some
algorithms that are simply difficult to research because labs don't have
access to fast enough machines. Also, there is beginning to be some effort to
port these algorithms to a Map/Reduce framework so they can be run at scale
(check out Apache Mahout). Lastly, I'm slightly biased towards machine
learning as that's where I chose to do my grad research. I'm not sure what
problem domains are under AI or ML or Statistics...I tend to clump them all
together.

~~~
earl
Gauss invented least squares. Before 1800 IIRC. Also, I had no idea svm
predated em. Cool.

As for the gpus... I'm dubious. Most optimization problems I'm aware of are
poorly suited for gpu style parallelism. More's the pity.

~~~
Retric
An obvious choice would be ridiculously large Neural Networks. However, any
problem where you start with a random seed and converge on a solution benefits
from starting at several different random seeds to avoid local minima. Also
search algorithms (chess programs etc) often paralyze vary well.

------
orangecat
Monte Carlo AIXI: <http://www.vetta.org/2009/09/monte-carlo-aixi/>. It's an
approximation of the optimal-but-uncomputable AIXI agent. Apparently it
learned to play a Pacman variant without being told the rules in advance.

------
ajj
In today's context, most work in AI is statistics-based. So if you are talking
about conventional AI (logic-based for example), yep it would be relatively
harder to find.

Lets not forget though that machine learning and data mining and their
applications: natural language processing, a lot of computer vision,
recommender systems, financial predictions, fraud monitoring, some types of
games, and innumerable others are huge advancements made towards the same goal
that AI was after, albeit with different means (statistics) than originally
attacked with.

Statistical machine learning is AI. And it is bigger than ever before.

------
_delirium
Large-scale data-mining, predictions, and machine learning are not new per se,
but have rapidly expanded in terms of available practical techniques, and
actual use of those techniques in applications. Palantir is one major startup
based around that, and there's a bunch of other less-high-profile stuff moving
AI into marketing (behavior prediction, etc.). There's also quantitative
finance. Less businessy, there's a bunch of research on applying machine
learning and datamining to huge datasets like NASA's sky surveys, or biology
or physics data repositories.

------
apu
To add to the many good responses here so far: AI is such an overloaded term
with many negative connotations that it's no longer used. It's also now
specialized into separate components, as researchers realize that it's not
"cheating" to study e.g. vision separately from natural language processing.
Even humans cheat, by having separate portions of the brain specialize in
different parts of "AI".

So some of the major AI fields are now known as:

    
    
      * machine learning - learning from data, aka applied statistics, aka how to "learn"
      * computer/machine vision - how to "see"
      * natural language processing - how to "read" and "write" and "translate"
      * speech recognition - how to "hear"
      * robotics - how to "do"
      * affective computing - how to "feel" and "act"
    

There are others as well, but as you can see from this simplistic breakdown,
the specialities loosely mirror those in biology/medicine/neuroscience, and
indeed there are also researchers who straddle the boundaries between the
natural side and the computation side of things.

As a vision researcher, I can tell you that there's a huge amount of work
being done, and progress being made, in our field -- both in academia and in
industry. While it often doesn't make the news, we consider this a feature,
not a bug.

For following progress in these fields, I have two comments:

1\. There's no good resource I know of to follow all the subfields of AI.
Instead, there are different sources for each field.

2\. While others have recommended journals and conferences, I think it can be
tough to read them if you're not already immersed in the field. So instead,
I'd recommend starting with the wikipedia pages for each field, seeing the
general list of topics, and then finding the appropriate papers if you're
really interested. A good way to find important papers is by looking on google
scholar for papers with lots of citations (insert usual disclaimers here about
citations != quality of work, etc.)

I can get you started in computer vision with two very influential papers in
the last decade that have also had a huge impact on industry:

 _P. Viola and M. Jones - Robust Real-time Object Detection_
[http://research.microsoft.com/en-
us/um/people/viola/Pubs/Det...](http://research.microsoft.com/en-
us/um/people/viola/Pubs/Detect/violaJones_CVPR2001.pdf)

This paper revolutionized face detection, and is the basis for automatic face
detection in most consumer cameras.

 _D. Lowe. Distinctive image features from scale-invariant keypoints_
<http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf>

This paper was the culmination of many years of work on detecting repeatable
features in images and representing them in a consistently-findable way and is
the basis for numerous object recognition algorithms, as well as those for
stitching multiple photos together into panoramas and Microsoft's
"PhotoSynth".

~~~
_delirium
I don't think it's true that it's no longer used; I certainly still consider
my primary research field to be "AI", and the main journals and conferences I
publish in have "AI" in the title. I do agree that some subfields have split
off more, especially machine-learning, robotics, and vision, but there's
plenty of stuff that still goes on under the rubric of AI. Even the more
split-off fields are still very present in big-tent AI conferences and
journals, especially when presenting work on integrated systems.

Despite being mostly known as an ML researcher, Leslie Pack Kaelbling's
AAAI-10 keynote slides make something of an argument for why AI is important
as well: <http://people.csail.mit.edu/lpk/AAAI10LPK.pdf>

~~~
nervechannel
The skills and techniques required are very very different between the fields
though; my PhD was in natural language processing but I don't have the first
clue about machine vision, for example.

Hell, I was working on parsing biomedical text -- meaning I don't really have
much of a clue about speech processing, machine translation, sentiment
analysis, question answering or most of the other subfields of linguistic
computing.

Of course there's plenty of room for cross-fertilization, but that's true
across comp sci as a whole. Thinking of all of these things as being part of
some coherent topic called AI is more of a historical legacy than a useful
category.

~~~
_delirium
I guess taking a mostly integrated-systems, applications-focused view, I see
AI as a useful organizing concept for what to build, what the issues are in
building it, etc. For example, I'm not sure what field Alan Newell's
"knowledge level" talk would go in if not into AI.

I do agree there are lots of areas of research that are maybe more
"algorithms" than "AI" (e.g. improving SAT-solving), but I disagree that that
sort of specialization is the _only_ way to research. From my perspective,
those areas of algorithms provide the raw-material research that can be used
to build AI systems. Even building AI systems is often specialized to some
extent as well, but I think it's useful to have a semi-coherent body of
knowledge and shared community around "AI" when doing so, rather than _just_
the domain-specific algorithms and approaches.

~~~
another
... and as someone whose research involves both SAT and statistical learning,
I'd agree: putting a variety of specialties under the AI umbrella can make it
easier to connect different areas in useful ways.

------
dlwh
As others have pointed out, a lot of things that "work" stop being considered
AI.

But here are a few recent advances off the top of my head:

The DARPA Grand Challenge. Robot cars that drive themselves.
<http://www.darpa.mil/grandchallenge/index.asp>
<http://www.youtube.com/watch?v=RY93kr8PaC4>

Google's Machine Translation system. Far from perfect, but it can usually do
something reasonable between over 40 different pairs of languages. They added
Latin on Thursday.

CMU researchers have developed a classifier that can look at your MRI scans
and tell what you're thinking. (Still in its infancy, but still.)
[http://www.computerworld.com/s/article/346917/The_Grill_Tom_...](http://www.computerworld.com/s/article/346917/The_Grill_Tom_Mitchell)

People working in my research lab are working to automatically reconstruct
words from the ProtoAustronesian language using just the modern words. Our
results are typically extraordinarily close to what historical linguists have
done by hand.

People in a neighboring lab are working on a system that can automatically
distinguish between a nuclear detonation and an earthquake as part of the Test
Ban Treaty Organization. They're making good progress.

Take a look at PhotoSynth: <http://photosynth.net/> which can automatically
reconstruct 3D scenes from disparate photos taken from different cameras with
no prior information needed to stitch them together.
<http://grail.cs.washington.edu/rome/>

The face detection that is now standard on many cameras was one of the great
problems in computer vision.

------
geuis
The field is most certainly not dead. Mass media (movies, books, etc) over the
last 60 years or so has presented a vary narrow vision of "A.I"; a vision of
what are more or less people that happen to be in synthetic versions of the
old flesh and bone varieties, that are normally repressed and enslaved or
trying to kill the carbon-bags for some faulty reason or another.

That vision has basically made the term Artificial Intelligence become yet
another meaningless marketing term.

The concept of A.I. is like the concept of organic chemistry. There are vast
sub-disciplines that are loosely collected under the term "organic chemistry"
because they more or less all involve carbon atoms at some point. A.I is like
this as well, because there are many, many sub-disciplines and they all
involve the concept of systems that are capable of "learning".

So, my first recommendation is to just forget any preconceptions of
"Artificial Intelligence" because its not something that really exists, just
like Brawndo the Thirst Mutilator does not have what cows crave.

What does exist is this vast field of scientific, practical, and commercial
activity involving learning systems. These systems are typically closely tied
to computation, aka hardware or software, but not all. Some work is done in
biology and some in organic chemistry (interesting cross connection there).

Another preconception to forget is that "A.I" died in the 80's and is
irrelevant today. What nearly _all_ people who are even loosely familiar with
the term, beyond killer robots from the Future or lonely little robot boys
with mommy issues, is that A.I. has no part in their lives. The truth is that
it has been all around us for many years.

The timely delivery of your new shoes from Zappos or buying oranges in the
winter: shipping. Tens of thousands of planes in the air at any moment:
transportation. The computer chips that are in your computer or handheld that
you are reading this on: computing (simulated annealing, fascinating!).
Millisecond stock trades: the economy. Automated stellar classification:
astronomy. The list goes on.

So once you put aside your preconceptions for a bit, start asking what kinds
of fields you're really interested and then looking at how learning systems
are influencing them. Sometimes you will be surprised to learn that what might
be classically called "A.I" has utterly invaded your favorite field and that
the people who work in it are entirely unaware that the learning systems
they're using might even be considered "A.I".

------
snikolov
Numenta (<http://www.numenta.com>) is working on simulating how the brain (the
neocortex in particular) recognizes patterns. It's an exciting recent field of
research, and has little to do with neural networks, or other supposedly
brain-inspired methods that don't actually bear much resemblance to neural
processes.

This TED talk by co-founder Jeff Hawkins (of Palm fame) gives an overview of
what they are doing.

[http://www.ted.com/talks/jeff_hawkins_on_how_brain_science_w...](http://www.ted.com/talks/jeff_hawkins_on_how_brain_science_will_change_computing.html)

------
npp
People have already explained the fact that the field now goes under a number
of different names, and that it is very active, so I won't rehash this. A few
pointers to things where you can keep track that are different from what
others suggested so far.

1\. Foundations and Trends in Machine Learning -- this is a journal aimed at
publishing a very small number of well-written survey papers on various trends
in ML. This is easier to follow than an entire conference (much lower traffic,
higher signal/noise), and should be readable for a wider audience (assuming
they are math-inclined).

2\. Conferences like Algorithms for Modern Massive Datasets are practically-
oriented, well attended by a lot of industry, and involve a lot of AI:
<http://www.stanford.edu/group/mmds/>. Look through the speakers and topics.
This is one example, there are others.

3\. A lot of important tech companies have teams that do AI and AI-type
things, at least using the modern definition of AI (Google, Facebook, Twitter,
LinkedIn, Netflix, Amazon, Microsoft, eBay, even Apple with its Siri
acquisition; there are others). This is not to mention people using this stuff
in other areas, like finance and bioinformatics. These groups sometimes talk
about what they're working on, so you can check this out.

------
bravura
On MetaOptimize, we were discussing the most influential ideas from 95-05:

[http://metaoptimize.com/qa/questions/867/most-influential-
id...](http://metaoptimize.com/qa/questions/867/most-influential-
ideas-1995-2005)

And just to clarify: Most of the contributions have been in _machine
learning_. Machines can learn a superset of AI (assuming that machines can
learn AI) because there are a handful of things a machine can learn that a
human can't.

------
ggchappell
> AI research and related topics and startups rarely make news.

Research of any sort rarely makes news. The exceptions involve universities
with very zealous, hard-working P.R. people (who pretty much always overstate
the importance and impact of the work they cover).

As for startups, they, and other businesses, are there to make money. And
there just isn't much money to be made in A.I. (yet?). The only commercially
viable idea to come out of A.I., that I know of, is the expert system. And
that turned out to be a pretty small market. [EDIT: Seems I was way off here.
See replies, and my apologies to all the hard-working A.I. researchers out
there.]

> Is it really such a dead field?

Well, not _dead_ , but it certainly is not what it once was. 50 years ago,
many knowledgeable people thought that machines with human-level capabilities
were just around the corner. Today it's just Ray Kurzweil. 50 years of rather
dismal results tends to dampen the enthusiasm of prospective researchers.

> There's never any breakthroughs announced, ....

"Breakthrough" is a word used by P.R. people. Real researchers know that
knowledge is almost always advanced in small steps, building on previous work.
When you read about a "breakthrough"--in any field--be skeptical.

> ... and there's no grandiose research projects underway that I know of.

Almost certainly not. This is because the U.S. Congress, and similar
organizations elsewhere, have not made A.I. a funding priority. No grant $$$
means no big research projects at universities. (And no obvious commercial
possibilities means no big research projects at corporations.)

But there are certainly plenty of little projects.

> Are there any websites that try to track the different AI projects going on?

That would be difficult, since, when a researcher starts a project, he
generally does not announce the fact to the world. Research projects often
have no official status. It's just a few people--or one person--playing around
with some ideas. If they come up with something interesting, then they publish
a paper about it, but that is only after the fact. So it would be hard to
gather the information for such a website.

Generally, the way to track current research in a field is to look at the
relevant journals. Unfortunately, this can be tricky without expertise in the
field. In any case, here are a few links to A.I. journals, to get you started:

Journal of Artificial Intelligence Research <http://www.jair.org/>

Journal of Machine Learning Research <http://jmlr.csail.mit.edu/>

Journal of Intelligent Systems <http://www.jisjournal.org/>

Another possibility is to find an online discussion group about A.I. research.
However, I can't help you there.

~~~
tansey
> The only commercially viable idea to come out of A.I., that I know of, is
> the expert system. And that turned out to be a pretty small market.

Oh come on, you're baiting AI people (like me) here. :P

* Spam Filters (Bayesian networks)

* Video Game AI (game trees, fuzzy logic, etc)

* Natural Language Processing (Markov networks)

* Computational Finance (pattern recognition)

The list goes on, but the thing about AI is this: if an algorithm is
successful, it quickly becomes a part of the industry or its own field and is
forgotten.

~~~
patio11
Amen. Stuff that doesn't work is "AI". Stuff that does work is "math".

An algorithm to predict which humans are trustworthy enough to keep their
promises to their creditors, which _murders_ the performance of human beings,
enabling billions of dollars of commerce which was previously economically
unfeasible? AI. FICO? Oh, that is just some number crunching on a big source
of data. Big deal. Come back when you get a chatbot working.

~~~
lmkg
Hofstadter said in GEB that "intelligence" is almost always defined implicitly
as "whatever we currently think computers can't do," and that goalpost gets
moved with astonishing regularity. The fact that a machine can do something
means that it must not take intelligence, right? Thus, computers are able to
do more and more that would have been thought impossible just 5 years ago, and
we still convince ourselves that A.I. lies far beyond the horizon.

------
ddewey
If you want AI literature treating human-level intelligence, try searching for
"artificial general intelligence".

Also, here are some links to the proceedings of recent AGI conferences:

<http://www.atlantis-press.com/publications/aisr/AGI-09/>

<http://www.atlantis-press.com/publications/aisr/AGI-10/>

------
nnevvinn
There is the Singularity Institute for Artificial Intelligence --
<http://singinst.org/>

They are tackling the problem of how to make AI "friendly" --
[http://wiki.lesswrong.com/wiki/Friendly_artificial_intellige...](http://wiki.lesswrong.com/wiki/Friendly_artificial_intelligence)

------
syntience
Our ideas are definitely quite a bit different; track THIS:
<http://artificial-intuition.com> (theory/motivational site)
<http://videos.syntience.com> (videos - most recent results)
<http://monicasmind.com> (blog) <http://syntience.com> (corporate. Investor
inquiries welcome) <http://ai-meetup.org> (something to do in Silicon Valley)
<http://www.youtube.com/watch?v=REzrYWOzhWc> (bonus robot)

------
leif
The "dead field" you speak of is deterministic A.I. research. Statistical
methods (read: machine learning) are doing extremely well these days, and only
get more powerful the more data we get our hands on.

------
jmspring
I'm sure my karma rating will take a hit, but my first thought was -- "Someone
developed a program to replace Mike Arrignton and his ability to insult
interviewees?"

But in all seriousness, the links to journals are your best resource for
specific topic areas. I've spent a lot of time in data modeling, computer
vision, etc. It is a lot about providing specific models (with some minor
adaptation / dynamism in the algorithm) to provide for "learning" /
"adjustment".

So start with areas that are directly related to your interests and try and
iterate/generalize from there.

------
nervechannel
... I like the fact that 15 places above this post, currently, is this one:

<http://news.ycombinator.com/item?id=1754357>

------
clevercode
One recent development in the field of artificial neural networks is "Deep
Learning":
[http://www.youtube.com/watch?v=VdIURAu1-aU&feature=playe...](http://www.youtube.com/watch?v=VdIURAu1-aU&feature=player_embedded#)!

------
mtraven
Apple bought Siri, a spinoff from SRI's AI division, earlier this year. That
was a pretty big deal.

------
matus
how bout amazon's recommendation systems? or ad placements? or google search?
google what did you mean? ocr? algorithmic trading?

what field are they in if not AI? sure, the techniques are heterogeneous and
there seems to be no one unifying theory but the field is a bit like that.

