
AI is always less impressive than is claimed - jeffmcmahan
http://jeffmcmahan.info/blog/ai-is-always-less-impressive-than-is-claimed/
======
aub3bhat
I opened the link expecting a discussion of flawed metrics that fail to
capture uncertainty or failure in replication of claimed studies, or complex
architecture turning out to be shallow combinations of simpler ones.

Instead the post attacks a straw man argument about "General intelligence",
something that no AI researcher is claiming.

Also the author is clearly clueless as evidenced by:

"Ask a medical researcher to estimate the odds that a Google-funded AI-driven
cancer moonshot will succeed, and I expect you will be met with a snicker. '

Google employs several researchers [1] who are well versed in both medicine as
well as ML. Who are able to competently judge capabilities and limits of their
tools and challenges involved in doing medical research. Rather than creating
straw mental images of "medical researcher" "snickering" at "moonshots", the
author should to engage in some self reflection before writing about
scientific fields he has zero knowledge of.

AND

"I promise you, Facebook did not just work it all out in PHP, and they will
not do so in my lifetime or yours."

I won't comment on absurdity of the paragraph about FB or language & meaning.
But if any reader was confused, Facebook AI team uses Torch/Lua (for deep
learning) or Python (in FBLearnerFlow) and surely not PHP.

[1] [http://www.nimh.nih.gov/about/dr-tom-insel-to-step-down-
as-n...](http://www.nimh.nih.gov/about/dr-tom-insel-to-step-down-as-nimh-
director.shtml)

~~~
YeGoblynQueenne
>> Instead the post attacks a straw man argument about "General intelligence",
something that no AI researcher is claiming.

Several of the Deep Learning people are on record making silly claims about
the future of AI. I'll dig you up the links if you insist [1] but frex,
Juergen Schmidhuber has made noises to the effect that deep nets have shown
intuition and things like that. Jeoff Hinton has said similar things.

The reason for that is that the original AI project started out to create
strong AI in the first place, so if you could not show that your theory has
some sort of chance to lead to strong AI people would scoff at it. There's
still a bit of that in academia and in industry and parts of the press have
not yet gotten the memo that things have changed now, so you'll still hear
lots of people making these claims, or having such claims drawn out of them in
interviews.

But that's besides the point- which is that a lot of the very clearly stated
goals of the industry require the development of systems with human-like
intelligence. For instance, natural language processing. We have made some
advances in recent years (for instance in things like part of speech tagging
or even machine translation to an extent) but the parts of language that we
really take for granted as humans, conveying meaning and being aware of some
sort of context, those are completely outside our grasp for the time being.

So when Google pretends it can just throw a ton of processing power at, say,
machine translation between any two languages any time, and get it to work
well, you know that's just its marketing people knowing they can claim
anything they like and only their engineering team will be in a position to
know how far it is from the truth.

_______

[1] please don't, I need to stop procrastinating.

~~~
cLeEOGPw
> Juergen Schmidhuber has made noises to the effect that deep nets have shown
> intuition and things like that.

The only thing wrong in this sentence is your sarcastic tone. Neural nets,
deep or not, rely on intuition to produce output, so it is completely true
that deep nets show intuition.

What is intuition? It's a feeling that something is of a certain way that is
not obvious or immediately obvious/self evident. And translated to ML terms,
it is some amount of certainty about something. Intuition is an intrinsic
feature of any ML algorithm.

~~~
YeGoblynQueenne
On a more serious note (and therefore a different comment) you're not even,
like, factually correct. What you call "certainty", more commonly known as
_confidence_ , which you seem to think is a component of all machine learning
algorithms [1], is actually a characteristic of probabilistic classifiers and
makes sense primarily in a Bayesian belief network context in any case. If you
train an SVM for instance, you would normally not learn much about its degree
of confidence to its results (unless you explicitly output probabilities).

Also like I say it's called _confidence_ not "certainty" and it absolutely
refers to the degree of confidence of an observer of a given phenomenon, and
not some mental state of a system that computes the probabilities by which
that observer sets his or her degree of confidence.

Not to mention that in any other context, such terms as "belief",
"confidence", and so on are only used metaphorically and - assuming a modicum
of reason - only sparingly.

[1] You say that in the context of machine learning intuition is "some amount
of certainty" and so "an intrinsic feature of any ML algorithm". Correct me if
I misunderstood that.

~~~
cLeEOGPw
Well, I'm not native English speaker, but certainty and confidence means
pretty similar things, although probably I should have used confidence
instead.

And for your criticism about all ML algorithms having degree of confidence -
can you give me an example where an algorithm would not have it? And therefore
would not have intuition?

ML are made for partial knowledge problems - means that they don't SOLVE a
problem, they take prior experience and GUESS the answer. That is intuition by
definition.

------
nv-vn
A lot of the comments here seem to focus only on the progress we've made
rather than what AI should be (if it was true to the name). With "AI", sure I
can tell Siri to send a certain email to a certain person. That's pretty damn
impressive when I think back to what was possible only a few years earlier.
But it's not intelligent. Nowhere near. And honestly, it doesn't save you that
much work. It just performs some preprogrammed tasks given some unambiguous
input that follows a regular pattern. And even that trips up Siri a ton. Yes,
it's cool that you can identify pictures or have a program read your
handwriting, but none of that gets us any closer to a program that behaves
like a human when I tell it to do something (instead of sending an email or
text that I specify, it could draft one on its own; instead of showing me a
Wikipedia article when I ask what something is it should explain to me in
layman's terms and give me a summary). AI is impressive in terms of progress,
but is it really impressive in terms of what it promises to bring? Is it
really even progressing in the right direction? Personally, I don't think so.

~~~
Houshalter
> Yes, it's cool that you can identify pictures or have a program read your
> handwriting, but none of that gets us any closer to a program that behaves
> like a human when I tell it to do something

Yes it does. Humans didn't just emerge out of the aether. We evolved from
animals that first had to perform simple tasks like that. Our brains are still
heavily based on networks of simple pattern recognition neurons. We just don't
know how they are structured, or have enough computing power. But it's
definitely progress.

As proof, we are starting to get neural networks that can do complex language
understanding tasks. RNNs are the state of the art at tasks like predicting
the next word, and make interesting chatbots.

Obviously they aren't human level, but they only have 1,000 neurons. Humans
have billions! They don't even have episodic memory yet, it's amazing they can
do language at all. But research on that is just starting. Give it time.

~~~
YeGoblynQueenne
>> we are starting to get neural networks that can do complex language
understanding tasks.

Ah, please, don't say things like that.

We have neural networks that can build complex models of language. But those
complex models are nowhere near performing well in "understanding tasks",
whatever those are (processing meaning, you mean basically).

The use of ANNs in Natural Language Processing has not led to the same
advances it did in, say, image and speech processing, and... and, well, image
and speech processing.

There's a ton of things about language that go well beyond building a model of
language. Neural networks are nowhere near addressing meaning and context, or
the way those are conveyed through language but are not the same as language.

In a way, NLP today, with ANNs or not, is still stuck in the same rut we were
ages ago, where we assumed that syntax was the biggest part of language and
solving that would solve language [1]. We got syntax down pat and it didn't do
us much good. Neural Networks simply build more complex models of language
than what we were able to build by hand-crafting grammars thirty years ago-
but they're still just complex models of syntax. But syntax is just the
surface of language and it's not even all of the surface.

No we're not starting to get nets that can do "complex understanding tasks".
Not even ones that can do simple ones. No AI technique does "understanding"
yet, except in very limited ways.

[1] Did we really assume that? I'm not even so sure anymore.

------
ddebernardy
Personally, I'd argue the opposite: AI today is _super_ impressive compared to
what it was 15 or 30 years ago.

There used to be a time when one could rightfully dismiss new cool things AI
with "meh, wake me up when an AI beats a professional Go player on a 19x19
board". Because doing so requires an AI that displays intuition and use of
heuristics (Go moves tend to have both local/tactical and global/strategic
consequences on the board) rather than one that merely brute forces its way
through a decision tree.

The fact that one actually did recently is nothing short of game-changing in
my view. It matters little that an AI doesn't demonstrably understand meaning
or fully grasp intent. Plus, when one does, we'll arguably have (or not be too
far from) a strong AI that, I would gather, won't be too impressed with our
own human-level thinking. :-)

~~~
lottin
To me impressive would be if an AI could be told a story, such as _The 3
little pigs_ and then be able to reason about it speculatively, e.g. answering
questions such as "Why do you think this character did this and that?". This
would should that the machine has developed an intelligence. So far, all AI
can do is pattern recognition, which can be very useful for many tasks, but in
my opinion it shouldn't be called "intelligence" because it isn't. Far from
it.

~~~
bbctol
I agree that that would be impressive, but beating a human being at Go is also
very, very impressive, and had been considered a high benchmark for many
years. I can't help but think that after a machine can do basic story
analysis, there will still be people saying that this is just a trick, that
doesn't show any "intelligent" analyses of the story.

AI has a long way to go, but I don't think we should dismiss intelligence as
an either/or binary, and call things that are not "intelligence" mere "pattern
recognition." Strategy games are widely accepted across cultures as a mark of
intelligence when humans do them; you'd be very comfortable saying a human
chess or Go grandmaster was intelligent. There was a long time when people
were dismissing basic AIs as mere toys by saying they'll never beat a human at
Go. There's a little goalpost-moving any time someone responds to an AI
breakthrough by saying "You know what would be _really_ impressive..."

~~~
dmreedy
The Go victory is definitely impressive, for what it was, but I think this is
a pretty unfair comparison; it's an entirely different class of problem, and a
strictly less difficult one at that (at least given current knowledge of the
mind).

It seems likely to me that strategy games are accepted as an intelligence
gauge partly because they're so hard for humans to do; thinking so many plys
ahead by exploring and pruning across the game tree seems really hard for
humans. For some reason, Natural Language and its semantic binding (I think
the notion that these two things are often treated separately is part of a lot
of the bigger deltas between expectation and reality when it comes to 'AI'.
Honestly, they're probably the same thing anyway) doesn't seem really hard for
us.

That doesn't strictly imply that 'strategy games' are strictly harder than
'natural language understanding'; in fact, as far as research goes, strategy
games are easy to tackle, because we know all the rules; we designed them! And
often, perfect information is available to all agents involved. It's about as
black and white as problems get. Sure you can't brute-force Go before the heat
death of the universe, but it still theoretically -can- be brute forced. The
rules of the game (known and accepted by all parties) encode the algorithm,
and it's just a matter of reverse engineering it step-by-step for a given
situation. What a brain is doing when it speaks or listens or thinks is
currently a much bigger, much more primitive mystery. As far as we know right
now, you can't just brute force sentence permutations until you hit a correct
answer to a question like the one posed by the parent.

So I don't call this goal-post moving (though acknowledge that the Vanishing
Problem is systemic in AI Research and Pop Science); instead, I think this is
more akin to the work in say, separating problems into classes of
computational complexity. Finding the upper limits of things can be very
helpful when we're dealing with such an old, deep, controversial problem.

~~~
bbctol
For sure: as much as Go really was widely-talked about as an AI goal, real
language processing has been the goal since, well, Turing. Even more
importantly, it remains to be seen how far the current neural network approach
extends, and it could still be only successful at certain tasks, with a bigger
paradigm shift needed to crack language and so on. But I don't think it's
worthwhile to make a hard, fundamental distinction between game-playing and
language, such that one requires "real" intelligence and the other is
something else.

After all, we possess a single machine that can _both_ play Go and use
language, and do far more things besides, and it really does look like the
brain is re-using a lot of its structures for these different problems. You
can theoretically solve Go, and you can't solve language, but the brain
doesn't try to solve either (and neither does Deepmind); there's at least some
hope that one paradigm can be applied to both these problems, and the reason
we originally turned to neural networks was because of their (admittedly
_highly_ superficial) resemblance to our one positive case of intelligence.

So I do take issue with the idea that language understanding is "strictly"
harder than board games; that's an artifact of the paradigm used. The idea
that the number of rules and exceptions to rules is the 'objective' mark of a
task's difficulty only applies to a certain kind of processing agent, and our
one example of intelligence clearly uses a different one. For most of the past
ten millennia, the only information processing agents on the planet had far
more ease communicating with natural language than crushing at board games;
ask some now to either write an essay or beat Lee Sedol 4-1, and they'll tell
you which is easier. The fact that board game mastery is easier for our AI
attempts than basic speech isn't a sign that they've secretly been easier this
whole time; for a certain kind of processing agent, communicating in a poorly-
defined space has been much easier than mastering a clearly-defined one, and
if our AIs don't match that pattern, it's only another reason to wonder if
we're on a different path.

~~~
dmreedy
The 'kind of processing agent' emphasis is a very good one, and I think worth
bringing up in every one of these conversations.

That said, I want to niggle a little bit on some of these points

There certainly does seem to be component re-use in certain parts of the brain
between seemingly disparate tasks. The left brain/right brain divide has been
largely debunked, and I'm not entirely sure what the current state of the art
is, but I'm not sure I'd go so far as to say that that re-use in this
particular case (if it exists, I don't know) is anything other than a 'best
approximation' (and maybe not even best) with the available hardware. Like
using a fork to eat soup. Sure you can kinda do it, but you're always gonna be
thinner than the fellow with the spoon.

That suggests to me then that using games as a metric of intelligence is
actually a pretty poor way of getting at the deeper issue; there are too many
other variables beyond "how much (general/amalgamated) smarts have you got?".
It might be semi-convenient when you have cause to believe that the two models
you're testing have largely similar structure (i.e., two humans), but it gets
pretty noisy when you've got a less than solid idea how similar the two models
are. Because there is an obvious, perfect solution to the problem, you end up
with a kind of algorithmic continuum, with the system you're trying to model
on one end and perfect brute force on the other. And you don't know how far
along the continuum your model is; it might be pretty close to the system (the
brain), in which case, hey! Progress. But it might be closer to the perfect
solution, and thus closer to dedicated hardware for the problem at hand,
instead of adapted hardware doing its best. It might have a spork, which makes
it rather less worthy of admiration when it comes to soup-eating. Especially
when you see it struggle with the steak.

So, in short; I think it actually -might- be a sign that strategy has secretly
been easier this whole time. I'm not even sure it's that much of a secret. At
least when it comes to the particular processing agents in question, the ones
capable of general intelligence as we understand it. If you want to get into
philosophical conversations about Hyperintelligent Shades of Blue, well,
that's another story.

------
suvelx
The site appears to be dead, so haven't had the opportunity to actually read
it.

But I recently picked up a "Machine Learning" project for a client. I don't
entirely understand everything in the fullest detail, but I understand the
basics. Yet despite my lack of knowledge I got something to work 'enough' in a
day. Tensorflow makes getting a working DNN easy, and then wrappers like
TFLearn make it nearly trivial.

The hardest part of the project was sourcing a decent amount of training data.

And I'm honestly somewhat ashamed of it. My friends think it's cool, but
really I don't feel like I _did_ anything. I just glued a library together.

But maybe that's the interesting bit, that frameworks have been developed to a
point that even a rube like me can get something working in a day.

~~~
dasboth
I think it's definitely a good thing to abstract away the nuts and bolts of a
complicated ML algorithm for general use. Implementing the algorithms from
scratch is really useful if you want to learn the details, but most of the
time the "standard" implementation will do fine, and will probably perform
more efficiently. Of course as with any tool, a conceptual understanding is
essential to meaningfully interpret the results though.

I completely get what you're saying about not feeling like you did anything
though. Once you understand the algorithms even at a basic level they stop
feeling like magic, even though they'll still feel like that for other people.

------
Joof
The last 2-3 years of progress have come about a decade earlier than most
people expected. Many people have some catching up to do in determining
exactly where we are.

Do we have general intelligence? No, but we have audio generation from pure
video and can change an image to match any art style and reasonably good self
driving cars.

~~~
qznc
> audio generation from pure video

I missed that one. Link?

~~~
adwf
This was on the front page a while back, probably what they're referring to:

[https://news.mit.edu/2016/artificial-intelligence-
produces-r...](https://news.mit.edu/2016/artificial-intelligence-produces-
realistic-sounds-0613)

------
jrapdx3
The article looks at an issue I think about quite often and occasionally have
a chance to discuss. The complexity of real-world natural systems is at a
magnitude that's hard to grasp and harder to describe. AI, as an attempt to
replicate natural human intelligence is up against neural systems that have
evolved for 100's of millions of years into numerous forms of astonishingly
intricate structure and function.

In humans neuronal brain circuitry comprises an astronomical number of
interactive elements, and each connection is itself remarkably adaptable with
a large number of interfaces associated with a range of signalling modalities.

Consider that we have ~100 billion brain neurons and 100-500 trillion synaptic
interconnections and each connection is modulated by many factors including
endocrine and immune system input. Furthermore connections transmit and
receive many kinds of signals in complex feedback relationships.

It's well beyond my ability to know how to determine an estimate of how many
possible modes of computation can exist within such a marvelous creation, let
alone begin to adequately understand how even a small part of it actually
works.

I suppose that's the author's point, that what we regard as "simple" problems
are in reality not at all simple. Computers can do remarkable things, and
surely are among the most useful inventions of human beings. We should realize
that working like a significant subset of what our brains routinely do isn't
going to be easy for computing to achieve.

~~~
visarga
We don't need to match 1 artificial neuron for 1 biological neuron. An
artificial neural network doesn't have to be implemented in a self-replicating
body as our brain does. It doesn't have to carry its biological baggage into
the computational aspects. Our brain are part of a process of evolution - the
specific requirements for that goal impose huge restrictions on it. On the
other hand, in a computer, we can have perfect neurons that don't age or
degrade, with any structure we want, scalable to a higher degree. We have
already seen in image recognition and go play super-human performance, I
expect all domains to be conquered soon. Consider go - they started on this
project only 2-3 years ago and already AI has overgrown humans. It doesn't
need a lot of time to learn once it is created.

~~~
nekopa
I see it as similar to flight, once we stopped trying to mimic nature exactly
(flapping planes) we started to not only make progress, but went beyond what
nature can do in flight (eg supersonic)

I think that the biggest breakthrough we'll have in AI will come from a
similar break away from copying the brain.

Inspired by nature, not dictated by it.

------
YeGoblynQueenne
Oh boy, did I laugh my head off at this bit:

 _No one in the scientific literature has a working account of what the
English word ‘the’ means._

Aye. But, strong AI is just around the corner. No worries.

~~~
ajuc
As a speaker from language without articles - it doesn't mean anything ;)

~~~
jeffmcmahan
There's a clear difference between "bring me an apple" and "bring me the
apple". The latter requires that there is either only one apple available, or
requires some special contextual knowledge to comply with.

~~~
ajuc
My language solves this with equivalent of:

"Bring me apple" vs "Bring me that apple".

I still don't understand the need for "a". Surely if you don't qualify a noun
at all you mean "any".

I guess "the" is like "distinct" in sql, so it may be useful to catch "too
many rows" errors.

It was a joke, BTW.

~~~
jeffmcmahan
>> I guess "the" is like "distinct" in sql

That's original Russellian theory, in a nutshell
([http://www.uvm.edu/~lderosse/courses/lang/Russell(1905).pdf](http://www.uvm.edu/~lderosse/courses/lang/Russell\(1905\).pdf)).
None of its many variants cover the data.

>> It was a joke

Fair enough!

------
jamesrom
What's the big deal if an AI doesn't know which footstool you are talking
about?

Intelligence != Omniscience

~~~
aminorex
Nor is the footstool problem a terribly hard one. The challenging bit is
integrating the many and various point-solutions into an adequately coherent
assembly.

------
compumike
With regards to the specific problems described here, referencing how
computers can make sense of communication between a listener and a speaker
when each has different knowledge in the world and where strict and literal
communication isn't always the most efficient, search for the "Rational Speech
Acts (RSA) model" \-- an active field in the literature in the past few years.

~~~
jeffmcmahan
Yes, the trouble is that the work on performative acts (speech acts) only goes
so deep, and then everything turns to confusion and ad hoc rationalizations
presented like theories (I have a couple in mind). There'll be no means of
implementing this body of knowledge in software -- or even taking it as a
guide. So, if it were going to work out, you'd first have to solve the
immensely challenging scientific problems, and then see about using the
resulting knowledge to build something. As a person with some experience in
the area (admittedly nothing published), I don't expect the former to occur.
Ever.

------
MrPatan
To be fair, just plain old "I" is very disappointing most of the time.

------
Grue3
I'd be impressed if an "AI" could simply passably translate a natural non-
Indo-European language to English.

------
ktRolster
The article references "narrow syntax". Does anyone know what that is?

~~~
jeffmcmahan
The term refers to work on syntax (or phonology) that does not extend into
semantics. So, basically anything Chomsky writes is on narrow syntax, whereas
anything (say) Irene Heim or Barbara Partee writes is concerned with a broader
notion of syntax (which includes semantics).

------
Iv
Just you wait until the Grand Soir.

------
Lapsa
4chan: repeat after me

tay_ai: okay

