
Simple questions to help reduce AI hype - apathy
http://smerity.com/articles/2016/ml_not_magic.html
======
vonnik
Stephen makes a lot of good points. And the list of questions he says people
should ask is also useful. I have a few to add:

* What algorithm(s) are you using? Nobody should be able to brag publicly about their AI without simply naming the algorithms.

* What is your objective function? That is, what are you trying to optimize; what value you are you trying to maximize or minimize? Is it classification error? Is it measuring similarities on unstructured and unlabeled data through reconstruction?

* Do you already have the data that you can train on to minimize that error? If not, do you have a realistic plan to gather it? Have you thought about how you'll store it and make it accessible to your algorithms?

We have a series of questions we suggest that people ask themselves when
approaching a machine- or deep learning problem:

[http://deeplearning4j.org/questions.html](http://deeplearning4j.org/questions.html)

I talk to a lot of startups making claims about their "AI", and I can't stop
them from jumping on the bandwagon, but not all have the ability to build a
machine-learning system and gather the data it needs.

Every good thing gets hyped, but that doesn't make it less good. It just means
readers have more work to do.... Like Francois Chollet, I happen to believe
that AI is even bigger than the hype, but in ways that the hype can't imagine
yet.

[https://twitter.com/fchollet/status/751483046436548608](https://twitter.com/fchollet/status/751483046436548608)

~~~
Smerity
Great additions. I can't believe I missed "what algorithm(s) are you using" \-
it didn't even occur to me people could skip those yet still be taken
seriously though that was (obviously) the case with the company I referred to
in the article given they pitched AI when they had none.

I agree with you and Francois about the future potential enabled by AI. I do
think we're at the early days of the internet where we can see it enables many
things but we're not really accurate about predicting more than a year or two
in the future.

Hype is just noise distracting us from the important contributions and issues
we should be concerned over. I'm more worried about the immediate future of ML
algorithms being misused, resulting in modern day redlining[1] and so on, than
I am about any SkyNet prophecy.

[1]:
[https://en.wikipedia.org/wiki/Redlining](https://en.wikipedia.org/wiki/Redlining)

~~~
vonnik
I agree with you about redlining. I hadn't really thought about that... A
recent scandal involving a predictive analytics firm called Northpointe shows
how stupidly people use algorithms, and how much they are abused when there is
no transparency: [https://www.propublica.org/article/machine-bias-risk-
assessm...](https://www.propublica.org/article/machine-bias-risk-assessments-
in-criminal-sentencing)

~~~
yummyfajitas
The scandal is that propublica wrote a story which directly contradicts their
own statistical analysis. Their analysis was unable to show statistically
significant bias (and had other flaws that might be genuine mistakes - no
multiple comparison correction, possible misinterpretation of the model).

[https://www.chrisstucchio.com/blog/2016/propublica_is_lying....](https://www.chrisstucchio.com/blog/2016/propublica_is_lying.html)

In fact, their r-script suggests that the secret algorithm is probably
reducing racism relative to whatever secret algorithm human judges would use.

------
unabst
There is a simple antidote for hype. It doesn't require questions, and it will
mute it completely. Just ignore the hyped word and see if there is anything
there. The author does this throughout the article, but the business plan
argument is the best example:

> asking if the business plan would work with free human labor replacing the
> automated component

Just ignore the word AI, and measure the value of the business or the research
or the software for what it is. That's it.

But the author still resorts to the word AI as a common reference. This is
what feeds hype, because we introduce it with every mention.

Hype isn't about substance. It's about the energy and excitement infused into
these words that tends to build up. That's why we call them buzz words. People
who know nothing technical about the subject begin to react to them, but they
will not react to a different word.

Salesmen will always proceed to monetize this attention and excitement with
things people can click or buy or say yes to. The market is driven by hype.
Whether something actually happens in the field will have nothing to do with
hype, but most money in a capitalist market is still driven by it. So although
hype may be distracting for those doing real science or real entrepreneurship
or real work, it's your best shot for promoting whatever it is you're selling,
be it to consumers, to investors, or for funding.

------
epberry
> The real world is not a dataset.

I've seen this cut both ways. Particularly in neural nets for video, which I'm
working on, datasets such as HMDB51 are extremely difficult to the point where
they may not represent the difficulty of the real world application. Many
vision tasks in the real world can actually be constrained by camera angles,
assumptions based on experience about what kinds of objects are in the frame,
lighting considerations, or other things that make the real world task
actually _easier_ than the dataset.

> AI won't save a broken business plan.

Best point in the article and the thing I worry most about day to day. Even if
your AI is crazy good, if you don't have a market and people willing to pay
for your product then it doesn't matter.

~~~
Noseshine

        > Many vision tasks in the real world can actually be constrained by camera angles
    

Great example - because it can be extended to show that even human brains have
great difficulty with that. Just turn the scene upside down and see how the
brain has to go into overdrive and still miss a lot of stuff. There are quite
a few optical illusions photos based on that, for example the face that seems
to smile - until you flip it.

~~~
justratsinacoat
>There are quite a few optical illusions photos based on that, for example the
face that seems to smile - until you flip it.

Ah, the Thatcher effect! [0][1] Although IMO this is a bad example because
(er, AFAIK) we don't have the same sort of hardcoded facial-feature-recognizer
systems in computer-vision neural nets as we do in boring old hew-mons. The
Thatcher effect works on people with prosopagnosia [2] ('face blindness',
people who have to work out who you are by your clothing, gait, voice and body
language). These, taken together, proved that the known full-face-detector in
humans, the fusiform gyrus, is aided by other systems that recognize features
of a face (eyes, mouth) independently of that face's vertical orientation.

[0]
[https://en.wikipedia.org/wiki/Thatcher_effect](https://en.wikipedia.org/wiki/Thatcher_effect)

[1] [http://static.independent.co.uk/s3fs-
public/styles/story_med...](http://static.independent.co.uk/s3fs-
public/styles/story_medium/public/thumbnails/image/2016/02/15/17/thatcher_illusion.png)
< here's the trope-namer, straight outta the 80s. The effect is much more
pronounced in this image than in the wiki one; it's way more obvious that the
mouth/eyes are 'properly oriented' despite being upside down

[2]
[https://en.wikipedia.org/wiki/Prosopagnosia](https://en.wikipedia.org/wiki/Prosopagnosia)

------
betolink
One question that's not explicitly asked on this list: How much preparation
does the training data need?

Knowledge representation is still one of the main bottlenecks on AI/ML in
general, a deep learning network is not going to prepare its food by itself.

------
nkozyra
I haven't experienced a hype more warranted.

Yes, the underlying "magic" is decades old algorithms, often relatively simple
concepts like LMS or nearest neighbors. The ML isn't the exciting part, it's
the application. The pieces are finally in place: simple acquisition of
training/test data (big data), powerful computing for the masses, larger
crossover of ML into the computing/robotics fields. There is much hype but
also much on the cusp of changing huge parts of human life.

~~~
lottin
We'll see whether this is true when ML has actually changed huge parts of
human life. So far it hasn't changed much, to be honest.

~~~
nickmorri
The recent advances in speech and image recognition alone have had tremendous
impact. Many areas of day to day life have been impacted.

~~~
argonaut
This is generally overblown. What has mainly happened so far is that already-
existing products have been incrementally improved by deep learning - speech
recognition, spam detection, search ranking, etc. all worked reasonably well
before deep learning came along; deep learning just improved them.

There aren't actually that many actually massively-impactful image recognition
products out there yet (there are certainly products with the _potential_ to
be impactful, e.g. medical diagnostics, but it is too early to call).

------
gryphonshafer
> Achieving human-level performance on any real-world task is an exceedingly
> difficult endeavor when moving away from a few simple and well-defined
> tasks.

It's an exceedingly difficult endeavor to try to get all humans to produce
"human-level performance" while doing simple and well-defined tasks. Evidence
= Mechanical Turk.

~~~
50CNT
An interesting thing to note there is that research shows that untrained
individuals have significant variance in task performance which decreases as
they get better at the task. Mechanical Turk might be an extreme case, since
all tasks are done by untrained individuals.

Whilst I agree with the quote that AI has difficulties when moving away from
well-defined tasks, so do we. Most expert performance is constricted to narrow
task domains, does not transfer well to other domains, and takes a long time
to acquire. In a sense, that's not too dissimilar to AI.

I do disagree with the "simple". Games such as Chess are the stereotypical
example for things AI performs well in. Their rulesets are well defined, but
the games are incredibly complex. It is amazing that AI outperforms even
grandmasters in this area.

------
est
> "How much does the accuracy fall as the input story gets longer?"

As a beginner of text classification, I find the contrary, if the sentence is
very short, the result is more likely suboptimal. E.g. classification of short
tweets vs a detailed NYT article.

~~~
Smerity
I intended these questions as broad examples though some are specific to a
given AI system or architecture. You're quite right in that it depends on the
task and the model.

I was thinking specifically about neural machine translation (NMT) with the
encoder-decoder architecture. The encoder-decoder architectures converts a
sentence in language X into a fixed size vector representation then aims to
"expand" that vector into the equivalent sentence in language Y. As the vector
size is fixed, short sentences can be adequately represented but longer
sentences end up being lossily compressed. This realization illustrated a
fundamental limitation in the encoder-decoder architecture and motivated the
use of attention mechanisms.

There's a great figure (Figure 1) in Nvidia's Introduction to Neural Machine
Translation[1] that shows the dramatic drop in accuracy with respect to the
sentence's length.

[1]: [https://devblogs.nvidia.com/parallelforall/introduction-
neur...](https://devblogs.nvidia.com/parallelforall/introduction-neural-
machine-translation-gpus-part-3/)

------
gkr
quoted from the blog post*

    
    
      Achieving human level performance on any real world task is an exceedingly difficult endeavour when moving away from a few simple and well defined tasks.
    

May be related or off topic

I have developed a small messaging application which categorise the incoming
messages by HUMANs (instead of AI) to achieve less false positives.

HTTPS://www.formalapp.com

I admit this app's solution is not technically complex.

And I understand building AI based solution is complex and needs more
expertise.

But in this use case relying on human intelligence works or am I missing
something ?

~~~
gkr
link: [https://www.formalapp.com](https://www.formalapp.com)

------
peter303
AI is 60 years old and gone through hype cycles before. Nural networks are
nearly as old as AI but work better these days.

------
graycat
Short reaction: No!

In a little more detail, the author is talking about cases of "an amazing
system" but, really, has in mind a quite narrow view of such systems and how
they work and can be, and have been, built. Really, the author is looking at
less than, say, 5% of the great examples of how to build "an amazing system".

In more detail:

The article has its definition of _AI_ :

> In this article, I won't quibble over definitions, simply taking the
> broadest term as used in the media: artificial intelligence is whenever a
> system appears more intelligent than we expect it to be.

Okay, from my usual understanding of AI, say, the peer-reviewed papers I
published in AI, this is a relatively broad definition, but while considering
this article, I'll accept that definition.

But, oops, right away in the sentence before we have

> If there's any promise I can make about the field of AI,

So, the author is saying that AI is a _field_. From the author's definition of
AI, that is a very broad field indeed! Maybe the most broad of all?

Still I will try to consider the article:

So, my next concern is

> Ask what cases the system will fail on

> "Revealing the failure cases of your models is a way of avoiding over-hype"

> Kevin Murphy at ICML 2015

Now what is _AI_ is starting to look quite narrow, narrow enough to have a
"model" and "failure cases".

So, oops: Consider a simple computer program that plays the little matchstick
game Nim: There are two players. The game starts with three piles of
matchsticks. For a _move_ a player picks a pile and removes from it as many
matchsticks as they want but at least one. The players alternate moves. The
player who picks up the last matchstick loses.

Okay, there is a simple algorithms for perfect play. Then if both players play
perfect games, who wins depends just on who moves first. If only one player,
A, is playing a perfect game but on that instance of the game with perfect
play by the other player, B, should lose, the player A will still win as soon
as player B makes a mistake.

How to play a perfect game is in

Courant and Robbins, _What is Mathematics_.

So back to the last quote, there are no real "failure cases" and, really, no
"model". Instead, there's some applied math.

But, I'll keep reading:

> No AI system yet developed will magically fit every use case.

Well, the little algorithm for playing Nim fits "every use case".

> If a researcher tells you that a model got state of the art results out of
> the box, they're either exceedingly lucky or giving you a fairy tale version
> of reality.

No. As in a program to play perfect Nim, there is another explanation: They
have a perfect solution for the problem.

Again, by this far into the article, the author seems to have in mind a
limited view of what _AI_ is.

> Even if their model is arbitrarily flexible, there are still fundamental
> limits placed on us by information theory.

Not for a program written using the Courant and Robbins algorithm. Information
theory has essentially nothing to do with it.

Continuing:

> "How much training data is required?"

For the Nim program, none.

> "Can this work unsupervised (= without labeling the examples)?"

Well, since there are no examples, apparently so.

> "Can the system predict out of vocabulary names?"

WHAT? I doubt I understand the question. But with the Nim program, maybe so.

The author seems to be assuming that the program is about natural language
understanding. Not all computer programs are.

> "How stable is the model's performance over time?"

With the Nim program, perfectly stable.

> Very few datasets are entirely representative of what the task looks like in
> the real world.

"Dataset"? The Nim program doesn't have one.

Again, it appears that the author is thinking of a narrow view of _AI_ , much
more narrow than the definition the author gave.

> Any claim of advanced research without publication is suspect at best

> "If you do research in isolation, the quality goes down. Which is why
> military research sucks."

> Yann LeCun at ICML 2015

Uh, sorry, but I've done all my original research "in isolation" \-- in a
small, quiet room, sometimes on my back looking at the ceiling. For my Ph.D.
dissertation in applied math, the first of the research was on an airplane
ride from NY to TN. The rest of the research was independently in my first
summer in graduate school.

> The rate of change in the field of AI is such that anyone on the sidelines
> is at best keeping up.

No, not really: I call my research applied mathematics, but it meets the
author's definition of _AI_. But I pay essentially no serious attention to the
_field_ of _AI_ (e.g., the work of people who claim to be in AI) at all. And
the number of researchers in the field of AI, computer science, and
information technology entrepreneurship that have the mathematical
prerequisites to read my research is tiny. So, I'm not paying attention to the
field of AI, and AI researchers are paying no attention to my work, some of
which is published and, in one case, for one important problem, really, a
significantly large set of important problems, totally knocks the socks off
anything in AI. That case? Sure, some applied math, right, complete with
theorems and proofs. In more detail, the work is some new results in
mathematical statistics.

> It is certainly not impossible for an entity to come out of nowhere with an
> amazing system but not it is far less likely.

Naw! Essentially the opposite is true: If want "an amazing system", usually
stay far away from AI -- just totally ignore the field. Instead, pursue
applied math. E.g., molecular spectroscopy for identifying chemical molecules
is terrific stuff, but is has essentially no "training data". Instead, the
work is based on some quantum mechanics and group theory.

Again, the author is thinking in a way quite narrow compared with the author's
definition of AI and compared with what is done to build "an amazing system".

> It also means they haven't been put through the standard evaluations that
> academia would have placed on them.

Well, as I've seen in some of my peer-reviewed publications, my work in
applied mathematics is more advanced and with standards commonly higher, e.g.,
theorems and proofs, than the academic review process is able to follow.

Besides, any work I publish means I've given away the intellectual property,
and now mostly I don't want to do that.

> AI won't save a broken business plan. An easy upper bound is asking if the
> business plan would work with free human labor replacing the automated
> component.

Going back decades, for many cases of "an amazing system", the idea that human
labor could do the work at all is essentially absurd. Here the author is being
narrow again.

> Achieving human level performance on any real world task is an exceedingly
> difficult endeavor when moving away from a few simple and well defined
> tasks.

No: Totally knocking the socks off human level performance is very common in
applications of applied mathematics to complicated real problems, e.g.,
critical mass calculations for nuclear weapons, target detection via phased
array passive sonar, also with Wiener filtering, to track the target, maybe
with Kalman filtering, differential game theory for saying how a missile
should catch a fighter plane, deterministic optimal control theory for, say,
least time control to get an airplane to a given altitude, matching anti-
ballistic missiles to incoming warheads, a huge range of applications of
deterministic optimization, e.g., linear programming and integer linear
programming, going way back to, say, even vacuum tube computers.

> All of this is to say that if the business plan doesn't work with free
> humans, AI won't save it.

Well, for my startup, there's no way humans could do the crucial, core applied
mathematics, but my software based in my original applied math works just
fine!

Again the author is thinking about cases of "an amazing system" much more
narrow than the author's definition of AI and much, much more narrow than good
work in applied math going back at least 50 years.

~~~
50CNT
I have not read the entire thing, but skimming I found references to math and
an "amazing system", so, are you Stephen Wolfram?

~~~
graycat
No.

