
Deep Learning: A Critical Appraisal (2018) - gozzoo
https://arxiv.org/abs/1801.00631
======
vonnik
The weird thing about Gary Marcus's constant critique of deep learning is that
the most prominent researchers in the field actually agree with him (and are
doing something about it), yet he casts himself as a maverick, and that plays
well in the media. Here's an example of real researchers making real progress
combining DL with other approaches to AI:
[https://openreview.net/pdf?id=rJgMlhRctm](https://openreview.net/pdf?id=rJgMlhRctm)

~~~
SubiculumCode
His audience is probably the media, not other researchers. It is a critique to
calm down the hype machine.

------
GCA10
It's great to see Marcus's 3.8: "Deep learning presumes a largely stable
world, in ways that may be problematic." Most interesting problems in the
world don't mirror Go's orderly rules. Players don't always take turns; the
size of the board changes, and the pieces' powers mutate.

Marcus cites politics and economics as fields that pose fierce challenges to
deep learning. I'd add marriage counseling, stand-up comedy, full-length
fiction, and most sustained interactions between multiple people.

~~~
another-one-off
> the pieces' powers [don't] mutate [in a Go game]

That comment in particular suggests to me that you havn't spent a lot of time
playing Go. The pieces' powers mutate quite spectacularly as the situation
around them changes.

> Most interesting problems in the world don't mirror Go's orderly rules

This is true, but it isn't obvious this is going to slow deep learning down.
For example, you cite players taking turns which is a _significant_ handicap
for a computer. If it comes down to reflexes, robots can win even with worse
decision making algorithms than a human.

In the context of deep learning, if the situation mutates in ways that the
training regime didn't then an AI will have trouble. However, people love to
overestimate both how often exceptional circumstances come up (the correct
answer is rarely) and how good humans are at responding to them (correct
answer is badly).

My favorite part of learning the training system for artificial neural
networks was that it incidentally explains a lot of human failure modes really
well. It isn't at all obvious the humans have a sustainable advantage here.

~~~
scottLobster
"Rarely" over large populations comes out to quite a few people. And humans
may very well fail in more predictable ways (that can then be accounted for)
than multiple AIs trained on widely varying data sets. In fact AI's advantage,
speed of processing ("reflexes") means that it's capable of failing in all
sorts of ways that humans physically couldn't.

Deep Learning is great for pure-data problems. I doubt it will be sufficient
when interacting with the real world's inherent real-time randomness on large
scales.

------
mark_l_watson
I agree with much of what Gary Marcus says. Using trained deep models with out
of sample data in the real world will cause problems. There is a good reason
that self driving car companies want millions of miles of training data: to
reduce out of training sample incidents.

For almost four years, my work has been almost 100% development of sometimes
novel, sometimes mundane deep learning models so I am a fan but I still wait
eagerly for breakthroughs in hybrid AI systems, new Bayesian techniques that
can work with less data, handle counter factual conjectures, etc.

------
sctb
Discussed last year:
[https://news.ycombinator.com/item?id=17216536](https://news.ycombinator.com/item?id=17216536).

------
bawana
After reading this article I felt like asking the author, 'why havent we built
DNNs that can write code?' That seems like a very domain specific problem and
for the purposes of research, one could even stick to a single language. I
remember being amazed by SHRDLU
([https://en.wikipedia.org/wiki/SHRDLU](https://en.wikipedia.org/wiki/SHRDLU))
and had a sense of deja vu after reading this paper
[https://openreview.net/pdf?id=rJgMlhRctm](https://openreview.net/pdf?id=rJgMlhRctm)

Curiously, I had totally forgotten SHRDLU but had a vague recollection of
colored blocks in an AI paper in the 70s. Google pulled the paper as the first
hit from the terms('red block, AI, 1970s')

In 40 years it seems we are still doing the same thing. Moore's law has
enabled us to build much fancier searching,sorting and filtering algorithms.
But there has been no progress in being able to ask a machine if something is
good or bad. Fortunately.

------
yters
Why is the idea that the human mind is beyond Turing machines not taken
seriously? Seems to make the most sense from the data we do have. E.g. how
could we possible write functioning code so consistently if we are limited by
the halting problem for Turing machines?

~~~
sago
Heuristically.

There is no reason to suspect we _solve_ the halting problem or even any
restricted variant. In fact, there is no reason to suspect we _solve_ any
problem of even modest computational complexity for anything but the tiniest
domains.

It is 'not taken seriously' because the only evidence seems to be handwaving
and philosophical argument (perhaps with a dollop of quantum weirdness, a la
Penrose).

There are a number of models of hypercomputation. Finding any with a physical
basis that might credibly match neurobiology is highly dubious. It is not even
clear that any of them have any physical basis.

~~~
bitL
Berkeley already uses electricity for finding instant solutions to quadratic
optimization problems[1], why do we restrict ourselves to some primitive
digital mode in humans? In addition, we already know neurons do some form of
protein-based computation, alongside to what looks like spiking/timing-based
electric computation. Some neurons can be 1 meter long as well, interfacing
with many parts of brain. Can we rule out some super clever electric potential
optimization or even something better isn't going on in the background? I am
not saying Penrose is right that brain can compute BQP and is using collapses
of quantum gravity to solve halting problem, but why can't we keep open mind
about it?

Hypercomputation is a cool theoretical device, again the models are way too
primitive for now and likely any finding in nature would have super
complicated mapping to the current theory. But hey, there might be some
graduate student with an idea already...

[1] [http://www.mpc.berkeley.edu/research/analog-
optimization](http://www.mpc.berkeley.edu/research/analog-optimization)

~~~
yters
Exactly. Turing himself left the door open to such ideas with his notion of
the oracle machine. It seems odd that we have narrowed the range of possible
explanations so dramatically. There are many more oracle machines and even
higher order machines on the Turing hierarchy than there are Turing machines.

------
hellllllllooo
They are a tool to be used where suitable and not the answer to everything.
They are significantly better for detection and classification than what came
before but improvement has plateaued. When they fail, how and why is not
characterizable. They're good for some things but you have to be careful about
how you build the whole system and not just throw a CNN at it unless you
really don't mind undefined behavior.

