
Understand what AI sees - dirtPUNK
https://hackernoon.com/understanding-what-artificial-intelligence-actually-sees-7d4e5b9e648e
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lottin
To state the obvious, AI doesn't see anything. What people call AI is simply a
statistical model, a system of equations. By solving it, we find a solution
that solves the equations simultaneously. The model doesn't see anything,
because it doesn't exist as such. It simply is a series of calculations done
on a computer or on a piece of paper.

~~~
gok
Then biological brains don’t see anything either.

~~~
oliveshell
I’m not sure your conclusion follows. We agree that we “see” things because we
have the conscious experience of doing so and we talk about it.

Nothing remotely analogous happens with current AI algorithms.

~~~
schiffern
>We agree that we “see” things

That's precisely the epistemic problem. _We_ agree that we see things. Since
"we" have no experience of being an AI, we have no way of affirming or denying
what "their" experience may or may not be (apart from mere chauvinistic
dismissal, ie "'they' [and their hardware+software] aren't like us [and our
brains], therefore 'they' can't be conscious").

~~~
a13n
They don't have a conscious experience.

~~~
schiffern
I happen to agree with your conclusion, but since _we 're_ not part of "they,"
neither of us can know for sure. That's my point.

Merely by examining the hardware, the human brain doesn't look like it should
support consciousness either. Our inability to identify consciousness by
inspection does not deny consciousness _in vivo_ , why should it do so _in
silico_?

Re: burden of proof, let me be clear what I'm saying here. _In silico_
consciousness has not been proven _or_ ruled out, because we have not yet
developed a _material_ definition of consciousness, ie one that can be applied
merely be examining the hardware. It's not that we've disproven it (as "they
don't have conscious experience" suggests), it's that _we don 't yet even know
what we should be disproving!_

It's not like we have a Consciousness Detector Box ala C&H, which flawlessly
classifies all human brains as conscious merely by examining the configuration
of the atoms in the box. All we have are functional definitions which look at
human behavior.

If we don't even know conscious hardware+software when we see it (namely our
brains), how can we say for sure whether X is or isn't conscious, for any
arbitrary X?

~~~
visarga
> because we have not yet developed a material definition of consciousness, ie
> one that can be applied merely be examining the hardware

It's not a metaphysical puffy thing (unexplainable or inscrutable) or a
property of the brain itself - it's the ability of an agent to act in an
environment in a way that maximises its rewards. Biological rewards are tied
to survival and self reproduction. So consciousness is what happens when there
is an agent, an environment and a stream of rewards to be gained, where the
agent learns to understand its situation and acts in an intelligent way,
learning from its past mistakes and experiences. All these concepts are
covered by unsupervised learning and reinforcement learning. That's a material
definition of consciousness.

------
grenoire
I was actually giving an example to my friend on ML vision applications over
the hotdog app. The underlying network probably only recognises a red shape
surrounded by lighter parts, as opposed to understanding _conceptually_ what a
hotdog is. The USB example of course is an extreme case of that.

~~~
memebox3v
Eh? What does conceptually mean?

~~~
k_sh
The AI sees data/markers/patterns that look like something it's seen before,
as opposed to actually comprehending that it sees a tube of meat that people
call a hot dog.

The best metaphor I can think of is the cognitive difference between
navigating a transit station that has signs in your native language, and one
that you spent a couple of hours learning on Duolingo - with the latter, you
aren't really understanding anything, just associating a:b::x:y.

~~~
shawn
This might be another formulation of the "Chinese Room" argument:
[https://en.wikipedia.org/wiki/Chinese_room](https://en.wikipedia.org/wiki/Chinese_room)

If every action is the same -- that is, if you produce some actions which
would have been produced if you "conceptualized" it rather than merely
"memorized" it -- isn't that identical?

The only thing we can do in life is make decisions. Regardless of how they're
derived, if those decisions are identical to yours, isn't that entity "you" in
some sense?

~~~
schiffern
>if those decisions are identical to yours, isn't that entity "you" in some
sense?

If by "decisions" you mean every single nerve impulse in response to every
possible set of stimuli, then that's pretty exacting. Every wobble while
standing, every mouth movement answering any possible question, etc.

Also, how do you determine if the responses are "identical?" It's not like we
can rewind reality and play it back, substituting yourself for an AI. And due
to quantum nondeterminism, even if you played it back with no substitution
your actions will diverge over time! If you're not considered identical to
_yourself_ , how is that a useful definition/test of "identicality"?

At the required fidelity, this thought-experiment is problematic both in
theory and in practice. It obscures more than it illuminates imo.

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hal9000xp
I was always puzzled by human's experience of "seeing". When you "see redness"
for example. It was very hard for me to explain it to other people before I
found that there is special term called "qualia":

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

And I have absolutely no idea how to make machines experience "qualia". Any
sophisticated image/motion recognition is rather trivial stuff compared to
achieving mysterious "qualia".

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buboard
I believe models with attention are more appropriate for ‘telling’ what they
re focusing on , at least if the question is which part of the image they re
focusing on

~~~
minimaxir
Attention is only (edit: typically) for sequential models (e.g. time series),
not image-based convolution models.

~~~
sdenton4
Untrue! Three are a number of valid strategies for using attention in non
sequential models. Attention is really just generating a mask to apply on a
feature representation; it generalized perfectly well to classical models.

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foota
Isn't this pretty far behind the state of the art for white box neural nets?
Which would use the first layer's activation to do something similar?

~~~
shawn
Could you give some references for an ML novice to learn these techniques?
(Papers, books, anything.) I've been studying ML, but I'm not advanced enough
yet to know effective ways to learn state of the art methods.

~~~
foota
I just try to follow along, I don't really get into the details. The bit I was
referencing was from a blog post that was on here a while ago:
[https://ai.googleblog.com/2018/03/the-building-blocks-of-
int...](https://ai.googleblog.com/2018/03/the-building-blocks-of-
interpretability.html). That said, I've seen the following listed as good
resources:

fast.ai

The Google ML crash course

Andrew Ng's Coursera course

If reddit's your thing, it also looks like there's a sub,
[https://www.reddit.com/r/learnmachinelearning/](https://www.reddit.com/r/learnmachinelearning/),
that might be helpful.

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Viker
We need to invent a quad type machine ( opposed to the current binary model)
where we have a "maybe" type of data structure.

Yes and a No and a MaybeYes and MaybeNo.

~~~
shakna
Other kinds have existed.

Ternary computers [0], where the most famous examples come from the Soviet
Union.

And whilst I'm not aware of any computer that used the quaternary numerical
system, it should be possible.

However, none of that necessarily means it's a good idea or necessary to
perform quaternary logic.

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

