

Probabilistic AI can't be AI - middayc
http://refaktorlabs.blogspot.com/2014/02/probabilistic-ai-cant-be-ai.html

======
resu_nimda
_then people would be naturally great at probability_

Where do you get that conclusion? Think of a baseball player with exceptional
hand-eye coordination, who also knows absolutely nothing about any of the math
or calculus behind it. There is a vast chasm between conscious and
subconscious thought (where the latter might be viewed as the underlying
"intelligence model"). I don't agree that a (partially) probabilistic model
would necessarily imply a proficiency at the conscious, communicable level.

 _we wouldn 't benefit from learning about and consciously using probability
to solve problems, as our brain would already do it on a lower level._

Don't understand this one either. Back to the baseball player - there is a lot
of value we gain from our brains "naturally" solving e.g. inverse kinematics
problems on the fly. But, there is also a lot we gain from understanding these
mathematical concepts at the conscious level. Totally different realms, both
useful.

 _Statistical methods generally need a big learning set to learn anything._

Ok, that's a limitation of the current AI models. They're not smart enough to
infer the essential characteristics of an elephant from a small set (it's also
a very narrow approach...you don't start a child off by showing them pictures
of elephants, first they have to spend a long time acquiring basic
fundamentals of knowledge and perception). I don't see how that implies a more
deterministic nature of organic intelligence.

Even the child might think something is an elephant and be wrong. But (IMO)
it's the same process of using the bits of knowledge you have to make a guess
(and an estimated degree of certainty). "It could be this thing or it could be
that thing, and my model says it has the greatest probability of being this
one, so that's what I'm going with."

~~~
middayc
Ok, based on yours and many other similar comments below I only now see where
my argument isn't understood as I wanted it to be. Not that I think that now
you and others will agree with me, but just to make this clear.

I do not think that people would "then be consciously good at probability" (I
said naturally / later added instinctively), but that the results of their
unconscious processes / intuition, would then be better matching the
probability-wise view of the problem. While I think that exactly the intuition
seems to fail the most at probability (planes, terror attacks, ...).

------
eamsen
_...people would be naturally great at probability, and we know we suck at
probability_

Human bodies are based on molecules, it doesn't imply that people understand
chemistry.

Computer chips are based on transistors, it doesn't imply that computers
understand electronics.

Sorry, but your assumption that knowledge is inherited by foundations would
imply that everything understands the Universe.

~~~
middayc
I see now that I probably didn't express myself well. I am not saying we
should understand probability but the result of our instincts should be in
tune with probabilistic results on the problem. For example: We would not
naturally fear airplanes, terror, etc. or..

If human body is made from molecules it does mean that we function as blobs of
molecules.

If computer chips are based on transistors, the output of computer chips is
transistor like.

------
epistasis
First off, the title is terribly imprecise, you're not arguing that
probabilistic AI can't be AI, you're arguing that animal intelligence is not
probabilistic.

Second off, I personally find your theses unconvincing because the simple
statements are easily refuted, and there's not enough supporting information
to make them into tighter arguments.

For example, for your first bullet point, the underlying mechanism could be
highly probabilistic, without our consciousness having access to the
probabilities as they are executed. We do not have conscious knowledge of
neuron potentials, or of any other internal state of the physical mechanisms
of our consciousness, but that does not mean that they don't exist.

Further, people are bad at reasoning period, and have to be taught how to do
it in school. It's only through much training that we learn reasoning from
certainties, and reasoning with uncertainty will similarly require training.

Finally, I don't think that anybody is saying that our brains operate SVMs or
RBMs or anything like that, they are merely computational mechanisms to
replicate the behavior of complex networks of neurons; there may be direct
analogs to computation in how neurons integrate chemical, electrical, and
epigenetic signals, but it's not clear that they're exactly the same thing;
and that doesn't mean that even if it's completely different, that a
probabilistic AI can't be an Intelligence.

These are heady matters that people put a lot of careful thought into, and
while there's definitely room for light discussion among friends, I'm not sure
that this is the right material for HN.

~~~
middayc
you are right about the title, sorry.

is there a consensus/definition on what is AI without relying on what
animal/human intell. looks like/functions?

~~~
wlievens
I offer one crude definition: intelligence is a requirement for an agent that
can solve a wid range of problems, without being specifically designed for
these problems.

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BjoernKW
The argument itself is flawed as pointed out by others.

As for the subject of probabilistic AI there's quite a lot of evidence that
natural language grammar to a large extent is probabilistic. In fact I'd say
that most natural language grammar rules are just surface representations of
probabilistic models such as Hidden Markov models.

------
lvh
This article is deeply flawed. First of all, it's premise is flawed; secondly,
it's supporting evidence is flawed.

Example from supporting evidence:

"then people would be naturally great at probability, and we know we suck at
probability"

No, this does not follow at all. There is absolutely no reason to assume that
a probabilistic algorithm would somehow lead to better results when dealing
with probabilities at a much higher level.

Example from premise:

\- No argument for why "if real intelligences aren't probabilistic, therefore
probabilistic intelligences can't work" is given.

\- Just because your models come from statistical learning (such as SVMs or
Bayesian methods, as the author names) does not mean your resulting algorithm
is probabilistic.

Flagging.

~~~
middayc
so you think that we use probability (and are good at it) on a low level but
that doesn't translate to being sufficient in probability on a higher level.
Where is the border between low and high?

I am not saying any probabilistic intelligence can't work. Please define any
"intelligence" or link to definition. I said I think "we" or organic
intelligence doesn't with use of probability.

I am also not saying probabilistic models are useless / bad / or not worth
exploring further for making better programs or anything, I just don't think
human intelligence uses them as a basis.

------
fiatmoney
\- SVMs aren't really probabilistic models. They don't need to be thought of
in a way that assumes underlying statistical properties of what they're
measuring. Many machine learning methods (decision trees for one) are
similarly agnostic.

\- Reality is a probabilistic process. Brains work probabilistically at least
in the sense that the underlying biomechanics have some statistical
distribution.

I have the feeling the author doesn't really understand what "probability"
means, as he more or less admits.

~~~
sieisteinmodel
> Brains work probabilistically at least in the sense that the underlying
> biomechanics have some statistical distribution.

No, it's us that use these distribution to model them. Reality _might_ be
deterministic after all (no proof of the contrary), and then brains are not
working probabilistic at all.

~~~
wlievens
I would think even entirely deterministic systems need to be treated
probabilistically if you don't have perfect information.

------
PeterWhittaker
The argument is fundamentally and fatally flawed by a lack of understanding of
how the "mind" works.

The basic flaw in the argument is that there is only one "mind" and that all
its workings are available to us.

But as much recent research shows, especially that of Tversky and Kahneman, we
have at least two "minds", the so-called "fast" and "slow" systems, and the
"fast" system is not directly accessible to "us" \- and "us", what we
recognize as "us", is the working of "slow", the deliberate, conscious,
effortful problem solver, often called upon to cook up a "good enough story"
to explain an exception "fast" couldn't make sense of.

"Fast" might be terrifically good at probability, with some survival
heuristics favouring particular conclusions - better to always conclude that
those weird pattens in the bush are a tiger than to be wrong once.

Since "fast" is not really accessible to "slow", it matters not how good
"fast" is at probability - and "fast" could be 100% probabilistic AI and we'd
never know it...

...not without detailed neurophychological study, at least. It certainly
wouldn't "just be evident".

~~~
middayc
My argument doesn't assume that we don't have unconscious processes. Maybe I
didn't express well what I think. I do think we have multiple "minds" (I even
wrote a blogpost on this subject on another blog once).

Maybe my "then people would be naturally great at probability" came out as if
we would then consciously know about probability, know the formulas etc
without the need to learn about them.

No. I tried to say that if so then results of our "fast" mind would be very
well attuned to probabilistic study of the problem. For example, you wouldn't
become scared when boarding a plane.

I will try to write another blogpost being more precise. Not that it's worth
anything, more for myself. I won't post it here, not because of opposing
comments, but because many think this is not a HN material and I don't want to
spam.

Not that it matters, obviously. But I don't think there is no probability
processing anywhere in the system (we do conscious probability if not else).
My thoughts were that it can't be the main or important driver behind it all.
I think the memory itself (storage and "soft" retrieval of information .. my
theory on models that we make) plays much more important role in our function
than any special or complex algorithm. Again, not that it matters what I
think.

------
Houshalter
I originally intended to make a comment about how wrong this is because almost
all approaches to AI and ML are deeply rooted in probability and statistics.
The entire problem of intelligence is based on making accurate predictions and
then acting on them. But there is some truth in this. Humans _are_ terrible at
probability and that's something I wouldn't have expected if I didn't already
know it. It is a clue as to what kind of algorithm the brain is using.

I still disagree that the brain doesn't use probability at all or that we
shouldn't focus on it in AI research.

>There are more external signs that we don't do probability. Statistical
methods generally need a big learning set to learn anything. A small child
doesn't have to see a set of 500 cartoon elephants in different poses to
recognize elephants from then on in various different cartoons and in real
life __*)

No but they do see hundreds of hours of visual feed from their eyes from which
they learn high level features. Learning an elephant from one example wouldn't
be possible without first learning thousands of other concepts such as how to
detect edges, shapes, 3d objects, the properties of animals, etc.

~~~
middayc
I am not saying that it doesn't use something that efectively is a probability
calculation anywhere in the system, but that that isn't the main or the most
important "engine".

I agree with your comment on "first learning thousands of other concepts". But
that is a ton of (hierarchical) concepts that have to bo stored somehow and
isn't then the storage/retrieval itself maybe more cruicial to the whole
function than any special algo we just haven't figured out yet? (I mention
this in my "wild" speculation on "models") :)

~~~
Houshalter
Perhaps but my understanding is that, at least for vision, the brain learns by
trying to predict nearby neurons as well as the future, thus learning an
internal model of the world.

Algorithms that do something like this are called Deep Learning and have
recently proven extremely effective at machine vision and other tasks. They
don't all work by prediction _per se_ but they do learn to compress the input
down to a smaller representation of features that can be used to recreate it
and are very related to prediction.

------
therobot24
TL;DR: "I'm not an AI/ML anything by any stretch of imagination"

~~~
middayc
good one, I accept it :)

edit: so you think human intelligence is mainly a probability machine?

~~~
nabla9
Something to read:

Hierarchical Bayesian Modeling of Human Decision-Making Using Wiener Diffusion
[http://gandalf.psych.umn.edu/users/schrater/schrater_lab/cou...](http://gandalf.psych.umn.edu/users/schrater/schrater_lab/courses/Labmeeting/LeeEtAl_2007.pdf)

Decision Theory and Human Behavior
[http://www.umass.edu/preferen/Class%20Material/Bounds%20of%2...](http://www.umass.edu/preferen/Class%20Material/Bounds%20of%20Reason/BOR%20Decision%20Theory%20and%20Human%20Behavior.pdf)

Beyond Accuracy: How Models of Decision Making Compare to Human Decision
Making
[http://fileadmin.cs.lth.se/cs/Personal/Carl_Christian_Rolf/c...](http://fileadmin.cs.lth.se/cs/Personal/Carl_Christian_Rolf/ccr-
msccog.pdf)

Forgetful Bayes and myopic planning: Human learning and decision-making in a
bandit setting [http://papers.nips.cc/paper/5180-forgetful-bayes-and-
myopic-...](http://papers.nips.cc/paper/5180-forgetful-bayes-and-myopic-
planning-human-learning-and-decision-making-in-a-bandit-setting.pdf)

>Our result shows that subjects’ choices, on a trial-to- trial basis, are best
captured by a “forgetful” Bayesian iterative learning model [21] in
combination with a partially myopic decision policy known as Knowledge
Gradient [7].

------
deong
The problem with this assumption is that one doesn't have to be aware of how
their own brains work in order for them to work.

The fact that people seem to be universally bad at assessing probabilities
means only that however their brains work, that mechanism doesn't produce
intelligences that are finely adapted at assessing probabilities. The
machinery that is producing that intelligence could still be completely
probabilistic itself.

Humans are fantastic at pattern recognition, but that doesn't mean that our
intelligence must have been _created_ by a pattern recognition algorithm.

~~~
guard-of-terra
We can't say that humans are fantastic at pattern recognition either. We may
only be sure that our built-in pattern recognition is better than our ability
to create pattern recognition algorithms.

------
Mikeb85
I disagree with some of the premises.

We are great at probability, we compute it unconsciously. Every person knows
the approximate probability of flipping a coin to show heads, pulling a card
from a stack, or being rejected/successful when asking a date out (we always
say our 'chances' are good/bad, depending on factors we think are important).

In our day to day lives, many decisions are driven by what we think the 'odds'
are of our endeavor being successful.

~~~
middayc
I am not sure if I intuitively know it or do we have to consciously compute /
infer it? (the coin/card example).

I guess I have some aprox feeling, but it's highly susceptible to being
skewed.

