‘{
“epsilon”: 1e-07,
“float”: “float32”,
“image_data_format”:”channels_last”,
“backend”: “tensorflow”
}
In “Build Deeper - Deep Learning Beginner's Guide by Thimira
Amaratunga
This book confirms other predictive system results that I have seen,
where it has often been found that we human as a species who fancy ourselves as
psychics or using other la-di-da methodologies can at best achieve around an
80% accuracy rate, even with good regular practice and tuning. The more
accustomed you are toward reaching ever higher accuracy & precision
percentile targets the more the distance to the next little increase in goal
horizon. Still it does bring into question the abilities of Science and machine
systems designing new machine systems, often through excluding what are
regarded as unrepeatable subjective methods in favour of repeatable
objectiveness. Outliers and other non-obvious patterns & so on are pushing
back the boundaries at the edge of our cultural belief systems.
I don't think that any computer scientist would dispute the point
that modern AI or machine learning is nowhere near the threshold of
'consciousness' or even 'general intelligence'. But it's not uncommon for words
to have a different meaning within a technical field compared to how they are
used in everyday communication. In regular English 'chaos' means unpredictable,
whereas in mathematics it refers to the tendency of sensitive nonlinear systems
to exhibit emergent attraction basins that can potentially be extremely
predictable. Those are arguably even antonyms. Another example would be terms
'deterministic/nondeterministic' in Computer Science, which also differ
strongly from their meanings in regular English. The point is that if you feel
the need to grandstand on these trivialities, you clearly don't understand the
fundamentals of the subject matter under discussion.
The human mind works mainly by analogy: This situation looks rather
like that other one, so if I act in a similar way, I will probably get a
similarly good result (that's a sort of fundamental though largely unconscious
meta-rule that is itself endlessly confirmed with a few notable exceptions:
I've worked by analogy in the past and it's worked... more or less, and I can
of course learn by mistakes by learning to avoid false analogies in future).
The math on which computer and other science is based is also in a
strong sense itself based on analogies, or formal mappings between partly
equivalent structures. But in 'science' the analogies are usually defined
starting from a well-defined base of, as it were, logical atoms that cannot
fully reproduce the non-explicit analogies in which human thought, perception
and action are rooted. At least, that's how I see the question / problem of AGI
(Artificial General Intelligence) for now. You can map a lot of analogical
processing into neural nets, but you're still starting with well-defined
analogies rather than the fuzzy logic of the wetware that is our brains, which
to me makes the programme very interesting methodologically, but ultimately flawed
conceptually - based ultimately on a false analogy of our minds as physical
machines). It's an analogy that like so many others works up to a point. And
that point is the question 'what is a machine?' physical or otherwise.
My background is in math, specifically mathematical logic and the
philosophy of math and mathematical physics. I'm constantly amazed by the naive
doctrinaire scientism, rooted in an outdated Victorian conception of mechanism
that simply assumes that because everything we do or think can be mapped into a
material word including our bodies and brains, then that mapping provides a
complete model of our actual individual lives, thought, consciousness and
separate identities.
In formal logic, Gödel's incompleteness theorems show that the very
mathematical space in which physical space-time is modelled cannot be both
formally complete and consistent. And recent results in mathematical physics
show that Quantum Mechanics is essentially 'incomplete' as a mathematical
theory in various ways: hidden variable extensions cannot reduce the
indeterminacy of the outcome of a quantum measurement - and most importantly of
all, quantum measurement cannot itself be modelled in Quantum Mechanics.
Last but not least, I was at a conference on modelling of the
cerebral dynamics of feeling and action recently and was once more amazed at
the essential crudeness of the methodological models on show, which assumed
that human central nervous system or brain physiology can be fully modeled in
abstraction from its interaction with the various components of the autonomic
nervous system and the endocrine system (and even the immune system, the third
of the primary integrative systems of the human body).
Good luck with the research if you're involved in it, it's important
and probably useful, but it won't tell you who or what you are, or what to do.
That is not really how modern Machine Learning algorithms operate. Without
going into too much detail, they are usually used for learning problems for
which there is no simple state transition map or combinatorial solution. This
means that the answer cannot be encoded into machine instructions through
conventional means. So instead, heuristics are used to simulate the behavior of
a probabilistic automata, and then the internal state logic is matched to a
training set using back-propagation (there are other algorithms as well).
So while this is almost certainly the result of human bias, the
problem is likely localized within the training data sets. I presume that they
gathered pictures of faces, then had people rate them based on perceived
beauty, and trained the algorithm to emulate that particular map. Which means
that the results of the algorithm is a reflection of trends within society.
That makes sense, seeing as people tend to rate the faces of minorities as
being less 'beautiful'. This trend is attributed to multiple factors, but most
significant are probably the history of colonialism and poor minority
representation in pop culture. I would be willing to bet that if you were to
take a survey of Guardian readers, you would find a very similar trend, because
based on broad data analysis it tends to be expressed across many populations
and communities in the West (and in other parts of the world as well).
Almost certainly due to entrenched bias, but far more likely those
of the people who contributed to the training data as opposed to the
programmers themselves. Also, research has shown that pretty much everybody
displays similar biases, so it's a little bit disingenuous to pass it off on
Machine Learning or these particular Programmers when it is clearly a societal
problem for which we all share the responsibility to do something about.
NB: To follow all the examples in this book you’ll need to install
the following: Anaconda Python, Packages from conda, OpenCV, Dlib, Theano,
Keras, TensorFlow. Some of thew links in the book didn’t work (I had to build
the frigging environment without any help whatsoever!!!!):
http://www.codeofinterest.com/p/build-deeper.html,
http://www.codeofinterest.com/search/label/Installation,
(...)
Sem comentários:
Publicar um comentário