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Machine learning: how to create an Artificial Intelligence in one infographic - EnjoyDigitAll by BNP Paribas
DIFFERENT WAYS OF LEARNING
ALGORITHMS & MACHINE LEARNING
MACHINE LEARNING IN THE COMPANY
The algorithm is fed with “training data”, i.e. data
that has been previously labeled by experts.
Inferring a function here depends on instructors
teaching the machine what type of result it is
supposed to come up with.
Area of machine learning inspired by behavioral
psychology, based on the trial and error method.
It is concerned with how software agents ought to
take actions in an environment so as to maximize
some notions of cumulative reward.
Is used to learn autonomously from unstructured
data, i.e. data that cannot be organized in a table.
It is a way for the AI not to be contained to one
programmed task. This way of learning comprises
and is generated by the four previous techniques.
For example, our artificial intelligence now
knows if the image it is presented with
features a cat or not. We then ask it to
learn how to recognize crocodiles. The AI
will use its visual recognition techniques,
thus training and improving them, and
will come back way better at recognizing
cats in future images.
Are determinists, their
operating criteria are
explicitly laid out by their
It is interesting to note that thanks to deep learning, research has
produced unprecedent results, from which experts will undertake new
research to understand the new findings. That’s what we call “reverse
search”: discoveries that could not have occurred while following the
classic path of hypothesis and prospective trials.
Almost always requires human
interaction, with the risk of
outdated, or biased databases.
Experts have to stay alert and
make sure to be inclusive with
the data they choose to feed
the algorithm with.
Lest we forget @TayAndYou,
Microsoft’s AI that started to
produce hate speeches after
being connected to Twitter for
less than 24 hours…
Are defined as probabilist. If the technology behind them is
way more powerful than the one behind classic algorithms,
their results are inconsistent and depend on the
ever-changing learning database used to teach them.
Machine learning is what
makes artificial intelligence,
well, “intelligent” – it makes it
able to learn, and not only be
a powerful calculator.
It gathers data and has it
go through an algorithm
that in turn feeds on it to
adapt its information
managing process and to
take decisions. Artificial
intelligence is now able
to learn and adapt.
1 0 10
The data is unlabeled, and it is the
algorithm’s task to come up with its own
classification system, often using statistics.
This way of learning means it is free to
evolve towards any type of final state.
A technique in machine learning where an
algorithm learns how to perform one task,
and builds on that knowledge when
learning a different but related task.
This type of learning is often used for visual
recognition: for example, we give a thousand
images to the AI, labelling them “cat” or “not
cat”. It is then the AI that, by confrontation of
all the tagged images, comes up with the
criteria that enables it to recognize cats in the
new images it receives.
Sources: Les usages de l'intelligence artificielle, Olivier Ezratty • Statistics: Forbes
Donner un sens à l'intelligence artificielle pour une stratégie nationale et européenne, Cédric Villani
In banks, machine learning has been proved to increase sales of new products
by 10% and reduce churn by 20% while augmenting client satisfaction.
Of the data used by
companies to take
day-to-day decisions is
unstructured – hence
the necessity to have
powerful algorithms to
Of companies say
their turnover has
to improve sales