.. _l-jfall2017:
Democratization of AI: from Researchers to Any User
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Summary
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Beaucoup de sociétés de réfléchissent à leur propre évolution dans un monde
de plus en plus digital et le machine learning est devenu incontournable.
Ces techniques techniques existent depuis longtemps et elles se sont récemment
démocratisées. Cette présentation revient sur les ingrédients qui ont permis
leur popularisation.
Machine learning somehow became one magic wand able to solve many things.
It can be seen as a way to automate an existing pipeline which became very time
consuming, it could also be seen as a way to process information we could not
handle so far such as images or videos. We usually forget it is first a statistical
method which is right in most cases but not in all cases. This talk will expose
a particular case where machine learning and deep learning will be used to reduce the
amount of manual work.
`Democratization of AI: from Researchers to Any User `_
Key Ideas
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Many things which did exists ten years ago were not as easy to use
as they are now. It was more like a researcher's job than an engineer's job.
That's something which changes in the past five years because four pieces
became available at the same time.
**Democratization of algorithms**
Open Source became more and more popular, :epkg:`github` without being
very new was a tremondous catalyst as it hosts the vast majority
of open source projects. Anybody can find an implementation of
algorithms described in research papers also available through
initiative such as `arxiv `_ or
`jmlr `_. Most of these algorithms are
now available in :epkg:`Python`.
**Democratization of hardware**
The technology improved but also the economical model.
Computing power can still be acquired by getting machines
or cluster of machines but it can also be rent for a short
period of time.
**Democratization of AI**
By AI, I mostly mean deep learning. Difficult to optimize and very
expensive to train ten years ago, it is now more accessible.
Frameworks are open source such as :epkg:`CNTK` or
:epkg:`pytorch` and many trained models are available.
Competing into a challenge such as `ImageNet `_
require a lot computing power not everybody can afford.
The winner of Image Net 2015 was
`Microsoft Research Asia `_.
But these trained models can be leveraged with
`transfer learning `_
and adapted to a new similar problem without paying the cost of
a full training process.
**Democratization of knowledge**
The first reflex is now to go to internet to find an answer
to a technical question.
`Stackoverflow `_
or `Quora `_ have gathered so many
answers that they pop up in first position to many queries
sent to a search engine. People share technical knowledge.
Teachers also share theoritical knowledge through videos, tutorials
on `Coursera `_, `EdX `_
or `Fun MOOC `_ (France).
Even the documentation of libraries is not only a list
of functions but also contains many examples and tutorials
users can copy paste and adapt to their own problems.
Trends
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.. list-table::
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* - :epkg:`github`
- .. image:: images/github.png
:width: 400
* - :epkg:`Python`
- .. image:: images/python.png
:width: 400
* - :epkg:`scikit-learn`
- .. image:: images/sklearn.png
:width: 400
* - `data science `_
- .. image:: images/datascience.png
:width: 400