(liveblog starts after
a general paragraph on the two keynotes that preceded her talk, and really her
talk was really GREAT! And with fresh,
relevant structure).
First of a talk on the
skill sets of future workers (the new skills needed, referring to critical thinking,
but not mentioning what is understood with critical thinking) and the
collective intelligence (but clearly linking it to big data not small data, as
well described in an article by Stella Lee).
Self-worth idea for the philosophy session, refer tot he Google map approach
where small companies who offer one particular aspect of what it took to build
google maps were bought by Google, and as such producing something that was bigger
than the sum of its parts). But this of course means that the identity and the
self-versus-the-other becomes under pressure, as people that really make a
difference at some point, do not have the satisfying moment to think they are
on top of the world (you can no longer show off your quality easily… for there
are so many others just like you… as you can see when you read the news, follow
people online…). While feeling important was easier, or possible in a ‘smaller’
world, where the local tech person was revered for her or his knowledge. So, in
some way we are loosing the feeling of being special based on what we do. Additionally,
if AI enters more of the working world, how do we ensure that work will be
there for everyone, as work is also a way to ‘feel’ self-worth. I think keeping
self-worth will be an increasing challenge in the connected, and AI supported
world. As a self-test, simply think of yourself, and wanting to be invited to
be on a stage… it is a simple yet possibly mentally alarming aspect. Our
society is promoting ‘being the best’ at something, or having the most ‘likes’,
what can we do to install or keep self-worth?
Than a speaker on the
promise of online education, referring to MOOCs versus formal education, the
increase of young people going to college… which strangely contradicts what the
most profiles of future jobs seems to be like (professions that are rather
labour intensive). The speaker Kaplan managed to knock down people who get into
good jobs based on non-traditional schooling (obviously, my eye-brows went up,
and I am sure there are more of us in the audience pondering which conservative
thinking label can be put on that type of scolding stereotype speech,
protecting the norm, he is clearly not even a non-conformist).
Here a person in the
line of my interest takes the stage: Anita Schjoll Brede. Anita founded an AI company
Iris.ai , and tries to simplify the AI, machine learning and data science for
easier implementation. So… of interest.
Learning how to learn sets us human beings apart. We are in the era where machines
will learn, based on how we learn… inevitably changing what we need to learn.
She gives what AI is
seen by most people, and where that model is not really correct.
Machine learning is
based on the workings of a human brain. Over time the machine will adapt based
on the data, and it will learn new skills. It is a great model to see the
difference. One caveat, we still not sure how the human mind really works.
If we think of AI, we
think of software, hardware, data … but our brains are slightly different and our
human brains are also flawed. We want to build machines that are complementary to
the human brain.
Iris.ai started with
the idea that there are papers and new
research published every day, humans can no longer read all. Iris.ai goes
through the science and the literature process is relatively automated. The
process is currently possible with a time decrease of 80%. Next step is
hypothesis extraction, than build a truth tree of the document based on scientific
arguments. Once you have the truth trees are done, link that to a lab or
specific topic, … with an option of the machine learning results leading to
different types of research. Human beings will still do the deeper understanding.
Another example is one tutor per child. Imagine that there is one tutor
for that child, which grows with that child, helps with lifelong learning. The
system will know you so well, that it will know how to motivate you, or get you
forward. It might also have filters to identify discriminatory feelings or
actions (remark of myself: but I do wonder, if this is the case, then isn’t
this limiting the freedom of saying what you want and being the person you want
to be… it might risk becoming extreme in either way of the doctrine system).
Refers to the Watson
Lawyer AI, which makes that the junior lawyers will no longer do all the groundwork.
So the new employees will have to learn other stuff, and be integrated
differently. But this relates to critical ideas of course, as you must choose
for employing people (but make yourself less competitive) or you only higher senior
lawyers (remark of myself: but than you loose diversity and workforce).
Refers to doctors
built by machine learning, used in sub-Saharan settings, to analyse human blood
for malaria. Which saves time for the doctors, health care workers… but
evidently, this has an impact on the health care worker jobs.
Cognitive bias codex
(brain picture with lots of links). Lady in the red dress experiment.
Her take on what we need to learn:
Critical thinking, refers
to source criticism she learned during her schooling.
Who builds the AI, lets
say Google will transgress the first general AI… their business model will
still get us to buy more soap.
Complex problem solving: we need to hold this uncertainty
and have that understanding. To understand why machines were lead to specific
choices.
Creativity: machines can be creative, we can learn this.
Rehashing what is done, and making it to something of your own is something
that is (refers to lawyer commercial that was built by AI based on hours of
legal commercials).
Empathy: is at the core of human capabilities like
this. Machines are currently doing things, but not yet empathic. But empathy is
also important to build machines that can result in positive evolutions for
humans. If we can support machines that will be able to love the world,
including humans.
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