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Hannah Fry's argument
By
Steve Draper,
Department of Psychology,
University of Glasgow.
The RI-Xmas lectures 2019 as a model of the multidisciplinarity of important
current uses of computation
by Hannah Fry
https://www.bbc.co.uk/iplayer/episodes/b00pmbqq/royal-institution-christmas-lectures
These are aimed at children about 12 years old.
Her lectures (unlike previous series on the whole) did NOT say what the
implications were. In fact, unlike most lecturers and against old-fashioned
advice, she did NOT tell them what she would say and why; then say it; then
tell them what it was that she had just told them.
An interpretation of the implicit argument in her lectures
There is a suite of disciplines behind many of the famous and important
innovative applications of today.
If you want to be part of innovative applications, you
should probably realise that it will be a multidisciplinary collaboration.
This is an introductory tour of what/how each contributes.
- Mathematics: the great thing about maths is the degree of
certainty. You can calculate how to do successfully and right first time,
things which our ordinary thinking tells us are very unlikely [e.g. sky dive
from 30,000 feet without a parachute into a big catch net.] Main problems with
this apart from slips in the calculations are if you leave out of your maths
model of the situation, a factor which matters.
- Computers: can do exact calculations much faster than humans. Given
time limits in some problems, this can be a critical benefit e.g. matching
kidney donors to recipients.
- Statistics: some important situations do not allow you to calculate
deterministically BUT are highly regular when many trials are combined. This
is true of a lot of human behaviour. Also true of how infectious disease does
and doesn't spread, with or without vaccinations. High certainty collective
outcomes from low certainty individual outcomes; and how to exploit this.
- Probability: correctly combining multiple bits of partial
information.
Safety in systems by combining faulty sources of knowledge. Autonomous
drones; driverless road cars. They typically have multiple sensors, all may
fail to detect what they should. These can be combined in two ways: a) AND-ed
together: if any one says it's dangerous, shut the vehicle down. b) Bayes
theorem says how to combine conditional probabilities; and this can be used to
combine sensor outputs to give an accurate total probability given multiple
bits of (sources of) partial information.
This also lets you use (medical) diagnostic tests that are imperfect, and work
out the true chance of you being ill / clear of illness.
- Machine learning: Programming machines requires you to write down
ALL the details of the actions. This is so onerous in many important cases
(e.g. how to get a machine to recognise a dog) that it seems more practicable
to use AI learning to automate this, giving it only examples => motivation
for machine learning.
This leads to creating computer programs that were hitherto too hard to write,
but also that humans do not know how to
write and so do not understand what they are doing.
- Putting several of the above together can achieve:
- [Facebook, Google] taking rather few bits of a person's online
behaviour, predict their behaviour and interests with high accuracy.
- [used in premier league football] Using a big mass of data on each
player's abilities (e.g. running speed, passing accuracy) for each team member
AND each of the opposing team's players, it can predict what is the best thing
a player can do in any situation (best = most likely to lead to a goal in the
next 15 seconds). That is, not just predict someone's voluntary behaviour,
but advise which bits of their voluntary behaviour they should select in order
to achieve their (team's) objectives / success. (I.e. planning of a whole new
kind; not structural and deterministic, but probabilistic.)
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