FAQ : Machine Learning
The world of Machine Learning
A future where machine beats man is not a reality yet
What is Machine Learning?
Artificial Intelligence (AI) may be the great
buzzword of our times but Machine Learning (ML) is really the brass tacks. For
general conversation, however, the terms have considerable overlap. Some
decades ago, a chess playing programme that beat human players could’ve been
crowned ‘artificially intelligent’ but now there are mundane AIs, like the ones
Amazon uses to recommend purchases and ‘true’ AI that theorists reserve for a
point when machines become intelligent enough to take over the planet.
However the path to achieving this is due to
advances in designing algorithms, cheap hardware, access to ‘training data’— or
datasets of all kinds of stuff that computers can use to ‘learn’ new
associations — that have allowed computers to perform several useful tasks
better.
How have machines learnt to learn?
Computer science in the 1990s had laid much
of the theoretical background for machine learning namely via developing neural
networks. This involved, in essence, reviving a philosophy of designing
circuits to simulate the way neurons connect in the brain.
The brain with its billions neurons and each
connected to a 1000 others is now the dominant metaphor for how ML programs are
organised. Rather than older approaches that tried to program the most
‘efficient’ way to solve a problem (like what’s the best sequence of moves to
checkmate) ML systems are increasingly organised around letting the systems figure
out the rules from scratch. Circuits achieve their goals — differentiating cats
from dogs and recognising signatures on cheques—by repetitively blitzing
through ‘rewards’ and ‘penalties’ and are limited only by the efficiency of the
underlying algorithms and computing power at their programmer’s disposal.
Realistically, what has ML achieved so far?
Using approaches of Deep Learning — an
approach where layers of ‘neurons’ are hierarchically arranged to recognise an
object — machines can beat human champions of games that require computation
and intuition, such as Go. More usefully, it can look at pictures of biopsies
and picking out possible cancers. Some ML (or AI) are being taught to predict
the outcome of legal cases, writing press releases and composing music. However
the sci-fi future where a machine beats a human in every conceivable department
and is constantly self-learning isn’t a reality yet.
Regards
Pralhad Jadhav
Senior Manager @ Knowledge
Repository
Khaitan & Co
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