As titled, the entire book is an
optimal guide for all thinking humans. The author begins the book with a sketch
of the history of AI research from its birth in the 1950s, and outlines its key
figures and significant ideological branches. These are broadly classified as
symbolic (conscious reasoning) and sub-symbolic (sub-conscious learning) with
the latter being biologically inspired structures which learn patterns and
rules via lots of data.
Here is the brief summary of the various
sections in the book, rather guide:
# The first and the simple start is ‘definition’ - “Define your terms or we shall never understand one another”. IQ which is measured in single scale can thus be differentiated with various dimensions – emotional, verbal, spatial, logical social etc.
That GPS actually was shorted for 'General Problem Solver' rather than what we have assumed today was a surprise for me.
The author explains the initiation of logical coding with a simple old school puzzle of two men with a man-eater trying to cross a river with one boat so that only two species are allowed in the boat or trip once or at a time and the man should never be left alone at the mercy of man-eater. The coding would then be working out various combinations using symbols, ifs, buts….
Mitchell wonders over why we trust a review from a person who is a friend and give weightage to his opinion over others’. She meant to convey that the machine might not be able to analyze the ‘trust’ factor despite it having all the data! There is a lot more discussion on perception of figures by the machine so that data alone (imagine all sorts of) cannot help it.
Getting
along new ideas create lot of optimism breakthrough!
In this connection Ray Kurzweils ‘Text to Speech’ analysis is suggested to give futuristic prognostications machine. His books “The age of spiritual machines” and “The singularity is near” is suggested for further reading.
# The
concept of exponential growth is mentioned with a story of a Sage visiting a neighboring
rich king and challenging to answer any question asked by the ‘Darbar’. The
sage extracts promise from the king that he be given grains that double over
the chessboard, each time he answers correctly. The king at first laughs over
it and agrees only to realize in dismay that by the time second row of the
chess (16 squares) get covered, about 65,536 grains got accumulated (~ 2 kg).
Here it should make a difference between the man and machine. While the king
realizes the wisdom of the sage, the machine might not!
# Raw
info from pictures in obtained from simple queries like: who, what, when, where
and why. This section has many examples that are simple as well. But when it
comes to transformation into ‘wisdom’, the marvelous nature of the brain could never
be understood. How brain transforms visual info into what the scene can be hard
for the machine to accomplish. For instance, a dog with human is hard for the
machines to recognize if the picture is not clear. So, the data scientists
built a network called ConvNet (Convolutional Neural network), where the Shades
of pictures are pixelated and the values given and evaluation is done.
# PASCAL
gets a tribute as one of the early machine level language and is implemented by
Amazon as Mechanical Turk which still requires human intelligence to work with
for its market place.
# That Facebook with which we were happier when it was started as a social platform had actually a hidden objective. It gathered all our data and registered a patent for classifying our photos with emotions behind expressions. Using similar logic Twitter filtered pornographic images. And for the analytical part it required huge number of CPU’s and it was then that the Nvidia’s GPUS stock prices increased 1000% when ConvNet and ImageNet usage doubled.
# Facebook says ‘thankyou’ because it was able to
differentiate persons using imaging techniques and was able to offer ‘tag’ for
persons. Likewise, Flickr used pictures for its training to recognize them via
machines. There is description of Long tail graph with many good examples,
simple though and that might give an idea on how anyone can consider simple
failure possibilities into avoiding catastrophes. This is basically a figuring
graph that talks about the likelihood of things that may appear while
performing (i.e. if you want to train a driver-less car you have to mention
what all it can come across, like more traffic lights and less often a lion on
the street).
# Images
with blurry background predicting animals and Camera face determination seeing
blinking Asians or racially denigrating species have been some output by the
machines, particularly with Deep Neural network.
#
Ethical AI section deals a lot with the ethics a machine might not ‘know’. If
you have asked a driver-less car to take you home it cannot determine if it really
knows what it need to know. It might warrant a misuse of the car. Google’s
DeepMind has thus postulated a lot for the beneficial ethics of face
recognition (similar to FB asking to tag you).
“A robot
must not injure a human” and further rules make a pleasant reading. It is here
that the author talks about a simple Trolley problem. There is a picture of a
woman with a trolley trying to cross a road and she is engrossed over mobile
while doing the cross. If an unmanned vehicle suddenly came across such a woman and if it
were to avoid hitting her it would have to make a swerve in a opposite
direction that may kill more than this single woman. So then should the car
go ahead and kill one instead of six?
# That
Steve Jobs started his career when Atari, a breakout game was assigned to him
might provide solace to all the gamers who are struggling with a slow IT
future. But Steve was a hard worker and he probably knew the future of the
machines that might have helped him achieve his goals. The concept of Supervised
learning vs Reinforcement learning is then discussed to lessen the projected
dangers expected of the machines. Similarly, another stalwart gets his name
mentioned: Checkers and Chess code writer Arthur Samuel of IBM who coined the
word ML.
The probability and statistics are
useful when you have prediction rather than performance. Deep Blue, another network-based
ML firm has made a good foray into many areas, particularly chess where every
position may have 35 moves on an average. Similarly, Monte Carlo, the
simulation techniques on probability, including electron position evaluation, was
first used to design the atom bomb (Manhattan project) with a family of
computer algorithm and so these still do a great job (including Quantum
Mechanics).
# The
information derived from data with any result or conclusion often takes time
for the machine. If not for the processor, it would never beat humans who have
now started to lag behind machines leaving the latter do all the stuff. The
various conclusions drawn from a single story is discussed in many pages with
interesting results that prove that AI cannot
beat human intelligence. Here is the story: In a restaurant a customer orders
some food which gets unfortunately charred by the cook and the waitress presents the food
to the customer with an excuse but the man leaves the restaurant murmuring some
words (machines cannot get that while humans can guess his dismay). The waitress' last words were “why is he so bent”?
Now this story is fed to machine with a
translator and different languages interpret different views which makes the
reading interesting (Word2Vec initially). But it is only the human being who
can understand well that the customer went without eating! (Gracias: Neural network layer). One language interpreting
machine was able to conclude the angular geometry!!! (bent).
A new rule of the thumb probably displaces 80-20 rule for the learners to 90-10 – the first 90% of a project takes about 10% of time and the last 10% taking 90% of time.
In the end, the author puts forward the
speculations around AI as the expectations associated with it are very high.
There is no exact conclusion about the future of the AI and only incremental or infinitesimal changes in the technological front over a period would be to the fore. People
who know coding, algorithms and data science would have tough time to train machines to get closer to the natural intelligence. And those without any data or
algorithm would be left pondering in uncertainty.
