{
What is AI?
It's a lot of different things to a lot of
different people:
How to tell if a machine is considered AI
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Does the Machine mimic human
behaviour
 Programs that behave (externally) like
humans.
 This is the original idea from Turing and
the well known Turing Test is to use to
verify this
The Turing Test
What is AI?
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Does the Machine have human “thought” ?
 Programs that operate (internally) the way
humans do
Does the Machine behave intelligently?
 But what does it mean to behave
intelligently?
Does the Machine behave rationally
 Take the right/ best action to achieve the
goals, based on its knowledge and belief
 The answer is dependent on its
knowledge and belief
Artificial general
intelligence (AGI)
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AGI is the intelligence of a machine that could
successfully perform any intellectual task that a
human being can.
Many believe that it should be the primary goal
of any AI researcher.
To distinguish the difference between strong AI
and weak AI, is the use of software to
accomplish problem solving and reasoning
tasks.
The weak AI will not attempt to perform the
full range of human cognitive abilities.
History of Artificial
intelligence
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In the 1940’s and 1950’s a group of scientists from all
different fields began to discuss the possibility of
creating an artificial brain. This was the birth of the
field for AI research
“In 1950 Alan Turing published a paper in which he
speculated about the possibility of creating
machines that can think. He noted that "thinking" is
difficult to define and devised his famous Turing
Test. If a machine could carry on a conversation that
was indistinguishable from a conversation with a
human being, then it was reasonable to say that the
machine was "thinking". ”
Game AI
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In 1951 using the Ferranti Mark 1 machine two
different programs started one for checkers and the
other for chess.
Christopher Strachey was the writer of the checkers
program, when developed it was able to challenge a
respectable amateur.
Dietrich Prinz was the writer of the one for chess.
While at first it had no major benefits it was
recreated later on to become incredibly influential
Game AI would be used as a guide line to measure
the progress of AI through its history.
Ferranti Mark 1 &
Checkers game
History of AI

In 1951 The term “Artificial Intelligence” was
coined at the Darthmouth conference.
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The four men that organized this conference
was Matvin Minsky, John McCarthy, Claude
Shannon and Nathan Rochester
“The Golden Years”
1956-1974
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Reasoning as search
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Many early AI programs used the same basic
algorithm. The algorithm would take a step by
step towards its goal as if searching through a
maze. Once it hit a dead end it would back track
and take a different path.
Natural language
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Joseph Weizenbaum’s ELIZA could carry out a
conversation that was so realistic that users were
sometimes fooled into thinking it was
communicating with a human.
However ELIZA actually had no idea what she
was saying and was just responding with basic
answers or the same thing.
ELIZA
The Grant
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In June 1963, MIT received a 2.2 Million dollar
grant from DARPA. The money was used to
fund project MAC ( the “AI Group”)
Then they received 3 Million dollars a year until
the 70’s.
The down fall 1974-1980
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In the 70’s AI had some setbacks, AI researchers
had tremendous optimism which raised the
expectations impossibly high. The promises
that scientists couldn't materialize caused
concern among the investors. This caused most
funding for AI to disappear.
The problems that caused
the downfall
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The main problem that caused scientists to not
be able to bring there promises to fruition, was
the limitations from hardware limitations.
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Limited computer power – There was not
enough memory or processing speed at this time
to accomplish anything useful to create a strong
AI.
Many AI applications like vision and language
required enormous information about the
machines surroundings. In the 70’s they did not
have a database large enough to contain this
information nor the hardware such as infared
lazers to help detect this world.
Back on track
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In the 1980s a form of AI program called "expert
systems" was adopted by corporations around
the world and knowledge representation became
the focus of mainstream AI research
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The power of expert systems came from the expert
knowledge using rules that are derived from the
domain experts. The system emulates or acts with
decision making capabilities of a human expert
In 1980, an expert system called XCON was
completed for the Digital Equipment Corporation.
It was an enormous success: it was saving the
company 40 million dollars annually by 1986
By 1985 the market for AI had reached over a
billion dollars
The money returns: the fifth generation project
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Japan aggressively funded AI within its fifth
generation computer project (but based on another
AI programming language - Prolog created by
Colmerauer in 1972)
This inspired the U.S and UK governments to
restore funding for AI research
Back on track cont.
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The expert system is based on a version of the
“rule-based” approach to knowledge
representation.
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For instance like a if statement
If<condition> then <action>
The second collapse
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In 1987 desktop computers from apple and IBM
was gaining speed and power and eventually
become more powerful than the more
expensive LISP machines
The successful expert systems, such as XCON.
Became too expensive to maintain, due to
difficulty in updating the systems.
In the late 80’s and early 90’s funding was once
again cut because not being met again.
Early 90’s - Present
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AI’s biggest advances have came recently the reasons
are the same that held AI back in the earlier years.
 the incredible power of computers today
 a greater emphasis on solving specific sub
problems
 the creation of new ties between AI and other fields
working on similar problems
 a new commitment by researchers to solid
mathematical methods and rigorous scientific
standards, in particular, based probability and
statistical theories
 Significant progress has been achieved in neural
networks, probabilistic methods for uncertain
reasoning and statistical machine learning,
machine perception (computer vision and Speech),
optimisation and evolutionary computation, fuzzy
systems, Intelligent agents.
AI problems and applications today
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Problem Solving
 Theorem-problems, solve puzzles, play board
games(chess,checkers,tic-tac-toe)
Machine Learning and Perception such as
 detecting credit card fraud, stock market
analysis, classifying DNA sequences, speech
and handwriting recognition, object and facial
recognition in computer vision
AI in todays society
Artificial Narrow Intelligence
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ANI is a machine intelligence that equals or
exceeds human intelligence or efficiency at a
specific thing.
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Cars are full of ANI systems, computer that
figures out when the anti-lock breaks should
kick in. Google's self driving car, uses ANU
systems to perceive and react to the world
around it.
https://www.youtube.com/watch?v=uCezICQNg
JU
The worlds best checkers, chess, backgammon
and Othello players are all ANI’s.
Is AI a threat to humanity
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http://www.technologyreview.com/review/5348
71/our-fear-of-artificial-intelligence/
I wont touch on this topic to much ill leave
Stephen hawking's opinion here.
In a AMA on Reddit that he did this was the
top voted question along with his answer
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“Professor Hawking- Whenever I teach AI, Machine Learning,
or Intelligent Robotics, my class and I end up having what I call
"The Terminator Conversation." My point in this conversation is
that the dangers from AI are overblown by media and nonunderstanding news, and the real danger is the same danger in
any complex, less-than-fully-understood code: edge case
unpredictability.
Answer:
You’re right: media often misrepresent what is actually said.
The real risk with AI isn’t malice but competence. A
superintelligent AI will be extremely good at accomplishing
its goals, and if those goals aren’t aligned with ours, we’re in
trouble. You’re probably not an evil ant-hater who steps on
ants out of malice, but if you’re in charge of a hydroelectric
green energy project and there’s an anthill in the region to be
flooded, too bad for the ants. Let’s not place humanity in the
position of those ants. Please encourage your students to think
not only about how to create AI, but also about how to ensure
its beneficial use.”
TensorFlow
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Google made there AI engine open source for
all to use, the TensorFlow.
This could push the progress of AI within
startup 10 fold.
Resources
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https://en.wikipedia.org/wiki/Artificial_intellig
ence
http://wwwformal.stanford.edu/jmc/whatisai/whatisai.htm
l
http://www.iep.utm.edu/art-inte/
http://plato.stanford.edu/entries/logic-ai/