1. Artificial Intelligence
Michael Haley

2. What is AI? The Turing Test
It's a lot of different things to a lot of different people:
How to tell if a machine is considered AI
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

3. What is AI? 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

4. Artificial general intelligence (AGI)
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.

5. History of Artificial intelligence
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". ”

6. Game AI
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.

7. Ferranti Mark 1 & Checkers game

8. History of AI
In 1951 The term “Artificial Intelligence” was coined at the Darthmouth conference.
The four men that organized this conference was Matvin Minsky, John McCarthy, Claude Shannon and Nathan Rochester

9. “The Golden Years” 1956-1974 Reasoning as search Natural language
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
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.

10. ELIZA

11. The Grant
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.

12. The down fall
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.

13. The problems that caused the downfall
The main problem that caused scientists to not be able to bring there promises to fruition, was the limitations from hardware limitations.
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.

14. Back on track
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
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
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

15. Back on track cont.
The expert system is based on a version of the “rule-based” approach to knowledge representation.
For instance like a if statement
If<condition> then <action>

16. The second collapse
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.

17. Early 90’s - Present
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.

18. AI problems and applications today
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

19. AI in todays society Artificial Narrow Intelligence
ANI is a machine intelligence that equals or exceeds human intelligence or efficiency at a specific thing.
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.
The worlds best checkers, chess, backgammon and Othello players are all ANI’s.

20. Is AI a threat to humanity
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

21. “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 non-understanding 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.”

22. TensorFlow
Google made there AI engine open source for all to use, the TensorFlow.
This could push the progress of AI within startup 10 fold.

23. Resources https://en.wikipedia.org/wiki/Artificial_intelligence