Course OverviewWhat is AI?
What are the Major Challenges?
What are the Main Techniques?
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?
Done
Done
Course Overview What is AI?
What are the Major Challenges?
What are the Main Techniques? (How do we do it?)
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?
Course Overview What is AI?
What are the Major Challenges?
What are the Main Techniques? (How do we do it?)
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?
Search Logics (knowledge representation and reasoning) Planning Bayesian belief networks Neural networks Evolutionary computation Reinforcement learning
Course Overview What is AI?
What are the Major Challenges?
What are the Main Techniques? (How do we do it?)
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?
Search Logics (knowledge representation and reasoning) Planning Bayesian belief networks Neural networks Evolutionary computation Reinforcement learning
Course Overview What is AI?
What are the Major Challenges?
What are the Main Techniques? (How do we do it?)
Where are we failing, and why?
Step back and look at the Science
Step back and look at the History of AI
What are the Major Schools of Thought?
What of the Future?
Search Logics (knowledge representation and reasoning) Planning Bayesian belief networks Neural networks Evolutionary computation Reinforcement learning
These are all in fact types of
“Machine Learning”
Dealing with Uncertainty The need to deal with uncertainty arose in “expert systems”
Code expertise into a computer systemExample: Medical diagnosis: MYCIN Equipment failure diagnosis in a factory
Sample from MYCIN: IF
The infection is primary-bacteremia AND The site of the culture is one of the sterile sites AND The suspected portal of entry is the gastrointestinal tract
THEN There is suggestive evidence (70%) that the infection is bacteroid
Expert systems often have long chains IF X THEN Y … IF Y THEN Z … IF Z THEN W … If uncertainty is not handled correctly, errors build up, wrong diagnosis Also, there may be dependencies, e.g. X and Y depend on each other Leads to more errors…
Need a proper way to deal with uncertainty
How do Humans Deal with Uncertainty? Not very well…
Consider a form of cancer which afflicts 0.8% of people (rare) A lab has a test to detect the cancer The test has a 98% chance to give an accurate result Mr. Bloggs goes for the test
The result comes back positive i.e. the test says he has cancer
What is the chance that he has the cancer? 28%
Afflicts experts too Studies have shown: human experts thinking of likelihoods do not reason
like mathematical probability
A
B C
D E
Increased total serum count
Metastatic cancer
Brain Tumour
Severe headachesComa
A
B C
D E
Increased total serum count
Metastatic cancer
Brain Tumour
Severe headachesComa
No Link
A
B C
D E
Increased total serum count
Metastatic cancer
Brain Tumour
Severe headachesComa
Serum count
Brain tumour
Coma
Yes Yes 95%
Yes No 94%
No Yes 29%
No No 0.1%
A
B C
D E
Increased total serum count
Metastatic cancer
Brain Tumour
Severe headachesComa
Serum count
Brain tumour
Coma
Yes Yes 95%
Yes No 94%
No Yes 29%
No No 0.1%
Brain tumour
headache
Yes 70%
No 1%
A
B C
D E
Increased total serum count
Metastatic cancer
Brain Tumour
Severe headachesComa
Serum count
Brain tumour
Coma
Yes Yes 95%
Yes No 94%
No Yes 29%
No No 0.1%
Brain tumour
headache
Yes 70%
No 1%
… …
… …
… …
… …
… …
… …
… …
… …
… …
Inference in Belief Networks Questions for a belief network:
Diagnosis Work backwards from some evidence to a hypothesis
Causality Work forwards from some hypothesis to likely evidence Test a hypothesis, find likely symptoms
In general – mixed mode Give values for some evidence variables Ask about values of others
No other approach handles all these modes
Reasoning can take some time Need to be careful to design network Local structure: few connections
How Good are Belief Networks? Relieves you from coding all possible dependencies
How many possibilities if full network?
Tools are available Build network graphically System handles mathematical probabilities
Case study: Pathfinder a medical expert system
Assists pathologists with diagnosis of lymph-node diseases Pathfinder is a pun
User enters initial findings Pathfinder lists possible diseases User can
Enter more findings Ask pathfinder which findings would narrow possibilities
Pathfinder refines the diagnosis Pathfinder version based on Belief Networks performs significantly better
than human pathologists
QUIZ
“Machines will be capable,
within _____ years,
of doing any work that a man can do.”
Herbert Simon, 1965.
What disciplines are considered as sub-areas of Cognitive Science?
1966: “Any task that requires real understanding of natural language is
too difficult for a computer” - Bar-Hillel
Time flies like an arrow.
Give an example which shows why speech recognition is hard for computers.
Major tasks in robotics:
1. Localisation/mapping Range finders Landmarks Always uncertainty
2. ?
DARPA Grand Challenge 2004
150-mile route in Mojave Desert (off-road course)
Best performance? (require accuracy to nearest 2 miles)
Vision Hierarchy
4. High level Models
3. Mid level ????
2. Putting together Multiple images
1. Low level processing on a single image
0. The physics of image formation
Go (Wei Qi)Humans don’t want to play computers because ?
John McCarthy, "Programs with Common
Sense", 1958.
"Our ultimate objective is to make "Our ultimate objective is to make programs that learn from their programs that learn from their
experience as effectively as humans experience as effectively as humans do. We shall…say that a program do. We shall…say that a program
has common sense if ?”has common sense if ?”
DefenceA big user of AI.
"... the deployment of a single ??????
called DART during the Desert Shield/Storm Campaign paid back all US government investment in AI/KBS research over a 30 year
period."
Tate A. Smart Planning. ARPI Proc. 1996.
Can use logic to represent a hierarchy of concepts isa(Tweety, canary) isa(canary, bird) isa(bird, animal) isa(animal, living_thing) isa(living_thing, physical_thing) isa(physical_thing, tangible_thing) isa(tangible_thing, thing)
What do we call this?
“Machines will be capable,
within _____ years,
of doing any work that a man can do.”
Herbert Simon, 1965.
“Machines will be capable,
within twenty years,
of doing any work that a man can do.”
Herbert Simon, 1965.
What disciplines are considered as sub-areas of Cognitive Science?
What disciplines are considered as sub-areas of Cognitive Science?
Cognitive Science
Psychology
Philosophy
Neuroscience
Artificial Intelligence
Linguistics
Anthropology
1966: “Any task that requires real understanding of natural language is
too difficult for a computer” - Bar-Hillel
1966: “Any task that requires real understanding of natural language is
too difficult for a computer” - Bar-Hillel
Time flies like an arrow.
Time flies like an arrow.
(You should) time flies as you would (time) an arrow
Time flies in the same way that an arrow would (time them)
Time those flies that are like arrows
Fruit flies like a banana
each of above
Time magazine travels straight when thrown
Give an example which shows why speech recognition is hard for computers.
Give an example which shows why speech recognition is hard for computers.
“eat I scream” vs. “eat ice cream”
Major tasks in robotics:
1. Localisation/mapping Range finders Landmarks Always uncertainty
2. ?
Major tasks in robotics:
1. Localisation/mapping Range finders Landmarks Always uncertainty
2. Motion planning For body location in world For arms/fingers
DARPA Grand Challenge 2004
150-mile route in Mojave Desert (off-road course)
Best performance? (require accuracy to nearest 2 miles)
DARPA Grand Challenge 2004
150-mile route in Mojave Desert (off-road course)
Best performance: 7.36 miles
Vision Hierarchy
4. High level Models
3. Mid level ????
2. Putting together Multiple images
1. Low level processing on a single image
0. The physics of image formation
Vision Hierarchy
4. High level Models
3. Mid level Segmentation
2. Putting together Multiple images
1. Low level processing on a single image
0. The physics of image formation
Go (Wei Qi)Humans don’t want to play computers because ?
Go (Wei Qi)Humans don’t want to play computers because computers are too bad
John McCarthy, "Programs with Common
Sense", 1958.
"Our ultimate objective is to make "Our ultimate objective is to make programs that learn from their programs that learn from their
experience as effectively as humans experience as effectively as humans do. We shall…say that a program do. We shall…say that a program
has common sense if ?”has common sense if ?”
John McCarthy, "Programs with Common
Sense", 1958.
"Our ultimate objective is to make "Our ultimate objective is to make programs that learn from their programs that learn from their
experience as effectively as humans experience as effectively as humans do. We shall…say that a program do. We shall…say that a program
has common sense if it has common sense if it automatically deduces for itself a automatically deduces for itself a sufficient wide class of immediate sufficient wide class of immediate consequences of anything it is told consequences of anything it is told
and what it already knows.”and what it already knows.”
DefenceA big user of AI.
"... the deployment of a single ??????
called DART during the Desert Shield/Storm Campaign paid back all US government investment in AI/KBS research over a 30 year
period."
Tate A. Smart Planning. ARPI Proc. 1996.
DefenceA big user of AI.
"... the deployment of a single logistics support aid called DART during the Desert Shield/Storm
Campaign paid back all US government investment in AI/KBS research over a 30 year period."
Tate A. Smart Planning. ARPI Proc. 1996.
Can use logic to represent a hierarchy of concepts isa(Tweety, canary) isa(canary, bird) isa(bird, animal) isa(animal, living_thing) isa(living_thing, physical_thing) isa(physical_thing, tangible_thing) isa(tangible_thing, thing)
What do we call this?
Can use logic to represent a hierarchy of concepts isa(Tweety, canary) isa(canary, bird) isa(bird, animal) isa(animal, living_thing) isa(living_thing, physical_thing) isa(physical_thing, tangible_thing) isa(tangible_thing, thing)
What do we call this? Ontology
