View
303
Download
0
Category
Preview:
Citation preview
Artificial IntelligenceIntroduction
Andres Mendez-Vazquez
May 15, 2016
1 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
2 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
3 / 48
Introduction
The ConceptInformally, we have four main fields to define AI
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
This divide the field in two main groupsA human-centered approach, an empirical science, involvinghypothesis and experimental confirmation.
A rationalist approach involves a combination of mathematicsand engineering.
4 / 48
Introduction
The ConceptInformally, we have four main fields to define AI
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
This divide the field in two main groupsA human-centered approach, an empirical science, involvinghypothesis and experimental confirmation.
A rationalist approach involves a combination of mathematicsand engineering.
4 / 48
Introduction
The ConceptInformally, we have four main fields to define AI
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
This divide the field in two main groupsA human-centered approach, an empirical science, involvinghypothesis and experimental confirmation.
A rationalist approach involves a combination of mathematicsand engineering.
4 / 48
Introduction
The ConceptInformally, we have four main fields to define AI
Systems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
This divide the field in two main groupsA human-centered approach, an empirical science, involvinghypothesis and experimental confirmation.
A rationalist approach involves a combination of mathematicsand engineering.
4 / 48
We have a PROBLEM!!!
Did you notice the following?There is not a single viable definition of intelligence...
OOPSSS!!!
5 / 48
Actually the situation is much worse
Something NotableAt MIT’s "Brains, Minds and Machines" symposium, 2012
I Chomsky contends that many AI theorists have gotten bogged downwith such things as statistical models and fMRI scans.
He told themAI developers and neuroscientists need to sit down and describe theinputs and outputs of the problems that they are studying.
I Something that they do not actually do.... OOPSSS!!!
6 / 48
Actually the situation is much worse
Something NotableAt MIT’s "Brains, Minds and Machines" symposium, 2012
I Chomsky contends that many AI theorists have gotten bogged downwith such things as statistical models and fMRI scans.
He told themAI developers and neuroscientists need to sit down and describe theinputs and outputs of the problems that they are studying.
I Something that they do not actually do.... OOPSSS!!!
6 / 48
Actually the situation is much worse
Something NotableAt MIT’s "Brains, Minds and Machines" symposium, 2012
I Chomsky contends that many AI theorists have gotten bogged downwith such things as statistical models and fMRI scans.
He told themAI developers and neuroscientists need to sit down and describe theinputs and outputs of the problems that they are studying.
I Something that they do not actually do.... OOPSSS!!!
6 / 48
Actually the situation is much worse
Something NotableAt MIT’s "Brains, Minds and Machines" symposium, 2012
I Chomsky contends that many AI theorists have gotten bogged downwith such things as statistical models and fMRI scans.
He told themAI developers and neuroscientists need to sit down and describe theinputs and outputs of the problems that they are studying.
I Something that they do not actually do.... OOPSSS!!!
6 / 48
We have harsher words
Sydney BrennerGeneticist and Nobel-prize
He went to say thatHe was equally skeptical about new system approaches to understandingthe brain.
He went to say thatThe new AI and neuroscientist approach is some “form of insanity”
7 / 48
We have harsher words
Sydney BrennerGeneticist and Nobel-prize
He went to say thatHe was equally skeptical about new system approaches to understandingthe brain.
He went to say thatThe new AI and neuroscientist approach is some “form of insanity”
7 / 48
We have harsher words
Sydney BrennerGeneticist and Nobel-prize
He went to say thatHe was equally skeptical about new system approaches to understandingthe brain.
He went to say thatThe new AI and neuroscientist approach is some “form of insanity”
7 / 48
Brenner’s Criticism
An unlikely pairSystem Biology - a computational and mathematical modeling ofcomplex biological systemsArtificial Intelligence - attempts for “intelligence” in machines
ProblemBoth face the same fundamental task of reverse-engineering a highlycomplex system whose inner workings are largely a mystery.
Why?Although ever-improving technologies yield massive data related tothe system!!!
I Only a fraction of it is relevant!!! Question Which one?
8 / 48
Brenner’s Criticism
An unlikely pairSystem Biology - a computational and mathematical modeling ofcomplex biological systemsArtificial Intelligence - attempts for “intelligence” in machines
ProblemBoth face the same fundamental task of reverse-engineering a highlycomplex system whose inner workings are largely a mystery.
Why?Although ever-improving technologies yield massive data related tothe system!!!
I Only a fraction of it is relevant!!! Question Which one?
8 / 48
Brenner’s Criticism
An unlikely pairSystem Biology - a computational and mathematical modeling ofcomplex biological systemsArtificial Intelligence - attempts for “intelligence” in machines
ProblemBoth face the same fundamental task of reverse-engineering a highlycomplex system whose inner workings are largely a mystery.
Why?Although ever-improving technologies yield massive data related tothe system!!!
I Only a fraction of it is relevant!!! Question Which one?
8 / 48
Brenner’s Criticism
An unlikely pairSystem Biology - a computational and mathematical modeling ofcomplex biological systemsArtificial Intelligence - attempts for “intelligence” in machines
ProblemBoth face the same fundamental task of reverse-engineering a highlycomplex system whose inner workings are largely a mystery.
Why?Although ever-improving technologies yield massive data related tothe system!!!
I Only a fraction of it is relevant!!! Question Which one?
8 / 48
Brenner’s Criticism
An unlikely pairSystem Biology - a computational and mathematical modeling ofcomplex biological systemsArtificial Intelligence - attempts for “intelligence” in machines
ProblemBoth face the same fundamental task of reverse-engineering a highlycomplex system whose inner workings are largely a mystery.
Why?Although ever-improving technologies yield massive data related tothe system!!!
I Only a fraction of it is relevant!!! Question Which one?
8 / 48
This is good but....
The ControversyIt will keep raging for the foreseeable future!!!
Therefore, we will use this classificationSystems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
9 / 48
This is good but....
The ControversyIt will keep raging for the foreseeable future!!!
Therefore, we will use this classificationSystems that think like humans Systems that think rationally
Systems that act like humans Systems that act rationally
9 / 48
Instead
We will look at this classificationAs a way to human beings try to solve problems...
Thus, we have the following new hierarchySystems that think like humans Systems that think rationallySolving problems as humans Solving problems using logic
Basically Solving Problems
⇓Systems that act like humans Systems that act rationally
Resulting of solving problems as humans Resulting of solving problems logically
10 / 48
Instead
We will look at this classificationAs a way to human beings try to solve problems...
Thus, we have the following new hierarchySystems that think like humans Systems that think rationallySolving problems as humans Solving problems using logic
Basically Solving Problems
⇓Systems that act like humans Systems that act rationally
Resulting of solving problems as humans Resulting of solving problems logically
10 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
11 / 48
The Turing Test
You have...I A human judge engages in a
natural language conversationwith one human and onemachine, each of which triesto appear human.
I All participants are placed inisolated locations.
I If the judge cannot reliablytell the machine from thehuman, the machine is said tohave passed the test.
12 / 48
The Turing Test
You have...I A human judge engages in a
natural language conversationwith one human and onemachine, each of which triesto appear human.
I All participants are placed inisolated locations.
I If the judge cannot reliablytell the machine from thehuman, the machine is said tohave passed the test.
12 / 48
The Turing Test
You have...I A human judge engages in a
natural language conversationwith one human and onemachine, each of which triesto appear human.
I All participants are placed inisolated locations.
I If the judge cannot reliablytell the machine from thehuman, the machine is said tohave passed the test.
12 / 48
The Turing Test
You have...I A human judge engages in a
natural language conversationwith one human and onemachine, each of which triesto appear human.
I All participants are placed inisolated locations.
I If the judge cannot reliablytell the machine from thehuman, the machine is said tohave passed the test.
12 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
13 / 48
Implications of the Turing Test
Passing the Turing Test has implications in the following fieldsNatural Language Processing
I The machine needs to understand what you are saying.
Knowledge representationI A precise talk needs a good knowledge representation of the subject.
Automated ReasoningI Without logic who cares what are you saying
Machine LearningI Learn to adapt depending on the data.
14 / 48
Implications of the Turing Test
Passing the Turing Test has implications in the following fieldsNatural Language Processing
I The machine needs to understand what you are saying.
Knowledge representationI A precise talk needs a good knowledge representation of the subject.
Automated ReasoningI Without logic who cares what are you saying
Machine LearningI Learn to adapt depending on the data.
14 / 48
Implications of the Turing Test
Passing the Turing Test has implications in the following fieldsNatural Language Processing
I The machine needs to understand what you are saying.
Knowledge representationI A precise talk needs a good knowledge representation of the subject.
Automated ReasoningI Without logic who cares what are you saying
Machine LearningI Learn to adapt depending on the data.
14 / 48
Implications of the Turing Test
Passing the Turing Test has implications in the following fieldsNatural Language Processing
I The machine needs to understand what you are saying.
Knowledge representationI A precise talk needs a good knowledge representation of the subject.
Automated ReasoningI Without logic who cares what are you saying
Machine LearningI Learn to adapt depending on the data.
14 / 48
Total Turing Test’s Implication
Total Turing TestIt uses a video signal so that the interrogator can test the subject’sperceptual abilities.
Computer VisionI It is used to perceive objects.
RoboticsI A way to manipulate objects and to move in the environment
15 / 48
Total Turing Test’s Implication
Total Turing TestIt uses a video signal so that the interrogator can test the subject’sperceptual abilities.
Computer VisionI It is used to perceive objects.
RoboticsI A way to manipulate objects and to move in the environment
15 / 48
Total Turing Test’s Implication
Total Turing TestIt uses a video signal so that the interrogator can test the subject’sperceptual abilities.
Computer VisionI It is used to perceive objects.
RoboticsI A way to manipulate objects and to move in the environment
15 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
16 / 48
Is the Turing Test Relevant?
Some researchers have pointed out that the Turing test is not enoughto talk about intelligent machines.
In the most extreme John Searle, professor of philosophy at UCBerkeley published “The Chinese Room” paper.He claimed that Strong AI is not even possible!!!
17 / 48
Is the Turing Test Relevant?
Some researchers have pointed out that the Turing test is not enoughto talk about intelligent machines.
In the most extreme John Searle, professor of philosophy at UCBerkeley published “The Chinese Room” paper.He claimed that Strong AI is not even possible!!!
17 / 48
Recently
Eugene GoostmanThe computer program designed by a team of Russian and Ukrainianprogrammers.
Against 30 JudgesIt was able to fool them 33% of the time
HoweverGraeme Hirst (University of Toronto) et al. dismissed the test because theTuring Test requires 50%.
18 / 48
Recently
Eugene GoostmanThe computer program designed by a team of Russian and Ukrainianprogrammers.
Against 30 JudgesIt was able to fool them 33% of the time
HoweverGraeme Hirst (University of Toronto) et al. dismissed the test because theTuring Test requires 50%.
18 / 48
Recently
Eugene GoostmanThe computer program designed by a team of Russian and Ukrainianprogrammers.
Against 30 JudgesIt was able to fool them 33% of the time
HoweverGraeme Hirst (University of Toronto) et al. dismissed the test because theTuring Test requires 50%.
18 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
19 / 48
Thinking Humanly: Cognitive ApproachSome Rea searchers
They think that we should understand the human mind.I Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing thisThought’s InspectionPsychological experimentsBrain Imaging
I Also known as Cognitive Brain Imaging...
ExampleNewell and Simon used the traces of their General ProblemSolver (GPS) to compare the traces generated by humansubjects when solving the same problem.
20 / 48
Thinking Humanly: Cognitive ApproachSome Rea searchers
They think that we should understand the human mind.I Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing thisThought’s InspectionPsychological experimentsBrain Imaging
I Also known as Cognitive Brain Imaging...
ExampleNewell and Simon used the traces of their General ProblemSolver (GPS) to compare the traces generated by humansubjects when solving the same problem.
20 / 48
Thinking Humanly: Cognitive ApproachSome Rea searchers
They think that we should understand the human mind.I Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing thisThought’s InspectionPsychological experimentsBrain Imaging
I Also known as Cognitive Brain Imaging...
ExampleNewell and Simon used the traces of their General ProblemSolver (GPS) to compare the traces generated by humansubjects when solving the same problem.
20 / 48
Thinking Humanly: Cognitive ApproachSome Rea searchers
They think that we should understand the human mind.I Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing thisThought’s InspectionPsychological experimentsBrain Imaging
I Also known as Cognitive Brain Imaging...
ExampleNewell and Simon used the traces of their General ProblemSolver (GPS) to compare the traces generated by humansubjects when solving the same problem.
20 / 48
Thinking Humanly: Cognitive ApproachSome Rea searchers
They think that we should understand the human mind.I Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing thisThought’s InspectionPsychological experimentsBrain Imaging
I Also known as Cognitive Brain Imaging...
ExampleNewell and Simon used the traces of their General ProblemSolver (GPS) to compare the traces generated by humansubjects when solving the same problem.
20 / 48
Thinking Humanly: Cognitive ApproachSome Rea searchers
They think that we should understand the human mind.I Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing thisThought’s InspectionPsychological experimentsBrain Imaging
I Also known as Cognitive Brain Imaging...
ExampleNewell and Simon used the traces of their General ProblemSolver (GPS) to compare the traces generated by humansubjects when solving the same problem.
20 / 48
Thinking Humanly: Cognitive ApproachSome Rea searchers
They think that we should understand the human mind.I Question: Understanding how the human mind solve problems and
react to the environment?
Three ways of doing thisThought’s InspectionPsychological experimentsBrain Imaging
I Also known as Cognitive Brain Imaging...
ExampleNewell and Simon used the traces of their General ProblemSolver (GPS) to compare the traces generated by humansubjects when solving the same problem.
20 / 48
Drawbacks of the Cognitive ApproachThought’s Inspection
To do this is quite difficult because you require snapshots of thethought process...
Psychological experimentsStatistics are quite iffy!!!Reproducibility Problems!!!Bias Problems!!!
Cognitive Brain ImagingResolution problem
I PET and MRI work at the range of mm, but you have in a cubicmm 1,000,000 neurons!!!
Difference Between IndividualsReproducibility and Replication Problems
21 / 48
Drawbacks of the Cognitive ApproachThought’s Inspection
To do this is quite difficult because you require snapshots of thethought process...
Psychological experimentsStatistics are quite iffy!!!Reproducibility Problems!!!Bias Problems!!!
Cognitive Brain ImagingResolution problem
I PET and MRI work at the range of mm, but you have in a cubicmm 1,000,000 neurons!!!
Difference Between IndividualsReproducibility and Replication Problems
21 / 48
Drawbacks of the Cognitive ApproachThought’s Inspection
To do this is quite difficult because you require snapshots of thethought process...
Psychological experimentsStatistics are quite iffy!!!Reproducibility Problems!!!Bias Problems!!!
Cognitive Brain ImagingResolution problem
I PET and MRI work at the range of mm, but you have in a cubicmm 1,000,000 neurons!!!
Difference Between IndividualsReproducibility and Replication Problems
21 / 48
Drawbacks of the Cognitive ApproachThought’s Inspection
To do this is quite difficult because you require snapshots of thethought process...
Psychological experimentsStatistics are quite iffy!!!Reproducibility Problems!!!Bias Problems!!!
Cognitive Brain ImagingResolution problem
I PET and MRI work at the range of mm, but you have in a cubicmm 1,000,000 neurons!!!
Difference Between IndividualsReproducibility and Replication Problems
21 / 48
Drawbacks of the Cognitive ApproachThought’s Inspection
To do this is quite difficult because you require snapshots of thethought process...
Psychological experimentsStatistics are quite iffy!!!Reproducibility Problems!!!Bias Problems!!!
Cognitive Brain ImagingResolution problem
I PET and MRI work at the range of mm, but you have in a cubicmm 1,000,000 neurons!!!
Difference Between IndividualsReproducibility and Replication Problems
21 / 48
Drawbacks of the Cognitive ApproachThought’s Inspection
To do this is quite difficult because you require snapshots of thethought process...
Psychological experimentsStatistics are quite iffy!!!Reproducibility Problems!!!Bias Problems!!!
Cognitive Brain ImagingResolution problem
I PET and MRI work at the range of mm, but you have in a cubicmm 1,000,000 neurons!!!
Difference Between IndividualsReproducibility and Replication Problems
21 / 48
Drawbacks of the Cognitive ApproachThought’s Inspection
To do this is quite difficult because you require snapshots of thethought process...
Psychological experimentsStatistics are quite iffy!!!Reproducibility Problems!!!Bias Problems!!!
Cognitive Brain ImagingResolution problem
I PET and MRI work at the range of mm, but you have in a cubicmm 1,000,000 neurons!!!
Difference Between IndividualsReproducibility and Replication Problems
21 / 48
Drawbacks of the Cognitive ApproachThought’s Inspection
To do this is quite difficult because you require snapshots of thethought process...
Psychological experimentsStatistics are quite iffy!!!Reproducibility Problems!!!Bias Problems!!!
Cognitive Brain ImagingResolution problem
I PET and MRI work at the range of mm, but you have in a cubicmm 1,000,000 neurons!!!
Difference Between IndividualsReproducibility and Replication Problems
21 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
22 / 48
Thinking Rationally: Use of Logic
Development of the formal logic in thelate 19th and early 20th century hasgive us:PROBLEM!!!
What?A precise notation about allkinds of thing in the worldand their relations betweenthem.
23 / 48
Thinking Rationally: Use of Logic
Development of the formal logic in thelate 19th and early 20th century hasgive us:PROBLEM!!!
What?It is not easy to takeinformal knowledge andstate in the way thelogical system need it.There is a big adifference between beingable to solve a problemin principle and doing inpractice.
23 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
24 / 48
Acting Rationally
Rational AgentsIn this approach, the agent acts so it can achieve its goals, givencertain beliefs about the environment.
It needs toIt needs to be able to make inferences.It needs to be able to act without inferences (HeuristicTriggers).
Norving and Company claim!!!It is more amenable to scientific development than
I Human behavior based models.I Human though based models.I After all the standards of rationality are clearly defined.
25 / 48
Acting Rationally
Rational AgentsIn this approach, the agent acts so it can achieve its goals, givencertain beliefs about the environment.
It needs toIt needs to be able to make inferences.It needs to be able to act without inferences (HeuristicTriggers).
Norving and Company claim!!!It is more amenable to scientific development than
I Human behavior based models.I Human though based models.I After all the standards of rationality are clearly defined.
25 / 48
Acting Rationally
Rational AgentsIn this approach, the agent acts so it can achieve its goals, givencertain beliefs about the environment.
It needs toIt needs to be able to make inferences.It needs to be able to act without inferences (HeuristicTriggers).
Norving and Company claim!!!It is more amenable to scientific development than
I Human behavior based models.I Human though based models.I After all the standards of rationality are clearly defined.
25 / 48
Acting Rationally
Rational AgentsIn this approach, the agent acts so it can achieve its goals, givencertain beliefs about the environment.
It needs toIt needs to be able to make inferences.It needs to be able to act without inferences (HeuristicTriggers).
Norving and Company claim!!!It is more amenable to scientific development than
I Human behavior based models.I Human though based models.I After all the standards of rationality are clearly defined.
25 / 48
Acting Rationally
Rational AgentsIn this approach, the agent acts so it can achieve its goals, givencertain beliefs about the environment.
It needs toIt needs to be able to make inferences.It needs to be able to act without inferences (HeuristicTriggers).
Norving and Company claim!!!It is more amenable to scientific development than
I Human behavior based models.I Human though based models.I After all the standards of rationality are clearly defined.
25 / 48
Acting Rationally
Rational AgentsIn this approach, the agent acts so it can achieve its goals, givencertain beliefs about the environment.
It needs toIt needs to be able to make inferences.It needs to be able to act without inferences (HeuristicTriggers).
Norving and Company claim!!!It is more amenable to scientific development than
I Human behavior based models.I Human though based models.I After all the standards of rationality are clearly defined.
25 / 48
Acting Rationally
Rational AgentsIn this approach, the agent acts so it can achieve its goals, givencertain beliefs about the environment.
It needs toIt needs to be able to make inferences.It needs to be able to act without inferences (HeuristicTriggers).
Norving and Company claim!!!It is more amenable to scientific development than
I Human behavior based models.I Human though based models.I After all the standards of rationality are clearly defined.
25 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
26 / 48
Strong AI vs. Weak AI
Strong AIStrong AI is artificial intelligence that matches or exceedshuman intelligence.
Weak AIWeak AI system which is not intended to match or exceed thecapabilities of human beings.
27 / 48
Strong AI vs. Weak AI
Strong AIStrong AI is artificial intelligence that matches or exceedshuman intelligence.
Weak AIWeak AI system which is not intended to match or exceed thecapabilities of human beings.
27 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
28 / 48
Arguments Against Strong AI
ArgumentsThe first argument against strong AI is that it is impossible forthem to feel emotions.The second argument against strong AI is that them cannotexperience consciousness.The third argument against strong AI is that machines neverunderstand the meaning of their processing.The fourth argument against strong AI is that machines cannothave free will.The fifth argument against strong AI is that God createdhumans as intelligent persons.
29 / 48
Arguments Against Strong AI
ArgumentsThe first argument against strong AI is that it is impossible forthem to feel emotions.The second argument against strong AI is that them cannotexperience consciousness.The third argument against strong AI is that machines neverunderstand the meaning of their processing.The fourth argument against strong AI is that machines cannothave free will.The fifth argument against strong AI is that God createdhumans as intelligent persons.
29 / 48
Arguments Against Strong AI
ArgumentsThe first argument against strong AI is that it is impossible forthem to feel emotions.The second argument against strong AI is that them cannotexperience consciousness.The third argument against strong AI is that machines neverunderstand the meaning of their processing.The fourth argument against strong AI is that machines cannothave free will.The fifth argument against strong AI is that God createdhumans as intelligent persons.
29 / 48
Arguments Against Strong AI
ArgumentsThe first argument against strong AI is that it is impossible forthem to feel emotions.The second argument against strong AI is that them cannotexperience consciousness.The third argument against strong AI is that machines neverunderstand the meaning of their processing.The fourth argument against strong AI is that machines cannothave free will.The fifth argument against strong AI is that God createdhumans as intelligent persons.
29 / 48
Arguments Against Strong AI
ArgumentsThe first argument against strong AI is that it is impossible forthem to feel emotions.The second argument against strong AI is that them cannotexperience consciousness.The third argument against strong AI is that machines neverunderstand the meaning of their processing.The fourth argument against strong AI is that machines cannothave free will.The fifth argument against strong AI is that God createdhumans as intelligent persons.
29 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
30 / 48
What is this?
Chinese RoomThe Chinese room was introduced in Searle’s 1980 paper "Minds, Brains,and Programs", published in Behavioral and BrainSciences.
Something NotableIt eventually became the journal’s "most influential target article".It is still generating an enormous number of commentaries andresponses.
David Cole, Philosophy Professor at University of Minnesota Duluth“The Chinese Room argument has probably been the most widelydiscussed philosophical argument in cognitive science to appear in the past25 years"
31 / 48
What is this?
Chinese RoomThe Chinese room was introduced in Searle’s 1980 paper "Minds, Brains,and Programs", published in Behavioral and BrainSciences.
Something NotableIt eventually became the journal’s "most influential target article".It is still generating an enormous number of commentaries andresponses.
David Cole, Philosophy Professor at University of Minnesota Duluth“The Chinese Room argument has probably been the most widelydiscussed philosophical argument in cognitive science to appear in the past25 years"
31 / 48
What is this?
Chinese RoomThe Chinese room was introduced in Searle’s 1980 paper "Minds, Brains,and Programs", published in Behavioral and BrainSciences.
Something NotableIt eventually became the journal’s "most influential target article".It is still generating an enormous number of commentaries andresponses.
David Cole, Philosophy Professor at University of Minnesota Duluth“The Chinese Room argument has probably been the most widelydiscussed philosophical argument in cognitive science to appear in the past25 years"
31 / 48
What is this?
Chinese RoomThe Chinese room was introduced in Searle’s 1980 paper "Minds, Brains,and Programs", published in Behavioral and BrainSciences.
Something NotableIt eventually became the journal’s "most influential target article".It is still generating an enormous number of commentaries andresponses.
David Cole, Philosophy Professor at University of Minnesota Duluth“The Chinese Room argument has probably been the most widelydiscussed philosophical argument in cognitive science to appear in the past25 years"
31 / 48
Against Strong AISearle’s Experiment
Suppose that artificial intelligence research has succeeded inconstructing a computer that behaves as if it understandsChinese.Suppose, says Searle, that this computer performs its task soconvincingly that it comfortably passes the Turing test inChinese.Now, a human is in a closed room and that he has a book withan English version of the aforementioned computer program.
ThenThen, a human are given Questions in Chinese, and He or Shesimply answers them using the book.
Question!Does He/She understand Chinese?
32 / 48
Against Strong AISearle’s Experiment
Suppose that artificial intelligence research has succeeded inconstructing a computer that behaves as if it understandsChinese.Suppose, says Searle, that this computer performs its task soconvincingly that it comfortably passes the Turing test inChinese.Now, a human is in a closed room and that he has a book withan English version of the aforementioned computer program.
ThenThen, a human are given Questions in Chinese, and He or Shesimply answers them using the book.
Question!Does He/She understand Chinese?
32 / 48
Against Strong AISearle’s Experiment
Suppose that artificial intelligence research has succeeded inconstructing a computer that behaves as if it understandsChinese.Suppose, says Searle, that this computer performs its task soconvincingly that it comfortably passes the Turing test inChinese.Now, a human is in a closed room and that he has a book withan English version of the aforementioned computer program.
ThenThen, a human are given Questions in Chinese, and He or Shesimply answers them using the book.
Question!Does He/She understand Chinese?
32 / 48
Against Strong AISearle’s Experiment
Suppose that artificial intelligence research has succeeded inconstructing a computer that behaves as if it understandsChinese.Suppose, says Searle, that this computer performs its task soconvincingly that it comfortably passes the Turing test inChinese.Now, a human is in a closed room and that he has a book withan English version of the aforementioned computer program.
ThenThen, a human are given Questions in Chinese, and He or Shesimply answers them using the book.
Question!Does He/She understand Chinese?
32 / 48
IMPORTANT
The Chinese RoomIt is the most damaging argument against “Strong AI”!!!
Even with the criticism against itIt is still a lingering question that the people in AI still cannnot answer!!!
33 / 48
IMPORTANT
The Chinese RoomIt is the most damaging argument against “Strong AI”!!!
Even with the criticism against itIt is still a lingering question that the people in AI still cannnot answer!!!
33 / 48
Funny Observations
Something NotableMost of the discussion consists of attempts to refute it.
Something Notable"The overwhelming majority," notes BBS editor Stevan Harnad, "still thinkthat the Chinese Room Argument is dead wrong."
It is more, Pat Hayes - An important AI researcher pointed out thatCognitive science ought to be redefined as "the ongoing research programof showing Searle’s Chinese Room Argument to be false"
34 / 48
Funny Observations
Something NotableMost of the discussion consists of attempts to refute it.
Something Notable"The overwhelming majority," notes BBS editor Stevan Harnad, "still thinkthat the Chinese Room Argument is dead wrong."
It is more, Pat Hayes - An important AI researcher pointed out thatCognitive science ought to be redefined as "the ongoing research programof showing Searle’s Chinese Room Argument to be false"
34 / 48
Funny Observations
Something NotableMost of the discussion consists of attempts to refute it.
Something Notable"The overwhelming majority," notes BBS editor Stevan Harnad, "still thinkthat the Chinese Room Argument is dead wrong."
It is more, Pat Hayes - An important AI researcher pointed out thatCognitive science ought to be redefined as "the ongoing research programof showing Searle’s Chinese Room Argument to be false"
34 / 48
There is even a novel (Hugo Award Finalist)Blindsight
A hard science fiction novelBy PhD Marine-Mammal biologist Petter Watts
Cover
35 / 48
Where
The human race confronts its first contact with terrifyingconsequences:
Conscious is not necessary... and the universe is full withnon-conscious intelligence!!!And the only way to survive is to allow an Hominid Vampire Branch(non-conscious) to exterminate the rest!!!
36 / 48
Where
The human race confronts its first contact with terrifyingconsequences:
Conscious is not necessary... and the universe is full withnon-conscious intelligence!!!And the only way to survive is to allow an Hominid Vampire Branch(non-conscious) to exterminate the rest!!!
36 / 48
Other Arguments Against AIThere are other people
Penrose’s ArgumentI In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.I Highly Criticized because of the following claims
HowUsing a variant of the Turing’s Halting Problem to demonstrate that a system canbe deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finerscale activities inside brain neurons (Orch-OR theory).
HoweverA discovery of quantum vibrations in microtubules by AnirbanBandyopadhyay of the National Institute for Materials Science in Japan.
I It “could” confirm the hypothesis of Orch-OR theory.37 / 48
Other Arguments Against AIThere are other people
Penrose’s ArgumentI In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.I Highly Criticized because of the following claims
HowUsing a variant of the Turing’s Halting Problem to demonstrate that a system canbe deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finerscale activities inside brain neurons (Orch-OR theory).
HoweverA discovery of quantum vibrations in microtubules by AnirbanBandyopadhyay of the National Institute for Materials Science in Japan.
I It “could” confirm the hypothesis of Orch-OR theory.37 / 48
Other Arguments Against AIThere are other people
Penrose’s ArgumentI In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.I Highly Criticized because of the following claims
HowUsing a variant of the Turing’s Halting Problem to demonstrate that a system canbe deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finerscale activities inside brain neurons (Orch-OR theory).
HoweverA discovery of quantum vibrations in microtubules by AnirbanBandyopadhyay of the National Institute for Materials Science in Japan.
I It “could” confirm the hypothesis of Orch-OR theory.37 / 48
Other Arguments Against AIThere are other people
Penrose’s ArgumentI In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.I Highly Criticized because of the following claims
HowUsing a variant of the Turing’s Halting Problem to demonstrate that a system canbe deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finerscale activities inside brain neurons (Orch-OR theory).
HoweverA discovery of quantum vibrations in microtubules by AnirbanBandyopadhyay of the National Institute for Materials Science in Japan.
I It “could” confirm the hypothesis of Orch-OR theory.37 / 48
Other Arguments Against AIThere are other people
Penrose’s ArgumentI In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.I Highly Criticized because of the following claims
HowUsing a variant of the Turing’s Halting Problem to demonstrate that a system canbe deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finerscale activities inside brain neurons (Orch-OR theory).
HoweverA discovery of quantum vibrations in microtubules by AnirbanBandyopadhyay of the National Institute for Materials Science in Japan.
I It “could” confirm the hypothesis of Orch-OR theory.37 / 48
Other Arguments Against AIThere are other people
Penrose’s ArgumentI In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.I Highly Criticized because of the following claims
HowUsing a variant of the Turing’s Halting Problem to demonstrate that a system canbe deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finerscale activities inside brain neurons (Orch-OR theory).
HoweverA discovery of quantum vibrations in microtubules by AnirbanBandyopadhyay of the National Institute for Materials Science in Japan.
I It “could” confirm the hypothesis of Orch-OR theory.37 / 48
Other Arguments Against AIThere are other people
Penrose’s ArgumentI In “The Emperor’s New Mind (1989),” he argues that known laws of
physics are inadequate to explain the phenomenon of consciousness.I Highly Criticized because of the following claims
HowUsing a variant of the Turing’s Halting Problem to demonstrate that a system canbe deterministic without being algorithmic.
In addition, he claimed that consciousness derives from deeper level, finerscale activities inside brain neurons (Orch-OR theory).
HoweverA discovery of quantum vibrations in microtubules by AnirbanBandyopadhyay of the National Institute for Materials Science in Japan.
I It “could” confirm the hypothesis of Orch-OR theory.37 / 48
For more, read...
ArticleHameroff, Stuart; Roger Penrose (2014). "Consciousness in the universe:A review of the ’Orch OR’ theory". Physics of Life Reviews 11 (1): 39–78.
38 / 48
Book based in this theory: Hyperion
Hyperion By Dan Simmons - Here a Nucleus of AI are trying to usehuman brains to simulate their own consciousness
39 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
40 / 48
History of AI: In the Beginning
Antiquity:Greek myths of Hephaestus and Pygmalion incorporated theidea of intelligent robots (such as Talos) and artificial beings(such as Galatea and Pandora).Sacred mechanical statues built in Egypt.
384-322 B.C.Aristoteles described the syllogism a method of mechanicalthought.
800 A.D.Jabir ibn Hayyan develops the Arabic alchemical theory ofTakwin, the artificial creation of life in the laboratory.
41 / 48
History of AI: In the Beginning
Antiquity:Greek myths of Hephaestus and Pygmalion incorporated theidea of intelligent robots (such as Talos) and artificial beings(such as Galatea and Pandora).Sacred mechanical statues built in Egypt.
384-322 B.C.Aristoteles described the syllogism a method of mechanicalthought.
800 A.D.Jabir ibn Hayyan develops the Arabic alchemical theory ofTakwin, the artificial creation of life in the laboratory.
41 / 48
History of AI: In the Beginning
Antiquity:Greek myths of Hephaestus and Pygmalion incorporated theidea of intelligent robots (such as Talos) and artificial beings(such as Galatea and Pandora).Sacred mechanical statues built in Egypt.
384-322 B.C.Aristoteles described the syllogism a method of mechanicalthought.
800 A.D.Jabir ibn Hayyan develops the Arabic alchemical theory ofTakwin, the artificial creation of life in the laboratory.
41 / 48
In the Beginning
1206Al-Jazari created a programmable orchestra of mechanicalhuman beings.
1495-1500Paracelsus claimed to have created an artificial man out ofmagnetism, sperm and alchemy.Leonardo created Robots for Ludovico Sforza.
42 / 48
In the Beginning
1206Al-Jazari created a programmable orchestra of mechanicalhuman beings.
1495-1500Paracelsus claimed to have created an artificial man out ofmagnetism, sperm and alchemy.Leonardo created Robots for Ludovico Sforza.
42 / 48
In the Beginning
1206Al-Jazari created a programmable orchestra of mechanicalhuman beings.
1495-1500Paracelsus claimed to have created an artificial man out ofmagnetism, sperm and alchemy.Leonardo created Robots for Ludovico Sforza.
42 / 48
In the Beginning
1206Al-Jazari created a programmable orchestra of mechanicalhuman beings.
1495-1500Paracelsus claimed to have created an artificial man out ofmagnetism, sperm and alchemy.Leonardo created Robots for Ludovico Sforza.
42 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
43 / 48
Modern Times1600 -1650
John Napier discovered logarithms.Wilhelm Schickard created the first mechanical calculatingmachine.Pascal developed the first real calculator. Addition andsubtraction were carried out by using a series of very lightrotating wheels. His system is still used today in car odometerswhich track a car’s mileage.
1818Mary Shelley published the story of Frankenstein.
1822-1859Charles Babbage & Ada Lovelace worked on programmablemechanical calculating machines.
44 / 48
Modern Times1600 -1650
John Napier discovered logarithms.Wilhelm Schickard created the first mechanical calculatingmachine.Pascal developed the first real calculator. Addition andsubtraction were carried out by using a series of very lightrotating wheels. His system is still used today in car odometerswhich track a car’s mileage.
1818Mary Shelley published the story of Frankenstein.
1822-1859Charles Babbage & Ada Lovelace worked on programmablemechanical calculating machines.
44 / 48
Modern Times1600 -1650
John Napier discovered logarithms.Wilhelm Schickard created the first mechanical calculatingmachine.Pascal developed the first real calculator. Addition andsubtraction were carried out by using a series of very lightrotating wheels. His system is still used today in car odometerswhich track a car’s mileage.
1818Mary Shelley published the story of Frankenstein.
1822-1859Charles Babbage & Ada Lovelace worked on programmablemechanical calculating machines.
44 / 48
Modern Times1600 -1650
John Napier discovered logarithms.Wilhelm Schickard created the first mechanical calculatingmachine.Pascal developed the first real calculator. Addition andsubtraction were carried out by using a series of very lightrotating wheels. His system is still used today in car odometerswhich track a car’s mileage.
1818Mary Shelley published the story of Frankenstein.
1822-1859Charles Babbage & Ada Lovelace worked on programmablemechanical calculating machines.
44 / 48
Modern Times1600 -1650
John Napier discovered logarithms.Wilhelm Schickard created the first mechanical calculatingmachine.Pascal developed the first real calculator. Addition andsubtraction were carried out by using a series of very lightrotating wheels. His system is still used today in car odometerswhich track a car’s mileage.
1818Mary Shelley published the story of Frankenstein.
1822-1859Charles Babbage & Ada Lovelace worked on programmablemechanical calculating machines.
44 / 48
Modern Times
1861Paul Broca, Camillo Golgi and Ramon y Cajal discover thestructure of the brain
1917Karel Capek coins the term ‘robot.’
1938John von Neuman’s minimax theorem.
1950Alan Turing proposes the Turing Test as a measure of machineintelligence.
45 / 48
Modern Times
1861Paul Broca, Camillo Golgi and Ramon y Cajal discover thestructure of the brain
1917Karel Capek coins the term ‘robot.’
1938John von Neuman’s minimax theorem.
1950Alan Turing proposes the Turing Test as a measure of machineintelligence.
45 / 48
Modern Times
1861Paul Broca, Camillo Golgi and Ramon y Cajal discover thestructure of the brain
1917Karel Capek coins the term ‘robot.’
1938John von Neuman’s minimax theorem.
1950Alan Turing proposes the Turing Test as a measure of machineintelligence.
45 / 48
Modern Times
1861Paul Broca, Camillo Golgi and Ramon y Cajal discover thestructure of the brain
1917Karel Capek coins the term ‘robot.’
1938John von Neuman’s minimax theorem.
1950Alan Turing proposes the Turing Test as a measure of machineintelligence.
45 / 48
Modern Times1956-1974
The Golden Years – The Promise of an intelligent MachineI Movies like “The Forbin Project” promised computers with Strong AI
1974-1980First AI winter
I It is shown that many problems in AI are NP-Complete.I Many projects are stopped in AI.
1980-1987AI Revival Experts Systems, Knowledge Revolution.
1987-1993Second AI winter
I Fall of the Expert System Market and the LISP Machines.
46 / 48
Modern Times1956-1974
The Golden Years – The Promise of an intelligent MachineI Movies like “The Forbin Project” promised computers with Strong AI
1974-1980First AI winter
I It is shown that many problems in AI are NP-Complete.I Many projects are stopped in AI.
1980-1987AI Revival Experts Systems, Knowledge Revolution.
1987-1993Second AI winter
I Fall of the Expert System Market and the LISP Machines.
46 / 48
Modern Times1956-1974
The Golden Years – The Promise of an intelligent MachineI Movies like “The Forbin Project” promised computers with Strong AI
1974-1980First AI winter
I It is shown that many problems in AI are NP-Complete.I Many projects are stopped in AI.
1980-1987AI Revival Experts Systems, Knowledge Revolution.
1987-1993Second AI winter
I Fall of the Expert System Market and the LISP Machines.
46 / 48
Modern Times1956-1974
The Golden Years – The Promise of an intelligent MachineI Movies like “The Forbin Project” promised computers with Strong AI
1974-1980First AI winter
I It is shown that many problems in AI are NP-Complete.I Many projects are stopped in AI.
1980-1987AI Revival Experts Systems, Knowledge Revolution.
1987-1993Second AI winter
I Fall of the Expert System Market and the LISP Machines.
46 / 48
Modern Times1956-1974
The Golden Years – The Promise of an intelligent MachineI Movies like “The Forbin Project” promised computers with Strong AI
1974-1980First AI winter
I It is shown that many problems in AI are NP-Complete.I Many projects are stopped in AI.
1980-1987AI Revival Experts Systems, Knowledge Revolution.
1987-1993Second AI winter
I Fall of the Expert System Market and the LISP Machines.
46 / 48
Modern Times1956-1974
The Golden Years – The Promise of an intelligent MachineI Movies like “The Forbin Project” promised computers with Strong AI
1974-1980First AI winter
I It is shown that many problems in AI are NP-Complete.I Many projects are stopped in AI.
1980-1987AI Revival Experts Systems, Knowledge Revolution.
1987-1993Second AI winter
I Fall of the Expert System Market and the LISP Machines.
46 / 48
Modern Times1956-1974
The Golden Years – The Promise of an intelligent MachineI Movies like “The Forbin Project” promised computers with Strong AI
1974-1980First AI winter
I It is shown that many problems in AI are NP-Complete.I Many projects are stopped in AI.
1980-1987AI Revival Experts Systems, Knowledge Revolution.
1987-1993Second AI winter
I Fall of the Expert System Market and the LISP Machines.
46 / 48
Modern Times1956-1974
The Golden Years – The Promise of an intelligent MachineI Movies like “The Forbin Project” promised computers with Strong AI
1974-1980First AI winter
I It is shown that many problems in AI are NP-Complete.I Many projects are stopped in AI.
1980-1987AI Revival Experts Systems, Knowledge Revolution.
1987-1993Second AI winter
I Fall of the Expert System Market and the LISP Machines.
46 / 48
Outline
1 MotivationWhat is Artificial Intelligence?Acting humanly: The Turing Test ApproachImplications of the Turing TestSome Issues About the Turing TestThinking HumanlyThinking Rationally: Use of LogicAct Rationally
2 Strong AI vs. Weak AIDefinitionAgainst Strong AISearle’s Chinese Room
3 History of AIThe Long DreamModern TimesFragmentation Years
47 / 48
Present - The Fragmentation Years - AI is still goingthrough a Winter
1993-PresentThe Fragmentation Years
I Computer VisionI RoboticsI Machine LearningI Fuzzy LogicI Bayesian NetworksI Evolutionary MethodsI etc
48 / 48
Recommended