1. Artificial Intelligence and Hierarchical Temporal Memory By E. Seamus O’Dunn November 28, 2012

2. Overview What is Artificial Intelligence? What is Artificial Intelligence? History History Strong AI vs. Weak AI Strong AI vs. Weak AI Recent Accomplishments Recent Accomplishments Knowledge Representation and HTM Knowledge Representation and HTM Intelligence vs. Consciousness Intelligence vs. Consciousness

3. What is Artificial Intelligence?

4. The goal of work in artificial intelligence is to build machines that perform tasks normally requiring human intelligence. -Nils J. Nelson

5. What is Artificial Intelligence? AI is a collective name for problems which we do not yet know how to solve properly by computer. -Donald Michie

6. History of AI

7. Turing Machine In 1937 a mathematician named Alan Turing published a paper on the computable numbers. In 1937 a mathematician named Alan Turing published a paper on the computable numbers. As a thought experiment, Turing describes a machine that writes, reads, and alters symbols on a infinitely long paper tape. As a thought experiment, Turing describes a machine that writes, reads, and alters symbols on a infinitely long paper tape. He goes on to show that this machine with a finite set of operations can perform any computable calculation, no matter the complexity. He goes on to show that this machine with a finite set of operations can perform any computable calculation, no matter the complexity.

8. The Turing Machine

9. Turing Sparks Interest in Intelligent Machines In 1950, Turing writes another influential paper entitled Computing Machines and Intelligence. In 1950, Turing writes another influential paper entitled Computing Machines and Intelligence. The paper proposes that brains must be machines, and can therefore be emulated by computers. The paper proposes that brains must be machines, and can therefore be emulated by computers. Turing proposed the famous “Turing Test” for intelligence. Turing proposed the famous “Turing Test” for intelligence. If a machine were indistinguishable from a human based on its answers to questions, It ought to be considered intelligent. If a machine were indistinguishable from a human based on its answers to questions, It ought to be considered intelligent. This paper generated a lot of excitement and interest in Artificial Intelligence. This paper generated a lot of excitement and interest in Artificial Intelligence.

10. Dartmouth Conference on AI We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. - Dartmouth AI Project Proposal; J. McCarthyDartmouth AI Project Proposal

11. Dartmouth Conference on AI First large gathering of AI researchers. The expectations of the conference were quite ambitious and a bit naïve. First large gathering of AI researchers. The expectations of the conference were quite ambitious and a bit naïve. During the Conference, Attempts were made to create machines that could, During the Conference, Attempts were made to create machines that could, Understand short stories enough to answer questions regarding them. Understand short stories enough to answer questions regarding them. Understand language to such a degree that it could accurately translate phrases. Understand language to such a degree that it could accurately translate phrases. Navigate through a crowded room. Navigate through a crowded room. All theses tasks proved to be quite difficult. The Dartmouth conference showed no success. All theses tasks proved to be quite difficult. The Dartmouth conference showed no success. By the mid to late sixties, the field of AI had seen no real progress. Research in AI heavily declined. By the mid to late sixties, the field of AI had seen no real progress. Research in AI heavily declined. Later the missing link in early AI research was found to be one of both processing power, and knowledge representation. Later the missing link in early AI research was found to be one of both processing power, and knowledge representation.

12. Two Schools of Thought Strong AI:Weak AI:

13. Strong AI vs. Weak AI Phrases first used by John Searle Phrases first used by John Searle Weak AI: Weak AI: Computers are powerful tools used to do things humans otherwise do and to study the nature of intelligence in general. Computers are powerful tools used to do things humans otherwise do and to study the nature of intelligence in general. Strong AI: Strong AI: A computer with the right programming is a conscious mind. A computer with the right programming is a conscious mind. John Searle showed philosophical precedence for disproof of the possibility of Strong AI with his famous Chinese room argument. John Searle showed philosophical precedence for disproof of the possibility of Strong AI with his famous Chinese room argument.

14. Recent Accomplishments in AI A DARPA-funded onboard computer system from Carnegie Melon University drove a van all but 52 of the 2849 miles from Washington, DC to San Diego, averaging 63 miles per hour day and night, rain or shine. A DARPA-funded onboard computer system from Carnegie Melon University drove a van all but 52 of the 2849 miles from Washington, DC to San Diego, averaging 63 miles per hour day and night, rain or shine. Deep Blue, a chess computer built by IBM researchers, defeated world champion Gary Kasparov in a landmark performance. Deep Blue, a chess computer built by IBM researchers, defeated world champion Gary Kasparov in a landmark performance. A computer system at Argonne National Laboratories proved a long- standing mathematical conjecture about algebra using a method that would be considered creative if done by humans. A computer system at Argonne National Laboratories proved a long- standing mathematical conjecture about algebra using a method that would be considered creative if done by humans. A NASA system learned to classify very faint signals as either stars or galaxies with superhuman accuracy, by studying examples classified by experts. A NASA system learned to classify very faint signals as either stars or galaxies with superhuman accuracy, by studying examples classified by experts. Jeff Hawkins and others invented HTM systems based on neocortex models from neuroscience. Jeff Hawkins and others invented HTM systems based on neocortex models from neuroscience.

15. The Problem of Knowledge Representation

16. Knowledge Representation “Realistic performance of the computer, required that the computer program have an extremely large knowledge base. Constructing the knowledge base was problem enough and it was compounded by the problem of how to access just the contextually relevant part.” -Patricia Churchland

17. Three Problems It takes a huge amount of data to make a machine intelligent It takes a huge amount of data to make a machine intelligent We don’t know how to collect that data. We don’t know how to collect that data. Even if we did, we don’t know how to adequately define relationships within the data. Even if we did, we don’t know how to adequately define relationships within the data. These arise because computers on the most basic level are unbiased (make no assumptions). These arise because computers on the most basic level are unbiased (make no assumptions).

18. Example: A Simple Short Story Tara saw a puppy in the window. She wanted it. What did Tara want? What did Tara want? From where she is now, can she pick up the puppy? From where she is now, can she pick up the puppy? Say we want to create a program that can understand the following short story enough to answer most questions we could ask.

19. Example: Language Translation English phrase: “The spirit is willing but the flesh is weak.” English phrase: “The spirit is willing but the flesh is weak.” Direct translation to Russian and back to English transforms the phrase to: “ The vodka is good, but the meat is rotten,” Direct translation to Russian and back to English transforms the phrase to: “ The vodka is good, but the meat is rotten,” To translate correctly, the machine either needs to truly understand the meaning of the phrase or know all pairings of figures of speech across languages of interest. To translate correctly, the machine either needs to truly understand the meaning of the phrase or know all pairings of figures of speech across languages of interest. This is a huge amount of information. This is a huge amount of information.

20. Problem may be Solved Using Models from Neuroscience

21. The Neocortex Thin sheet composing the outermost layer of the mammalian brain. Thin sheet composing the outermost layer of the mammalian brain. Is involved in higher functions such as sensory perception, generation of motor commands, spatial reasoning, conscious thought, and language Is involved in higher functions such as sensory perception, generation of motor commands, spatial reasoning, conscious thought, and language Stores information using a hierarchical structure. Higher level meanings are composed of lower level meanings. Stores information using a hierarchical structure. Higher level meanings are composed of lower level meanings. Every region of the neocortex is 98% identical and processes information in the same way. The type of information that is gathered depends on which sensing organ the region is connected to Every region of the neocortex is 98% identical and processes information in the same way. The type of information that is gathered depends on which sensing organ the region is connected to

22. Hierarchical Temporal Memory System of storing and categorizing information modeled after the neocortex System of storing and categorizing information modeled after the neocortex Identifies both spatial patterns and sequences of spatial patterns which allow time-based inferrence. Identifies both spatial patterns and sequences of spatial patterns which allow time-based inferrence. Are able to both make predictions and identify causal lines based on observations. Are able to both make predictions and identify causal lines based on observations. HTM systems are self learning. HTM systems are self learning.

23. HTM

24. HTM Algorithm Basics There is some sort of sensor to gather(observe) data. There is some sort of sensor to gather(observe) data. Spatial Pooling identifies frequently observed patterns and memorizes them as coincidences. Patterns that are significantly similar to each other are treated as the same coincidence. A large number of possible input patterns are reduced to a manageable number of known coincidences (ref 1). Spatial Pooling identifies frequently observed patterns and memorizes them as coincidences. Patterns that are significantly similar to each other are treated as the same coincidence. A large number of possible input patterns are reduced to a manageable number of known coincidences (ref 1). Temporal pooling partitions coincidences that are likely to follow each other in the training sequence into temporal groups. Each group of patterns represents a "name" or causeof the input pattern (ref 1). Temporal pooling partitions coincidences that are likely to follow each other in the training sequence into temporal groups. Each group of patterns represents a "name" or causeof the input pattern (ref 1). After sufficient training, HTMs identify new patterns by calculating the probability they belong to a certain node and a certain sequence (temporal groups). After sufficient training, HTMs identify new patterns by calculating the probability they belong to a certain node and a certain sequence (temporal groups).

25. HTM

27. What can HTM Do? Perception center need not parallel human senses. Perception center need not parallel human senses. Can be used to experiment with memory systems that are much larger and perhaps more complex than that of humans. Can be used to experiment with memory systems that are much larger and perhaps more complex than that of humans. Will be much better at identifying trends in data because it can observe it purely. Will be much better at identifying trends in data because it can observe it purely. Time-based inference allows these systems to both, make predictions and trace the cause based on observed patterns. Time-based inference allows these systems to both, make predictions and trace the cause based on observed patterns. Very useful tool for modeling. Should prove to be a useful tool in experimental physics. Very useful tool for modeling. Should prove to be a useful tool in experimental physics.

28. Intelligence vs. Consciousness

29. Summary Artificial intelligence aims to create intelligent machines. Artificial intelligence aims to create intelligent machines. If intelligence is adequately defined, computers will be able to model it. If intelligence is adequately defined, computers will be able to model it. We don’t know the best way to represent knowledge in a computer We don’t know the best way to represent knowledge in a computer HTM, modeled after the neocortex in the brain, seems to be a solution to knowledge representation in machines. HTM, modeled after the neocortex in the brain, seems to be a solution to knowledge representation in machines.

30. References http://en.wikipedia.org/wiki/Hierarchical_temporal_mem ory http://en.wikipedia.org/wiki/Hierarchical_temporal_mem ory http://en.wikipedia.org/wiki/Hierarchical_temporal_mem ory http://en.wikipedia.org/wiki/Hierarchical_temporal_mem ory http://en.wikipedia.org/wiki/Jeff_Hawkins http://en.wikipedia.org/wiki/Jeff_Hawkins http://en.wikipedia.org/wiki/Jeff_Hawkins http://en.wikipedia.org/wiki/DARPA_Grand_Challenge http://en.wikipedia.org/wiki/DARPA_Grand_Challenge http://en.wikipedia.org/wiki/DARPA_Grand_Challenge http://www.youtube.com/watch?v=oozFn2d45tg http://www.youtube.com/watch?v=oozFn2d45tg http://www.youtube.com/watch?v=oozFn2d45tg

31. Questions?