Mehdi Ghayoumi Kent State University Computer Science Department Summer 2015 Exposition on Cyber Infrastructure and Big Data

Learn A cognitive system learns. The system leverages data to make inferences about a domain, a topic, a person, or an issue based on training and observations from all arieties, volumes, and velocity of data.

Model To learn, the system needs to create a model or representation of a domain (which includes internal and potentially external data) and assumptions that dictate what learning algorithms are used. Understanding the context of how the data fits into the model is key to a cognitive system.

Generate hypotheses A cognitive system assumes that there is not a single correct answer. The most appropriate answer is based on the data itself. Therefore, a cognitive system is probabilistic. A hypothesis is a candidate explanation for some of the data already understood. A cognitive system uses the data to train, test, or score a hypothesis.

Individual (between the ears) including mental processes and structures in a given person’s brain. Extended (in the hand) including objects that we think, learn and create with. For example, an artist’s favorite paint brush or an architect’s model of a building. Group (among the heads) including any collection of individuals. For example, a partnership, product development team or therapy group.

Machine (in a black box) including hardware and software that automates one or more mental processes or structures. For example, the buzzer on your clothes dryer or an expert system a car mechanics uses to diagnosis a problem. Emergent (beyond the heads) including a group and/or machine intelligence the delivers a new mental state or level of performance. For example, a prediction market that forecasts a presidential election or the success of a new product better than any individual.

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Blueprint for intelligent agents. It proposes (artificial) computational processes that act like cognitive systems (human) An approach that attempts to model behavioral as well as structural properties of the modeled system. Aim : to model systems that accounts for the whole of cognition, i.e., systems with Artificial Consciousness – which can not only respond but also think, perceive and believe like a human !

Artificial Consciousness is broadly classified as access and phenomenal consciousness. Brain processes neural impulses from the eyes and determines that this image is physically unstable – pattern recognizability. What about pain, anger, motivation, attention, feeling of relevance, modeling other people's intentions, anticipating consequences of alternative actions, or inventing ?

Newell introduces Soar, an architecture for general cognition. Soar is the first problem solver to create its own sub goals and learn continuously from its own experience. Soar has the ability to operate within the real-time constraints of intelligent behaviour, such as immediate- response and item-recognition tasks.

Soar is a symbolic cognitive architecture. An AI programming language. It provides a (cognitive) architectural framework, within which you can construct cognitive models. It can be considered as an integrated architecture for knowledge-based problem solving, learning, and interaction with external environments.

Soar can be divided into 3 levels : Memory Level Decision Level Goal Level

Digital computers are Made from silicon Accurate (essentially no errors) Fast (nanoseconds) Execute long chains of serial logical operations (billions) Irritating to humans

Brains are Made from carbon compounds Inaccurate (low precision, noisy) Slow (milliseconds, 106 times slower) Execute short chains of parallel alogical associative operations (perhaps 10 operations) Understandable to humans

Huge disadvantage for carbon: more than 1012 in the product of speed and power. But we do better and faster in many tasks: speech recognition, object recognition, face recognition, motor control most complex memory functions, information integration. Implication: Cognitive “software” uses only a few but very powerful elementary operations.

1. Engineering: Many of the important practical computer applications of the next decade will be cognitive: · Language understanding. · Internet search. · Cognitive data mining. · Decent human-computer interfaces. We feel it will be necessary to have a brain-like architecture to run these applications efficiently.

2. Kinship Recognition, Human Factors: To be recognized as intelligent by humans, a machine has to have a somewhat human-like intelligence. There may be many kinds of intelligence, but we can only understand and communicate with one of them! Successful human-computer interactions will require a brain- like computer doing cognitive computation.

3. Personal: A technological vision: In 2050 the personal computer you buy in Wal-Mart will have two CPU’s with very different architecture: First, a traditional von Neumann machine that runs spreadsheets, does word processing, keeps your calendar straight, etc. What they do now. Second, a brain-like chip To handle the interface with the von Neumann machine, Give you the data that you need from the Web or your files

Thank you!