CS 557 - Artificial Intelligence

1. CS 557 - Artificial Intelligence • Class Syllabus

2. What is (Artificial) Intelligence?  No agreed upon scientific definition, except that intelligence is demonstrated by people  AI has been a field trying to solve problems that people are good at (and that other things are not good at)  Should we try to do it the same way as people?  Better than people?

3. Can a Machine Be Intelligent? • Ongoing Argument • Weak AI – Machines can be made to act as if they were intelligent • Strong AI – Machines that act intelligently, have real, conscious minds. • Does computation = intelligence? • Is a spider intelligent? • Are the genes of a human intelligent?  The Turing test.

4. Acting Humanly: The Turing test (1950) “Computing machinery and intelligence”: Can machine’s think? or Can machines behave intelligently? An operational test for intelligent behavior: the Imitation Game Predicted that by the year 2000, a machine would have a 30% chance of fooling a lay person for 5 minutes Anticipated all major arguments against AI in the following 50 years Suggested major components of AI: knowledge, reasoning, language, understanding, learning. Problem: Turing test is not reproducible, constructive, or amenable to mathematical analysis. Intelligence not determinable by surface behavior alone. The test is not sufficient since the behaviors under adjudication are too limited. As a sufficient condition for intelligence, the test is so difficult as to be uninteresting.

5. Philosophy – Mind over matter OR mind is matter? Biological Naturalism (phisicalism, materialism) - "Brains Cause Minds“: • Mental states, such as being in pain, knowing that one is driving a car, or thinking that your mother neglected you as a child, are a direct result of brain states. • Some brain states = the same mental state. • Avoids speculation about nonphysical processes beyond the ken of science. What about free will? Is everyone a deterministic machine? What about consciousness? How does consciousness arise from a certain organization of matter? What is consciousness? Why? Sentience: 1.   The quality or state of being sentient; consciousness. 2.   Feeling as distinguished from perception or thought. 3.   A sense of one's own personal thoughts, including the attitudes, beliefs, and sensitivities held by or considered characteristic of an individual. Mind is spiritual: However, physical changes in mind affect it. Damage to certain areas of brain can change behavior. Dualism: There is a part of mind that lies outside of nature, is not physical. Rene Descartes: first clear discussion of the distinction between mind and matter. A proponent of dualism. Held that only man (not animals) posses this dualist quality – animals can be viewed as machines. Alternative to dualism: mind is purely physical but cannot be completely explained by a reduction to ordinary physical processes. Perhaps mind could be an “emergent” property of the physical characteristics of your brain, for example.

6. Consciousness - The Chinese Room Experiment – Does running the right program generate consciousness? • Human – only understands English • Rule book – written in english • Stacks of paper – some blank, some with indecipherable symbols on them • Small opening to outside world • Pieces of paper with symbols on them are passed through the opening • The human follows the instructions in the rule book • Eventually the human hands a piece of paper with symbols on it through the opening • Argument: • Certain kinds of objects are incapable of conscious understanding • The human, paper, and rule book are objects of this kind • If each object is incapable, the entire whole is incapable • Therefore there is no conscious understanding in the room

7. The Brain Prosthesis Experiment Replace neurons in your brain one at a time with artificial neurons that *exactly* replicate the behavior of the original neurons (then reverse the process). By definition, the subjects external behavior must remain unchanged. What happens? • We have two choices, either • The causal mechanisms involved in consciousness in the electronic brain are still functioning, and it is therefore conscious. • Conscious mental events in the normal brain have no effect on behavior. If we accept neuron replacement maintains consciousness, then you must also accept that consciousness is also maintained when any larger functional unit of the brain (clumps of neurons, a lobe, hemisphere, etc) is replaced. In other words, if neuron replacement is conscious, replacing the brain with a circuit/lookup table that mapped inputs to outputs *must* also be conscious!!!

8. What is learning? • Learning….in a rather broad sense: • improvement of performance on the basis of experience • Machine learning…… • improve for task T • with respect to performance measure P, • based on experience E

9. Learning from Observations. • There are 3 main types of learning: • Supervised learning – used in environments where an action is followed by immediate feedback • Reinforcement learning – used in environments where feedback on actions is not immediate • Unsupervised learning – used where there isn’t any feedback on actions!

10. Inductive learning is defined to be the process of learning from pre-classified examples. T = {e1, e2, . . . en}, where each ei = (a, o) = (a1a2. . .am ,o) 1. Choose h such that is minimized 2. Hypothesis "goodness"

11. Inductive Learning – Supervised Learning  Gather a set of input-output examples from some application: Training Set i.e. Stock Forecasting  Train the learning model (decision tree, neural network, etc.) on the training set until “done”  The Goal is to “generalize” on novel data not yet seen  Gather a further set of input-output examples from the same application: Test Set in order to validate what the system is doing  Use the learning system on actual data  Formally, given  a function f  a set of examples (x, f(x))  produce h such that h approximatesf

12. Motivation • Cost and Errors in Programming A Solution • Domain knowledge limited - financial forecasting • Encoding/extracting of domain knowledge may be expensive • Augment existing domain knowledge • Adaptability • General, easy-to use mechanism for a large set of applications • Do better than current approaches

13. Learn This!

14. Which?

15. What explanations do we prefer? Common Biases in learning  Minimize Error on known examples  Information gain  Ockham’s Razor – Prefer the simplest hypothesis that describes the data (mml, mdl).  We’ll find out that, without bias, you cannot learn.  Bias influences what you will learn. Sometimes these biases are inherent in the basic learning algorithm you choose, sometimes they are implicit in the error function you are using.  So which biases are the “good” biases?