Doing Philosophy Philosophical theories are not primarily about facts. Therefore, there is no right or wrong. Philosophical arguments are well-argued opinions. A philosophy course such as this concerns both facts and opinions, e.g. What is functionalism (fact)? What is the problem of multiple realization (fact)? Is functionalism a good theory of the mind (opinion)? Is materialism a better theory than dualism (opinion)?

Doing Philosophy in this Course Ask questions –in class –on the course blog Think for yourself. Justify your opinions with good logical arguments, also can appeal to scientific evidence and personal experience

Tutorials There are four tutorial groups: All groups meet in the Philosophy Department, Room MB 305 Group 1: Thurs. 2:00 Sept. 20, Oct. 4, Nov. 1, Nov. 15 Group 2: Tues. 2:00 Sept. 25, Oct. 23, Nov. 6, Nov. 20 Group 3: Tues. 3:00 Sept. 25, Oct. 23, Nov. 6, Nov. 20 Group 4: Tues. 1:00 Oct. 2, Oct. 30, Nov. 13, Nov. 27 Please sign up today in the break. Otherwise, send me an .

Functionalism Things are defined by their functions Two ways to define function 1)Function = inputs and outputs (machine functionalism) e.g. mathematical function, e.g. +, -, x, / 2 x 3 = 6, when input is 2 and 3, output is 6 Multiple realizability: can be realized in different materials or through different processes

Functionalism defined as inputs and outputs continued e.g. beliefs, desires “I am thirsty” (i.e. I desire water) is defined in terms of inputs and outputs. When there are inputs x and y, there is output z: InputOutput (x) Water is available(z) I drink water (y) There is no reason not to drink the water

2) Function = use (teleological functionalism) Function is defined by what something does. e.g. a heart pumps blood. e.g. a belief plays a role in reasoning: a premise in a practical syllogism Premise 1 I believe x is water Premise 2 I desire water Premise 3There is no reason not to drink x Conclusion I drink x

No matter if you interpret functional as an input- output relation (machine functionalism) or use (teleological functionalism), mental states, such as thirst are multiply realizable. A computer can conduct multiplication. An alien can have thirst, pain, etc. A computer can have thirst, pain, etc.

Functional definition of mind If x acts like a mind, it is a mind. If, when compared to a mind given similar inputs, x gives similar outputs, x is a mind. If a computer can converse (take part in linguistic input and output exchanges/play the role of an intelligent conversational partner) just like a person, the computer is as intelligent as a person. It has a mind.

The Chinese Room Argument

Background Thought Experiments Instead of scientific experiments, philosophers have thought experiments Thought experiments are conducted in the imagination They test concepts looking for consistency and contradictions, often using intuitions to make judgments

The Turing Test In 1950, a computer scientist, Alan Turing, wanted to provide a practical test to answer “Can a machine think?” His solution -- the Turing Test: If a machine can conduct a conversation so well that people cannot tell whether they are talking with a person or with a computer, then the computer can think. It passes the Turing Test. In other words, he proposed a functional solution to the question, can a computer think?

There are many modern attempts to produce computer programs that pass the Turing Test. In fact, in 1991 Dr. Hugh Loebner started the annual Loebner Prize competition, with prize money offered to the author of the computer program that performs the best on a Turing Test. The winner of the Loebner prize in 2004 was a program called ALICE. You can try her (and other talkbots) out on this website:

Searle’s Chinese Room Argument John Searle Famous philosopher at the University of California, Berkeley Most well-known in philosophy of language, philosophy of mind and consciousness studies Wrote “Minds, Brains and Programs” in 1980, which described the “Chinese Room Argument”

Searle’s Chinese Room Argument The Chinese Room argument is one kind of objection to functionalism, specifically to the Turing Test Also an attack on “strong AI” Searle makes distinction between strong AI and weak AI Strong AI: “the appropriately programmed computer really is a mind, in the sense that computers, given the right programs can be literally said to understand” Weak AI: Computers can simulate thinking and help us to learn about how humans think Searle objects only to strong AI.

The Chinese Room Searle cannot understand any Chinese. He is in a room with input and output windows, and a list of rules about manipulating Chinese characters. The characters are all “squiggles and squoggles” to him. Chinese scripts and questions come in from the input window. Following the rules, he manipulates the characters and produces a reply, which he pushes through the output window.

The Chinese answers that Searle produces are very good. In fact, so good, no one can tell that he is not a native Chinese speaker! Searle’s Chinese Room passes the Turing Test. In other words, it functions like an intelligent person. Searle has only conducted symbol manipulation, with no understanding, yet he passes the Turing Test. Therefore, passing the Turing Test does not ensure understanding. In other words, although Searle’s Chinese Room functions like a mind, it is not a mind, and therefore functionalism is wrong.

Syntax vs. semantics Searle argued that computers can never understand because computer programs are purely syntactical with no semantics. Syntax: the rules for symbol manipulation, e.g. grammer Semantics: understanding what the symbols (e.g. words) mean Syntax without semantics: The bliggedly blogs browl aborigously. Semantics without syntax: Milk want now me.

Searle concludes that symbol manipulation alone can never produce understanding. Computer programming is only symbol manipulation. Computer programming can never produce understanding. Strong AI is false and functionalism is wrong.

What could produce real understanding? Searle: “it is a biological phenomenon” and “only something with the same causal powers as brains can have [understanding]”.

Objections The Systems Reply Searle is part of a larger system. Searle doesn’t understand Chinese, but the whole system (Searle + room + rules) does understand Chinese. The knowledge of Chinese is in the rules contained in the room. The ability to implement that knowledge is in Searle. The whole system understands Chinese.

Searle’s Response to the Systems Reply 1)It’s absurd to say that the room and the rules can provide understanding 2) What if I memorized all the rules and internalized the whole system. Then there would just be me and I still wouldn’t understand Chinese. Counter-response to Searle’s response If Searle could internalize the rules, part of his brain would understand Chinese. Searle’s brain would house two personalities: English-speaking Searle and Chinese- speaking system.

The Robot Reply What if the whole system was put inside a robot? Then the system would interact with the world. That would create understanding.

Searle inside the robot

Searle’s response to the Robot Reply 1)The robot reply admits that there is more to understanding than mere symbol manipulation. 2)The robot reply still doesn’t work. Imagine that I am in the head of the robot. I have no contact with the perceptions or actions of the robot. I still only manipulate symbols. I still have no understanding. Counter-response to Searle’s response Combine the robot reply with the systems reply. The robot as a whole understands Chinese, even though Searle doesn’t.

The Complexity Reply Really a type of systems reply. Searle’s thought experiment is deceptive. A room, a man with no understanding of Chinese and “a few slips of paper” can pass for a native Chinese speaker. It would be incredibly difficult to simulate a Chinese speaker’s conversation. You need to program in knowledge of the world, an individual personality with simulated life history to draw on, and the ability to be creative and flexible in conversation. Basically you need to be able to simulate the complexity of an adult human brain, which is composed of billions of neurons and trillions of connections between neurons.

Complexity changes everything. Our intuitions about what a complex system can do are highly unreliable. Tiny ants with tiny brains can produce complex ant colonies. Computers that at the most basic level are just binary switches that flip from 1 to 0 can play chess and beat the world’s best human player. If you didn’t know it could be done, you would not believe it. Maybe symbol manipulation of sufficient complexity can create semantics, i.e. can produce understanding.

Conclusion 1)The Turing Test: Searle is probably right about the Turing Test. Simulating a human-like conversation probably does not guarantee real human-like understanding. Certainly, it appears that simulating conversation to some degree does not require a similar degree of understanding. Programs like ALICE presumably have no understanding at all..

2) Functionalism Functionalists can respond that the functionalist identification of the of the room/computer and a mind is carried out at the wrong level. The computer as a whole is a thinking machine, like a brain is a thinking machine. But the computer’s mental states may not be equivalent to the brain’s mental states. If the computer is organized as a really long list of questions with canned answers, the computer does not have mental states such as belief or desire. But if the computer is organized like a human mind, with concepts, complex organization and homuncular modules, the computer can have beliefs, desires, etc.

3) Strong AI: Could an appropriately programmed computer have real understanding? Too early to say. I am not convinced by Searle’s argument that it is impossible. The right kind of programming with the right sort of complexity may yield true understanding. e.g. homuncular modularity mixing of levels self-updating

4) Syntax vs. Semantics How can semantics (meaning) come out of symbol manipulation? How can 1s and 0s result in real meaning? It’s mysterious. But then how can the firing of neurons result in real meaning? Also mysterious. One possible reply: meaning is use (Wittgenstein). Semantics is syntax at use in the world.

5) Qualia Qualia = raw feels = phenomenal experience = what it is to be like something Can a computer have qualia? Again, it is hard to understand how silicon and metal can have feelings. But it is no easier to understand how meat can have feelings. If a computer could talk intelligently and convincingly about its feelings, we would probably ascribe feelings to it. But would we be right?

5) Searle’s claim: understanding can only occur in biological systems with the same causal properties as the brain: There is no basis for this hypothesis. It is unclear what special causal properties the brain meant to have. I doubt that Searle is right about this.

Readings for next week Sterelny, Kim, The Representational Theory of Mind, Section 1.3, pgs (on reserve in Philosophy Dept.) Sterelny, Kim, The Representational Theory of Mind, Section , pgs (on reserve in Philosophy Dept.)

More optional readings On the Chinese Room: Searle, John. R. (1990), “Is the Brain's Mind a Computer Program?” in Scientific American, 262, pgs (in main library) Churchland, Paul, and Patricia Smith Churchland (1990) “Could a machine think?” in Scientific American 262, pgs (in main library) On modularity of mind: Fodor, Jerry A. (1983), The Modularity of Mind, pgs at: Pinker, Steven (1999), “How the Mind Works”, William James Book Prize Lecture at: www3.hku.hk/philodep/joelau/wiki/pmwiki.php?n=Main.Pinker-HowTheMindWorks