Researchers have succeeded in controlling a group of beetles’ flight initiation, cessation, and elevation via stimulus of the brain which elicited, suppressed, or modulated wing oscillation¹.

The control system, mounted on the upper thorax, consisted of neural stimulators, muscular stimulators, a radio transceiver-equipped microcontroller, and a microbattery. After a series of electrical stimuli cause a beetle to take flight, no further stimulus is needed to maintain flight. Turns, triggered by directly stimulating the wing muscle on the side opposite the direction of the desired turn, were 75% successful. A stimulus to the brain can cause a descent of 60 cm on average, but only one electric pulse is required to cause a landing. Average trial flight time was only 45 seconds, but one flight persisted for more than half an hour.

Two species of beetle were used: Cotinus texana (a.k.a. green June beetles) from the southern U.S. and Mecynorrhina torquata from Africa. The difference between the two in response to stimulus was minimal but only the larger Mecynorrhina torquata (which can reach the size of a human palm) could handle the weight of the electronic gear and its battery well enough to fly freely under remote control.

The UC Berkeley engineers that created the cyborg insects were led by Michel Maharbiz and Hirotaka Sato. Their efforts were funded by the Pentagon’s Defense Advanced Research Projects Agency. The project’s goal is to create insects that are completely remote-controlled and are able to perform such tasks as spying or looking for disaster survivors. However, in the short term it seems the research may be more helpful to biologists in understanding insect brains than in creating a battalion of cyborg spies.

What, if any, are the implications in this for the free will basis of action? Do parts of the human brain function similarly enough to the brain of insects for there to be any relevance in this to humans at all? What of apes and chimps? What empirical or theoretical (as opposed to conjectural) basis is there to suppose that humans are partly constituted by something that can rightly be called a will? It seems to me that the possibility of a remote-controlled human prompts us toward another order of skepticism alongside Cartesian skepticism and meaning skepticism: control skepticism. In other words, how can we know that we are in control of our own actions in any way—whether in the free will sense, merely in the sense that no one outside ourselves is controlling our actions, or an of the shades of grey in between?

Much of cognitive science and philosophy of mind is oriented around computational and functionalist theories of mind which hold, respectively, that the human mind is a digital computer (in a particular sense) and that what makes a mind a mind is not what it’s made of but rather how it functions (in which case, human minds and computers may function in the same way). Strong AI is the thesis that computers are (at least potentially) capable of human-like thought. John Searle’s Chinese Room thought experiment and related argument for the impossibility of Strong AI is commonly referenced in philosophical discussions of the mind and has been cited as central to cognitive science. If Searle is right and Strong AI is false, then much of the current efforts in cognitive science and philosophy of mind are futile. However, Searle’s reasoning contains a modality problem and an oversight of computers’ inherent semanticity.

Briefly, the thought experiment and argument run as follows¹. A person who understands only English sits alone in a room following English instructions for manipulating Chinese linguistic characters such that to those outside the room it appears that the person inside it understands Chinese. (The following argument is my own formulation.)

• 01) The person who merely manipulates characters of a natural language does not truly understand the language.
• 02) AI is analogous to the person who merely manipulates characters of a natural language.
• 03) If 01 and 02, then AI cannot truly understand natural language.
• 04) If AI cannot truly understand natural language, then Strong AI is false.
• 05) Therefore, Strong AI is false.

Many of Searle’s respondents have pointed out that Strong AI need not be oriented around a mere symbol-manipulating computer with no perceptual access to the referents of the symbols as would be analogous to the Chinese Room scenario. For instance, an android with such access and with such symbol-manipulating power may very well come to understand what the symbols mean in the same way that children come to understand language. We simply don’t yet know enough about how that happens to know if that process can be duplicated in silicon and until we do our tendency to impugn said android’s capability to understand language is based merely in untutored intuition. Therefore, like many others, I don’t believe we can assent to Premise 02.

The first premise of Searle’s argument is widely regarded as having brought to light another point, though, which is that syntax doesn’t seem to be sufficient for or constitutive of semantics. Incorporating that notion into the Chinese Room scenario has inspired this variation (again, my own formulation of others’ thoughts based on Searle’s 1984²):

• 06) Computers are merely syntactic.
• 07) Syntacticity is not a sufficient condition for semanticity.
• 08) Semanticity is a necessary condition for mind-hood.
• 09) If 06, 07, and 08; then computer-hood is not a sufficient condition for mind-hood.
• 10) If computer-hood is not a sufficient condition for mind-hood, then Strong AI is false.
• 11) Therefore, Strong AI is false.

Premise 08 is widely agreed upon and I can see no reason to dispute it. Such is also the case with 10 which is developed simply from the Strong AI thesis itself.

If our basis for evaluation is the current state of computer science we are prompted to assent to 06. Such a qualified version of 06 would run as follows:

• 06b) Computers as we know them are merely syntactic.

However, 06b is not strong enough to oppose Strong AI because it is a thesis concerned with both present and future manifestations of computer science. In other words, if the antecedent of 09 includes 06b, then 09 will be false. What is needed for 09 to be true is a modal version of 06:

• 06c) Computers are necessarily merely syntactic.

Now we are left with the task of evaluating 06c and 07. To my knowledge, there is no currently-known theoretical or empirical reason to assert either premise.

On the metaphysical/physical construal of 06c’s modality, one could make 06c true by restricting the referents of ‘computers’ to merely-syntactic machines, but, as the above discussion of 06b makes clear, that would not strike at the heart of the Strong AI thesis. Therefore, short of interpreting 06c in this bizarre fashion, we simply don’t know if computers are, by metaphysical/physical necessity, merely syntactic and therefore cannot judge 06c to be true. Furthermore, on the epistemological construal of 06c’s modality, the premise is false. There is neither a credible theory nor any empirical evidence our countenance of which forces upon us the necessary conclusion that computers are merely syntactic.

Moreover, I believe we have a reason to reject 06c. Executing programs causes computers to undergo state changes. The only way we can make any sense of this is to conclude that the executing computer understands the instructions for execution–an understanding that involves semantics. To make the point clear, consider a computer/robot in an automobile factory sticking out its screwdriver and rotating it in response to its programming. In order to understand the program’s instructions the computer must know that an expression in those instructions (let’s say the binary code or simply the voltage equivalent of ’screwdriver’) refers to one definite object (its screwdriver), that another expression refers to an action (rotating), and so on. Just as the computer in the auto factory acts on parts of itself that we can see and therefore must be capable of semanticity where the referential terms for those parts appear in a program it is executing, so all computers must be capable of semanticity where the referential terms for their microscopic parts appear in the programs they execute.

Note that my insistence on the semanticity of computers is compatible with both the internalistic and externalistic approaches to explaining semanticity by way of causation. Also note that this is not to say that computers are conscious in the sense of having second-order awareness. I am not claiming that the computer is aware that it is a causal agent, that it is aware that it is manipulating symbols, or that it knows what symbols are. Lastly, if my reasoning is correct thus far, then Premise 07 is irrelevant: computers cannot be merely syntactic so the sufficiency of mere syntacticity does not figure into the arguments for or against Strong AI.

Concept formation is a hallmark of intelligence, but we know very little of how brains form and use concepts. Using fMRI scanning, Dharshan Kumaran and his colleagues at the Wellcome Trust Centre for Neuroimaging, part of University College London, identified the hippocampus and the ventromedial prefrontal cortex (vMPFC), respectively, as the relevant regions of the brain¹.

Initially, the researchers repeatedly showed  human volunteers pairs of fractal patterns representing the night sky and asked them to forecast rainy or sunny weather based on the patterns. Predictions congruent with certain conceptual rules resulted in cash rewards but participants were not told what these rules were. The participants could simply memorize previous outcomes to make accurate predictions but they also had the opportunity to figure out the concepts that associated given fractal patterns with certain weather outcomes.

In the study’s second phase, participants received less information so as to encourage them to use the rules they had learned. The researchers were thus able to determine who had formed the relevant concepts and those who had not.

The fMRI  scanning revealed a correlation between high activity in the hippocampus (known to be important in learning and memory) in the first phase of the study and the ability to make accurate predictions with less information in the study’s second phase. The vMPFC (known to be important in decision-making) was active in the second phase. Kumaran and his team concluded that the hippocampus forms and stores concepts and passes them to the vMPFC for use in making decisions.

People suffering from amnesia are known to also have problems forming concepts. In 1997  Faraneh Vargha-Khadem of the University College London Institute of Child Health studied three young amnesiacs with hippocampal damage. Upon discovering that the children were able to reach normal levels of speech and language competence, literacy, and factual knowledge she concluded that the acquisition of conceptual knowledge did not involve the hippocampus. However, Kumuran believes the three young amnesiacs’ brains were able to compensate for the hippocampal damage since it was present from birth or a very early age.

On a sidenote, is the plasticity of the brain exemplified by the three young amnesiacs? If so, is this a testament to functionalism?

TIME has published Joel Stein’s new commentary on the panels that determine who does and who does not receive medical treatment. The column is largely humorously facetious but is also poignant at its beginning.

» Read column »

The Alliance for Climate Protection’s Repower America Hotline is now live. To help lessen U.S. dependence on foreign oil, reduce harmful pollution, and improve the U.S. economy by creating new jobs, U.S. voters can do the following:

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Berkeley Professor Benson Mates (b. 1919) died May 13, 2009. A brief outline of his career:

• 1940: B.A. from University of Oregon (Eugene) in philosophy and mathematics.
• 1941-1942: at Cornell University, studied philosophy with Richard Robinson and Anders Wedberg and mathematics with Wallie Abraham Hurwitz and J. Barkley Rosser.
• 1941: involved in the U.S. Navy’s cryptanalysis program.
• 1942–1945: Chief of Naval Operations, Communications Security, for U.S. Navy.
• 1945–1948: at U.C. Berkeley, studied philosophy with William Ray Dennes and Paul Marhenke, classics with Harold Cherniss and Ivan Linforth, and mathematics with Alfred Tarski.
• 1948: Ph.D. from U.C. Berkeley. Dissertation entitled “The Logic of the Old Stoa”.
• 1948: became an Instructor for the Berkeley philosophy dept.
• 1948-1958: Assistant Professor and then Associate Professor at Berkeley.
• 1958: Professor at Berkeley.
• 1963-1978: Associate Editor of the Journal of the History of Philosophy.
• 1986: The Philosophy of Leibniz published.
• 1989: retired Emeritus Professor from Berkeley.