There was an interesting blog post on the NY Times today about an experiment that showed that the decisions of monkeys could be predicted before the monkeys knew they had made the decision. The part I found more interesting connected with my previous post on free will and how the ability to predict our decisions does not have to mean that we don’t have free will:

Let me explain what I mean by way of an example. Imagine we suspend a steel ball from a magnet directly above a vertical steel plate, such that when I turn off the magnet, the ball hits the edge of the plate and falls to either one side or the other.

Very few people, having accepted the premises of this experiment, would conclude from its outcome that the ball in question was exhibiting free will. Whether the ball falls on one side or the other of the steel plate, we can all comfortably agree, is completely determined by the physical forces acting on the ball, which are simply too complex and minute for us to monitor. And yet we have no problem assuming the opposite to be true of the application of the monkey experiment to theoretical humans: namely, that because their actions are predictable they can be assumed to lack free will. In other words, we have no reason to assume that either predictability or lack of predictability has anything to say about free will. The fact that we do make this association has more to do with the model of the world that we subtly import into such thought experiments than with the experiments themselves.

The rest of the blog post was a bit crazy and I think extrapolated a bit much from these experiments, but I think the excerpt above makes clear the argument that we can still have free will even if it turns out we can predict all our decisions by looking at all the neurons, chemicals, signals, inputs, etc to the brain.

I just read “The Brain that Changes Itself” by Norman Doidge. It’s all about brain plasticity and how our brain is constantly changing. Basically all of our brain is maps, like auditory map where different areas respond to different frequencies. Or higher up, different areas respond to different syllables. When we’re young, these maps quickly differentiate, just on inputs, i.e. your syllable map will adjust to the syllables you hear, part of why its difficult to learn a 2nd language when you’re an adult. But they continue to change and adapt as you grow older. If you blindfold someone, their visual cortex starts helping out with other senses almost immediately. When they train a monkey to perform a new task using its index finger, its index finger quickly takes up more of its map. People with brain damage from strokes or other accidents, are able, through training, to regain many skills by re-training other parts of the brain to take over.

One important point that is emphasized in the book is the need to keep your brain plastic. The more often you do some experience, the more learned in the brain it is, the more of the map it will take up, and the harder it will be to learn more stuff. So you should continue changing things up and trying new experiences to keep your brain lively. And the less learning you do, the less plastic your brain may become. So do your brain exercises as you get older!

A significant part of the book focuses on research by Michael Merzenich. He has developed some software programs to help improve people’s brain maps. Due to defects in the brain or body, or the inputs the brain is getting from the environment itself, certain maps may develop abnormally, but can be fixed through training. For example, you can re-train your audio map to properly hear sounds. He has software to do this for thousands of kids who have struggled with language, speaking, listening, and reading, due to this abnormality. He also has software for the elderly. And after the same software proved very helpful with autistic children, he has some interesting theories there as well. Here’s an interesting TED talk by Merzenich.

I just read a fascinating edge talk by Stanislas Dehaene on consciousness. He’s a neuroscientist studying the “easy” problem of consciousness, meaning he’s studying how conscious awareness arises in the brain, not how we get this subject perception of the world. He does some pretty interesting experiments, using MRI to study people’s brain’s as they consciously and subliminally perceive various stimuli. He comes the conclusion that consciousness is when multiple areas of the brain begin to communicate and synchronize on one concept or stimulus. Then it becomes conscious and you can think about, process it in different areas of the brain, etc. Here’s what he says:

This idea is relatively simple, and it is not far from the one that Daniel Dennett proposed when he said that consciousness is “fame in the brain”. What I propose is that “consciousness is global information in the brain” — information which is shared across different brain areas. I am putting it very strongly, as “consciousness is”, because I literally think that’s all there is. What we mean by being conscious of a certain piece of information is that it has reached a level of processing in the brain where it can be shared.

Because it is sharable, your Broca’s area (or the part of it involved in selecting the words that you are going to speak) is being informed about the identity of what you are seeing, and you become able to name what you are seeing. At the same time, your hippocampus is perhaps informed about what you have just seen, so you can store this representation in memory. Your parietal areas also become informed of what you have seen, so they can orient attention, or decide that this is not something you want to attend to… and so on and so forth. The criterion of information sharing relates to the feeling that we have that, whenever a piece of information is conscious, we can do a very broad array of things with it. It is available.

It’s pretty interesting that the main purpose of consciousness may just be so that different parts of the brain can use the information. Suddenly we’re able to think about, visualize, memorize and talk about some particular thing. We can decide to pay attention to it or not. I thought the “all or nothing” principle was particularly interesting:

Another important feature that I have briefly mentioned already is the all-or-none property. You either make it into the conscious workspace, or you don’t. This is a system that discretizes the inputs. It creates a digital representation out of what is initially just a probability distribution. We have some evidence that, in experiments where we present a stimulus that is just at threshold, subjects end up either seeing it perfectly and completely with all the information available to consciousness — or they end up not having seen anything at all. There doesn’t seem to be any intermediate state, at least in the experiments that we’ve been doing.

Having a system that discretizes may help solve one of the problems that John Von Neumann considered as one of the major problems facing the brain. In his book The computer and the brain, Von Neumann discusses the fact that the brain, just like any other analog machine, is faced with the fact that whenever it does a series of computations, it loses precision very quickly, eventually reaching a totally inaccurate result in the end. Well, maybe consciousness is a system for digitizing information and holding on to it, so that precision isn’t lost in the course of successive computations.

I thought the analogy to the computer here was particularly interesting. In robotics, there are plenty of times where we have an uncertain estimate of what is going on. For example in robot localization, we have some distribution over possible places we might be located. But its really hard to determine what to do based on a range of places we might be. So we typically take a single location, the most likely one, and determine behavior based on this. It’s hard to process a large uncertain analog symbol, so we reduce it to a discrete point that we are. It seems like we’re losing valuable information by doing this, but perhaps this is exactly what consciousness is doing. It has to pick out one interesting thing to process at a time, because it’s too difficult to actively do that with many things at once.

This is a pretty interesting article on how the brain processes time and whether time really “slows down” during dangerous events: Edge: Brain Time

There’s an interesting article in the latest edge on free will and consciousness. Basically confirming what I already believed after reading lots of Dennett and others’ books on the mind. Most of our decision making occurs in the subconscious. Our conscious self either only does the difficult decisions, or is only told after the fact so that we feel that ‘we’ made the decision. This doesn’t mean we don’t have free will: our subconscious decisions are still made based on who we are, our genes, our lives, how we grew up, etc. And Bargh raises another interesting point, do we really know who we are if our decisions are made by our subconscious and not our conscious selves as we believe? Here are some highlights from the article:

All organisms are purposive and have reasons for what they do. We certainly have that of course. So it’s not that will doesn’t exist; it’s that the free part is problematic — a lot of people see free will and say, “Well, you’re showing there’s no free will; therefore, people have no intentions or will.” No.

There is will, and will can be shaped by a host of factors: your genetic background, your early experience with your home and your family, your caretakers, you playmates, cultural influences bombarding us through the media and through socializing with your peers (and, thus what they like and what they think and what they believe from their parents). All this is being soaked up like a sponge by little kids.

…

But there is another question that is more pragmatic and I think it’s a wonderful question, “If all these things are going on without my knowledge, then I don’t really know why I’m doing what I’m doing, and I don’t really know myself that well apparently. So how can I make the right decisions or make the right choices for myself when all these biases are throwing my decisions all over the place?”

As a grad student studying artificial intelligence, it might be useful to know what exactly intelligence is.  No one can really define it.  We know that we have it.  We know that tables and chairs do not.  Everything in between is up for grabs.  I’m going to discuss what some historical views on intelligence are and what I think it might be.

The classic definition of intelligence in AI is based on the Turing Test.  This is a test created by Alan Turing to determine if something is intelligent.  You have a person and a computer in separate rooms.  In a third room is a judge.  He chats with both of them over a computer.  If he can’t tell which one is the person, then the computer is intelligent.  Basically the computer is considered to be intelligent if it can imitate a person in conversation.  If it doesn’t have this ability then its not intelligent.  This test can be extended to a more complicated task, say if you can’t tell a person and a computer/robot apart in every day life, then the computer/robot would be intelligent.

Then there are a class of people who believe that having intelligent behavior is not enough.  They say its critical what is going on to make that intelligent behavior.  Usually they say that having computer circuits create such behavior is not intelligent.  Some of them say that only having an exact human brain create the behavior is intelligence.  The classic argument from this side is John Searle’s Chinese Room.  There is a man in a room.  He receives pieces of paper with weird symbols on them.  He has a set of directions of what to do when he receives these pieces of paper, which generally involves him creating new weird symbols and passing them back out.  The symbols are Chinese and he is actually answering someone’s questions with someone in Chinese this way. Searle’s main argument is that the man does not know Chinese even though he can act like he does this way.  The room also does not speak Chinese.  Searle goes on to say that even if the man and directions were replaced by a fully accurate simulation of the human brain it would not be intelligent, you need the magic stuff of the brain to be intelligent.  Another point to make is that this example is utterly impossible, it would not be possible to create a set of directions to converse fluently in Chinese.

Another argument from a more computer science perspective is that of Simon and Newell.  They put forth the ‘Physical Symbol System Hypothesis’, which states that “A physical symbol system has the necessary and sufficient means for general intelligent action.”  They define a physical symbol system as any system which physical patterns (symbols) that are related somehow (in a system).  Basically, anything.  They later go on to say that one of the signs of intelligence is avoiding exponential search (Many problems can be reduced to searching through a space of possible solutions.  If the search space is exponential, then it is impractical or impossible to search it exhaustively.  Avoiding this exponential search requires some intelligence in deciding how to avoid it).  This is a pretty interesting idea.  I often feel that one of the key things to do to be successful is to know how much time to put into something and when its not worthwhile anymore, which seems somewhat related to this idea of avoiding exponential search.

Recently, our reading group read a paper by Ned Block that also discussed this topic.  Block is part of the camp that says that the internal processing that creates the intelligent behavior is critical to whether something is intelligent.  He provides a counter-example to the Turing Test.  A computer is loaded with a database of every conceivable conversation that is less than an hour in length.  Then when taking the Turing Test, it looks up a conversation that matches up to the current sentence, and speaks the next sentence in that conversation.  He says that this machine is clearly not intelligent, and therefore intelligent behavior is not enough.  He does allow that computers could be intelligent, but they would have to be doing something better than this, such as learning or adapting.  Another student in the group brought up a great point that the Turing Test is fine if you say that it has to be a feasible machine that passes for a human.  Both this counterexample and the Chinese room are not feasible agents (the number of hour long conversations is much greater than the number of particles in the universe).  Any feasible agent (reasonable memory and computation power) that passes the Turing Test must be doing something intelligent to be acting that way with limited resources.  This is an interesting definition that lets the Turing Test stand and does put a restriction on the internal processing to be ‘feasible’, which any actually realized agent would be.

So where do I stand?  I’m not sure.  Intelligence may be defined on some scale by behavior.  More complex or more efficient behaviors come from more intelligent beings.  I’m not sure that a definition with a restriction on the internal processing of a being will work.  Intuitively I don’t think an agent that is simply looking up a conversation in its database like above is intelligent.  An agent that is learning or adapting seems to me to be more intelligent.  But depending on what level of abstraction you look at, even we aren’t doing anything that exciting.  Sure, you say that we’re understanding what is said to us, thinking of a response, using our memories, and responding.  Which sounds intelligent. But at its basest level, its just chemicals and ions flowing back and forth in our brain, following the inexorable laws of physics.  Which sounds very unintelligent.  The same thing applies in the case of AI.  You can write some cool learning algorithm that adapts and learns how to behave in some environment over time.  But of course how it learns was written by you and is pre-determined and the entire course of what it would do in that environment could probably be predicted if you knew all the variables.  So I don’t think any definition of intelligence that involves the internal processing going on could ever work, because every agent simply has very boring particles following the laws of physics at the core of their internal processing.

In summary, I think a definition of intelligence has to be about producing intelligent behavior and not about the internal processing that creates it.  The Turing Test seems pretty reasonable if you restrict it to feasible agents.  And I still think algorithms that let machines learn and adapt are more exciting that simply programming in a static solution to a computer (I prefer to think of things at the learning abstraction level than the level of particles following the laws of physics).

In our reading group this week, we read John Searle’s classic paper “Minds, Brains, and Programs.” In the paper, he makes the claim that digital computers doing symbol manipulation cannot have intelligence. His motivating example is what he calls the Chinese Room Experiment. Basically, a man is inside a room. He is given a set of instructions to follow when he receives papers full of Chinese symbols. By following the instructions, he creates a second set of symbols which he returns. Although he does not personally understand Chinese, to an observer outside the room, they have just received fluent answers to questions in Chinese. Searle claims that this shows that intelligence cannot be created in a digital computer (the man) with a program (the instructions) because in this example the man clearly does not understand Chinese. I would argue that the room as a whole understands Chinese, as its the whole system that understands it (you wouldn’t expect a computer to be intelligent without its program). He argues that the system as a whole cannot have intelligence, and that some subsystem must have it. But the same systems rule applies to the brain, the brain as a whole has intelligence, but you can’t ascribe the intelligence to any subsystem of the brain such as a neuron or brain region.

The other main thing that bothers me about Searle’s essay is that he claims that human intelligence can never be replicated on a digital computer because a computer does not have the same “causal properties” as the brain. He claims that our intentionality is somehow created by these “causal properties” inherent to the brain that cannot be replicated in a computer. He makes no claim as to what they are or why they can not be replicated. I find it highly unlikely that our intentionality is due to some specific physical feature of the brain as opposed to an emergent property of the coalition of neurons in our brain. It seems like Searle is searching for some little homonculi in our brain that is directing things, and that we can’t recreate that homonculi in a computer. But there is no little man inside our brains.

One interesting point that Searle brings up is the separation of mind and brain. The separation of mind and brain may not be the same as the separation of a program and a computer. While a program can be run on any number of equivalent computers, the mind may be dependent on specific properties of the brain, or may be integrated in the brain rather than being some program that can be applied on top of the brain. I can agree that there are probably some aspects of the brain that are essential to mind, although I don’t see why we won’t be able to simulate those somehow in a computer.

There was a good article in the New York Times today on the effect dreaming has on learning and memory.  The fact that dreaming and sleep are good for learning and memory is commonly known I think.  Their description of how dreaming can help you make connections between memories is pretty cool though.  Your dreams could be your brain re-organizing your memories and what you’re learned and trying to find the connections between them.  Leading to inspiration apparently!  Now I have a good excuse for why I want to sleep 9-10 hours a night.

A new study has shown that exercise makes you smarter by promoting neurogenesis in the brain.  In short, exercising helps create new neurons in the brain, which then improves your cognitive abilities.  Exactly why I bike to school everyday

I saw this article on Scientific American’s website about how our expectations relate to the effects of placebos. They did both a study with a placebo painkiller and a study using a gambling game. Subjects who had higher dopamine activity (and thus higher expectations of reward) during the gambling game were the same subjects who received more of an effect from the placebo. The conclusion is that your expectations of rewards are closely linked to the effectiveness of the placebo.

This is pretty interesting for a number of reasons. One is that it shows that an important aspect of having placebos work is simply being convinced that they will work. Looking at it from a reinforcement learning perspective, the dopamine activity can be viewed as the prediction of the reward signal. And then somehow expecting a higher reward signal can reduce the pain? Is the pain signal just a negatively valued feedback? What is the relationship between the expectation of reward and the actual reward received? Can simply expecting more or less reward affect the actual amount of reward received?