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Tag Archives: cognitive computing

Turing’s Nightmares: Variations on Prospects for The Singularity.

16 Sunday Aug 2015

Posted by petersironwood in Uncategorized

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AI, cognitive computing, the singularity, Turing

caution IMG_1172The title of this series of blogs is a play on a nice little book by Alan Lightman called “Einstein’s Dreams” that explores various universes in which time operates in different ways. This first blog lays the foundation for these variations on how “The Singularity” might play out.

For those who have not heard the term, “The Singularity” refers to a hypothetical point in the future of human history where a super-intelligent computer system is developed. This system, it is hypothesized, will quickly develop an even more super-intelligent computer system which will in turn develop an even more super-intelligent computer system. It took a fairly long time for human intelligence to evolve. While there may be some evolutionary pressure toward bigger brains, there is an obvious tradeoff when babies are born in the traditional way. The head can only be so big. In fact, human beings are already born in a state of complete helplessness so that the head and he brain inside can continue to grow. It seems unlikely, for this and a variety of other reasons, that human intelligence is likely to expand much in the next few centuries. Meanwhile, a computer system designing a more intelligence computer system could happen quickly. Each “generation” could be substantially (not just incrementally) “smarter” than the previous generation. Looked at from this perspective, the “singularity” occurs because artificial intelligence will expand exponentially. In turn, this will mean profound changes in the way humans relate to machines and how humans relate to each other. Or, so the story goes. Since we have not yet actually reached this hypothetical point, we have no certainty as to what will happen. But in this series of essays, I will examine some of the possible futures that I see.

Of course, I have substituted “Turing” here for “Einstein.” While Einstein profoundly altered our view of the physical universe, Turing profoundly changed our concepts of computing. Arguably, he also did a lot to win World War II for the allies and prevent possible world domination by Nazis. He did this by designing a code breaking machine. To reward his service, police arrested Turing, subjected him to hormone treatments to “cure” his homosexuality and ultimately hounded him literally to death. Some of these events are illustrated in the recent (though somewhat fictionalized) movie, “The Imitation Game.”

Turing is also famous for the so-called “Turing Test.” Can machines be called “intelligent?” What does this mean? Rather than argue from first principles, Turing suggested operationalizing the question in the following way. A person communicates with something by teletype. That something could be another human being or it could be a computer. If the person cannot determine whether or not he is communicating with a computer or a human being, then, according to the “Turing Test” we would have to say that machine is intelligent.

Despite great respect for Turing, I have always had numerous issues with this test. First, suppose the human being was able to easily tell that they were communicating with a computer because the computer knew more, answered more accurately and more quickly than any person could possibly do? (Think Watson and Jeopardy). Does this mean the machine is not intelligent? Would it not make more sense to say it was more intelligent? 

Second, people are good at many things, but discriminating between “intelligent agents” and randomness is not one of them. Ancient people as well as many modern people ascribe intelligent agency to many things like earthquakes, weather, natural disasters plagues, etc. These are claimed to be signs that God (or the gods) are angry, jealous, warning us, etc. ?? So, personally, I would not put much faith in the general populous being able to make this discrimination.

Third, why the restriction of using a teletype? Presumably, this is so the human cannot “cheat” and actually see whether they are communicating with a human or a machine. But is this really a reasonable restriction? Suppose I were asked to discriminate whether I were communicating with a potato or a four iron via teletype. I probably couldn’t. Does this imply that we would have to conclude that a four iron has achieved “artificial potatoeness”? The restriction to a teletype only makes sense if we prejudge the issue as to what intelligence is. If we define intelligence purely in terms of the ability to manipulate symbols, then this restriction might make some sense. But is that the sum total of intelligence? Much of what human beings do to survive and thrive does not necessarily require symbols, at least not in any way that can be teletyped. People can do amazing things in the arenas of sports, art, music, dance, etc. without using symbols. After the fact, people can describe some aspects of these activities with symbols.But that does not mean that they are primarily symbolic activities. In terms of the number of neurons and the connectivity of neurons, the human cerebellum (which controls the coordination of movement) is more complex that the cerebrum (part of which deals with symbols).

Fourth, adequately modeling or simulating something does not mean that the model and the thing are the same. If one were to model the spread of a plague, that could be a very useful model. But no-one would claim that the model was a plague. Similarly, a model of the formation and movement of a tornado could prove useful. But again, even if the model were extremely good, no-one would claim that the model constituted a tornado! Yet, when it comes to artificial intelligence, people seem to believe that if they have a good model of intelligence, they have achieved intelligence. When humans “think” things, there is most often an emotional and subjective component. While we are not conscious of every process that our brain engages in, there is nonetheless, consciousness present during our thinking. This consciousness seems to be a critical part of what it means to have human intelligence. Regardless of what one thinks of the “Turing Test”, per se, there can be no doubt that machines are able to act more accurately and in more domains than they could just a few years ago. Progress in the practical use of machines does not seem to have hit any kind of “wall.”

In the next blog, we begin exploring some possible scenarios around the concept of “The Singularity.”

55.683257 12.588479

Ban Open Loops: Part Two – Sports

14 Friday Aug 2015

Posted by petersironwood in management, psychology, sports

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AI, cognitive computing, Customer experience, customer service, education, learning

Sports and open loops.

Sports offers a joy that many jobs and occupations do not. A golfer putts the ball and it sinks into the cup — or not. A basket-baller springs up for a three pointer and —- swish — within seconds, the shooter knows whether he or she was successful. A baseball hitter slashes the bat through the air and send the ball over the fence —- or hears the ball smack into the catcher’s mitt behind. What sports offers then is the opportunity to find out results quickly and hence offers an excellent opportunity for learning. In the previousiPhoneDownloadJan152013 593 entry in this blog, I gave examples of situations in life which should include feedback loops for learning, but, alas, do not. I called those open loops.

Sports seem to be designed for closed loop learning. They seem to be. Yet, reality complicates matters even here. There are three main reasons why what appears to be obvious opportunities for learning in sports is not so obvious after all. Attributional complexity provides the first complication. If you miss a putt to the left, it is obvious that you have missed the putt to the left. But why you missed that putt left and what to do about it are not necessarily obvious at all. You might have aimed left. You might not have noticed how much the green sloped left (or over read the slant to the right). You may not have noticed the grain. You might not have hit the ball in the center of the putter. You might not have swung straight through your target. So, while putting provides nice unambiguous feedback about results, it does not diagnose your problem or tell you how to fix it. To continue with the golf example, you might be kicking yourself for missing half of your six foot putts and therefore three-putting many greens. Guess what? The pros on tour miss half of their six foot putts too! But they do not often three-putt greens. You might be able to improve your putting, but your underlying problems may be that your approach shots leave you too far from the pin and that your lag putts leave you too far from the hole. You should be within three feet of the hole, not six feet, when you hit your second putt.

A second issue with learning in sports is that changes tend to cascade. A change in one area tends to produce other changes in other areas. Your tennis instructor tells you that you are need to play more aggressively and charge the net after your serve. You try this, but find that you miss many volleys, especially those from mid-court. So, you spend a lot of time practicing volleys. Eventually, your volleys do improve. Then, they improve still more. But you find that, despite this, you are losing the majority of your service games whereas you used to win most of them. You decide to revert to your old style of hanging out at the baseline and only approaching the net when the opponent lands the ball short. Unfortunately, while you were spending all that time practicing volleys, you were not practicing your ground strokes. Now, what used to work for you, no longer works very well. This isn’t the fault of your instructor; nor is it your fault. It is just that changing one thing has ripple effects that cannot always be anticipated.

The third and most insidious reason why change is difficult in sports springs from the first two. Because it is hard to know how to change and every change has side-effects, many people fail to learn from their experience at all. There is opportunity for learning at every turn, but they turn a blind eye to it. They make the same mistakes over and over as though sports did not offer instant feedback. I think you will agree that this is really a very close cousin of what people in business do when they refuse to institute systems for gathering and analyzing useful feedback.

If learning is tricky —- and it is —- is there anything for it? Yes. There is. There is no way to make learning in sports —- or in business —- trivial. But there are steps you can take to enhance your learning process. First, be open-minded. Do not shut down and imagine that you are already playing your sport as well as can be expected for a forty year old, or a fifty year old, or someone slightly overweight or someone with a bad ankle. Take an experimental approach and don’t be afraid to try new things. Second, forget ego. Making mistakes are opportunities to learn, not proof that you are no good. Third, get professional help. A good coach can help you understand attributional complexity and they can help you anticipate the side-effects of making a change.

Soon, I suspect that the shrinking size and cost and weight of computational and sensing devices will mean that training aids will help people with attributional complexity. I see big data analytics and modeling helping people foresee what the ramifications of changes are likely to be. There are already useful mechanical training aids for various sports. For example, the trade-marked Medicus club enables golfers to get immediate feedback during their full swings.as to whether they are jerking the club. Dave Pelz developed a number of useful devices for helping people understand how they may be messing up their putting stroke.

It may take somewhat longer before there are small tracking devices that help you with your mental attitude and approach. We are still a long way from understanding how the human brain works in detail. But it is completely within the realm of possibility to sense and discover your optimal level of stress. If you are too stressed, you could be prompted to relax through self-talk, breathing exercises, visualization, etc. You do not need technology for that, but it could help. You may already notice that some of the top tennis players seem to turn their backs from play for a moment and talk to an “invisible friend” when they need to calm down. And why not? Nowhere is it law that only kids are allowed to have invisible friends.

“The mental game” and which kinds of adaptations to make over what time scales are dealt with in more detail in The Winning Weekend Warrior How to Succeed at Golf, Tennis, Baseball, Football, Basketball, Hockey, Volleyball, Business, Life, Etc. available at Amazon Kindle.

Intra-Psychic Learning

08 Saturday Aug 2015

Posted by petersironwood in psychology

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AI, cognitive computing, learning, sports

Intra-Psychic Learning plays a crucial yet largely unacknowledged role in human intelligence. It will also play a critical role in so-called “artificial intelligence” or “the singularity.” In general, the paradigm most talked about in learning, whether by psychology professors or the general public, focuses on the role of external experiences. Famous examples include Pavlov’s dogs who exhibited classical conditioning. A bell was rung whenever food was presented and eventually the bell sound alone caused the dog to salivate. This works for humans as well. Just watch someone cut open a fresh lemon and you will find yourself puckering up and salivating! In operant conditioning, a rat learns, probably through a shaping process, that some behavior, say, pressing a lever, results in a reward such as receiving a food pellet. Eventually, the rat presses the lever. Both of these kinds of mechanisms are important and play a part in animal learning as well as human learning. Both kinds of learning are useful for AI as well. In humans (and to some extent in other animals as well), you do not have to “be in the loop” in order for learning to take place. You can *observe* another person getting a reward doing X and you might immediately try that behavior for yourself. Indeed, human beings take this one step further and can be induced to try (or not try) something based on what someone *says* about a behavior leading to a consequence. You don’t *have* to touch a hot stove and get burned or even watch someone else get burned by touching a hot stove in order to fear touching a hot stove. For most people most of the time, you can be told about hot stoves and that is enough. All these forms of learning focus on personal, observed, or bespoken information that actually exists about consequences in the real world.

However, there is another important way that we learn and it is based on checking intermediate results against each other without the need for any ground truth observation in the real world. I first mentioned this in my dissertation. I was studying human problem solving and fascinated by the observation that human chess players, who have excellent memories for real chess positions, would often examine one branch of a move tree, study another branch and then return to study the first branch again. This is not likely because they forgot. Instead, I believe that looking at the second branch taught them fundamental things about what was true for this particular chess position, and they then used that information to re-evaluate what they saw during their re-examination of the first portion of the game tree. Notice that in all of this thought process, they had not actually made a move in the real world and not seen their opponent’s actual response. They certainly did not yet get feedback about the ultimate outcome of the game.

In chess, as in many if not most endeavors in life, one may learn a great deal by examining things from various mental angles and comparing the results without waiting for actual feedback from the external world. Consider the case of a playwright writing a script. As they are writing, they are imagining the action, the facial expressions, the tone of voice. They are “checking” how the various characters react to what is being done and said. If something doesn’t “ring true” they will alter what they are writing. Of course, this process is not perfect and they may well make additional changes based on a reading and based on rehearsals. But many of the potential paths are already examined, selected and modified based on imagination alone.

Consider another interesting case that was extremely common through most of our evolutionary history and is still somewhat common today. A person walks through a physical environment. As they walk, they see before them a host of objects in a hypothesized set of physical relationships. In many cases, the information that is presented is extremely minimal at first. It is hard to tell whether that is a stranger over there or your cousin Bill. That looks like an oak tree, but maybe not. Is that a painting of some cedar trees on the side of that building or are those actual cedar trees over there? The brain is making a huge number of perceptual hypotheses about what these objects are and how they are arranged. As you move forward, you gain more detailed information. Now, you can clearly see that that is not your cousin Bill. That tree is definitely a sugar maple. Those are just well executed paintings of cedar trees and so on. You can use the difference in hypothesis weights between every two physical steps to update the weighting functions on all these perceptual hypotheses! You need not wait until you actually get verification that that is a maple tree. You do not wait until you reach the Bill-like stranger to make a modification in your weighting functions. In fact, you will probably pay little more attention to this figure as you approach. You already have enough information to learn. If, indeed, as you approach still more closely and Uncle Bill calls out to you —- making you suddenly realize you have prematurely concluded this was not Bill — you will again update your recognition function weightings. This may even come to consciousness and you may remark, “Uncle Bill! I hardly recognized you without your beard!”

This type of learning also plays an important part in improving sports performance. As a person improves their skill in golf, basketball, tennis, baseball, etc., they begin to anticipate earlier and earlier whether they have “executed” the move properly. An experienced tennis server, for example, generally knows long before their serve is called “out” that they have made an error. This process is not infallible, of course, but it is statistically better than chance, and for very skilled athletes it is much better than chance. You can see it when a slugger hits a home run and they take a skip step and watch the ball go out of the park. (There can be a downside to this facility of intra-psychic learning in sports under certain circumstances as explained in chapter 23 of The Winning Weekend Warrior). This means that the skilled athlete gets “feedback” from their own mental model of what they did critical seconds before a beginner does who must wait for feedback from the real world.

These kinds of phenomena are not limited to sight, or indeed, any one sense. You hear a very faint noise. You imagine it to be a cardinal singing. As you walk closer to the bird, you get a better signal and are more certain it is a cardinal. You can use the difference in certainty to internally reward those neuronal paths who were shouting “cardinal! cardinal!” And, you demote those neuronal paths who were shouting, “car backfire” or “firecracker” or “church bell.” If you get close enough to see the cardinal, you do even more internal tuning based on the inter-sensory verification. Similarly, if you walk toward what appears to be an uneven patch in the terrain, you imagine what you must do to compensate for that variation in the terrain. As you step on the uneven spot, your tactile and kinesthetic senses give you feedback about the terrain. You use this panoply of information from various senses to tune all of them.

While it is vital that, at the end of the day, we obtain feedback about actual consequences, a huge amount of human learning takes place simply by comparing what we think we know based on scant evidence to what we think we know based on slightly less scant evidence. I believe we are doing this continually within and across all our senses and that it actually accounts for the majority of our learning.

The Winning Weekend Warrior

Learning by modeling; in this case by modeling something in the real world.

Learning by modeling; in this case by modeling something in the real world.

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