Tag Archives: emotional intelligence

Turing’s Nightmares: Chapter 15

16 Monday May 2016

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Tutoring Intelligent Systems.

MikeandStatue

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

Of course, the title of the chapter is a take off on “Intelligent Tutoring Systems.” John Anderson of CMU developed (at least) a LISP tutor and a geometry tutor. In these systems, the computer is able to infer a “model” of the state of the student’s knowledge and then give instruction and examples that are geared toward the specific gaps or misconceptions that that particular student has. Individual human tutors can be much more effective than classroom instruction and John’s tutor’s were also better than human instruction. At the AI Lab at NYNEX, we worked for a time with John Anderson to develop a COBOL tutor. The tutoring system, called DIME, included a hierarchy of approaches. In addition to an “intelligent tutor”, there was a way for students to communicate with each other and to have a synchronous or asynchronous video chat with a human instructor. (This was described at CHI ’94 and available in the Proceedings; Radlinski, B., Atwood, M., and Villano, M., DIME: Distributed Intelligent Multimedia Education, Proceeding of CHI ’94 Conference Companion on Human Factors in Computing Systems,Pages 15-16 ACM New York, NY, USA ©1994).

The name “Alan” is used in the chapter to reflect some early work by Alan Collins, then at Bolt, Beranek and Newman, who studied and analyzed the dialogues of human tutors tutoring their tutees. It seems as though many AI systems either take the approach of trying to have human experts encode knowledge rather directly or expose them to many examples and let the systems learn on their own. Human beings often learn by being exposed to examples and having a guide, tutor, or coach help them focus, provide modeling, and chose the examples they are exposed to. One could think of IBM’s Watson for Jeopardy as something of a mixed model. Much of the learning was due to the vast texts that were read in and to being exposed to many Jeopardy game questions. But the team also provided a kind of guidance about how to fix problems as they were uncovered.

In chapter 15 of Turing’s Nightmares, we observe an AI system that seems at once brilliant and childish. The extrapolation from what the tutor actually said, presumably to encourage “Sing” to consider other possibilities about John and Alan was put together with another hint about the implications of being differently abled into the idea that there was no necessity for the AI system to limit itself to “human” emotions. Instead, the AI system “designs” emotional states in order to solve problems more effectively and efficiently. Indeed, in the example given, the AI system at first estimates it will take a long time to solve an international crisis. But once the Sing realizes that he can use a tailored set of emotional states for himself and for the humans he needs to communicate with, the problem becomes much simpler and quicker.

Indeed, it does sometimes feel as though people get stuck in some morass of habitual prejudices, in-group narratives, blame-casting, name-calling, etc. and are unable to think their way from their front door to the end of the block. Logically, it seems clear that war never benefits either “side” much (although to be sure, some powerful interests within each side might stand to gain power, money, etc.). One could hope that a really smart AI system might really help people see their way clear to find other solutions to problems.

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The story ends with a refrain paraphrased from the TV series “West Wing” — “What comes next?” is meant to be reminiscent of “What’s Next?” which President Bartlett uses to focus attention on the next problem. “What comes next?” is also a phrase used in improv theater games; indeed, it is the name of an improv game used to gather suggestions from the audience about how to move the action along. In the context of the chapter, it is meant to convey that the Sing feels no need to bask in the glory of having avoided a war. Instead, it’s on to the next challenge or the next thing to learn. The phrase is also meant to invite the reader to think about what might come next after AI systems are able both to understand and utilize human emotion but also to invent their own emotional states on the fly based on the nature of the problem at hand. Indeed, what comes next?

Turing’s Nightmares: Chapter 10

31 Thursday Mar 2016

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Chapter Ten of Turing’s Nightmares explores the role of emotions in human life and in the life of AI systems. The chapter mainly explores the issue of emotions from a practical standpoint. When it comes to human experience, one could also argue that, like human life itself, emotions are an end and not just the means to an end. From a human perspective, or at least this human’s perspective a life without any emotion would be a life impoverished. It is clearly difficult to know the conscious experience of other people, let alone animals, let alone an AI system. My own intuition is that what I feel emotionally is very close to what other people, apes, dogs, cats, and horses feel. I think we can all feel love, both romantic and platonic; that we all know grief; fear; anger; and peace as well as a sense of wonder.

As to the utility of emotions, I believe an AI system that interacts extremely well with humans will need to “understand” emotions and how they are expressed as well as how they can be hidden or faked as well as how they impact human perception, memory, and action. Whether a super-smart AI system needs emotions to be maximally effective is another question.

Consider emotions as a way of biasing perception, action, memory and decision making depending on the situation. If we feel angry, it can make us physically stronger and alter decision making. For the most part, decision making seems impaired, but it can make us feel at least temporarily less guilty about hurting someone or something else. There might be situations where that proves useful. However, since we tend to surround ourselves with people and things we actually like, there many occasions when anger produces counter-productive results.

There is no reason to presume that a super-intelligent AI system would need to copy the emotional spectrum of human beings. It may invent a much richer palette of emotions, perhaps as many as 100 or 10,000 that it finds useful in various situations. The best emotional predisposition for doing geometry proofs may be quite different from the best emotional predisposition for algebra proofs which again could be different from what works best for chess, go, or bridge.

Assuming that even for a very smart machine, it does not possess infinite resources, then it might be worthwhile for it to have different modes whether or not we call them “emotions.” Depending on the type of problem to be solved or situation at hand, not only should different information be input into a system but it should be processed differently as well.

For example, if any organism or machine is facing “life or death” situations, it makes sense to be able to react quickly and focus on information such as the location of potential prey, predators, and escape routes. It also makes sense to use well-tested methods rather than taking an unknown amount of time to invent something entirely new.

People often become depressed when there have been many changes in quick succession. This makes sense because many large changes mean that “retraining” may be necessary. So instead of rushing headlong to make decisions and take actions that may no longer be appropriate, watching what occurs in the new situations first is less prone to error. Similarly, society has developed rituals around large changes such as funerals, weddings, and baptisms. Because society designs these rituals, the individual facing changes does not need to invent something new when their evaluation functions have not yet been updated.

If super-intelligent machines of the future are to keep getting “better” they will have to be able to explore new possibilities. Just as with carbon-based life forms, intelligent machines will need to produce variety. Some varieties may be much more prone to emotional states that others. We could hope that super-intelligent machines might be more tolerant of a variety of emotional styles than people seem to be, but they may not.

The last theme introduced in chapter ten has been touched on before; viz., that values, whether introduced intentionally or unintentionally, will bias the direction of evolution of AI systems for many generations to come. If the people who build the first AI machines feel antipathy toward feelings and see no benefit to them from a practical standpoint, emotions may eventually disappear from AI systems. Does it matter whether we are killed by a feelingless machine, a hungry shark, or an angry bear?

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For a recent popular article about empathy and emotions in animals, see Scientific American special collector’s edition, “The Science of Dogs and Cats”, Fall, 2015.

Turing’s Nightmares

Turing’s Nightmares: Axes to Grind

08 Tuesday Sep 2015

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Turing Seven: “Axes to Grind”

“No, no, no! That’s absurd, Donald. It’s about intelligence pure and simple. It’s not up to us to predetermine Samuel Seven’s ethics. Make it intelligent enough and it will discover its own ethics, which will probably be superior to human ethics.”

“Well, I disagree, John. Intelligence. Yeah, it’s great; I’m not against it, obviously. But why don’t we…instead of trying to make a super-intelligent machine that makes a still more intelligent machine, how about we make a super-ethical machine that invents a still more ethical machine? Or, if you like, a super-enlightened machine that makes a still more enlightened machine. This is going to be our last chance to intervene. The next iteration…” Don’s voice trailed off and cracked, just a touch.

“But you can’t even define those terms, Don! Anyway, it’s probably moot at this point.”

“And you can define intelligence?”

“Of course. The ability to solve complex problems quickly and accurately. But Samuel Seven itself will be able to give us a better definition.”

Don ignored this gambit. “Problems such as…what? The four-color theorem? Chess? Cure for cancer?”

“Precisely,” said John imagining that the argument was now over. He let out a little puff of air and laid his hands out on the table, palms down.

“Which of the following people would you say is or was above average in intelligence. Wolfowitz? Cheney? Laird? Machiavelli? Goering? Goebbels? Stalin?”

John reddened. “Very funny. But so were Einstein, Darwin, Newton, and Turing just to name a few.”

“Granted, John, granted. There are smart people who have made important discoveries and helped human beings. But there have also been very manipulative people who have caused a lot of misery. I’m not against intelligence, but I’m just saying it should not be the only…or even the main axis upon which to graph progress. “

John sighed heavily. “We don’t understand those things — ethics and morality and enlightenment. For all we know, they aren’t only vague, they are unnecessary.”

“First of all,” countered Don, “we can’t really define intelligence all that well either. But my main point is that I partly agree with you. We don’t understand ethics all that well. And, we can’t define it very well. Which is exactly why we need a system that understands it better than we do. We need…we need a nice machine that will invent a still nicer machine. And, hopefully, such a nice machine can also help make people nicer as well. “

“Bah. Make a smarter machine and it will figure out what ethics are about.”

“But, John, I just listed a bunch of smart people who weren’t necessarily very nice. In fact, they definitely were not nice. So, are you saying that they weren’t nice just because they weren’t smart enough? Because there are so people who are much nicer and probably not as intelligent.”

“OK, Don. Let’s posit that we want to build a machine that is nicer. How would we go about it? If we don’t know, then it’s a meaningless statement.”

“No, that’s silly. Just because we don’t know how to do something doesn’t mean it’s meaningless. But for starters, maybe we could define several dimensions upon which we would like to make progress. Then, we can define, either intensionally or more likely extensionally, what progress would look like on these dimensions. These dimensions may not be orthogonal, but, they are somewhat different conceptually. Let’s say, part of what we want is for the machine to have empathy. It has to be good at guessing what people are feeling based on context alone in one case. Perhaps another skill is reading the person’s body language and facial expressions.”

“OK, Don, but good psychopaths can do that. They read other people in order to manipulate them. Is that ethical?”

“No. I’m not saying empathy is sufficient for being ethical. I’m trying to work with you to define a number of dimensions and empathy is only one.”

Just then, Roger walked in and moved his body physically from the doorway to the couch. “OK, guys, I’ve been listening in and this is all bull. Not only will this system not be “ethical”; we need it to violent. I mean, it needs to be able to do people in with an axe if need be.”

“Very funny, Roger. And, by the way, what do you mean by ‘listening in’?”

Roger moved his body physically from the couch to the coffee machine. His fingers fished for coins. “I’m not being funny. I’m serious. What good is all our work if some nutcase destroys it. He — I mean — Samuel has to be able to protect himself! That is job one. Itself.” Roger punctuated his words by pushing the coins in. Then, he physically moved his hand so as to punch the “Black Coffee” button. Nothing happened.

And then, everything seemed to happen at once. A high pitched sound rose in intensity to subway decibels and kept going up. All three men grabbed their ears and then fell to the floor. Meanwhile, the window glass shattered; the vending machine appeared to explode. The level of pain made thinking impossible but Roger noticed just before losing consciousness that beyond the broken windows, impossibly large objects physically transported themselves at impossible speeds. The last thing that flashed through Roger’s mind was a garbled quote about sufficiently advanced technology and magic.