engaging experience

One Hundredth Post

This is my one hundredth post. The gravity of that milestone has muted me for weeks. What can I say that's special enough for the occasion?

Since launch, engaging experience has enjoyed 10,139 pageviews (and one more, now that you're reading this).  The average is 10.42 per day, the majority to posts I blogged live at the AI@50 Conference. Visitors seem also to be interested in meaning. Some even come straight to the home page. Now that's flattery.

So this one hundredth post is a tautology. It is about itself. And now I can move on.

Absolute Pronouncements Corrupt Absolutely

In the last few days I've been treated to an overabundance of blanket pronouncements by experts. Here's a sampling:

"There are only five or six, maybe seven, real authors alive today."
"There are really only three or four pieces of literature that have ever been written about the Holocaust."
"There is almost no literature now. There's a lot of writing, but little of it is literature."
"The Back button is the button of doom."
"Users do not come to browse your site. They have a purpose."
"Web users want actionable content; they don't want to fritter away their time on (otherwise enjoyable) stories that are tangential to their current goals.

Okay, you're entitled to you opinion, and I'm entitled to mine. If you back up your opinion with data, you're more likely to convince me. But if you postulate easily disprovable axioms, or if you postulate axioms that are impossible to prove or disprove, I'm going to shut my mind to you. And that's not really what a pundit wants, is it?

Simplicity

I've been following the work of John Maeda off and on for a few years. Maeda is a graphic designer, artist, and computer scientist who teaches at the MIT media lab. So we're a lot alike, except that he's famous.

Maeda's been writing and thinking recently about simplicity. He has a new book, The Laws of Simplicity, which I have not read, and a new eponymous blog, which I have, and recommend.

Here are Maeda's 10 Laws:

• Law 1: Reduce - The simplest way to achieve simplicity is through thoughtful reduction
• Law 2: Organize - Organization makes a system of many appear fewer
• Law 3: Time - Savings in time feel like simplicity
• Law 4: Learn - Knowledge makes everything simpler
• Law 5: Differences - Simplicity and complexity need each other
• Law 6: Context - What lies in the periphery of simplicity is definitely not peripheral
• Law 7: Emotion - More emotions are better than less
• Law 8: Trust - In simplicity we trust
• Law 9: Failure - Some things can never be made simple
• Law 10: The One - Simplicity is about subtracting the obvious and adding the meaningful

Verbing Nouns

In college we learned to use the word "party" as a verb. The grammarians would have argued with us about this, but nobody invited them, because they were no fun. They would just hang on the walls, drinking herbal tea, smoking clove cigarettes, and refusing every invitation to dance.

Anyway, over at Language Log, linguist Geoffrey Pullum writes about the latest fashion in verbing nouns: the practie of completely redefining things as actions. In a recent Salon article, for instance, the author insists that "Science" is a verb. Pullum writes:

It has become clear to me that there's no point in railing against this trope, or telling these people to get the dictionary out. They cannot conceivably think they are talking about the correct part-of-speech classification of words. They don't need or want a dictionary. When they say "is a verb" they clearly mean something like "is something that must be engaged in, or be engaged with, as an active practice".

So, okay, here's my stake in the ground: "experience" is a verb. And "engaging experience" is even more verby. (But verby definitely isn't a verb.)

Signing Music

NPR ran a story last night about making the concertgoing experience rich and meaningful for those with hearing impairments. Sign language interpreters rehearse for weeks to get the songs right:

Signing music is not about word-for-word translation, Bailey said. It is about trying to convey meaning. Sign interpreters think conceptually, considering flow, rhythm and whether the signs convey visually the mood that each song tries to convey.

Read more, listen to the story, and watch clips of the interpreters on the story page.

Engaging Experience Hits Wikipedia

Engaging Experience's brief abstracts of AI@50 papers are now posted on Wikipedia's AI@50 article.

[AI@50] Selected Submitted Papers: Future Possibilities for AI

[AI@50]
Filene Auditorium, Moore Hall
Dartmouth College
Hanover, NH

Conference notes by Meg Houston Maker

Eric Steinhart
Survival as a Digital Ghost

A "digital ghost" is an instantiation of you. [MHM: A machine doppelganger.] The name comes from William Gibson. It's a personalized artificial intelligence, and it could maybe pass a personalized Turing test, convincing your friends and loved ones the machine was you. So it would have to know your history, beliefs, etc.

People now have massive amounts of personal information; there is a digital vapor trail that you leave behind you as you go through the world: videos, images, bookmarks, etc. You go through life generating data, and much of it is not saved or archived. But if you were to start archiving it, you'd get a big temporal database. A little work is being done on this, there's a SIG in the ACM for it, and MicroSoft has its "my life's bits" project.

Think of this collection as a digital diary. Of course you would have to tag all that information -- that's my wife, that's my dog. Tagging and indexing is not there yet -- they're too cumbersome. So instead, you might want a personalized AI that could look at the diary and interpret the diary data as if it were you. The AI tool would have to be a model of your psychology that would interpret it the way YOU would interpret it. Some recomendation engines (Amazon.com) are pretty promising and pretty good at capturing preferences. Mate selection in online data sites could certainly use this technology. E.g. the website Hot or Not, where you rate pictures of people; these could be sent to dating sites.

So, how do you get this? One recommendation is a questionnaire with 20,000 questions you could answer to construct a profile. No way! Too onerous. But maybe we could analyze your telephone conversations, GPS data about where you are at any given time. Or you could do simple query-response about where you were or why you did a certain action. Descriptive and explanatory, in other words. Brain implants are another possibility, or non-invasive brain scanning approaches, to create a highly-personalized analog of your own architecture.

Digital ghosts should also be a simulation of your body. These could be pretty much generic models that could then be tuned with personal history and prefs. Faces and voices are finely tuned personal features, and are interesting to others, so it would be essential to incorporate these. It would have speech synthesizers that could replicate your voice. Your medical data could be incorporated. We can then build a model that looks like you in addition to having your history.

How might we interact wtih something like this? Maybe initially it would be chat-based, but in a more advanced state it would be an animated VR or simulation. It raises a lot of privacy questions, and issues of restricted access.

C. T. A. Schmidt, LeMans and Sorbonne
Did You Lean That "Contraption" Alone with Your Little Sister?

Schmidt's research areas: the dialogical aspects of cognition and communication, and the context for learning -- the physical environment including other people. Key question that interests him: how can the machine learn if it can't communicate?

Robotics-embedded AI should be, or maybe must be, dialogical. In order for advanced humanoid robotics to be fully accepted by others, they will need the proper identity features or they will remain at the fringe of human communities.

Social roles and human institutional involvement seem to have been left out in all forms of AI. To make robots dialogical, we need to work on the pragmatic aspects of communication.

Michael Anderson, U. of Hartford
Susan Leigh Anderson , UCONN
The Status of Machine Ethics: A Report from the AAAI Symposium

Michael is a computer scientist, and Susan is a philosopher. They're here presenting summaries of the papers presented recently at the AAAI Symposium on machine ethics.

The time has come for adding an ethical dimesion to machines -- CareBots, unmanned aircraft, defense uses, etc. This will ensure their actions, especially in self-evolving or learning systems, remain ethical. (Note to contrast this with computer ethics, which concerns hacking and the like.)

The Nature of Machine Ethics:
1) Normative computer agents: computers are normative, because they're designed with a purpose in mind, but not necessarily an ethical purpose. Their performace is assessed according to how well they do what we've told them to.

2) Ethical Impact agents: these not only perform certain tasts, but have an ethical impact on the world. E.g. a robot jockey that guides camels in races, replacing young boys who are slaves who are otherwise forced to do this.

3) Implicit ethical agents: these are machines that are programmed to behave ethically and are designed to perform ethically. E.g. ATMs that are programmed not to cheat the bank or its customers, and automatic pilots entrusted with human safety.

4) Explicit Ethical Agents: machines that are able to calcuate best actions in ethical dilemmas.

5) Autonomous ethical agents: these can calculate the best action in an ethcial dilemma and function independently. E.g., a robo-soldier, sent into battle, which makes ethical decisions that guide its own behavior.

6) Full ethical agents: this term is used to describe human ethical decision makers. Are intentionality, consciousness and free will essential to genuine ethical decision making? Would it be sufficient that machines have "as if it does" versions of these qualities? Could it pass a "Moral Turing Test" for understanding ethics?

If humans create laws that allow them to mistreat entities that resemble human beings, it increases the chances that they will find it easier to mistreat human beings.

Developing an explicit ethical agent is a compelling goal of AI. Many approaches are being pursued. Democracy-dependent algorithms have been created, wherein agents could look up ethical information on the web, giving the machine a kind of "average" or "averaged" ethics. This is probably not good enough. Other methods use neural nets, or offer the human user a case-based reasoning engine with natural language inputs and outputs. Other researchers recommend using deontic and default logics to iteratively construct a theory of ethics. The Andersons have developed a system that extrapolates from experts' intuitions about particular ethical dilemmas.

See machineethics.com

Marcello Guarini, University of Windsor
Computation, Coherence, and Ethical Reasoning

Thagard-Verbeurgt Coherence Theory of Constraints (1998): hypotheses, evidence statements, negative and positive constraints. Used in moral reasoning problems. This system can be encoded in an associative neural network.

Thadarg identifies four types of coherence reasonings contributing to ethical reasoning: explanatory, deducive, deliberative, and analogical coherence. Ethical reasoning is a "multi-coherence" problem. The idea is that it can provide prescriptive or normative recommendations.

People obviously don't arrive at ethical decisions through brute force computation. We don't work out coherence values using internal computation. And we certainly don't do so consciously.

The roll-up: Guarini is critical of the Thagard-Verbeurgt approach, and that coherence is required for moral reasoning in machines. Read his paper for more.

[AI@50] Final Poll

40% believe that to produce a successful artifact, the most important approach is to make it interact with the world in which in it operates. Meanwhile, 44% think it should primarily be made to encode and exploit knowledge in the domain

60% strongly agree that AI should take a multidiscipmalary approach, incorporating stats, machine learning, linguistics, computer science, cognitive psychology, philosophy, and biology.

[AI@50] Selected Submitted Papers: Future Strategies for AI

[AI@50]
Filene Auditorium, Moore Hall
Dartmouth College
Hanover, NH

Conference notes by Meg Houston Maker

J. Storrs Hall, autogeny.org
Self-improving AI: An Analysis

Self-improvement and self-improving machines: from Turing's "child machines" that would learn and grow, to so-called "stick-built" AI that produces intelligence but doesn't necessarily learn and grow. But human adults do learn and grow, so we need an AI that does that, too.

Universal Learning Machines
There are limits to learning: many animals learn, and have speciific learning programs, but none improve recursively without limit. On the other hand, it's not clear that there are limits to learning for some individuals, and as a species we have been successful, so it's possible that limitless learning is a factor in our collective success. Similarly, our biology is so complex that it's likely we have more capacity. And it also looks like evolution has "learned how to learn;" we didn't evolve to do physics and chemistry, so continued learning was required for these advances.

[MHM editorial: this all seems a bit unrigorous. Is he talking about the simple acquisition of knowledge, or about learning as a process? He's using a lot of analogies to make the case that a universal learning machine is possible. Also, he's conflating the capabilties of the scientific community as a whole, in its capacity to develop a machine that can learn to play chess better than Kasparaov, with the capabilities of an individual human learning agent.]

Selmer Bringsjord
The Logicist Manifesto+

Why the +? You shouldn't just use logic to do AI, you should use logic to discuss ai, too. Logic occupies a unique, indestructibly central position in human cognition.

Types of AI:

- Strong AI: We can engineer systems that have attributes constitutive of human personhood; this is replication, not simulation.
- Weak AI: engineer systems that appear to be intelligent; this is simulation
- Strong/Weak AI: we can manage to create a system that is behaviorially indistinguishable from mere animals and humans, over finite intervals of time.

Strong AI is not likely in 50 years. Weak AI we already have, and Strong/Weak AI, maybe.

How can we ensure that robots always will behave in an ethically correct manner? How can we know ahead of time, via rationales expressed in clear English? We should regulate the behavior of robots wtih computational logic, so that all robotic actions will be sure to be ethical.

How Can We Ensure This?
- Humans select an ethical theory, principles, and rules
- The selection is formalized in logic
- It is mechanized
- Every action that's performed has to be permissible according to this mechanization.

Vincent Muller, American College of Thessaloniki
Is There a Future for AI Without Representation?

Can we do AI without representing the world?

Rodney Brooks suggests a layered subsumption architecture, and situating (embodying or grounding) the machine in the world. He rejects central control over emergence of intelligence. And he rejects representation in the classical sense.

So, what is representation? When should we say that X represents something? What are the conditions for something being a representation? And under what conditions does something represent something else? Representation is dependent on people's intention that something represents something else.

Icons - represent what they represent (e.g. portriat painting)
Indices - connected by a causal connection (e.g. smoke indicates fire)
Symbols - connected to what they represent through convention (like words of a natural language)

Information and Representation: a rock is wet if rainy, warm if sunny. The rock conveys information, but it doesn't represent anything. On the other hand, some things are made to convey informaton for us (clocks, thermometers).

Hilary Putnam, Jerry Fodor and others think that representational or intentional features of symbols and signs must be explained with the help of mental representations. But how do you find out the function (either natural or conventional) of the representation? Causal connection is necessary part of the equation.

Brooks's New AI appears strangely unmotivated -- discovering that classical AI is not represntation, we propose another system that is not representational.

Cognition without central control
Traditional AI/Cog Sci/Philosopy of mind holds that central control is assumed, and then representation is deemed necessary for the agent to succeed.

Theses:
- cognition is the processing of outside information
- the information comes in the form of representations
- the processing of representations is computational

The first two of those make the representational theory of the mind, and all three together yield the computational theory of mind (and the PSS hypothesis).

Indications against central control:
Mark Bickhard: critique of "encodingism:" - the mind does not decode and encode information: because who would do the decoding and encoding? Ways out of this: remove the notion of encoding from that of representation via natural functions, and abandon the notion of mental representation as a causal factor.

John Searle's Chinese Room Argument
Searle looked at the processor in a computer and asked what that agent understood: nothing! The systems reply agrees that the agent does not understand, but claims that the whole system, of which it is a part, might. Buty why should the system understand?

Brooks's inspried response: the system understands because there is no central agent in the system.
Abandon the cartesian theatre: searle was asking the wrotn question, just like eerybody else.

Embodiment of cognition: intelligences in the body as a whole. This involves the integration of cognition and action. Perceiving isi really a way of acting. Perception is not something that happens to us (see e.g. Noe). So there is an indication that cognition, emotion, and volition must be integrated.

We do not get conscious representation without central control. Conscious experience is the experience of "what it is like" -- the having of experience: I am the one that does the perceiving, thinking, palnning, and acting. Consciousness requries something being conscious, and this requires a central agent. The progelm is people move from that to saying that all of cognition has to be centrally organcized. But we don't know what we can do (or do without) central control.

[AI@50] Future Interactions with Intelligent Machines

[AI@50]
Filene Auditorium, Moore Hall
Dartmouth College
Hanover, NH

Conference notes by Meg Houston Maker

Daniela Rus, MIT
Making Bodies Smart

Robotics is a dream to build a machine in your ownd image; building platforms for stuying life and intelligence, intelligent conection of perception and action, outcome of visiouaries with energy and resources; the next disruptive technology. The field has been fueled by crazy dreamers.

The history of robotics is the history of automata, Golems, Frankensteins, all the way to current robotics. There has been some trepidation about these "soulless beings."

The hard problem is to capture what happens in the interaction between computation and the physical world. Hand-eye coordination, which we do to survive, is really one of the hardest problems.

Robotics methodology timeline:
- Knowledge representation -> symbolic reasoning
- Blocks world - kinematics
- Motion planning - automation and manufacturing
- Reactive control - exploration
- Probabilistic/expectation/verification - mobile/field, space, underwater
- Human-centered - home, medical, entertainment
- Modular/distributed systems - self-organization, RoboCup

Things we take for granted in robotics (and need to do our work): GPS, Real-time vision and stereo, WiFi, Rapid prototyping, GHz processors, Gumstix, Sick laster rangefindesrs, etc. These have all developed in the last 10 years, and have been a boon.

What we've gained: localization, planning and navigation, 3D perception. However, we have many challenges ahead. Of these, uncertainty is one of the biggest; the world is unpredictable, and the robot has to be able to handle it. Perception also remains a major challenge. Robotics has made huge strides in areas of navigation, but manipulation, which is just as important as navigation, is far behind. We need to be able to develop algorithms for enabling computers to interact better with humans, and we need deeper connections between perception and action.

"Simulations are always doomed to succeed:" taking the robots from the lab to the field, from simulation to actual world, remains a big challenge.

Many robots have not been constructed with the explicit goal of understanding human intelligence. Rather, they have been more task- and motion- oriented. What is the environment of the robot? Is it fully known? What is available to it? This is robotics for structured environments, and comprises the last few items on the list above (motion).

Rus shows her work in with robotic self-organization; really smart parts that know how the whole is to be organized. In application, imagine a robot encountering a river without a bridge, and constructing a bridge for itself.

Predictions for the future:
- Robotics will give us new theoris for intelligence -- we need to account for situatedness and embedding in natural world; we will move beyond Turing intelligence
- Robots/actuation will be pervasive
- Robots will accomplish the ultimate automation -- on-demand factories
- Robots will save the environment -- biologists are overwhelmed by simple tasks like data collection; it's not just to monitor environment, but also to effectively repair processes in which the system is damaged
- Robots will extend and enhance life -- the convergence of DNA and silicon will happen.

Human-centered robotics will enable us to move from personal computers to personal robots.

Sherry Turkle, MIT
From Building Intelligences to Nurturing Sensibilities

Technologies are never just tools; technologies are "evocative objects." Computational entities (computers, agents, robots) become relational companions. They provoke anthromorphizing. Her objects are designed to provoke nurturing behavior.

What is a relational artifact? It's an evocative object. These present themselves as having states of mind affected by interactions with humans. They impress not just through their intelligence, but through their sensibilities. In how we interact with them and reflect on our relationship to them, we learn about our own inner states.

E.g. Kismet, Aibo, Furby, My Real Baby. This last wasn't so successful with children but was very successful with seniors in senior centers. Children and seniors are both somewhat vulnerable populations. With seniors, sometimes it's interactions with robotics or nothing, and in some cases the robot is "good enough" to satisfice, as it were, for human interaction. But the robot is not really empathetic. This could be misused, morally, because the question is who will be given (actively) a robot as the best our culture can do to provide them with companionship: the disabled, the elderly, children.

The art and sciene of reltional artifacts is part of the art and science of understanding human psychology and human vulnerability. Interaction wtih these objects move from the psychology of project to the psychology of engagement and object relations. In talking about relationsl artifacts, the discourse of aliveness moves from the objects competetency (physical or psychological) to the person's relationship with the object. They push our Darwinian buttons. People respond to these objects as though they were sentient and feeling objects.

Children can differentiate -- and have to learn to differentiate -- between animal alive-ness and robotic alive-ness, between animal love and robotic love. Children nurture robots; they think of a nonfunctioning robot as wounded, they show belligerence towards robots when angry, and view robots as dialogue partners in conversation.

Nurturance may turn out to be the killer app.

In the 1980s Turkle met a teenager who spoke about the pleasure of working with a personal computer because it was like putting a piece of her mind into the computer's mind. Twenty years later, children and elder nursing home residents who have robotic agents report that they feel emotionally connected with the robot. They feel love. This is a sea change.

Relational artifacts present themselves as a possible Self objects. Now, Self objects are positive psychology when the object is a person. It presents a narcissistic experience to project self onto others, but robotics might be used this way. Perhaps there is therapeutic potential here, but what are we to make of this? Is it good for us that we have robots that convince us they care? Or will our lives be enhanced because we'll have to develop new ways of feeling toward these others, and our new kinds of relationships?