Projects
Today I completed a tiling job. I had started it in the fall, during a weeklong vacation from work. I set the underlayment and stone tile in a matter of a few days, but the thinset adhesive I used to bed the tile needed longer than expected to harden, and it was several weeks before it was possible to advance to the final stages: sealing the tile and applying grout. I managed to apply two coats of the quick-drying, volatile sealant over the course of three November days warm enough to keep the windows open. But the project languished as work, school, and the holidays took over my time and motivation.
My husband and I tried not to use the floor in its ungrouted state, but it is difficult to avoid using a floor, even one in a less-used area of the house. This is our mudroom. It is not our main entry, but is one we use for stomping in and out with dirty boots, firewood, tools, and trash. I resigned myself to using the room, knowing the crevices would need a good vacuuming prior to the final grouting, and reminding myself that extra steps are not unusual when one is simultaneously creating a home and living in it.
Finally, today, home on another vacation, I blocked the half-day required to grout the floor. Grouting is messy, tedious, demanding. One applies the sticky sludge to the cracks between the tiles, pressing it in with a rubber float and swiping off the excess before switching to a wet sponge to clear the surface of residue. The grout starts to harden in thirty minutes, so it’s important to work in sections to keep a dried haze from forming on the tile surface. The grout lines must be tooled evenly, smoothed while still damp. And the whole surface needs a final wipe and scrub before it hardens for good.
As with most creative endeavors, this one provided me moments of deep despair, when success seemed elusive, even impossible. The thinset was wrong for my application. The Indian slate tiles proved maddening to work with, being crumbly, brittle, and uneven. Grouting required innumerable buckets of fresh water, sponge wringing, and paper towels.
I’m not an accomplished tiler, having previously tiled only three small floors and a shower stall. But I am a determined tiler, someone who has undertaken, since a young age, many complicated projects I wanted done but wasn’t sure, exactly, how to do. In such projects, there is an almost inevitable moment when I say (or swear) to myself: This is a mess, a catastrophe. This will never come together. But as I continue to work, to test options and make progress, I learn how to solve the problems the project presents. The result is almost never what I had anticipated, planned, and expected. But the result is achievable, and inevitable. The only way to do a project is to do it, to make it happen.
Knowledge Workers Need Periods of Inattention
"I had this idea while I was in the shower..." How many times have you used that sentence to introduce your new idea that was so novel, so compelling, you knew it couldn't fail? Or maybe you weren't in the shower—maybe you were weeding the garden, chopping wood, or simply stepping away from your desk for a fresh cup of tea, when suddenly the puzzle you'd been trying to untangle for hours, days, or weeks is solved in an instant?
This is the power of thinking without thinking, the knowledge you can suddenly access when you turn your conscious brain off. It's the spontaneous understanding that arises when you've fully internalized a problem, but haven't fully formulated a solution. It's the idea you get when you stop thinking so hard.
Malcolm Gladwell gets at some of this in Blink, but there he's mostly talking about applying deeply cultivated expertise to new situations. I'm talking about another predicament, and one that's probably more common: the insight we can access when we pause, step back, and let our minds work on the problem while our hands work on something else. It's the idea we get when we remove ourselves from deliberation, and let ourselves sink into ideation.
Sometimes an idea comes during reverie—staring out the window, perhaps—but often it comes when we simply stop paying direct attention to an idea and start up a physical process. The process might be weeding or washing or driving, but it's likely one that uses our motor skills more than our intellect. I find that natural and repetitive actions let me work without a lot of conscious thinking, letting my mind explore freely. These are some of my most synthetic moments; the moments when I have good ideas.
It happened one morning this week while commuting in to work. I had the radio on, and was thinking about and noticing a lot of things along the way: the crows by the side of the road, my husband's trip this week to Chicago, my deadline at work. I wasn't thinking about anything in particular; I was paying attention, in a very inattentive way, to the road in front of me, and to the stories in my head. Suddenly I had an idea for a marketing promo to try at work, an idea that synthesized a story on the radio with the time of the year with my client's current marketing goals. When I got to work I tried my new idea, and it worked, famously. I don't believe I could have arrived at that idea while sitting at my desk trying to think it up. I needed to be away from the demands of focused thinking, steeped in a fluid near-trance of mechanical activity, to arrive at my good idea.
Knowledge workers—those of us who make our living by our subject matter expertise rather than by our manual labor—need to remember this important phenomenon. In spite of everything, we need, sometimes, to stop paying attention, to stop trying so hard. This might be the best way to produce our best ideas.
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.
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.
