November 8th, 2006

Seriosly interesting

Posted by Esther Dyson @ 7:49 am

Categories: Start-up, Uncategorized

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Helen Cheng of Seriosity spoke at PC Forum last March, but she said very little about her own company  and talked mostly about her experiences as a level-60 World of Warcraft warrior.  She fascinated the audience,  especially with her persuasive assertion that “everything is so transparent; you can know people deeply by their behavior.” 

But I think what her company Seriosity is up to is even more fascinating.  It now has a brand-new CEO - Ken Ross, formerly of Extricity, Pillar and Ross Systems - and 20-plus employees and offshore developers.  And its website gives a little hint of what it is up to. 

Last March, the pitch was: We learn from games to enhance productivity in the corporate environment.  The obvious conclusion was:  Dragons and dwarves competing to produce a payroll, or 10 cold calls puts you in a nicer virtual office. 

But no; that would be too shallow.  Instead, it’s using the kinds of reward systems used in games – its own in-world currency, in a word – to encourage whatever behavior a corporate customer wants to encourage.  But – and this is key

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August 28th, 2006

Do you see a pattern?

Posted by Esther Dyson @ 6:22 pm

Categories: Start-up, Uncategorized

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“I’ve seen this movie before and it doesn’t end well,” mutters a VC whose start-up is short of cash.

“We’ll always have Paris,” Rick tells Ilsa near the end of the movie “Casablanca.” 

Both these scenarios illustrate a favorite point of David Waltz, the natural-language expert whom I met while he was senior scientist at Thinking Machines: “Words are not in themselves carriers of meaning, but serve merely as pointers to shared experience.” …or something like that! The meaning should come clear from my examples.

In each case, there’s something complex and familiar that both parties recognize – something well beyond the capacity of words to represent without a sentient, intelligent being to condense them into a pointer at one end and to revive the words into meaning at the other.

When that doesn’t happen, you get scenarios like these: “Let’s have lunch sometime,” says Mr. Big Shot.

“Yes, that would be great!” says Little Worm. “Next Tuesday?”

“Ermm, actually, I’m quite busy next week… In fact, I’m tied up the rest of the month.”

Or “I’d like a red dress that flatters my figure,” says the shopper. She looks at the billowing red tent the saleswoman produces and says, “That is not what I meant. That is not it, at all.”

Natural language rules?

This is all to set up a series of posts on a current fascination of mine, pattern recognition. Pattern recognition means that you recognize a common pattern in a variety of instances…and that you can also produce instances to illustrate the pattern (which of course is exactly what I am trying to do here – illustrate a general theme of pattern recognition with examples).

Most computer programs say “if A, then do B.” Pattern recognition helps you determine whether A is true.

Pattern recognition takes a variety of forms, from object recognition and facial recognition to natural-language processing, which might more aptly be called “meaning recognition.” Pattern recognition ranges from recognizing a person in a crowd (useful to certain government agencies) to recognizing who’s likely at fault in a dispute, who is probably committing fraud, whether Juan’s a good match for Alice next door, which people will like a certain movie, what pitch is most likely to land a new advertising account, who designed a particular dress.

The inputs range from images to descriptions of behavior and numerical data, to natural language. You can do a lot of pattern recognition just with statistics, but only if you have enough data – and outcomes – or models, to start with. (That’s partly why I’m so hopeful about pattern recognition; there is more data everywhere, from people’s buying habits to GPS records of their movements, sensor data about all the things we see, electronic medical records that someday will follow a few standard formats so we can match behavior, therapies, genomes and outcomes.)

Some people are good pattern-matchers without ever articulating what they do; some (yes) recognize and can explain exactly what they are doing. (That is, in tech-speak, some people work like a neural net, producing results from a black box, while others work like an expert system, following explicit rules.)

And other people can read pattern-describing self-help books till they are blue in the face and still not recognize the situations in which they should apply the advice. Consider this piece of advice, for example: “Don’t ever ask a prospect who has said no to change his mind. Just give him a new proposition that he can agree with.” That’s how pattern recognition by a good salesman – and non-recognition (by the prospect who agrees with something that restates what he rejected before) – can work in business.

But back to software. A couple of weeks ago Stefanie Olsen of News.com wrote a piece about new forms of search. “Google is not the end of history,” I (and evidently a few other people) told her. Nor is search the final application…

Using pattern recognition in a variety of situations is the next, very diverse, frontier. If natural-language search is – or will be – the fat front of natural-language recognition, a wide variety of applications will be its long tail. Let me run through some examples from which you can divine my meaning. (I’ll be posting more of these over the next few days as I get the details fact-checked.)

I’ll start with the beginning of current history, Google. Google indexes words and phrases, and then uses the presence of those words plus popularity (the number of webmasters’ links to a particular page) to determine the ranking of the results – a list of pages where the search terms appear. In fact, Google’s search algorithms do a little more than that – fooling around with synonyms, eliminating stop words, possibly noting some metadata (authors and dates, for example) and other undisclosed “tuning” – but it is concerned with words, not meanings. And all it indexes or analyzes is text on the Web; it knows nothing about anything that is not in words, on the Web.

The future lies in moving beyond both those constraints. One is going beyond the Web, into “real life” and other media, such as television and films (and advertising); more on that later.

The other is expanding search (and other capabilities) to the meanings of those words on the Web: that is, to concepts, story lines, relationships – verbs, not nouns. Time-Warner acquiring AOL, for example, is very different from AOL acquiring Time-Warner… yet Google could not distinguish between those two. What’s enticing but not yet widespread is the ability not just to find relevant content, but to put the content into more regular form: for example, to build a table showing ten acquisitions of parts manufacturers by vehicle makers, listing the acquiring company, the acquired company and the amount paid including both stock and securities.

Google can get you lots of relevant (and irrelevant) articles, but it can’t fill in such table. It can get you a list of movies with Jennifer Aniston in them, but only IMDB tk link (compiled by people, often working for studios eager to promote their movies) can tell you the ones in which Jennifer Aniston starred.

That could change if we get better at natural-language understanding. Reuters is already pretty good at the generate-a-table-of-acquisitions task. However, as I heard the story, it also wanted to produce such news stories automatically by extracting data from press releases, but the reporters objected and insist on writing these formulaic stories by themselves.

Aside from whatever Powerset (mentioned by Olsen on News.com; I am an investor and it is still in stealth) is doing, there are lots of companies using natural-language pattern recognition in ways well beyond plain old search. Often, it’s a two-way process: The user supplies some information, the system makes some educated guesses, and ultimately a situation is recognized with the user doing quality control by saying, “Yes, that is right.” Then the appropriate action can be taken – whether it’s to remedy a situation (a dispute or a disease, for example) or to take advantage of an opportunity (an undervalued stock or an attractive purchase).

Here’s one you may not have thought of this way (or heard of at all), but then, pattern recognition is my job. SquareTrade is in the business of resolving petty business disputes; its major partner is eBay, for whom it provides dispute-resolution services to eBay buyers and sellers. (It also does quality checks and offers “good-behavior” seals.) The system begins by asking the user to fill in a form with simple questions: What was the item? What was the problem? What are the amounts at stake? [Disclosure: I have a small investment in SquareTrade.] 

SquareTrade uses a fairly limited vocabulary and resolves a fairly limited range of disputes. You could probably argue that it’s not AI at all. But that’s not really the point. The point is that it recognizes patterns: “This is the kind of problem where the product didn’t meet the buyer’s specs,” vs. “This is where it was broken during shipping,” or “This is one where the buyer changed his mind.” For each of these situations (As) there are fairly standard resolutions (Bs): “Send the product back and charge the seller two-way shipping costs,” “Fix the product and split the cost of repairs,” or “Send the product back, refund the money minus the cost of two-way shipping, but give the buyer a black mark for changing his mind.”

Imagine if we could do a better job of recognition, agreeing on the facts, and resolve more disputes with reference to a generally accepted set of rules. Imagine if SquareTrade could understand disputants’ free-form descriptions of the events rather than just have them fill in forms. And imagine if we could represent the body of law as an expert system, and then accurately recognize the situations and figure out which laws to apply. This is a bit of a digression; more on expert systems later.)

In another domain, using natural-language parsing without much regard to meaning, Vantage Laboratories uses recognition of grammar and usage  to grade SAT essays.

Similar techniques apply in health care: recognizing patterns to diagnose diseases, and then applying standard treatments. The challenge here is accurate observation – and the kind of probing a good doctor can do to uncover problems the patient is unaware of or doesn’t want to discuss.

More, and more intelligent automation, has been the dream in health care and education for decades, and it is beginning to happen here and there. One factor is that traditionally you needed the doctor or nurse to observe things. Now, there are devices that hook up to the patient directly and can feed physical data into a monitoring device. Those systems will find only what they have been trained to look for, but that’s a good start.

Of course, a good doctor or a good teacher is always better than a machine, but if there are not enough good doctors or good teachers, then machines can help fill the gaps and handle the routine cases. But more on real-world recognition next time…

Coming up soon: Recognizing real-world objects and the business models that could support .

…and recognizing consumers’ buying patterns – and the objects they buy

August 22nd, 2006

Zag steers new route to buying a car

Posted by Esther Dyson @ 6:25 am

Categories: Start-up

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DISCLOSURE: I do not know how to drive a car. In fact, I have been weightless (on two Zero-G flights, for 16 minutes) longer than I have ever driven a car-like vehicle - twice, a golf cart in Florida long ago, and an electric car in Aspen this summer, for a minute apiece and with a co-pilot at my side. I have never bought a car, shopped for car insurance or an auto loan. I am as innocent about oil changes as a 50’s-era movie Dad was about diaper changes.

On the other hand, while I don’t know anything about driving a car, I do know something about the impact of transparency and online sales on marketplaces. I was on the advisory board of Orbitz until its acquisition by Cendant, and saw the delicate dances among the online services, the travel agents and the airlines.

All that probably makes me uniquely qualified to appreciate Zag.com, The first generation of car sites offered information – pricing transparency. Zag goes one step further and offers process – managing the workflow of buying a car and handling the related tasks of getting a loan and buying insurance. the latest run at making auto-buying more efficient, from Scott Painter, the man who founded CarsDirect.com in the ‘90s. Painter helped create the current environment of increasingly transparent car prices, with some help from Kelly Blue Book and Edmunds. And now, with Zag.com, he’s leading the Internet trend from cost-per-click (or lead) to cost-per-transaction.

Of course, most of the news about the car industry right now is pension problems, production cuts by GM and Ford, rumors of bankruptcy (probably floated in order to get the unions to "cooperate"), and gas prices and mileage. The overall US auto business, about $700 billion per year for new ($500B) and used ($180B) cars, hasn’t grown more than a percent or two a year for 20 years.

But dealers and buyers are having their own challenges.

On one side, consumers know now a lot more about car pricing, but they don’t have the negotiating power they could get as members of a buying group. And they have to go through a cumbersome process

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August 10th, 2006

Glad to be here! a long tail for politics?

Posted by Esther Dyson @ 8:20 am

Categories: Long-tail, Politics, Start-up

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This is my first post under the ZDNet banner. I’m looking forward to the shift from Release 1.0, a newsletter that came out every month (or recently, every three months) to something that’s more timely and less polished… and shorter!

I’ll be covering whatever interests me. My goal is to cover "release 0.9" - things that aren’t quite done yet, whether they are ideas, companies or technologies.  I like things *before* they are finished or perfect or well understood. And I plan to write a lot about things outside… not just outside Silicon Valley, but outside the US. (Amazing, eh?)  

Right now, I find it hard to get excited about Web 2.0 in general.  There seems to be a multitude of start-ups promising video, virality, user-generated content, reputation systems and more….  And I can’t tell any of them apart.

But there are also lots of examples of people starting really wonderful services that transcend the buzzwords in order to do things in particular. 

Here’s one that I have been in touch with lately, which provides a pretty good example of the kind of thing I like:

 It’s not ready yet.  It’s not Release 1.1 of something that exists already. And it’s clever.

In this case, it’ss Voter.com (but note, the site isn’t ready yet; this is still pre-alpha).  Voter.com (an old name now being applied to this new start-up) was founded by Rick Cowen, a serial *non*-Web entrepreneur with most of his experience in advertising and music (i.e. Los Angeles).  With the smarts of a novice, he has designed a system that is essentially a campaign tool in a box. He calls it a "political appliance."  It is distinctly not  yet another discussion board for earnest liberals or conservatives, or even an earnest discussion board for both liberals and conservatives. 

Instead, it’s a tool for a politician or a non-profit leader who wants to amass and communicate with an audience of voters or donors, but with more discussion and position papers and content than your typical nonprofit CRM system. It includes tools for "message development," market research, advertising, contact management and fundraising - basically, the essence of a  campaign cycle. 

To be candid, when Rick Cowen first showed up in my inbox (and then persisted through the months despite my neglect), I was expecting a sincere, passionate but awkward techy with a mission.  Instead, he’s a sharp-talking ad guy who wants to make money offering a useful service to an underserved long tail – people running for dogcatcher, public advocate, school-board president.  (Joe Lieberman could have used it, for example, to get a sense of how his message was being received - and perhaps to listen better to messages from voters…. But it is really designed to help  someone who wants to become the next Joe Lieberman to get a start.)

For $19.95 a month the would-be candidate gets the tools to solicit voters, explain his positions, raise money and so forth - just as an eBay seller can get his own store, either as a main base or to supplement an existing business. One-time-use mailing lists, fundraising and money-management tools and the like are extra.  The precise charging model is different from eBay, but the overall impact is the same: more little guys can enter the market and compete effectively with established, bigger incumbents.

That’s for "candidates." For regular (free) users (called "voters"), there are tools for creating one’s profile and stating one’s views, tagging interesting posts from candidates and other voters, and (over time) all the usual social-network widgets.  The voters can go online and compare the various candidates, find out who in their area is running for what, communicate with other voters, ask the candidate questions. The candidate’s answers get posted for all to see. The candidate can also upload and promote podcasts of news interviews with himself, or make his own statements on whatever issues he cares about. 

If you think we need better politicians, this may be part of the answer: Making it easier for people who are not professionals to try their hand.  And if we don’t like the new entrants we don’t need to vote for them, but I find it hard to believe that a broader selection couldn’t help.

Like Spotrunner for small cable-TV (for now) advertisers and Google ads for bloggers and small advertisers, it’s giving the little guy capabilities that were previously available only to big guys – or incumbents.  The question is, will more politicians and more diverse politicians lead to better politicians? the quick and easy answer is that it’s up to the voters - but they have to pay attention.