You may have heard of Bus Rapid Transit -- a system to give a bus line a private or semi-private right-of-way, along with bus stops that are more akin to stations than bus shelters (with ticket-taking machines and loading platforms for multiple doors.) The idea is to make bus transit competitive with light-rail (LRT) in terms of speed and convenience. Aside from getting caught in slow traffic, buses also are slow to board. BRT is hoped to be vastly less expensive than light rail -- which is not hard because LRT (which means light capacity rail, not lightweight rail) has gotten up to $80 to $100M per mile. When BRT runs down the middle of regular roads, it gets signal timing assistance to help it have fewer stops. It's the "hot new thing" in transit. Some cities even give it bits of underground or elevated ROW (the Boston Silver Line) and others just want to wall off the center of a road to make an express bus corridor. Sometimes BRT gets its own highway lane or shares a special carpool lane.

At the same time just about anybody who has looked at transit and the internet has noticed that as the buses go down the street, they travel with tons of cars carrying only one person and lots of empty seats. Many have wondered, "how could we use those empty private car seats to carry the transit load?" There are a number of ride-sharing and carpooling apps on web sites and on smartphones, but success has been modest. Drivers tend to not want to take the time to declare their route, and if money is offered, it's usually not enough to counter the inconvenience. Some apps are based on social networks so friends can give rides to friends -- great when it works but not something you can easily do on demand.

But one place I've seen a lot of success at this is the casual carpooling system found in a number of cities. Here it's very popular to cross the Oakland-SF Bay Bridge, which has a $6 toll to cross into SF. It used to be free for 3-person carpools, now it's $2.50, but the carpools also get a faster lane for access to the highly congested bridge both going in and out of SF.

Almost all the casual carpool pickup spots coming in are at BART (subway) stations, which are both easy for everybody to get to, and which allow those who can't get a carpool to just take the train. There is some irony that it means that the carpools mostly take people who would have ridden BART trains, not people who would have driven, the official purpose of carpool subsidies. In the reverse direction the carpools are far fewer with no toll to be saved, but you do get a better onramp.

People drive the casual carpools because they get something big for for it -- saving over $1,000/year, and hopefully a shorter line to the bridge. This is the key factor to success in ride share. The riders are saving a similar amount of money in BART tickets, even more if they skipped driving.

Let's consider what would happen if you put in the dedicated lane for BRT, but instead of buses created an internet mediated carpooling system. Drivers could enter the dedicated lane only if:

• They declared their exit in advance to the app on their phone, and it's far enough away to be useful to riders.
• They agree to pick up riders that their phone commands them to.
• They optionally get a background check that they pay for so they can be bonded in some way to do this. (Only the score of the background check is recorded, not the details.)

Riders would declare their own need for a ride, and to what location, on their own phones, or on screens mounted at "stops" (or possibly in nearby businesses like coffee shops.) When a rider is matched to a car, the rider will be informed and get to see the approach of their ride on the map, as well as a picture of the car and plate number. The driver will be signaled and told by voice command where to go and who to pick up. I suggest calling this Carpool-Rapid-Transit or CRT.

The computer scientist world is abuzz with the game show world over the showdown between IBM's "Watson" question-answering system and the best human players to play the game Jeopardy. The first game has been shown, with a crushing victory by Watson (in spite of a tie after the first half of the game.)

Tomorrow's outcome is not in doubt. IBM would not have declared itself ready for the contest without being confident it would win, and they wouldn't be putting all the advertising out about the contest if they had lost. What's interesting is how they did it and what else they will be able to do with it.

Dealing with a general question has long been one of the hard problems in AI research. Watson isn't quite there yet but it's managed a great deal with a combination of algorithmic parsing and understanding combined with machine learning based on prior Jeopardy games. That's a must because Jeopardy "answers" (clues) are often written in obfuscated styles, with puns and many idioms, exactly the sorts of things most natural language systems have had a very hard time with.

Watson's problem is almost all understanding the question. Looking up obscure facts is not nearly so hard if you have a copy of Wikipedia and other databases on hand, particularly one parsed with other state-of-the-art natural language systems, which is what I presume they have. In fact, one would predict that Watson would do the best on the hardest $2,000 questions because these are usually hard because they refer to obscure knowledge, not because it is harder to understand the question. I expect that an evaluation of its results may show that its performance on hard questions is not much worse than on easy ones. (The main thing that would make easy questions easier would be the large number of articles in its database confirming the answer, and presumably boosting its confidence in its answer.) However, my intuition may be wrong here, in that most of Watson's problems came on the high-value questions.

It's confidence is important. If it does not feel confident it doesn't buzz in. And it has a serious advantage at buzzing in, since you can't buzz in right away on this game, and if you're an encyclopedia like the two human champions and Watson, buzzing in is a large part of the game. In fact, a fairer game, which Watson might not do as well at, would involve randomly choosing which of the players who buzz in in the first few tenths of a second gets to answer the question, eliminating any reaction time advantage. Watson gets the questions as text, which is also a bit unfair, unless it is given them one word a time at human reading speed. It could do OCR on the screen but chances are it would read faster than the humans. It's confidence numbers and results are extremely impressive. One reason it doesn't buzz in is that even with 3,000 cores it takes 2-6 seconds to answer a question.

Indeed a totally fair contest would not have buzzing in time competition at all, and just allow all players who buzz in to answer an get or lose points based on their answer. (Answers would need to be in parallel.)

Watson's coders know by now that they probably should have coded it to receive wrong answers from other contestants. In one instance it repeated a wrong answer, and in another case it said "What is Leg?" after Jennings had incorrectly answered "What is missing an arm?" in a question about an Olympic athlete. The host declared that right, but the judges reversed that saying that it would be right if a human who was following up the wrong answer said it, but was a wrong answer without that context. This was edited out. Also edited out were 4 crashes by Watson that made the game take 4 hours instead of 30 minutes.

It did not happen in what aired so far, but in the trials, another error I saw Watson make was declining to answer a request to be more specific on an answer. Watson was programmed to give minimalist answers, which often the host will accept as correct, so why take a risk. If the host doesn't think you said enough he asks for a more specific answer. Watson sometimes said "I can be no more specific." From a pure gameplay standpoint, that's like saying, "I admit I am wrong." For points, one should say the best longer phrase containing the one-word answer, because it just might be right. Though it has a larger chance of looking really stupid -- see below for thoughts on that.

The shows also contain total love-fest pieces about IBM which make me amazed that IBM is not listed as a sponsor for the shows, other than perhaps in the name "The IBM Challenge." I am sure Jeopardy is getting great ratings (just having their two champs back would do that on its own but this will be even more) but I have to wonder if any other money is flowing.

Being an idiot savant

Watson doesn't really understand the Jeopardy clues, at least not as a human does. Like so many AI breakthroughs, this result comes from figuring out another way to attack the problem different from the method humans use. As a result, Watson sometimes puts out answers that are nonsense "idiot" answers from a human perspective. They cut back a lot on this by only having it answer when it has 50% confidence or higher, and in fact for most of its answers it has very impressive confidence numbers. But sometimes it gives such an answer. To the consternation of the Watson team, it did this on the Final Jeopardy clue, where it answered "Toronto" in the category "U.S. Cities."

I shoot lots of large panoramas, and the arrival of various cheaper robotic mounts to shoot them, such as the Gigapan Epic Pro and the Merlin/Skywatcher (which I have) has resulted in a bit of a "mine's bigger than yours" contest to take the biggest photo. Some would argue that the stitched version of the Sloane Digital Sky survey, which has been rated at a trillion pixels, is the winner, but most of the competition has been on the ground.

Last year I wrote about an interesting but simple pedal powered monorail/PRT system called Shweeb which had won a prize/investment from Google. Recent announcements show they are not alone in this concept. Scott Olson, the original developer of the Rollerblade, has founded a company called Skyride Technologies to build their own version of a pedal powered suspended monorail.