If you check the mainstream news, chances are you've heard of the writer's strike. I don't know much about it, but my guess is they're "takin it in the shorts", as an ex-boss of mine used to say. But to tell you the truth, I don't watch much tv. ( Though I have discovered Scrubs and watch it late nights before bed, it's like I Love Lucy 2007 ). I peep ATHF, Daily Show, Colbert on a regular. And I've been known to plan my week around Lost; but I'm all about tv on dvd when I like the series. I'd rather blast through the season in 3 days than F around with commercials. (yeah, I haven't got a dvr yet, but again, I'm not around the tv that much to care) Anyway, the must-see tv these days ain't on tv, it's on the web.
There are so many cool shows out there right now. I'm hearing the term New Media Producers more lately, but I'm not sure if that's an apt title. They don't have anything in common with Old Media, and I don't know if they even consider themselves producers. These people are just doin what they do for the love of doin it! It's an exciting time for publishing this type of creativity. There are more than a few of these people out there, but below are some of the shows I enjoy regularly. I have to admit, I got the idea to share these from watchin Epic-Fu, it's my favorite, and it rocks.
My absolute favorite: Epic-Fu! (formely JetsetShow). If you haven't peeped it, check it out. You'll learn more there each week than you will in the news or on the Daily Show. Steve, Zadi, and Rick bring the epicness each week.
Another must-see Chad Vader. This series is hilarious. Can't wait for Season 2. Start with episode1 and work your way up.
If you're into sci-fi at all, you have to check out Galacticast. Rudy and Casey rock!
Ask-A-Ninja always delivers the goods. You got questions, he's got answers.
Another two fun ones are HappySlip (read the "about" for the name) and LisaNova (this chick is nuts, but frick'n hilarious).
And one I realized I haven't watched in awhile, so I have some catching up to do : Tiki Bar TV
Anyway. I spend a lot of time on the computer reading, coding, etc. When I take a break I peep these shows and they're perfect. 3-6 minute blasts of originality with no commercials. Just a bunch of creativity and heart served up freely courtesy of some talented people and the internets. Then its back to the lab.
Wednesday, November 14, 2007
Tuesday, November 13, 2007
Goodreads is Sweet
I really like goodreads.com. I just want to put that out there for everyone who's not familiar with it. It's a social network that's actually useful. If you enjoy reading, it gives you a place to share all you've read with others; so you can see what your friends are reading, and what they recommend or didn't like.
Anyway, it turns out that these days as soon as one person has an idea, the VCs rush to fund similar companies. It's like coffee shops, as soon as one opens on the block, you know another will follow shortly as someone wants their piece of the pie. So Shelfari and LibraryThing are two other companies that seem to be doing the same thing and are getting a lot of press lately for themselves by spamming others and complaining about it. I don't know who came first, and Shelfari and LibraryThing might actually be great sites, but I found goodreads first and I dig it so I'm rollin with it.
I'm endorsing them as I find the site very useful. Also, it's refreshing to find a site that seems to put the user first, it's not all about how they can make the money through ads or whatever. In print and on the internets all I'm seeing lately is the other 2 companies and people need to wake up and not sleep on goodreads. It's cool. If you make it over there I'm PT3.
Anyway, it turns out that these days as soon as one person has an idea, the VCs rush to fund similar companies. It's like coffee shops, as soon as one opens on the block, you know another will follow shortly as someone wants their piece of the pie. So Shelfari and LibraryThing are two other companies that seem to be doing the same thing and are getting a lot of press lately for themselves by spamming others and complaining about it. I don't know who came first, and Shelfari and LibraryThing might actually be great sites, but I found goodreads first and I dig it so I'm rollin with it.
I'm endorsing them as I find the site very useful. Also, it's refreshing to find a site that seems to put the user first, it's not all about how they can make the money through ads or whatever. In print and on the internets all I'm seeing lately is the other 2 companies and people need to wake up and not sleep on goodreads. It's cool. If you make it over there I'm PT3.
Oliver Selfridge conclusion
Ok, so I had this plan to rant about how screwed up it is that great computer scientists continue to crank out the badassness and deliver the goods while a bunch of fronters get all the media attention and ridiculous valuations for their <airfingerquotes>companies</airfingerquotes> based on what <airfingerquotes>they might be someday</airfingerquotes> or <airfingerquotes>they could be someday</airfingerquotes>. But, it's obvious, and you'll probably find this theme in future rants of mine just manifesting itself in different ways anyway, so I'm calling it off. In conclusion, Oliver's the man. I'm still uplifted just thinking about his presentation.
Saturday, November 10, 2007
TrueKnowledge, NLP, and HCI
It's exciting to see NLP present in so many applications these days. Some of the companies I follow and have a keen interest in seeing develop are: Attensity, Powerset, Hakia, and RadarNetworks. But out of the blue last week ( and I'm still not sure how I missed this as I'm usually on top of companies like this ) comes TrueKnowledge. If you haven't peeped their demo yet, you should do so. The site looks great but it's in "private beta" right now. I'm looking forward to testing their tech out once it's finally released.
TK bills themselves as a site for "Question-Answering" and "Enhanced Search". Based on the demo, you can type in a question in English such as, "Who invented Perceptrons?", and it will provide an Answer. It's not just using keywords, it does some parsing of the sentence you enter and then applies some inference rules. In the demo, they ask "What time is it at Google?", and the correct time is returned. Very cool stuff.
So I like asking a question and getting a straight answer, but I have to critique how the answer is returned. This doesn't apply to just TK either. I see other companies doing this, and it's because Engineers come up with this stuff. When we have the Engineer hat on, we do brilliant things, but forget about the little details like user experience. So...
In the demo, when you ask a question of their site, you get a straight answer.
Example: You ask "is J Lo single?", the site returns "No" and a link it thinks will back up it's answer. That parts awesome.
But then there's all this other verbose stuff with the answer. For the same question, the site also returns: "There are 2 defintions of "single" , then provides the 2 definitions,
then goes on to say "I took the meaning of your question to be.." and inserts something from the definitions previously listed. There can then be several links that represent the "inference path" for how the machine came up with the answer it's returning. In their Google-time example, a ton of links are returned.
Something about this nags at me that the HCI or UI design is off somewhere. It looks like they just jammed a bunch of System.outs in their code and blurt out the logging in the answer. They try to gussy it up and anthropomorphize it to make the logging seem helpful, but I'm not sure it really is. Maybe they should have a verbose mode, that will give you all that extra crap if you want it, and a trusted mode, where you just trust TK is giving you the right answer. Or maybe in trusted mode you get a straight answer and a confidence percentage. If the user questions the answer returned or the confidence rating, they can always go into verbose mode to validate the response, but I don't think logging should be on by default. Anyway, for me, I'll dig all that extra info, but I wonder if the average user will, or how all those inference links will look jammed on a cel phone.
Anyway. Back in 2001, I'd type a query in Google and it was like it could read my mind, I loved it. Their search just blew me away. Nowadays, there are a bunch of search engines out there, and I try to check them out occasionally, but I find myself going back to the Goog regularly cause "The devil I know....". But I'm almost always assured that the first few links will be trying to sell me items from ebay or amazon.
I'm all for a site that I can query that will return answers and not links. More power to 'em!
TK bills themselves as a site for "Question-Answering" and "Enhanced Search". Based on the demo, you can type in a question in English such as, "Who invented Perceptrons?", and it will provide an Answer. It's not just using keywords, it does some parsing of the sentence you enter and then applies some inference rules. In the demo, they ask "What time is it at Google?", and the correct time is returned. Very cool stuff.
So I like asking a question and getting a straight answer, but I have to critique how the answer is returned. This doesn't apply to just TK either. I see other companies doing this, and it's because Engineers come up with this stuff. When we have the Engineer hat on, we do brilliant things, but forget about the little details like user experience. So...
In the demo, when you ask a question of their site, you get a straight answer.
Example: You ask "is J Lo single?", the site returns "No" and a link it thinks will back up it's answer. That parts awesome.
But then there's all this other verbose stuff with the answer. For the same question, the site also returns: "There are 2 defintions of "single" , then provides the 2 definitions,
then goes on to say "I took the meaning of your question to be.." and inserts something from the definitions previously listed. There can then be several links that represent the "inference path" for how the machine came up with the answer it's returning. In their Google-time example, a ton of links are returned.
Something about this nags at me that the HCI or UI design is off somewhere. It looks like they just jammed a bunch of System.outs in their code and blurt out the logging in the answer. They try to gussy it up and anthropomorphize it to make the logging seem helpful, but I'm not sure it really is. Maybe they should have a verbose mode, that will give you all that extra crap if you want it, and a trusted mode, where you just trust TK is giving you the right answer. Or maybe in trusted mode you get a straight answer and a confidence percentage. If the user questions the answer returned or the confidence rating, they can always go into verbose mode to validate the response, but I don't think logging should be on by default. Anyway, for me, I'll dig all that extra info, but I wonder if the average user will, or how all those inference links will look jammed on a cel phone.
Anyway. Back in 2001, I'd type a query in Google and it was like it could read my mind, I loved it. Their search just blew me away. Nowadays, there are a bunch of search engines out there, and I try to check them out occasionally, but I find myself going back to the Goog regularly cause "The devil I know....". But I'm almost always assured that the first few links will be trying to sell me items from ebay or amazon.
I'm all for a site that I can query that will return answers and not links. More power to 'em!
Friday, November 9, 2007
Nopen Social
So Brandy and I were talking last night about Nopen Social and our general agreement with O'Reilly's latest, when she says: "Of course Open Social isn't open, good engineers generally aren't Socially Open, so it makes sense, they couldn't make it any other way."
Brilliant.
Brilliant.
Wednesday, November 7, 2007
Oliver Selfridge pt. 2
One of the greatest things to be a part of at Oliver's presentation, was listening to him reminisce about A.I. history with members of the audience. This wasn't intentional, it just sort of happened naturally as he presented his slides. He'd share a thought, someone in attendance would ask a question, then in response, he and his peers, also in attendance, would bring up past work that validated/invalidated their answers. In doing so, they couldn't help but go off on little sidetrips down memory lane.
Upon reflecting about their time at Bell Labs and DARPA, you could hear the joy and gratitude in his exchanges with McCarthy. They'd describe the work, and mention others working there. They looked back together fondly on a time working with Minsky. Oliver went on to express his sadness at losing Rosenblatt. He told the story of perceptrons and thought it unfortunate Frank didn't get the chance to counter Minsky's arguments against his findings. He also mentioned a brief time of greatness at DEC, and you could sense the early joy and then feel the anger and sadness as his tone changed; He described watching their great products and research efforts take a dive due to poor leadership in the company and he had some choice words for Ken Olsen. You could also hear Oliver's sad frustration at losing Licklider. He thought Licklider could have lived many more years if he would've just taken better care of himself at the end, and he wished they could of had those years. He had a great amount of respect for that man.
They were just brief mentions, but you felt like you were right there with him. I've always been a fan of computer science, and for me, being in that room gave me the chance to meet so many of my heroes. People who I've read about, and who's formulas and algorithms I've studied, all suddenly there live and in 3-d confirming my studies and enriching what I know with those little details that let you know it's real. It was awesome to get to go back to those places with them, just for those short moments.
Well, on that note. I thought of immediately hitting them all up and putting together a book on the history of A.I. Listening to him, I realized we've lost many greats already, and that losing more is a given. I wanted to capture those voices before they were lost. Upon looking them all up after the lecture, I found Nils Nilsson is actually doing just that.
From his homepage:
The Quest for Artificial Intelligence. I am writing a book on the history of AI. I hope to finish by the end of 2008
I'll be the first in line to buy it.
Upon reflecting about their time at Bell Labs and DARPA, you could hear the joy and gratitude in his exchanges with McCarthy. They'd describe the work, and mention others working there. They looked back together fondly on a time working with Minsky. Oliver went on to express his sadness at losing Rosenblatt. He told the story of perceptrons and thought it unfortunate Frank didn't get the chance to counter Minsky's arguments against his findings. He also mentioned a brief time of greatness at DEC, and you could sense the early joy and then feel the anger and sadness as his tone changed; He described watching their great products and research efforts take a dive due to poor leadership in the company and he had some choice words for Ken Olsen. You could also hear Oliver's sad frustration at losing Licklider. He thought Licklider could have lived many more years if he would've just taken better care of himself at the end, and he wished they could of had those years. He had a great amount of respect for that man.
They were just brief mentions, but you felt like you were right there with him. I've always been a fan of computer science, and for me, being in that room gave me the chance to meet so many of my heroes. People who I've read about, and who's formulas and algorithms I've studied, all suddenly there live and in 3-d confirming my studies and enriching what I know with those little details that let you know it's real. It was awesome to get to go back to those places with them, just for those short moments.
Well, on that note. I thought of immediately hitting them all up and putting together a book on the history of A.I. Listening to him, I realized we've lost many greats already, and that losing more is a given. I wanted to capture those voices before they were lost. Upon looking them all up after the lecture, I found Nils Nilsson is actually doing just that.
From his homepage:
The Quest for Artificial Intelligence. I am writing a book on the history of AI. I hope to finish by the end of 2008
I'll be the first in line to buy it.
Tuesday, November 6, 2007
Oliver Selfridge is a P.IM.P. pt. 1
Pretty damn IMportant Person in the world of A.I. !
I've been meaning to share this for a few days, but it'll probably take a few posts. So I'll just start with this: On Thursday, October 25th, I had the privilege of seeing Oliver Selfridge give a presentation to the Symbolic Systems posse at Stanford and it was incredible. I didn't know what to expect, but I walked in and there was John McCarthy, Nils Nillson, and Ed Feigenbaum sitting in the audience. It was a small affair, probably around 40 people, and half of them were O.G. veterans of A.I. There were probably other greats there too that I just didn't know or catch. Half the room was gray hairs and the other half were undergrads. It was kinda like something out of Dark Crystal, all the Mystics gathered and dropped some serious science while us Skeksis waited to merge, but anyway, onto the good stuff....
His lecture was titled "Let's Improve Machine Learning" and I took notes voraciously, so I'll just share some highlights. Also, the experience has given me 3 threads of thought I find myself meditating on, that's why I'll be breakin the posts up:
Part 1) His presentation itself and what it means
Part 2) Hearing the history of A.I. shared first person by himself and the attendees
Part 3) How come so many people ride FB's jock for the valuation, or attend the web2.0 summit but sleep on the greats who actually deliver? In fact, we still use and teach what these people delivered 40+ years later while I'm pretty confident we'll forget some of these dot-com frontrepeneurs after a short time.
Part 1: The Presentation
"I want software to give a shit."
That direct quote sums it up in a nutshell. He himself quoted Norbert Weiner who proposed "a machine can learn" , and Ed Berkeley who said, "a machine, therefore, can think." From this he went on to question how much does software think today? He also asked, well, if we want software to think, how might we do that?
To answer those questions, he focused on giving software a Purpose, and what that entails. His notion of Purpose: "Having Purpose means having a goal, and trying to achieve it."
To have software give a shit, he Proposed "Purpose Loops". Adaptive, learning loops, with an emphasis that software should remember what it has learned. He pointed out that today, most apps reach a goal, but forget how they got there and don't build off of it.
He also stated that Math, while great, should not be an impediment to getting software to care. His background is Math, and he didn't mean to offend any Mathematicians, he just thinks that sometimes scientists get lost in the equations and at that point stop getting things done in regards to advancing learning applications forward.
From there he stated some of the insights and challenges with Purposes, and he often used people and children as a metaphor for what the software could/might do:
Purpose Insights:
1)You can have many at once.
2)There are different kinds.
3)You remember them, and learning is not isolated.
(you build off experiences; sequences of met goals.)
4)You collaborate: sharing what you've learned;
we learn from our experiences and other's;
we don't reach goals or learn in a big-bang fashion, it builds.
5)If you don't reach the goal the first time, you try something else.
6)Learning is self-improvement; what the learner perceives to be important.
Purpose Challenges:
1)You can have a hierarchy of Purpose:
"Win the war!" - The Commander
"Take the city!" - Major
"Take the hill!" - Corporal
"Take out enemy tanks!" - Sgt.
"Stay Alive!" - Private
2)You can have many Purposes at once.
3) The world changes: values, cost, environments, you change...
From this he pointed out, that many times we work to deliver the very best software, but given this, he quotes Minsky: "The best is the enemy of the good". So again, the message was just get out there and make something happen!
4)Sharing; you can't always share and when you can there's no standard way. Language helps, but in some situations, you need examples, instructions, teaching. (He used Tennis as an example.) He also pointed out that many times Sharers have something in common.
Now, here's where it got kind of Matrixy; he proposed apps that help other apps in the way an adult helps a child. When people asked technical questions during the course of this event, he'd often respond: "How would you (insert noun/verb from question here) with a child?" But he made complete sense. It was Brilliant!
Some Challenges with the Challenges:
He provided some more examples where Learning and Sharing are just tough:
1) Learning to raise an eyebrow; or raise each individually.
2) Sometimes you can't teach how to do something, but how to learn how to do something.
3) Counting; how did man learn to count and why, it must be important.
4) How to get to the grocery? Requires maps/models; we often make the map in our head and remember landmarks.
5) Spoonerisms: I'm riding a well-boiled icicle
6) SkyDiving: Trying and failing may not work.
7) Learning a new skill
8) Solving quadratic equations (there are many ways: factoring, graphing, formulas, etc.)
What To Do:
He started to wrap up with proposals for what we can work on to help give software a purpose.
1) A full descriptive taxonomy of Purposes.
2) A language for expressing Purposes.
3) Flexible adaptive modules for our Purpose Loops and Learning.
4) A language for remember what we've learned
5) Hierarchies of Purposes
6) Kinds of Change
A particularly useful problem: Recognition of English Text, there's no ideal way.
(Note: NLP is something I'm keenly interested in and have been focused on for quite some time, so I was right there with him loving it by the time he got to this point.)
In Conclusion:
He concluded with this parting thought:
You can't program a kid, you can only open a door for a kid and show them what's out there, then they care. Do the same for software.
Final Thoughts:
Ok, some of those points above may be concise, but I hope you got something out of it. It was a great presentation, and some of McCarthy's quotes during the presentation were priceless:
When Oliver spoke on learning, McCarthy stated with authority in response:
"You can only learn what your system will permit."
When Oliver spoke on the advancement of machine learning and A.I., McCarthy pointed out:
"Complete success has not been achieved."
which caused many to laugh.
And finally, when people were questioning Oliver's points on scientists getting lost in Math, and not advancing machine learning as quickly as may be possible,
McCarthy blurted out: "It's not the fault of Mathematics!"
So marinate on that for awhile, I'll followup with some more A.I. goodness later.
I've been meaning to share this for a few days, but it'll probably take a few posts. So I'll just start with this: On Thursday, October 25th, I had the privilege of seeing Oliver Selfridge give a presentation to the Symbolic Systems posse at Stanford and it was incredible. I didn't know what to expect, but I walked in and there was John McCarthy, Nils Nillson, and Ed Feigenbaum sitting in the audience. It was a small affair, probably around 40 people, and half of them were O.G. veterans of A.I. There were probably other greats there too that I just didn't know or catch. Half the room was gray hairs and the other half were undergrads. It was kinda like something out of Dark Crystal, all the Mystics gathered and dropped some serious science while us Skeksis waited to merge, but anyway, onto the good stuff....
His lecture was titled "Let's Improve Machine Learning" and I took notes voraciously, so I'll just share some highlights. Also, the experience has given me 3 threads of thought I find myself meditating on, that's why I'll be breakin the posts up:
Part 1) His presentation itself and what it means
Part 2) Hearing the history of A.I. shared first person by himself and the attendees
Part 3) How come so many people ride FB's jock for the valuation, or attend the web2.0 summit but sleep on the greats who actually deliver? In fact, we still use and teach what these people delivered 40+ years later while I'm pretty confident we'll forget some of these dot-com frontrepeneurs after a short time.
Part 1: The Presentation
"I want software to give a shit."
That direct quote sums it up in a nutshell. He himself quoted Norbert Weiner who proposed "a machine can learn" , and Ed Berkeley who said, "a machine, therefore, can think." From this he went on to question how much does software think today? He also asked, well, if we want software to think, how might we do that?
To answer those questions, he focused on giving software a Purpose, and what that entails. His notion of Purpose: "Having Purpose means having a goal, and trying to achieve it."
To have software give a shit, he Proposed "Purpose Loops". Adaptive, learning loops, with an emphasis that software should remember what it has learned. He pointed out that today, most apps reach a goal, but forget how they got there and don't build off of it.
He also stated that Math, while great, should not be an impediment to getting software to care. His background is Math, and he didn't mean to offend any Mathematicians, he just thinks that sometimes scientists get lost in the equations and at that point stop getting things done in regards to advancing learning applications forward.
From there he stated some of the insights and challenges with Purposes, and he often used people and children as a metaphor for what the software could/might do:
Purpose Insights:
1)You can have many at once.
2)There are different kinds.
3)You remember them, and learning is not isolated.
(you build off experiences; sequences of met goals.)
4)You collaborate: sharing what you've learned;
we learn from our experiences and other's;
we don't reach goals or learn in a big-bang fashion, it builds.
5)If you don't reach the goal the first time, you try something else.
6)Learning is self-improvement; what the learner perceives to be important.
Purpose Challenges:
1)You can have a hierarchy of Purpose:
"Win the war!" - The Commander
"Take the city!" - Major
"Take the hill!" - Corporal
"Take out enemy tanks!" - Sgt.
"Stay Alive!" - Private
2)You can have many Purposes at once.
3) The world changes: values, cost, environments, you change...
From this he pointed out, that many times we work to deliver the very best software, but given this, he quotes Minsky: "The best is the enemy of the good". So again, the message was just get out there and make something happen!
4)Sharing; you can't always share and when you can there's no standard way. Language helps, but in some situations, you need examples, instructions, teaching. (He used Tennis as an example.) He also pointed out that many times Sharers have something in common.
Now, here's where it got kind of Matrixy; he proposed apps that help other apps in the way an adult helps a child. When people asked technical questions during the course of this event, he'd often respond: "How would you (insert noun/verb from question here) with a child?" But he made complete sense. It was Brilliant!
Some Challenges with the Challenges:
He provided some more examples where Learning and Sharing are just tough:
1) Learning to raise an eyebrow; or raise each individually.
2) Sometimes you can't teach how to do something, but how to learn how to do something.
3) Counting; how did man learn to count and why, it must be important.
4) How to get to the grocery? Requires maps/models; we often make the map in our head and remember landmarks.
5) Spoonerisms: I'm riding a well-boiled icicle
6) SkyDiving: Trying and failing may not work.
7) Learning a new skill
8) Solving quadratic equations (there are many ways: factoring, graphing, formulas, etc.)
What To Do:
He started to wrap up with proposals for what we can work on to help give software a purpose.
1) A full descriptive taxonomy of Purposes.
2) A language for expressing Purposes.
3) Flexible adaptive modules for our Purpose Loops and Learning.
4) A language for remember what we've learned
5) Hierarchies of Purposes
6) Kinds of Change
A particularly useful problem: Recognition of English Text, there's no ideal way.
(Note: NLP is something I'm keenly interested in and have been focused on for quite some time, so I was right there with him loving it by the time he got to this point.)
In Conclusion:
He concluded with this parting thought:
You can't program a kid, you can only open a door for a kid and show them what's out there, then they care. Do the same for software.
Final Thoughts:
Ok, some of those points above may be concise, but I hope you got something out of it. It was a great presentation, and some of McCarthy's quotes during the presentation were priceless:
When Oliver spoke on learning, McCarthy stated with authority in response:
"You can only learn what your system will permit."
When Oliver spoke on the advancement of machine learning and A.I., McCarthy pointed out:
"Complete success has not been achieved."
which caused many to laugh.
And finally, when people were questioning Oliver's points on scientists getting lost in Math, and not advancing machine learning as quickly as may be possible,
McCarthy blurted out: "It's not the fault of Mathematics!"
So marinate on that for awhile, I'll followup with some more A.I. goodness later.
Sunday, November 4, 2007
Urban Challenge Update Final
The Final Winners have been announced.! Details will probably eke out over time, but it looks like a questionable lane change by Stanford, and a traffic infraction by VT, cost them the first place crown. Congratulations to all the Teams! I can't wait to see how they step it up for the next challenge.
1) Tartan Racing (Carnegie Mellon)
2) Stanford Racing
3) Victor Tango (Virginia Tech)
1) Tartan Racing (Carnegie Mellon)
2) Stanford Racing
3) Victor Tango (Virginia Tech)
Saturday, November 3, 2007
Urban Challenge Update II
Phenomenal!
Spoiler Alert! (just kidding, but you know how people reveal the details of Heroes or Lost, well I'm about to do it with the DARPA Urban Challenge.)
Ok, I haven't gotten anything done all day because I've been watching the race online. 6 of 11 teams completed the course. Stanford crossed the finish first, but we won't know the actual winner until later. Along with the course completion times, the vehicles were awarded and deducted points based on their performance throughout several key tests. DARPA's crunchin the numbers and will probably announce the winner tomorrow morning.
Oh, how far it's come! I remember watching the "webcast" in 2005. Then it consisted of a map with different colored moving dots representing the bots. I was so excited when I saw the red dot of Stanford pass the CMU dot. The reporting was so sparse, I had to wait for official DARPA updates to confirm that they'd actually completed the course before CMU.
Today's webcast was brilliant. They had cameras everywhere and you could watch all the vehicles in action. Like a tv sports event, they even had the banner at the bottom of the screen. On the right of the banner, instead of espn, it had the DARPA emblem. And on the left, it would have numbers for the mission completed while the banner displayed the team name. They even had announcers calling the action. It was spectacular! I hope they release it on DVD, I'd buy it. It was amazing to see autonomous vehicles come to a Stop sign together, pause, then take action to merge into traffic, or continue on their way without hitting the other vehicles. There were some mishaps of course, but all in all, it was amazing! Congratulations to all the teams! I can't wait to find out who won.
Spoiler Alert! (just kidding, but you know how people reveal the details of Heroes or Lost, well I'm about to do it with the DARPA Urban Challenge.)
Ok, I haven't gotten anything done all day because I've been watching the race online. 6 of 11 teams completed the course. Stanford crossed the finish first, but we won't know the actual winner until later. Along with the course completion times, the vehicles were awarded and deducted points based on their performance throughout several key tests. DARPA's crunchin the numbers and will probably announce the winner tomorrow morning.
Oh, how far it's come! I remember watching the "webcast" in 2005. Then it consisted of a map with different colored moving dots representing the bots. I was so excited when I saw the red dot of Stanford pass the CMU dot. The reporting was so sparse, I had to wait for official DARPA updates to confirm that they'd actually completed the course before CMU.
Today's webcast was brilliant. They had cameras everywhere and you could watch all the vehicles in action. Like a tv sports event, they even had the banner at the bottom of the screen. On the right of the banner, instead of espn, it had the DARPA emblem. And on the left, it would have numbers for the mission completed while the banner displayed the team name. They even had announcers calling the action. It was spectacular! I hope they release it on DVD, I'd buy it. It was amazing to see autonomous vehicles come to a Stop sign together, pause, then take action to merge into traffic, or continue on their way without hitting the other vehicles. There were some mishaps of course, but all in all, it was amazing! Congratulations to all the teams! I can't wait to find out who won.
Friday, November 2, 2007
Urban Challenge Update
11 Teams Qualified. Tomorrow at 8:30 PT the real challenge begins. Wired has some great posts that give the whole event a more personal touch. Stories there include: teams working together so each bot can qualify, how a track had to be resized for team Oshkosh, and how team Virginia's entry is number 32 in honor of those they lost April 16th at their campus. Best of luck to all 11!
Subscribe to:
Posts (Atom)
