Robots: The Future of Artificial Intelligence

Robots: The Future of Artificial Intelligence: "

The latest episode of the Robots podcast interviews Kristinn R. Thórisson from Reykjavik
University on some of the great advances, but also some of the
disappointments of artificial intelligence, and where he thinks AI will
be used in the future. In the second part of this interview, we conclude
our quest for a definition of the word "robot" with a definition by Prof. Wendelin Reich
from the Swedish Collegium for
Advanced Study at Uppsala University, Sweden. He defines a
robot as an artificial, physically embodied ‘agent tool’ - and
gives some good
reasons for this definition. For details as well as a list of other
definitions have a look at the Robots
website.

"

Random Robot Roundup

Random Robot Roundup: "Nelson Bridwell points
out a NASA article on the increasing
autonomy of the Mars Exploration Rovers and also sent a link to an
essay he wrote on NASA's
future direction. If you're really interested in Mars, you may want
to check a links JPL's Cassie Bowman sent for anyone who wants to be a Martian. Closer to
Earth, Mark Miller will be showing off
his android
creations at the National High Magnetic Field Lab in Tallahassee, FL on
April 2. Mark's androids are part robot and part
artwork and they're well worth seeing if you're able to make it to the
show. Travis Deyle writes, 'thought you might like this
new robot from our lab' Roschler sent us an
article he's written on the Emotiv
EPOC headset
By the way, sorry for the slowdown in news lately, I've been doing a lot
of work with my local robot group and it's eaten into my time for
posting stories here. Hopefully I'll be able to work out a better
balance of time soon. Know any other robot news, gossip, or amazing
facts we should report? Send 'em our
way please. And don't forget to follow us on twitter."

Phantom of the Operating Shuttle?

Phantom of the Operating Shuttle?: "


Dvice.com
reports about a mysterious Robotic Shuttle that will be launched
April 19th. This is the first time I've even heard of such a shuttle
replacement. I mean, I thought NASA dumped the idea of a shuttle
completely and went for the super Apollo type mission to go to the Moon
or Mars?
So at a time when mothballing
the old Space Shuttle debate is going ballistic, what happens?
Well, it looks like the Air Force pulled a fast one and went ahead and
had it's own space shuttle secretly built by Boeing Phantom Works. The
new autonomous robotic Space Shuttle is dubbed the X37B.
Revealing a new Space Shuttle at this time is probably not going to
help the Obama administration with all the harsh
Space
Agency criticism they've been getting lately. In my opinion it shames
the Obama Administration and NASA because neither came up with this
dreamy vehicle, the Air Force had to. One could argue that NASA has
limited funds or how NASA and the Air Force is sort of two
sides of the same coin, yada yada. It probably shames the Air Force too
for not informing Obama they had a secret space shuttle.
Anyway, the Air Force didn't commission just any space shuttle to be
made, they had made a small, efficient, robotic,
autonomous space shuttle. It can go up, deploy some secret
payload, and come down and land
all on it's own the article says!
OK, well, details are sketchy so it's probably not completely autonomous
but it appears to be just as much autonomously controlled by robotic
equipment as the original shuttle was controlled by humans in the
cockpit. That's very impressive. Awesome. So... now that such a robotic
shuttle is
made public and known to exist, I wonder if the Air Force will let NASA
use it
for non-military missions? Naw, probably not."

Bots High: Documentary on BattleBots

Bots High: Documentary on BattleBots: "

Bots High Teaser Trailer from Joey Daoud on Vimeo.

Joey Daoud is working on a video
documentary, called Bots High, about high school teams
participating in the BattleBots competition and he needs your help. Joey
writes,

I'm working on a documentary on high school BattleBots. I've
been following multiple teams around since August, leading up to the
National Championship. I'm trying to raise funds to film the
championship with a multi camera crew, as well as travel to San
Francisco to interview the BattleBot creators and builders.

For those who don't remember, Robot
Wars (1998-2004) and BattleBots
(1999-2002), were
much-hyped game/reality television shows featuring competitions between
remote-controlled
vehicles designed to look like robots. Contests consisted of massive
machines that destroyed each other in entertaining ways. The
hype eventually died and the shows were cancelled. What you may not know
is that BattleBots
spun off a high school league known as BOTSIQ which still exists and attempts
to add an
educational aspect to the competition. The BOTSIQ
championship will be held April 14-18 in Miami, Florida.

"

Robots: Chaos Control

Robots: Chaos Control: "

Walking, swallowing, respiration and many other key functions in humans
and other animals are controlled by Central
Pattern Generators (CPGs). In essence, CPGs are small, autonomous
neural networks that produce rhythmic outputs, usually found in animal's
spinal cords rather than their brains. Their relative simplicity and
obvious success in biological systems has led to some success
in using CPGs in robotics. However, current systems are restricted
to very simple CPGs (e.g., restricted to a single walking gait). A
recent breakthrough at the BCCN
at the University of Göttingen, Germany has now allowed to achieve
11 basic behavioral patterns (various gaits, orienting, taxis,
self-protection) from a single CPG, closing in on the 10–20 different
basic behavioral patterns found in a typical cockroach. The trick: Work
with a chaotic, rather than a stable periodic CPG regime. For more on
CPGs, listen to the latest episode of the Robots
podcast on Chaos Control, which interviews Poramate
Manoonpong, one of the lead researchers in Göttingen, and Alex Pitti from the University of Tokyo who uses chaos
controllers that can synchronize to the dynamics of the body they are
controlling.

"

Explained: Regression analysis

Explained: Regression analysis

Regression analysis. It sounds like a part of Freudian psychology. In reality, a regression is a seemingly ubiquitous statistical tool appearing in legions of scientific papers, and regression analysis is a method of measuring the link between two or more phenomena.

Imagine you want to know the connection between the square footage of houses and their sale prices. A regression charts such a link, in so doing pinpointing “an average causal effect,” as MIT economist Josh Angrist and his co-author Jorn-Steffen Pischke of the London School of Economics put it in their 2009 book, “Mostly Harmless Econometrics.”

To grasp the basic concept, take the simplest form of a regression: a linear, bivariate regression, which describes an unchanging relationship between two (and not more) phenomena. Now suppose you are wondering if there is a connection between the time high school students spend doing French homework, and the grades they receive. These types of data can be plotted as points on a graph, where the x-axis is the average number of hours per week a student studies, and the y-axis represents exam scores out of 100. Together, the data points will typically scatter a bit on the graph. The regression analysis creates the single line that best summarizes the distribution of points.

Mathematically, the line representing a simple linear regression is expressed through a basic equation: Y = a0 + a1 X. Here X is hours spent studying per week, the “independent variable.” Y is the exam scores, the “dependent variable,” since — we believe — those scores depend on time spent studying. Additionally, a0 is the y-intercept (the value of Y when X is zero) and a1 is the slope of the line, characterizing the relationship between the two variables.

Using two slightly more complex equations, the “normal equations” for the basic linear regression line, we can plug in all the numbers for X and Y, solve for a0 and a1, and actually draw the line. That line often represents the lowest aggregate of the squares of the distances between all points and itself, the “Ordinary Least Squares” (OLS) method mentioned in mountains of academic papers.

To see why OLS is logical, imagine a regression line running 6 units below one data point and 6 units above another point; it is 6 units away from the two points, on average. Now suppose a second line runs 10 units below one data point and 2 units above another point; it is also 6 units away from the two points, on average. But if we square the distances involved, we get different results: 62 + 62 = 72 in the first case, and 102 + 22 = 104 in the second case. So the first line yields the lower figure — the “least squares” — and is a more consistent reduction of the distance from the data points. (Additional methods, besides OLS, can find the best line for more complex forms of regression analysis.)

In turn, the typical distance between the line and all the points (sometimes called the “standard error”) indicates whether the regression analysis has captured a relationship that is strong or weak. The closer a line is to the data points, overall, the stronger the relationship.

Regression analysis, again, establishes a correlation between phenomena. But as the saying goes, correlation is not causation. Even a line that fits the data points closely may not say something definitive about causality. Perhaps some students do succeed in French class because they study hard. Or perhaps those students benefit from better natural linguistic abilities, and they merely enjoy studying more, but do not especially benefit from it. Perhaps there would be a stronger correlation between test scores and the total time students had spent hearing French spoken before they ever entered this particular class. The tale that emerges from good data may not be the whole story.

So it still takes critical thinking and careful studies to locate meaningful cause-and-effect relationships in the world. But at a minimum, regression analysis helps establish the existence of connections that call for closer investigation.

Robot wheelchair finds its own way

Robot wheelchair finds its own way