From IEEE Spectrum
By Allison Marsh
28 Sep 2018 | 19:00 GMT
This voice-controlled robot could walk, talk, and smoke, and it captivated crowds
“Ladies and gentlemen, I’ll be very glad to tell my story. I am a smart fellow as I have a very fine brain of 48 electrical relays.” This is how Elektro the robot introduced itself to crowds at the 1939 New York World’s Fair. Standing 2.1 meters tall and weighing 118 kilograms, Elektro performed 26 different tricks, including walking, talking, counting, and singing. It had a vocabulary of approximately 700 words, although its responses were all prerecorded and played back from 33⅓-rpm records. One of Elektro’s pet lines was, “My brain is bigger than yours.” At 25 kg, it certainly was.
I never knew how badly I needed a self-parking slipper until now!
By Evan Ackerman
from IEEE Specturm
Nissan, like every other car manufacturer that doesn’t want to be rendered mostly obsolete within the next few decades, has been gradually developing autonomous technology for its vehicles. They’ve been going about it very sensibly, introducing discrete modules like highway assist and parking assist, and they’ve managed to get the parking bit working well enough to take it beyond cars. One such attempt at an even more challenging and important self-parking application: slipper arrangements.
At first glance, the ProPILOT Park Ryokan looks like any other traditional Japanese inn, or ryokan. Slippers are neatly lined up at the foyer, where guests remove their shoes. Tatami rooms are furnished with low tables and floor cushions for sitting. What sets this ryokan apart is that the slippers, tables and cushions are rigged with a special version of Nissan’s ProPILOT Park autonomous parking technology. When not in use, they automatically return to their designated spots at the push of a button.
Even the television remote is self parking! Brilliant!
For its primary application, Nissan’s ProPILOT Park system uses an array of four cameras and twelve sonar sensors to wedge its host vehicle into even the smallest of parking spaces—whether it’s nose-in parking, butt-in parking, or trickiest of all, parallel parking. It seems unlikely that the slippers use quite the same technology, although Nissan does suggest that the technology is at least similar, which would mean that the slippers are operating autonomously rather than relying on someone off-camera with a remote control. If you’d like to investigate further, Nissan is offering a free night for a pair of travelers at this particular ryokan, which located in Hakone, Japan—a lovely place that you should consider visiting even if self-parking slippers aren’t on the amenities list.
Our only question now is, why limit this technology to cars, slippers, and pillows? I’d like my cereal bowls to be self parking. And my socks. And how about the toothpaste? Just think about how much more convenient it would be if all of these things were self-parking, too. So let’s get going with this, Nissan. Make our lives better already.
From: IEEE Spectrum
The 2017 Top Programming Languages
Python jumps to No. 1, and Swift enters the Top Ten
By Stephen Cass
Date: July 18, 2017
It’s summertime here at IEEE Spectrum, and that means it’s time for our fourth interactive ranking of the top programming languages. As with all attempts to rank the usage of different languages, we have to rely on various proxies for popularity. In our case, this means having data journalist Nick Diakopoulos mine and combine 12 metrics from 10 carefully chosen online sources to rank 48 languages. But where we really differ from other rankings is that our interactive allows you choose how those metrics are weighted when they are combined, letting you personalize the rankings to your needs.
We have a few preset weightings—a default setting that’s designed with the typical Spectrum reader in mind, as well as settings that emphasize emerging languages, what employers are looking for, and what’s hot in open source. You can also filter out industry sectors that don’t interest you or create a completely customized ranking and make a comparison with a previous year.
So what are the Top Ten Languages for the typical Spectrum reader?
Python has continued its upward trajectory from last year and jumped two places to the No. 1 slot, though the top four—Python, C, Java, and C++—all remain very close in popularity. Indeed, in Diakopoulos’s analysis of what the underlying metrics have to say about the languages currently in demand by recruiting companies, C comes out ahead of Python by a good margin.
C# has reentered the top five, taking back the place it lost to R last year. Ruby has fallen all the way down to 12th position, but in doing so it has given Apple’s Swift the chance to join Google’s Go in the Top Ten. This is impressive, as Swift debuted on the rankings just two years ago. (Outside the Top Ten, Apple’s Objective-C mirrors the ascent of Swift, dropping down to 26th place.)
However, for the second year in a row, no new languages have entered the rankings. We seem to have entered a period of consolidation in coding as programmers digest the tools created to cater to the explosion of cloud, mobile, and big data applications.
Speaking of stabilized programming tools and languages, it’s worth noting Fortran’s continued presence right in the middle of the rankings (sitting still in 28th place), along with Lisp in 35th place and Cobol hanging in at 40th: Clearly even languages that are decades old can still have sustained levels of interest. (And although it just barely clears the threshold for inclusion in our rankings, I’m pleased to see that my personal favorite veteran language—Forth—is still there in 47th place).
Looking at the preset weighting option for open source projects, where we might expect a bias toward newer projects versus decades-old legacy systems, we see that HTML has entered the Top Ten there, rising from 11th place to 8th. (This is a great moment for us to reiterate our response to the complaint of some in years past of “HTML isn’t a programming language, it’s just markup.” At Spectrum, we have a very pragmatic view about what is, and isn’t, a recognizable programming language. HTML is used by coders to instruct computers to do things, so we include it. We don’t insist on, for example, Turing completeness as a threshold for inclusion—and to get really nitpicky, as user Jonny Lin pointed out last year, HTML has grown so complex that when combined with CSS, it is now Turing complete, albeit with a little prodding and requiring an appreciation of cellular automata.)
Finally, one last technical detail: We’ve made some tweaks under the hood to improve the robustness of the results, especially for less popular languages where the signals in the metrics are weaker and so more prone to statistical noise. So that users who look at historical data can make consistent comparisons, we’ve recalculated the previous year’s rankings with the new system. This could lead to some discrepancies between a language’s ranking in a given year as currently shown, versus the ranking that was shown in the original year of publication, but such differences should be relatively small and not affect the more popular languages in any case.
Last December, Duncan Haldane (whose research on incredibly agile bioinspired robots we’ve featured extensively in the past) ended up on the cover of the inaugural issue of Science Robotics with his jumping robot, Salto. Salto had impressive vertical jumping agility, and was able to jump from the ground onto a vertical surface, and then use that surface to change its direction with a second jump. It was very cool to watch, but the jumping was open-loop and planar, meaning that two jumps in a row was just about all that Salto could manage.
Haldane mentioned to us in December that future work on Salto could include chaining together multiple jumps, and in a paper just accepted to the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), he and co-author Justin Yim at UC Berkeley’s Biomimetic Millisystems Lab, led by Professor Ronald Fearing, show the improvements that they’ve made over the last six months. Thanks to some mechanical fine-tuning and the clever addition of a pair of thrusters, the new Salto-1P is jumping longer, faster, and higher than ever. Prepare to be amazed.
We’ve seen other jumping robots over the years, but Salto-1P takes the cake. Watch this:
Salto is short for “Saltatorial Locomotion on Terrain Obstacles,” a reference to saltatorial animals, which are adapted to locomotion by jumping. Kangaroos and rabbits are a few saltatorial animals that you’re probably familiar with, but Salto was particularly inspired by the galago, or bushbaby, which has a vertical jumping agility that no other animal can match. The galago is able to manage this thanks to a rather clever bit of leg design which uses variable mechanical advantage, leveraging the shape of their leg to amplify the force that their muscles can deliver. For all the details on the jumping ability of the original Salto (and how it’s different from other jumping robots), be sure and read our very in-depth article about it, because this article is focused on the new and upgraded Salto-1P.
The original Salto was able to control its pitch through the use of a rotating inertial tail: By spinning the tail one way, the robot could pitch itself in the other direction. This worked very well, but only in one plane, which made Salto difficult to control. Salto-1P is, according to Haldane, essentially “Salto with half of a mini-quadrotor glued to it.”
Those two little thrusters are able to control Salto-1P’s yaw and roll: When they’re thrusting in different directions, the robot yaws, and when they both thrust in the same direction, the robot rolls. Combined with the tail, that means Salto-1P (which only ways 98 grams) can stabilize and control itself in three dimensions, even in mid-air, which is what allows it to chain together so many jumps. Other hardware modifications include a deeper crouch than the original Salto, which allows more energy to be transferred from the jumping motor into the spring, giving it the highest vertical jumping agility of any battery powered robot at 1.83 m/s.
Haldane says one issue that came when they redesigned the leg mechanism to allow the robot to jump higher is that, as he puts it, “Salto lost its friendly and forgiving nature.” The robot would occasionally “fire pieces of itself across the room when the motor tore the leg-mechanism apart.” They had to do revise the design to keep everything in one piece. The video below is a compilation of Salto-1P’s “little acts of self-destruction”:
The software that Salto-1P is running to make all of this work is an adaptation of Marc Raibert’s hopping controller from 1984. Raibert’s 3D One-Leg Hopper weighed 170 times more than Salto-1P, and can’t jump nearly as high, but fundamentally the algorithm works just as well on Salto as it did on Raibert’s hopper more than 30 years ago. However, controlling Salto-1P involves some unique challenges, because the robot spends so little time on the ground. In fact, 92 percent of the time, the robot is in the air, which means that you really have to control it in the air, which is why the tail and thrusters are necessary (as opposed to control through the leg and foot).
This results in enormous accelerations (on the order of 14 g’s), and to put this in context, Haldane compares Salto-1P to a cheetah: The robot has “a lower duty cycle than a single cheetah limb at top speed,” he says, adding: “Imagine a cheetah running at top speed using only one leg, and then cut the amount of time that leg spends on the ground in half. That’s the duty factor of Salto-1P.”
“Imagine a cheetah running at top speed using only one leg, and then cut the amount of time that leg spends on the ground in half. That’s the duty factor of Salto-1P.” —Duncan Haldane, UC Berkeley
It’s important to note that when you see Salto-1P bouncing around in the video, it’s doing so untethered, but not completely autonomously: There’s a bunch of stuff going on in the background to get it to perform the way it does. The path it follows relies on motion capture, with an offboard computer (though not a particularly powerful one) receiving tracking data and wirelessly sending control commands to the robot.
“Motion capture is an easy way to track the robot that freed us up to work more on the robot mechanics,” co-author Justin Yim explains. “It’s also useful for gathering performance data since we can very closely track Salto-1P throughout its hopping.” It’s also worth noting that Salto-1P isn’t doing a lot of sensing on its own, and it’s still able to handle all those obstacles at the end of the video, which is impressive.
Known for: Inventing the computer compiler and leading the development of the programming language COBOL (common business-oriented language).
Why it matters: Hopper is considered one of the founders of the information age. Her compiler, a collection of coded instructions that could be reused, saved programmers from having to write each program anew. It significantly advanced the art of programming. By the late 1970s, COBOL was the most extensively used computer language in the world.
Where she started: Hopper was a mathematics professor at Vassar College, in Poughkeepsie, N.Y., when she joined the U.S. Navy Waves (women accepted for voluntary service) program in December 1943. She was commissioned a lieutenant the following year. She was named an IEEE Fellow in 1962 “for contributions in the field of automatic programming.”
Breakthrough: As a Navy lieutenant, she was assigned in 1944 to program the Mark I Automatic Sequence Controlled Calculator at Harvard under Howard Aiken, a computing pioneer. The Mark 1, one of the first programmable computers, is an IEEE Milestone.
