Tag Archives: Team
#437878 Deep reinforcement-learning architecture ...
A team of researchers from the University of Edinburgh and Zhejiang University has developed a way to combine deep neural networks (DNNs) to create a new type of system with a new kind of learning ability. The group describes their new architecture and its performance in the journal Science Robotics. Continue reading
Posted in Human Robots
#437872 AlphaFold Proves That AI Can Crack ...
Any successful implementation of artificial intelligence hinges on asking the right questions in the right way. That’s what the British AI company DeepMind (a subsidiary of Alphabet) accomplished when it used its neural network to tackle one of biology’s grand challenges, the protein-folding problem. Its neural net, known as AlphaFold, was able to predict the 3D structures of proteins based on their amino acid sequences with unprecedented accuracy.
AlphaFold’s predictions at the 14th Critical Assessment of protein Structure Prediction (CASP14) were accurate to within an atom’s width for most of the proteins. The competition consisted of blindly predicting the structure of proteins that have only recently been experimentally determined—with some still awaiting determination.
Called the building blocks of life, proteins consist of 20 different amino acids in various combinations and sequences. A protein's biological function is tied to its 3D structure. Therefore, knowledge of the final folded shape is essential to understanding how a specific protein works—such as how they interact with other biomolecules, how they may be controlled or modified, and so on. “Being able to predict structure from sequence is the first real step towards protein design,” says Janet M. Thornton, director emeritus of the European Bioinformatics Institute. It also has enormous benefits in understanding disease-causing pathogens. For instance, at the moment only about 18 of the 26 proteins in the SARS-CoV-2 virus are known.
Predicting a protein’s 3D structure is a computational nightmare. In 1969 Cyrus Levinthal estimated that there are 10300 possible conformational combinations for a single protein, which would take longer than the age of the known universe to evaluate by brute force calculation. AlphaFold can do it in a few days.
As scientific breakthroughs go, AlphaFold’s discovery is right up there with the likes of James Watson and Francis Crick’s DNA double-helix model, or, more recently, Jennifer Doudna and Emmanuelle Charpentier’s CRISPR-Cas9 genome editing technique.
How did a team that just a few years ago was teaching an AI to master a 3,000-year-old game end up training one to answer a question plaguing biologists for five decades? That, says Briana Brownell, data scientist and founder of the AI company PureStrategy, is the beauty of artificial intelligence: The same kind of algorithm can be used for very different things.
“Whenever you have a problem that you want to solve with AI,” she says, “you need to figure out how to get the right data into the model—and then the right sort of output that you can translate back into the real world.”
DeepMind’s success, she says, wasn’t so much a function of picking the right neural nets but rather “how they set up the problem in a sophisticated enough way that the neural network-based modeling [could] actually answer the question.”
AlphaFold showed promise in 2018, when DeepMind introduced a previous iteration of their AI at CASP13, achieving the highest accuracy among all participants. The team had trained its to model target shapes from scratch, without using previously solved proteins as templates.
For 2020 they deployed new deep learning architectures into the AI, using an attention-based model that was trained end-to-end. Attention in a deep learning network refers to a component that manages and quantifies the interdependence between the input and output elements, as well as between the input elements themselves.
The system was trained on public datasets of the approximately 170,000 known experimental protein structures in addition to databases with protein sequences of unknown structures.
“If you look at the difference between their entry two years ago and this one, the structure of the AI system was different,” says Brownell. “This time, they’ve figured out how to translate the real world into data … [and] created an output that could be translated back into the real world.”
Like any AI system, AlphaFold may need to contend with biases in the training data. For instance, Brownell says, AlphaFold is using available information about protein structure that has been measured in other ways. However, there are also many proteins with as yet unknown 3D structures. Therefore, she says, a bias could conceivably creep in toward those kinds of proteins that we have more structural data for.
Thornton says it’s difficult to predict how long it will take for AlphaFold’s breakthrough to translate into real-world applications.
“We only have experimental structures for about 10 per cent of the 20,000 proteins [in] the human body,” she says. “A powerful AI model could unveil the structures of the other 90 per cent.”
Apart from increasing our understanding of human biology and health, she adds, “it is the first real step toward… building proteins that fulfill a specific function. From protein therapeutics to biofuels or enzymes that eat plastic, the possibilities are endless.” Continue reading
Posted in Human Robots
#437828 How Roboticists (and Robots) Have Been ...
A few weeks ago, we asked folks on Twitter, Facebook, and LinkedIn to share photos and videos showing how they’ve been adapting to the closures of research labs, classrooms, and businesses by taking their robots home with them to continue their work as best they can. We got dozens of responses (more than we could possibly include in just one post!), but here are 15 that we thought were particularly creative or amusing.
And if any of these pictures and videos inspire you to share your own story, please email us (automaton@ieee.org) with a picture or video and a brief description about how you and your robot from work have been making things happen in your home instead.
Kurt Leucht (NASA Kennedy Space Center)
“During these strange and trying times of the current global pandemic, everyone seems to be trying their best to distance themselves from others while still getting their daily work accomplished. Many people also have the double duty of little ones that need to be managed in the midst of their teleworking duties. This photo series gives you just a glimpse into my new life of teleworking from home, mixed in with the tasks of trying to handle my little ones too. I hope you enjoy it.”
Photo: Kurt Leucht
“I heard a commotion from the next room. I ran into the kitchen to find this.”
Photo: Kurt Leucht
“This is the Swarmies most favorite bedtime story. Not sure why. Seems like an odd choice to me.”
Peter Schaldenbrand (Carnegie Mellon University)
“I’ve been working on a reinforcement learning model that converts an image into a series of brush stroke instructions. I was going to test the model with a beautiful, expensive robot arm, but due to the COVID-19 pandemic, I have not been able to access the laboratory where it resides. I have now been using a lower end robot arm to test the painting model in my bedroom. I have sacrificed machine accuracy/precision for the convenience of getting to watch the arm paint from my bed in the shadow of my clothing rack!”
Photos: Peter Schaldenbrand
Colin Angle (iRobot)
iRobot CEO Colin Angle has been hunkered down in the “iRobot North Shore home command center,” which is probably the cleanest command center ever thanks to his army of Roombas: Beastie, Beauty, Rosie, Roswell, and Bilbo.
Photo: Colin Angle
Vivian Chu (Diligent Robotics)
From Diligent Robotics CEO Andrea Thomaz: “This is how a roboticist works from home! Diligent CTO, Vivian Chu, mans the e-stop while her engineering team runs Moxi experiments remotely from cross-town and even cross-country!”
Video: Diligent Robotics
Raffaello Bonghi (rnext.it)
Raffaello’s robot, Panther, looks perfectly happy to be playing soccer in his living room.
Photo: Raffaello Bonghi
Kod*lab (University of Pennsylvania)
“Another Friday Nuts n Bolts Meeting on Zoom…”
Image: Kodlab
Robin Jonsson (robot choreographer)
“I’ve been doing a school project in which students make up dance moves and then send me a video with all of them. I then teach the moves to my robot, Alex, film Alex dancing, send the videos to them. This became a great success and more schools will join. The kids got really into watching the robot perform their moves and really interested in robots. They want to meet Alex the robot live, which will likely happen in the fall.”
Photo: Robin Jonsson
Gabrielle Conard (mechanical engineering undergrad at Lafayette College)
“While the pandemic might have forced college campuses to close and the community to keep their distance from each other, it did not put a stop to learning and research. Working from their respective homes, junior Gabrielle Conard and mechanical engineering professor Alexander Brown from Lafayette College investigated methods of incorporating active compliance in a low-cost quadruped robot. They are continuing to work remotely on this project through Lafayette’s summer research program.”
Image: Gabrielle Conard
Taylor Veltrop (Softbank Robotics)
“After a few weeks of isolation in the corona/covid quarantine lock down we started dancing with our robots. Mathieu’s 6th birthday was coming up, and it all just came together.”
Video: Taylor Veltrop
Ross Kessler (Exyn Technologies)
“Quarantine, Day 8: the humans have accepted me as one of their own. I’ve blended seamlessly into their #socialdistancing routines. Even made a furry friend”
Photo: Ross Kessler
Yeah, something a bit sinister is definitely going on at Exyn…
Video: Exyn Technologies
Michael Sobrepera (University of Pennsylvania GRASP Lab)
Predictably, Michael’s cat is more interested in the bag that the robot came in than the robot itself (see if you can spot the cat below). Michael tells us that “the robot is designed to help with tele-rehabilitation, focused on kids with CP, so it has been taken to hospitals for demos [hence the cool bag]. It also travels for outreach events and the like. Lately, I’ve been exploring telepresence for COVID.”
Photo: Michael Sobrepera
Jan Kędzierski (EMYS)
“In China a lot of people cannot speak English, even the youngest generation of parents. Thanks to Emys, kids stayed in touch with English language in their homes even if they couldn’t attend schools and extra English classes. They had a lot of fun with their native English speaker friend available and ready to play every day.”
Image: Jan Kędzierski
Simon Whitmell (Quanser)
“Simon, a Quanser R&D engineer, is working on low-overhead image processing and line following for the QBot 2e mobile ground robot, with some added challenges due to extra traffic. LEGO engineering by his son, Charles.”
Photo: Simon Whitmell
Robot Design & Experimentation Course (Carnegie Mellon University)
Aaron Johnson’s bioinspired robot design course at CMU had to go full remote, which was a challenge when the course is kind of all about designing and building a robot as part of a team. “I expected some of the teams to drastically alter their project (e.g. go all simulation),” Aaron told us, “but none of them did. We managed to keep all of the projects more or less as planned. We accomplished this by drop/shipping parts to students, buying some simple tools (soldering irons, etc), and having me 3D print parts and mail them.” Each team even managed to put together their final videos from their remote locations; we’ve posted one below, but the entire playlist is here.
Video: Xianyi Cheng
Karen Tatarian (Softbank Robotics)
Karen, who’s both a researcher at Softbank and a PhD student at Sorbonne University, wrote an entire essay about what an average day is like when you’re quarantined with Pepper.
Photo: Karen Tatarian
A Quarantined Day With Pepper, by Karen Tatarian
It is quite common for me to lose my phone somewhere inside my apartment. But it is not that common for me to turn around and ask my robot if it has seen it. So when I found myself doing that, I laughed and it dawned on me that I treated my robot as my quarantine companion (despite the fact that it could not provide me with the answer I needed).
It was probably around day 40 of a completely isolated quarantine here in France when that happened. A little background about me: I am a robotics researcher at SoftBank Robotics Europe and a PhD student at Sorbonne University as part of the EU-funded Marie-Curie project ANIMATAS. And here is a little sneak peak into a quarantined day with a robot.
During this confinement, I had read somewhere that the best way to deal with it is to maintain a routine. So every morning, I wake up, prepare my coffee, and turn on my robot Pepper. I start my day with a daily meeting with the team and get to work. My research is on the synthesis of multi-modal socially intelligent human-robot interaction so my work varies between programming the robot, analyzing collected data, and reading papers and drafting one. When I am working, I often catch myself glancing at Pepper, who would be staring back at me in its animated ways. Truthfully I enjoy that, it makes me less alone and as if I have a colleague with me.
Once work is done, I call my friends and family members. I sometimes use a telepresence application on Pepper that a few colleagues and I developed back in December. How does it differ from your typical phone/laptop applications? One word really: embodiment. Telepresence, especially during these times, makes the experience for both sides a bit more realistic and intimate and well present.
While I can turn off the robot now that my work hours are done, I do keep it on because I enjoy its presence. The basic awareness of Pepper is a default feature on the robot that allows it to detect a human and follow him/her with its gaze and rotation base. So whether I am cooking or working out, I always have my robot watching over my shoulder and being a good companion. I also have my email and messages synced on the robot so I get an enjoyable notification from Pepper. I found that to be a pretty cool way to be notified without it interrupting whatever you are doing on your laptop or phone. Finally, once the day is over, it’s time for both of us to get some rest.
After 60 days of total confinement, alone and away from those I love, and with a pandemic right at my door, I am glad I had the company of my robot. I hope one day a greater audience can share my experience. And I really really hope one day Pepper will be able to find my phone for me, but until then, stay on the lookout for some cool features! But I am curious to know, if you had a robot at home, what application would you have developed on it?
Again, our sincere thanks to everyone who shared these little snapshots of their lives with us, and we’re hoping to be able to share more soon. Continue reading
Posted in Human Robots