Tag Archives: experience
#437964 How Explainable Artificial Intelligence ...
The field of artificial intelligence has created computers that can drive cars, synthesize chemical compounds, fold proteins, and detect high-energy particles at a superhuman level.
However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation.
Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.
Learning From Experience
One field of AI, called reinforcement learning, studies how computers can learn from their own experiences. In reinforcement learning, an AI explores the world, receiving positive or negative feedback based on its actions.
This approach has led to algorithms that have independently learned to play chess at a superhuman level and prove mathematical theorems without any human guidance. In my work as an AI researcher, I use reinforcement learning to create AI algorithms that learn how to solve puzzles such as the Rubik’s Cube.
Through reinforcement learning, AIs are independently learning to solve problems that even humans struggle to figure out. This has got me and many other researchers thinking less about what AI can learn and more about what humans can learn from AI. A computer that can solve the Rubik’s Cube should be able to teach people how to solve it, too.
Peering Into the Black Box
Unfortunately, the minds of superhuman AIs are currently out of reach to us humans. AIs make terrible teachers and are what we in the computer science world call “black boxes.”
AI simply spits out solutions without giving reasons for its solutions. Computer scientists have been trying for decades to open this black box, and recent research has shown that many AI algorithms actually do think in ways that are similar to humans. For example, a computer trained to recognize animals will learn about different types of eyes and ears and will put this information together to correctly identify the animal.
The effort to open up the black box is called explainable AI. My research group at the AI Institute at the University of South Carolina is interested in developing explainable AI. To accomplish this, we work heavily with the Rubik’s Cube.
The Rubik’s Cube is basically a pathfinding problem: Find a path from point A—a scrambled Rubik’s Cube—to point B—a solved Rubik’s Cube. Other pathfinding problems include navigation, theorem proving and chemical synthesis.
My lab has set up a website where anyone can see how our AI algorithm solves the Rubik’s Cube; however, a person would be hard-pressed to learn how to solve the cube from this website. This is because the computer cannot tell you the logic behind its solutions.
Solutions to the Rubik’s Cube can be broken down into a few generalized steps—the first step, for example, could be to form a cross while the second step could be to put the corner pieces in place. While the Rubik’s Cube itself has over 10 to the 19th power possible combinations, a generalized step-by-step guide is very easy to remember and is applicable in many different scenarios.
Approaching a problem by breaking it down into steps is often the default manner in which people explain things to one another. The Rubik’s Cube naturally fits into this step-by-step framework, which gives us the opportunity to open the black box of our algorithm more easily. Creating AI algorithms that have this ability could allow people to collaborate with AI and break down a wide variety of complex problems into easy-to-understand steps.
A step-by-step refinement approach can make it easier for humans to understand why AIs do the things they do. Forest Agostinelli, CC BY-ND
Collaboration Leads to Innovation
Our process starts with using one’s own intuition to define a step-by-step plan thought to potentially solve a complex problem. The algorithm then looks at each individual step and gives feedback about which steps are possible, which are impossible and ways the plan could be improved. The human then refines the initial plan using the advice from the AI, and the process repeats until the problem is solved. The hope is that the person and the AI will eventually converge to a kind of mutual understanding.
Currently, our algorithm is able to consider a human plan for solving the Rubik’s Cube, suggest improvements to the plan, recognize plans that do not work and find alternatives that do. In doing so, it gives feedback that leads to a step-by-step plan for solving the Rubik’s Cube that a person can understand. Our team’s next step is to build an intuitive interface that will allow our algorithm to teach people how to solve the Rubik’s Cube. Our hope is to generalize this approach to a wide range of pathfinding problems.
People are intuitive in a way unmatched by any AI, but machines are far better in their computational power and algorithmic rigor. This back and forth between man and machine utilizes the strengths from both. I believe this type of collaboration will shed light on previously unsolved problems in everything from chemistry to mathematics, leading to new solutions, intuitions and innovations that may have, otherwise, been out of reach.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: Serg Antonov / Unsplash Continue reading
#437918 Video Friday: These Robots Wish You ...
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):
ICCR 2020 – December 26-29, 2020 – [Online]
HRI 2021 – March 8-11, 2021 – [Online]
RoboSoft 2021 – April 12-16, 2021 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.
Look who’s baaaack: Jibo! After being sold (twice?), this pioneering social home robot (it was first announced back in 2014!) now belongs to NTT Disruption, which was described to us as the “disruptive company of NTT Group.” We are all for disruption, so this looks like a great new home for Jibo.
[ NTT Disruption ]
Thanks Ana!
FZI's Christmas Party was a bit of a challenge this year; good thing robots are totally competent to have a part on their own.
[ FZI ]
Thanks Arne!
Do you have a lonely dog that just wants a friend to watch cat videos on YouTube with? The Danish Technological Institute has a gift idea for you.
[ DTI ]
Thanks Samuel!
Once upon a time, not so far away, there was an elf who received a very special gift. Watch this heartwarming story. Happy Holidays from the Robotiq family to yours!
Of course, these elves are not now unemployed, they've instead moved over to toy design full time!
[ Robotiq ]
An elegant Christmas video from the Dynamics System Lab, make sure and watch through the very end for a little extra cheer.
[ Dynamic Systems Lab ]
Thanks Angela!
Usually I complain when robotics companies make holiday videos without any real robots in them, but this is pretty darn cute from Yaskawa this year.
[ Yaskawa ]
Here's our little christmas gift to the fans of strange dynamic behavior. The gyro will follow any given shape as soon as the tip touches its edge and the rotation is fast enough. The friction between tip and shape generates a tangential force, creating a moment such that the gyroscopic reaction pushes the tip towards the shape. The resulting normal force produces a moment that guides the tip along the shape's edge.
[ TUM ]
Happy Holidays from Fanuc!
Okay but why does there have to be an assembly line elf just to put in those little cranks?
[ Fanuc ]
Astrobotic's cute little CubeRover is at NASA busy not getting stuck in places.
[ Astrobotic ]
Team CoSTAR is sharing more of their work on subterranean robotic exploration.
[ CoSTAR ]
Skydio Autonomy Enterprise Foundation (AEF), a new software product that delivers advanced AI-powered capabilities to assist the pilot during tactical situational awareness scenarios and detailed industrial asset inspections. Designed for professionals, it offers an enterprise-caliber flight experience through the new Skydio Enterprise application.
[ Skydio ]
GITAI's S1 autonomous robot will conduct two experiments: IVA (Intra-Vehicular Activity) tasks such as switch and cable operations, and assembly of structures and panels to demonstrate its capability for ISA (In-Space Assembly) tasks. This video was recorded in the Nanoracks Bishop Airlock mock-up facility @GITAI Tokyo office.
[ GITAI ]
It's no Atlas, but this is some impressive dynamic balancing from iCub.
[ IIT ]
The Campaign to Stop Killer Robots and I don't agree on a lot of things, and I don't agree with a lot of the assumptions made in this video, either. But, here you go!
[ CSKR ]
I don't know much about this robot, but I love it.
[ Columbia ]
Most cable-suspended robots have a very well defined workspace, but you can increase that workspace by swinging them around. Wheee!
[ Laval ]
How you know your robot's got some skill: “to evaluate the performance in climbing over the step, we compared the R.L. result to the results of 12 students who attempted to find the best planning. The RL outperformed all the group, in terms of effort and time, both in continuous (joystick) and partition planning.”
[ Zarrouk Lab ]
In the Spring 2021 semester, mechanical engineering students taking MIT class 2.007, Design and Manufacturing I, will be able to participate in the class’ iconic final robot competition from the comfort of their own home. Whether they take the class virtually or semi-virtually, students will be sent a massive kit of tools and materials to build their own unique robot along with a “Home Alone” inspired game board for the final global competition.
[ MIT ]
Well, this thing is still around!
[ Moley Robotics ]
Manuel Ahumada wrote in to share this robotic Baby Yoda that he put together with a little bit of help from Intel's OpenBot software.
[ YouTube ]
Thanks Manuel!
Here's what Zoox has been working on for the past half-decade.
[ Zoox ] Continue reading