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Category: robotics/AI – Page 2,116

Quantum computing isn’t going to revolutionize AI anytime soon, according to a panel of experts in both fields.

Different worlds: Yoshua Bengio, one of the fathers of deep learning, joined quantum computing experts from IBM and MIT for a panel discussion yesterday. Participants included Peter Shor, the man behind the most famous quantum algorithm. Bengio said he was keen to explore new computer designs, and he peppered his co-panelists with questions about what a quantum computer might be capable of.

Quantum leaps: The panels quantum experts explained that while quantum computers are scaling up, it will be a while—we’re talking years here—before they could do any useful machine learning, partly because a lot of extra qubits will be needed to do the necessary error corrections. To complicate things further, it isn’t very clear what, exactly, quantum computers will be able to do better than their classical counterparts. But both Aram Harrow of MIT and IBM’s Kristian Temme said that early research on quantum machine learning is under way.

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When moving through a crowd to reach some end goal, humans can usually navigate the space safely without thinking too much. They can learn from the behavior of others and note any obstacles to avoid. Robots, on the other hand, struggle with such navigational concepts.

MIT researchers have now devised a way to help robots navigate environments more like humans do. Their novel motion-planning model lets robots determine how to reach a goal by exploring the environment, observing other agents, and exploiting what they’ve learned before in similar situations. A paper describing the model was presented at this week’s IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Popular motion-planning algorithms will create a tree of possible decisions that branches out until it finds good paths for navigation. A that needs to navigate a room to reach a door, for instance, will create a step-by-step search tree of possible movements and then execute the best path to the door, considering various constraints. One drawback, however, is these algorithms rarely learn: Robots can’t leverage information about how they or other agents acted previously in similar environments.

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By translating a key human physical dynamic skill — maintaining whole-body balance — into a mathematical equation, the team was able to use the numerical formula to program their robot Mercury, which was built and tested over the course of six years. They calculated the margin of error necessary for the average person to lose one’s balance and fall when walking to be a simple figure — 2 centimeters.

“Essentially, we have developed a technique to teach autonomous robots how to maintain balance even when they are hit unexpectedly, or a force is applied without warning,” Sentis said. “This is a particularly valuable skill we as humans frequently use when navigating through large crowds.”

Sentis said their technique has been successful in dynamically balancing both bipeds without ankle control and full humanoid robots.

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From space colonization to resurrection of dinosaurs to machine intelligence, the most awe-inspiring visions of humanity’s future are typically born from science fiction.

But among an abundance of time travel, superheroes, space adventures, and so forth, biotech remains underrepresented in the genre.

This selection highlights some outstanding works (new and not so new) to fill the sci-fi gap for biotech aficionados.

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Scientists from Nanyang Technological University, Singapore (NTU Singapore) have developed a technology whereby two robots can work in unison to 3D-print a concrete structure. This method of concurrent 3D printing, known as swarm printing, paves the way for a team of mobile robots to print even bigger structures in the future. Developed by Assistant Professor Pham Quang Cuong and his team at NTU’s Singapore Centre for 3D Printing, this new multi-robot technology is reported in Automation in Construction. The NTU scientist was also behind the Ikea Bot project earlier this year, in which two robots assembled an Ikea chair in about nine minutes.

Using a specially formulated cement mix suitable for 3D , this new development will allow for unique concrete designs currently impossible with conventional casting. Structures can also be produced on demand and in a much shorter period.

Currently, 3D-printing of large concrete structures requires huge printers that are larger in size than the printed objects, which is unfeasible since most construction sites have space constraints. Using multiple that can 3D print in sync means large structures and specially designed facades can be printed anywhere, as long as there is enough space for the robots to move around the work site.

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If simulating the brain is proving tricky, why don’t we try decoding it?

“There’s a good reason the first flying machines weren’t mechanical bats: people tried that, and they were terrible.” — Dan Robitzski

In the current AI Spring, many people and corporations are betting big that the capabilities of deep learning algorithms will continue to improve as the algorithms are fed more data. Their faith is backed by the miracles performed by such algorithms: they can see, listen and do a thousand other things that were previously considered too difficult for AI.

Our guest for the third episode of the AGI Podcast, Pascal Kaufmann, is amongst those who believe such faith in deep learning is misplaced.

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Almost exactly a decade ago, I was cycling in a bike lane when a car hit me from behind. Luckily, I suffered only a couple bruised ribs and some road rash. But ever since, I have felt my pulse rise when I hear a car coming up behind my bike.

As self-driving cars roll out, they’re already being billed as making me – and millions of American cyclists, pedestrians and vehicle passengers – safer.

As a driver and a cyclist, I initially welcomed the idea of self-driving cars that could detect nearby people and be programmed not to hit them, making the streets safer for everyone. Autonomous vehicles also seemed to provide attractive ways to use roads more efficiently and reduce the need for parking in our communities. People are certainly talking about how self-driving cars could help build more sustainable, livable, walkable and bikable communities.

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