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Category: robotics/AI – Page 1,229

While automated manufacturing is ubiquitous today, it was once a nascent field birthed by inventors such as Oliver Evans, who is credited with creating the first fully automated industrial process, in flour mill he built and gradually automated in the late 1700s. The processes for creating automated structures or machines are still very top-down, requiring humans, factories, or robots to do the assembling and making.

However, the way nature does assembly is ubiquitously bottom-up; animals and plants are self-assembled at a cellular level, relying on proteins to self-fold into target geometries that encode all the different functions that keep us ticking. For a more bio-inspired, bottom-up approach to assembly, then, human-architected materials need to do better on their own. Making them scalable, selective, and reprogrammable in a way that could mimic nature’s versatility means some teething problems, though.

Now, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have attempted to get over these growing pains with a new method: introducing magnetically reprogrammable materials that they coat different parts with—like robotic cubes—to let them self-assemble. Key to their process is a way to make these magnetic programs highly selective about what they connect with, enabling robust self-assembly into specific shapes and chosen configurations.

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Ask a smart home device for the weather forecast, and it takes several seconds for the device to respond. One reason this latency occurs is because connected devices don’t have enough memory or power to store and run the enormous machine-learning models needed for the device to understand what a user is asking of it. The model is stored in a data center that may be hundreds of miles away, where the answer is computed and sent to the device.

MIT researchers have created a new method for computing directly on these devices, which drastically reduces this latency. Their technique shifts the memory-intensive steps of running a machine-learning model to a central server where components of the model are encoded onto light waves.

The waves are transmitted to a connected device using , which enables tons of data to be sent lightning-fast through a network. The receiver then employs a simple optical device that rapidly performs computations using the parts of a model carried by those light waves.

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Researchers from the Hefei Institutes of Physical Science (HFIPS) of the Chinese Academy of Sciences (CAS) have proposed a new artificial intelligence framework for target detection that provides a new solution for fast and high-precision real-time online target detection.

Relevant results were published in Expert Systems with Applications.

In recent years, theory has driven the rapid development of artificial intelligence technology. Object detection technology based on deep learning theory is also successful in many . Current research focuses on improving the speed or accuracy of and fails to take efficiency and accuracy into account. How to achieve fast and accurate object detection has become an important challenge in the field of artificial intelligence.

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A Brazilian study published in Scientific Reports shows that artificial intelligence (AI) can be used to create efficient models for genomic selection of sugarcane and forage grass varieties and predict their performance in the field on the basis of their DNA.

In terms of accuracy compared with traditional breeding techniques, the proposed methodology improved predictive power by more than 50%. This is the first time a highly efficient genomic selection method based on has been proposed for polyploid plants (in which cells have more than two complete sets of chromosomes), including the grasses studied.

Machine learning is a branch of AI and computer science involving statistics and optimization, with countless applications. Its main goal is to create algorithms that automatically extract patterns from datasets. It can be used to predict the performance of a plant, including whether it will be resistant to or tolerant of biotic stresses such as pests and diseases caused by insects, nematodes, fungi or bacteria, and or abiotic stresses such as cold, drought, salinity or insufficient soil nutrients.

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Thanks to its mild climate, expansive highway network, and lax regulations, Texas has become the country’s proving ground for driverless trucks. From cargo to produce, goods have been traveling the state’s highways partially driver-free (the trucks aim to use autonomous mode on highways, but safety drivers take over to navigate city streets) for a couple of years already. Now there’s another type of cargo traveling through Texas via autonomous trucks: furniture. This week Kodiak Robotics announced a partnership to transport IKEA products using a heavy-duty self-driving truck.

Kodiak has been moving furniture and other IKEA goods since August, but the companies carried out a testing period before making the agreement public. The route runs from an IKEA distribution center in Baytown, east of Houstin, to a store in Frisco, 290 miles away just north of Dallas. It’s mostly a straight shot on highway 45.

Like the self-driving trucks that’ve come before it, the vehicle has a safety driver on board. He or she picks up loaded trailers at the distribution center in the morning and provides driving help where needed, reaching the store by late afternoon; it’s about a five-hour drive in a car, so a bit more in a heavy-duty truck.

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Ben Goertzel, PhD, is author of many books on artificial intelligence including Ten Years to the Singularity if We Really Really Try; Engineering General Intelligence, Vols. 1 and 2; The Hidden Pattern: A Patternist Philosophy of Mind; and The Path to Posthumanity. He is also editor (with Damien Broderick) of an anthology about parapsychology titled, Evidence for Psi: Thirteen Empirical Research Reports. He is chief scientific officer for Hanson Robotics in Hong Kong.

Here he notes that, while the question of reincarnation in robots seems outlandish, most of our present technology would have seemed nonsensical and incomprehensible to earlier generations of humans. He quotes the 14th Dalai Lama who suggested (half-jokingly) that artificial intelligence programmers of the future might incarnate into robots. He cites Stephen Braude’s book, Immortal Remains, as demonstrating that we must consider some version of consciousness operating outside of the body. He outlines the sort of scientific and metaphysical models that might lead to such a development.

New Thinking Allowed host, Jeffrey Mishlove, PhD, is author of The Roots of Consciousness, Psi Development Systems, and The PK Man. Between 1986 and 2002 he hosted and co-produced the original Thinking Allowed public television series. He is the recipient of the only doctoral diploma in “parapsychology” ever awarded by an accredited university (University of California, Berkeley, 1980). He is also past-president of the non-profit Intuition Network, an organization dedicated to creating a world in which all people are encouraged to cultivate and apply their inner, intuitive abilities.

(Recorded on April 29, 2016)

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A new computational approach will improve understanding of different states of carbon and guide the search for materials yet to be discovered.

Materials—we use them, wear them, eat them and create them. Sometimes we invent them by accident, like with Silly Putty. But far more often, making useful materials is a tedious and expensive process of trial and error.

Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have recently demonstrated an automated process for identifying and exploring promising new materials by combining machine learning (ML)—a type of artificial intelligence—and computing. The new approach could help accelerate the discovery and design of useful materials.

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