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, deep learning theory has driven the rapid development of artificial intelligence technology. Object detection technology based on deep learning theory is also successful in many industrial applications. Current research focuses on improving the speed or accuracy of target detection 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.
The artificial intelligence speech translation system can decipher Hokkien, a spoken language.
Meta has created a new speech translator that can translate Hokkien, a predominantly oral language spoken in the diaspora of China and one of the national languages of Taiwan.
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 machine learning 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.
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.
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.
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 high performance computing. The new approach could help accelerate the discovery and design of useful materials.
The goal is to enable the printing of large, complex shaped structures, on any surface, using a swarm of drones, each depositing whatever material is required. It’s a bit like a swarm of wasps building a nest, into whatever little nook they come across, but on the wing.
Even in technical disciplines such as engineering, there is much we can still learn from nature. After all, the endless experimentation and trials of life give rise to some of the most elegant solutions to problems. With that in mind, a large team of researchers took inspiration from the humble (if rather annoying) wasp, specifically its nest-building skills. The idea was to explore 3D printing of structures without the constraints of a framed machine, by mounting an extruder onto a drone.
As you might expect, one of the most obvious issues with this attempt is the tendency of the drone’s to drift around slightly. The solution the team came up with was to mount the effector onto a delta bot carrier hanging from the bottom of the drone, allowing it to compensate for its measured movement and cancel out the majority of the positional error.
The printing method relies upon the use of two kinds of drone. The first done operates as a scanner, measuring the print surface and any printing already completed. The second drone then approaches and lays down a single layer, before they swap places and repeat until the structure is complete.
As if it weren’t enough to have AI tanning humanity’s hide (figuratively for now) at every board game in existence, Google AI has got one working to destroy us all at Ping-Pong as well. For now they emphasize it is “cooperative,” but at the rate these things improve, it will be taking on pros in no time.
The project, called i-Sim2Real, isn’t just about Ping-Pong but rather about building a robotic system that can work with and around fast-paced and relatively unpredictable human behavior. Ping-Pong, AKA table tennis, has the advantage of being pretty tightly constrained (as opposed to playing basketball or cricket) and a balance of complexity and simplicity.
“Sim2Real” is a way of describing an AI creation process in which a machine learning model is taught what to do in a virtual environment or simulation, then applies that knowledge in the real world. It’s necessary when it could take years of trial and error to arrive at a working model — doing it in a sim allows years of real-time training to happen in a few minutes or hours.
