And it is on Singles’ Day, automation, robots, AI and machine learning will be widely applied to all aspects of the annual shopping ritual, right from product selection to delivery.
This year’s November 11 shopping ritual will engage a recommendation algorithm, robots, and chatbots capable of understanding human emotion.
Thank a new approach to spoofing image recognition AIs, developed by a team from Kyushu University in Japan, for that joke.
Trying to catch out AIs is a popular pastime for many researchers, and we’ve reported machine-learning spoofs in the past. The general approach is to add features to images that will incorrectly trigger a neural network and have it identify what it sees as something else entirely.
The new research, published on the arXiv, describes an algorithm that can efficiently identify the best pixels to alter in order to confuse an AI into mislabeling a picture. By changing just one pixel in a 1,024-pixel image, the software can trick an AI about 74 percent of the time. That figure rises to around 87 percent if five pixels are tweaked.
Google unveiled software aimed at making it easier for scientists to use the quantum computers in a move designed to give a boost to the nascent industry.
The software, which is open-source and free to use, could be used by chemists and material scientists to adapt algorithms and equations to run on quantum computers. It comes at a time when Google, IBM, Intel Corp., Microsoft Corp. and D-Wave Systems Inc. are all pushing to create quantum computers that can be used for commercial applications.
A group of astronomers from the universities of Groningen, Naples and Bonn has developed a method that finds gravitational lenses in enormous piles of observations. The method is based on the same artificial intelligence algorithm that Google, Facebook and Tesla have been using in the last years. The researchers published their method and 56 new gravitational lens candidates in the November issue of Monthly Notices of the Royal Astronomical Society.
When a galaxy is hidden behind another galaxy, we can sometimes see the hidden one around the front system. This phenomenon is called a gravitational lens, because it emerges from Einstein’s general relativity theory which says that mass can bend light. Astronomers search for gravitational lenses because they help in the research of dark matter.
The hunt for gravitational lenses is painstaking. Astronomers have to sort thousands of images. They are assisted by enthusiastic volunteers around the world. So far, the search was more or less in line with the availability of new images. But thanks to new observations with special telescopes that reflect large sections of the sky, millions of images are added. Humans cannot keep up with that pace.
One of the most defining scientific discoveries in recent decades is the development of induced pluripotent stem cells, which lets scientists revert adult cells back into an embryonic-like blank state and then manipulating them to become a particular kind of tissue.
But now a new model could do away with this time-consuming process, taking out the middle step and directly programming cells to become whatever we want them to be.
“Cells in our body always self-specialise,” explains bioinformatics researcher Indika Rajapakse from the University of Michigan.
A lobbying group representing top artificial-intelligence companies including Amazon.com Inc., Facebook Inc. and Google issued a warning to lawmakers on Tuesday: hands off our algorithms.
An international, interdisciplinary research team of scientists has come up with a machine-learning method that predicts molecular behavior, a breakthrough that can aid in the development of pharmaceuticals and the design of new molecules that can be used to enhance the performance of emerging battery technologies, solar cells, and digital displays.
The work appears in the journal Nature Communications.
“By identifying patterns in molecular behavior, the learning algorithm or ‘machine’ we created builds a knowledge base about atomic interactions within a molecule and then draws on that information to predict new phenomena,” explains New York University’s Mark Tuckerman, a professor of chemistry and mathematics and one of the paper’s primary authors.
Intel says it is shipping an experimental quantum computing chip to research partners in The Netherlands today. The company hopes to demonstrate that its packaging and integration skills give it an edge in the race to produce practical quantum computers.
The chip contains 17 superconducting qubits—the quantum computer’s fundamental component. According to Jim Clarke, Intel’s director of quantum hardware, the company chose 17 qubits because it’s the minimum needed to perform surface code error correction, an algorithm thought to be necessary to scaling up quantum computers to useful sizes.
Intel’s research partners, at the TU Delft and TNO research center Qutech, will be testing the individual qubits’ abilities as well as performing surface code error correction and other algorithms.