For cosmologists trying to study the formation of the Universe, knowing the mass of everything is critical. But the need to estimate the mass of dark matter, which can't be observed directly, limits their accuracy. A team of scientists led from Carnegie Mellon University (CMU) has trained artificial intelligence (AI) on data from simulated clusters of galaxies, in which the composition of all the components is known. This AI went on to predict a mass for the real-world Coma Cluster of galaxies that agrees with those from earlier, more human-labor-intensive attempts. The result offers the possibility of faster, more accurate assessment of the masses of galaxy clusters.

Christopher Intagliata: Artificial intelligence systems have bested humans at chess, poker, Jeopardy, Go, and countless other games. Susan Hespos: They still can't do what 3-month-olds do. And I'm a champion for babies at the end of the day and this is a clear win for babies. Babies are still slam dunking our most powerful computers when it comes to intuitive physics. Intagliata: Cognitive psychologist Susan Hespos of Northwestern University listed off a few examples of those "intuitive physics" principles.

Engineers at the company DeepMind built a machine-learning system based on … Christopher Intagliata: Artificial intelligence systems have bested …

Artificial intelligence is present in almost all areas of daily life, from the algorithms that predict the content we want to see based on our past choices to those that help detect diseases from medical images. This branch of computer science that ensures that machines have the same capabilities as people, such as learning or reasoning, has endless applications that improve decision making, although its benefits go hand in hand with a huge impact on the environment. To get an idea, the use of ICT solutions today represents between 5 and 9% of electricity consumption worldwide, a figure that could reach 20% in 2030, according to a report by the European Parliament published in May of the year past. In the case of artificial intelligence, both the training of the algorithms and the processing of the data generate an ecological bill that is difficult to digest. A group of researchers from the University of Amherst (Massachusetts, United States), for example, revealed in a 2019 study that feeding information to a computer for human language processing involves the emission of about 284,000 kg of carbon dioxide equivalent, five times more than what a car produces during its useful life, including manufacturing.

England [UK], May 27 (ANI): According to new research, artificial intelligence (AI) can track the health of coral reefs by learning the "song of the reef." The research has been published in the journal, "Ecological Indicators". Coral reefs have a complex soundscape - and even experts have to conduct painstaking analyses to measure reef health based on sound recordings. In the study, University of Exeter scientists trained a computer algorithm using multiple recordings of healthy and degraded reefs, allowing the machine to learn the difference. The computer then analysed a host of new recordings, and successfully identified reef health 92 per cent of the time.

England: According to new research, artificial intelligence (AI) can track the health of coral reefs by learning the "song of the reef." The research has been published in the journal, "Ecological Indicators". Coral reefs have a complex soundscape – and even experts have to conduct painstaking analyses to measure reef health based on sound recordings. In the study, University of Exeter scientists trained a computer algorithm using multiple recordings of healthy and degraded reefs, allowing the machine to learn the difference. The computer then analysed a host of new recordings, and successfully identified reef health 92 per cent of the time.

Researchers from the Department of Computer Systems Engineering at Universidad Politécnica de Madrid,in collaboration with the University of Strathclyde (UK), used a deep learning approach that had previously shown promising performance in other forecasting problems, to forecast the F10.7 solar radio flux over days-ahead timescales relevant to space operations. The F10.7 index, which is a measurement of the intensity of solar radio emissions with a wavelength of 10.7 cm, is used as a proxy for solar activity, a main driver in changes in the density of the atmosphere, and therefore atmospheric drag. As atmospheric drag is one of the most prominent sources of uncertainty in the orbit prediction of the satellites in the Low Earth Orbit (LEO), the accurate modeling of this index is of great importance to help predict the positions of satellites and space debris, and ensure no collisions occur. The methodology followed for the development of this model is based on the use of Deep Learning methods, more specifically, using a new approach based on Deep Residual Networks. This method, based on the neural network architecture "N-BEATS" was found to be effective in single-point forecasting up to 27 days ahead, as well as able to predict the uncertainty in the forecast using an ensemble of models.

How does artificial intelligence learn? Machine learning is the process of using computers to detect patterns in a large number of data sets, and then make predictions based on what the computer learns from these patterns. This makes machine learning a specific and narrow artificial intelligence. Fully artificial intelligence involves machines that can perform the thinking capabilities of humans and intelligent animals, such as perception, learning, and problem-solving. All machine learning is based on algorithms.

For this project, Champ mentioned he had a lot of fun art directing, designing and animating this piece. The vision for this came out beyond what he expected and in the end Champ says "I felt that I really pushed myself. I can say that I'm very proud of it."

Convolutional Neural Networks (CNNs) are a form of bio-inspired deep learning in artificial intelligence. The interaction of thousands of'neurons' mimics the way our brain learns to recognize images. 'These CNNs are successful, but we don't fully understand how they work', says Estefanía Talavera Martinez, lecturer and researcher at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence of the University of Groningen in the Netherlands. She has made use of CNNs herself to analyse images made by wearable cameras in the study of human behaviour. Among other things, Talavera Martinez has been studying our interactions with food, so she wanted the system to recognize the different settings in which people encounter food.