In the future Cdr Ramsey believes submarines will be equipped with the ability to launch their own autonomous vessels. The next generation of surface warships entering service are already planned to feature mission-bays for launching uncrewed vessels. And autonomous submarines can be deployed by transport aircraft, giving the batteries a rest and the XLUUV a global reach.

The new dataset contains more than 1,000 anonymised sets of chest CT scans. This expands on the earlier database of CT studies of patients with laboratory-confirmed infection created by scientists at the Diagnostics and Telemedicine Centre. The data set aims to inform AI to diagnose COVID-19. The dataset is the largest to date, and all CT studies in the dataset have a special marking made according to the classification, which reflects the manifestation of pathological abnormalities of COVID-19 in the lung tissue based on the chest computed tomography. According to experts at the Diagnostics and Telemedicine Center, a database with CT scans converted into the'research' Neuroimaging Informatics Technology Initiative (NIFTI) format is intended for developing artificial intelligence algorithms.

GANpaint Studio could also be used to improve and debug other GANs that are being developed, by analyzing them for "artifact" units that need to be removed. In a world where opaque AI tools have made image manipulation easier than ever, it could help researchers better understand neural networks and their underlying structures. "Right now, machine learning systems are these black boxes that we don't always know how to improve, kind of like those old TV sets that you have to fix by hitting them on the side," says Bau, lead author on a related paper about the system with a team overseen by Torralba. "This research suggests that, while it might be scary to open up the TV and take a look at all the wires, there's going to be a lot of meaningful information in there." One unexpected discovery is that the system actually seems to have learned some simple rules about the relationships between objects.

This is the first method that can convincingly translate between visual and touch signals

In Canadian author Margaret Atwood's book "Blind Assassins," she says that "touch comes before sight, before speech. It's the first language and the last, and it always tells the truth." While our sense of touch gives us a channel to feel the physical world, our eyes help us immediately understand the full picture of these tactile signals. Robots that have been programmed to see or feel can't use these signals quite as interchangeably. To better bridge this sensory gap, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have come up with a predictive artificial intelligence (AI) that can learn to see by touching, and learn to feel by seeing.

Researchers at Mt. Sinai's Icahn School of Medicine in New York at have a unique collaborator in the hospital: Their in-house artificial intelligence system, known as Deep Patient. The researchers taught Deep Patient to predict risk factors for 78 different diseases by feeding it electronic health records from 700,000 patients. Doctors now turn to the system to aid in diagnoses. While not a person, Deep Patient is more than just a program. Like other advanced AI systems, it learns, makes autonomous decisions, and has grown from a technological tool to a partner, coordinating and collaborating with humans.

In October 2015, when Google invited the European Go champion Fan Hui to play a few games against a computer program called AlphaGo, his response was: "Oh, it's just a program. He lost all five games. A rather jovial fellow, he said his wife told him after the game that he shouldn't check the internet "because people are saying terrible things about you… that a champion has been beaten by a computer". Fan was later hired as an adviser by DeepMind, a Google-owned company that had developed AlphaGo, an Artificial Intelligence (AI) program. Six months later, Google asked the world's finest Go player, grandmaster Lee Sedol of South Korea, to a game of five matches. As Lee walked in to play against the machine, he said: "Human intuition is still too advanced for AI to have caught up.

Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells us about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. A technique that lends itself well to such a task of classification in the face of vast amounts of data is the use of artificial neural networks. These networks, inspired by the workings of the human brain, have been increasingly successful in analysing large datasets, performing medical diagnoses, identifying handwriting, playing games, or recognising images. In this work we demonstrate the use of an artificial neural network which we train to'read' electropherograms and show that it can generalise to unseen profiles.

We live in a world that's increasingly being shaped by complex algorithms and interactive artificial intelligence assistants who help us plot out our days and get from point A to point B. According to a new Princeton study, though, the engineers responsible for teaching these AI programs things about humans are also teaching them how to be racist, sexist assholes. The study, published in today's edition of Science magazine by Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan, focuses on machine learning, the process by which AI programs begin to think by making associations based on patterns observed in mass quantities of data. In a completely neutral vacuum, this would mean that AI would learn to provide responses based solely on objective, data-driven facts. But because the data sets fed to the AI are selected and influenced by humans, there's a degree to which certain biases become a part of the AI's diet. To demonstrate this, Caliskan and her team created a modified version of an Implicit Association Test, an exercise that tasks participants to quickly associate concrete ideas like people of color and women with abstract concepts like goodness and evil.

The Department of Computer Science at the University of Southern California recently created two new degree programs, namely a Bachelor's Program in Computer Science (Games) and a Master's Program in Computer Science (Game Development). In this paper, we discuss two projects that use games as motivator. First, the Computer Games in the Classroom Project develops stand-alone projects on standard artificial intelligence topics that use video-game technology to motivate the students but do not require the students to use game engines. Second, the Pinball Project develops the necessary hardware and software to enable students to learn concepts from robotics by developing games on actual pinball machines.