A method for approximating continuous functions $\mathbb{Z}_{p}^{n}\rightarrow\mathbb{Z}_{p}$ by a linear superposition of continuous functions $\mathbb{Z}_{p}\rightarrow\mathbb{Z}_{p}$ is presented and a polynomial regression model is constructed that allows approximating such functions with any degree of accuracy. A physical interpretation of such a model is given and possible methods for its training are discussed. The proposed model can be considered as a simple alternative to possible $p$-adic models based on neural network architecture.

In May 2017, National Health Service (NHS) hospitals in England and Scotland were virtually shut down for several days because of the global WannaCry cyberattack. The attack resulted in the cancellation of thousands of appointments and operations and some NHS services had to turn away noncritical emergencies. Up to 70,000 devices, including computers, MRI scanners, blood-storage refrigerators, and operating room equipment may have been affected. And in 2016, the Hollywood Presbyterian Medical Center in Los Angeles paid $17,000 in bitcoin to a hacker to unlock data that had been encrypted in an attack. Hospital staff struggled to deal with the loss of email and access to patient data.

Amazon's 2019 Climate Pledge calls for a commitment to net zero carbon across their businesses by 2040. Since then, the company has reduced the weight of their outbound packaging by 33%, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.5 billion shipping boxes. With less packaging used throughout the supply chain, volume per shipment is reduced and transportation becomes more efficient. The cumulative impact across Amazon's enormous network is a dramatic reduction in carbon emissions. To make this happen, the customer packaging experience team partnered with AWS to build a machine learning solution powered by Amazon SageMaker.

Amazon's 2019 Climate Pledge calls for a commitment to net zero carbon across their businesses by 2040. Since then, the company has reduced the weight of their outbound packaging by 33%, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.5 billion shipping boxes. With less packaging used throughout the supply chain, volume per shipment is reduced and transportation becomes more efficient. The cumulative impact across Amazon's enormous network is a dramatic reduction in carbon emissions. To make this happen, the customer packaging experience team partnered with AWS to build a machine learning solution powered by Amazon SageMaker.

If there's one thing a hospital patient doesn't want to think about as they prepare for a medical scan it's the possibility a cyberattacker might have found a way to remotely tamper with the diagnostic images, or even quietly upped the radiation levels used to generate them. The good news is that nobody has ever been confirmed to have done such a thing to a computed tomography (CT) X-ray scanner, which along with MRI (magnetic resonance imaging) and ultrasound systems form the backbone of modern hospital diagnosis. There is a caveat of course – the moment when somebody tries must be growing closer, leaving researchers searching for a reliable way to head off the troubling possibilities. Now a team at Israel's famous Ben-Gurion University of the Negev thinks it has come up with a solution to the problem of defending medical imaging devices (MIDs) using an AI system trained with families of open source algorithms to monitor commands sent to CT scanners for something that doesn't look right. In a proof of concept study due to be published this month, this splits the AI defense into a context-free (CF) layer that filters for obviously suspect commands (an excessive radiation level, say), and a more sophisticated context-sensitive (CS) layer that compares an apparently legitimate command to the medical context in which it is being used (giving a child an adult radiation dose).

Researchers at Ben-Gurion University of the Negev have developed a new AI technique that will protect medical devices from malicious operating instructions in a cyberattack as well as other human and system errors. Complex medical devices such as CT (computed tomography), MRI (magnetic resonance imaging) and ultrasound machines are controlled by instructions sent from a host PC. Abnormal or anomalous instructions introduce many potentially harmful threats to patients, such as radiation overexposure, manipulation of device components or functional manipulation of medical images. Threats can occur due to cyberattacks, human errors such as a technician's configuration mistake or host PC software bugs. As part of his Ph.D. research, BGU researcher Tom Mahler has developed a technique using artificial intelligence that analyzes the instructions sent from the PC to the physical components using a new architecture for the detection of anomalous instructions.

A researcher at the Ars Electronica Futurelab research center in Austria used open-source artificial intelligence software to mimic classical symphonies. Ali Nikrang, who works at the Ars Electronica Futurelab research center in Austria, is using open-source artificial intelligence (AI) software to mimic classical symphonies. Nikrang debuted the program at the recent Ars Electronica Festival in Linz, Austria, which aims to highlight connections between science, art, and technology. During the festival, a traditional orchestra performed Gustav Mahler's unfinished Symphony No. 10, which was immediately followed by six minutes of "Mahleresque" music written by the MuseNet software. The software used the first ten notes from Mahler's Symphony No. 10 and produced four suggested segments.

Linz (Austria) (AFP) - Can artificial intelligence turn out symphonies to match one of the greats of classical music? That was the question posed by one unusual orchestra performance in the Austrian city of Linz on Friday, in which Gustav Mahler's unfinished Symphony No.10 was played -- immediately followed by six minutes of "Mahleresque" music written by software. The project's creator says that the two are clearly distinguishable but not everyone in the audience agreed. "I couldn't really feel the difference... I believe it was really well done," Maria Jose Sanchez Varela, 34, a science and philosophy researcher from Mexico, told AFP.

LINZ (Austria): Can artificial intelligence turn out symphonies to match one of the greats of classical music? That was the question posed by one unusual orchestra performance in the Austrian city of Linz on Friday, in which Gustav Mahler's unfinished Symphony No 10 was played -- immediately followed by six minutes of "Mahleresque" music written by software. The project's creator says that the two are clearly distinguishable but not everyone in the audience agreed. "I couldn't really feel the difference... I believe it was really well done," Maria Jose Sanchez Varela, 34, a science and philosophy researcher from Mexico, said.

Can artificial intelligence turn out symphonies to match one of the greats of classical music? That was the question posed by one unusual orchestra performance in the Austrian city of Linz on Friday, in which Gustav Mahler's unfinished Symphony No.10 was played -- immediately followed by six minutes of "Mahleresque" music written by software. The project's creator says that the two are clearly distinguishable but not everyone in the audience agreed. "I couldn't really feel the difference... I believe it was really well done," Maria Jose Sanchez Varela, 34, a science and philosophy researcher from Mexico, told AFP.