Engineers at the University of Waterloo have developed artificial intelligence (AI) technology to predict if women with breast cancer would benefit from chemotherapy prior to surgery. The new AI algorithm, part of the open-source Cancer-Net initiative led by Dr. Alexander Wong, could help unsuitable candidates avoid the serious side effects of chemotherapy and pave the way for better surgical outcomes for those who are suitable. "Determining the right treatment for a given breast cancer patient is very difficult right now, and it is crucial to avoid unnecessary side effects from using treatments that are unlikely to have real benefit for that patient," said Wong, a professor of systems design engineering. "An AI system that can help predict if a patient is likely to respond well to a given treatment gives doctors the tool needed to prescribe the best personalized treatment for a patient to improve recovery and survival." In a project led by Amy Tai, a graduate student with the Vision and Image Processing (VIP) Lab, the AI software was trained with images of breast cancer made with a new magnetic image resonance modality, invented by Wong and his team, called synthetic correlated diffusion imaging (CDI).

On May 14, the AI Facebook Research Team has published an article regarding Expire-Span, a human-brain like algorithm which learns to forget the irrelevant information. Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. In practice, Expire-Span is a deep learning algorithm, which first predicts the most relevant information for a given task, and then it equips every information with an expiration date, namely a deadline. When the date expires, the associated information is forgotten. This aspect permits the Expire-Span algorithm to be very scalable in terms of memory.

Medical imaging is an important part of modern healthcare, enhancing both the precision, reliability and development of treatment for various diseases. Artificial intelligence has also been widely used to further enhance the process. However, conventional medical image diagnosis employing AI algorithms require large amounts of annotations as supervision signals for model training. To acquire accurate labels for the AI algorithms--radiologists, as part of the clinical routine, prepare radiology reports for each of their patients, followed by annotation staff extracting and confirming structured labels from those reports using human-defined rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and various NLP tools.

Deepmind introduces PonderNet, a new algorithm that allows artificial neural networks to learn to think for a while before answering. This improves the ability of these neural networks to generalize outside of their training distribution and answer tough questions with more confidence than ever before. The time required to solve a problem is not just influenced by the size of inputs but also the complexity. Also, the amount of computation used in standard neural networks is not proportional to the complexity, but rather it's proportional with size. To address this issue, Deepmind, in its latest research, presents PonderNet, which builds on Adaptive Computation Time (ACT; Graves, 2016) and other adaptive networks.

While artificial intelligence (AI) has the potential to revolutionize a number of industries, the technology isn't without its controversies. Over the past few years, researchers and developers have raised concerns around the potential impacts of widespread AI adoption--and how a lack of existing ethical frameworks may put consumers at risk. These concerns may be especially relevant to medical device manufacturers, which are increasingly using AI in new medical devices like smart monitors and health wearables. New standards and regulations on ethical AI may provide essential guidance for medical device manufacturers interested in leveraging AI. The widespread use of AI could pose a number of ethical challenges.

It's February 2021 and the coronavirus that knocked on the door in late 2019 is still here to cause chaos. As each day passes, new positive cases are emerging all around the world. While there is some recovery rate and strict measures are being duly followed to keep the demonic virus from spreading, a sneaky way the number is going up is through asymptomatic COVID-19 carriers. These are people who are infected by the virus but should show or feel any symptoms. Most of the time, they are clueless that they have been affected.

The aim of the AI algorithm is to separate healthy patients from ill but asymptomatic ones based on coughing tone, feeling, and other biomarkers. In a continuation of the involvement of artificial intelligence (AI) in healthcare, a new app may help diagnose asymptomatic COVID-19 carriers through a simple listening test -- a cough from someone infected versus a regular cough. It may seem straight from science fiction, but a research team at MIT believes that asymptomatic carriers may be showing subtle signs through the sound of their cough. They've built an app, programming it with thousands of data points from healthy and sick volunteers. AI was able to identify coughs coming from those infected with the virus with a 98.5% accuracy rate.

Computer scientists have developed a new artificial intelligence (AI) system that may be able to identify malicious codes that hijack supercomputers to mine for cryptocurrency such as Bitcoin and Monero. "Based on recent computer break-ins in Europe and elsewhere, this type of software watchdog will soon be crucial to prevent cryptocurrency miners from hacking into high-performance computing facilities and stealing precious computing resources," said Gopinath Chennupati, a researcher at Los Alamos National Laboratory and co-author of a new paper in the journal IEEE Access. "Our deep learning artificial intelligence model is designed to detect the abusive use of supercomputers specifically for the purpose of cryptocurrency mining." Legitimate cryptocurrency miners often assemble enormous computer arrays dedicated to digging up the digital cash. Less savory miners have found they can strike it rich by hijacking supercomputers, provided they can keep their efforts hidden.

As artificial intelligence evolves and becomes more intelligent, it's important for organizations to question its power. Certainly, AI is being designed to help organizations make jobs more efficient, streamline business processes, and acquire and retain more customers. Companies that haven't yet incorporated AI are tempted by its operational promises. They're also tempted by a sense that AI is a tool that will be vital to ensuring competitive advantage, relevance in a rapidly changing world, and not being inadvertently left behind. But even though AI should be designed to improve human function, it can also hinder it.

The tests, set to take place at Edwards Air Force Base in Kern County, California, are expected to be conducted on a "small, but representative high-speed surrogate aircraft," Cara Bousie, the service's spokesperson, told Aviation Week. Although Bousie steered clear of offering any additional details regarding the looming tests, she did indicate that the move is part of a two-year campaign for the department to determine just how the technology will perform in a controlled setting. Will Roper, assistant secretary of the Air Force for acquisition, previously revealed in a March interview that aircraft candidates that may be used during the summer trials include the Kratos XQ-58A Valkyrie, Composite Engineering BQM-167 Skeeter and Boeing QF-16. Disclosed to the public just in March, the Skyborg program's objective is to deliver a combat-ready, autonomous, unmanned aerial vehicle prototype by the end of 2023. The aircraft is expected to act as a robotic wingman for service members, using its AI tech to manage combat mission tasks on its own when the need arises.