It's been another eventful year for cyber attacks. More than 4 billion records have been breached so far – and we're not even to the end of the year yet! But 2019 will soon be behind us. It's now time to look toward 2020 and speculate on what will transpire next in the ongoing cybersecurity battle. What new and evolving technologies will be at the forefront of cybersecurity?

With the help of computational pathology firm Paige, healthcare technology giant Royal Phillips is bringing clinical artificial intelligence to pathology laboratories to help improve a pathologist's workflow and treatment planning for patients. According to a joint news release Thursday, this strategic collaboration will first start with Paige Prostate to help pathologists quantify and characterize cancer in tissue samples and make precise and efficient diagnoses. The release noted the need for more advanced cancer diagnosis technology as the number of cancer cases rises. Glass slide-based laboratory workflows are being converted to digital using solutions like ones offered by Phillips. Once digital images are created, the CE-marked Paige Prostate software is applied automatically to detect and localize prostate cancer, providing pathologists with valuable information they can use to evaluate prostate biopsies.

While some business owners are happy and excited about the prospect of artificial intelligence (AI) and how it could improve their operations, some are scared of it, some don't even know they're using it already, and some don't know how they could benefit from the emerging tech, at all. This is according to a new report from Esme Loans, which polled 250 business owners in the UK to understand how they perceive AI. As it turns out, 43 per cent don't understand how they could benefit from AI, while six in ten feel they've overlooked AI's potential. Three quarters (73 per cent) said they didn't use AI, but when presented with a long list of tools, 29 per cent said to have been using at least one. That means that these business owners have been using AI, unknowingly.

Nvidia unveiled a new federated learning edge computing reference application for radiology to help hospitals crunch medical data for better disease detection while protecting patient privacy. Called Clara Federal Learning, the system relies on Nvidia EGX, a computing platform which was announced earlier in 2019. It uses the Jetson Nano low wattage computer which can provide up to one-half trillion operations per second of processing for tasks like image recognition. EGX allows low-latency artificial intelligence at the edge to act on data, in this case images from MRIs, CT scans and more. Nvidia made its announcement of Clara on Sunday at the Radiological Society of North America conference in Chicago.

Employers engage artificial intelligence solutions amid a talent shortage. As employers grapple with a widespread labor shortage, more are turning to artificial intelligence tools in their search for qualified candidates. Hiring managers are using increasingly sophisticated AI solutions to streamline large parts of the hiring process. The tools scrape online job boards and evaluate applications to identify the best fits. They can even stage entire online interviews and scan everything from word choice to facial expressions before recommending the most qualified prospects.

ESA's SpaceBok robot is designed to walk, hop, and run in low-gravity environments. From free-flying droids to humanoids, from crawlers to inflatable torsos, space robots of myriad types are now being considered for missions in low Earth orbit, on interplanetary spacecraft, and on other worlds. It might sound like a prop list from a Star Wars movie, but space agencies and their contractors are developing a panoply of robotic assistants with a serious aim in mind: to boost the productivity and safety of astronauts. The idea behind robot assistants is multifaceted: one aim is to offload time-consuming repetitive tasks like space station cleaning and inventory making from crew members to free-flying or humanoid robots. Ground robots controlled from, say, spacecraft orbiting the Moon or Mars could construct human habitats ahead of a landing, or perform reconnaissance ahead of human exploration missions.

If I told you only 4% of all high school students in the U.S. were taking science or math classes, you'd be aghast. If 96% of students were not getting science or math classes, you could reasonably argue it does not exist in any practical sense. Over the last few months, several reports provided new insights about U.S. high school computer science (CS): California and Texas are two largest states based on U.S. population, but we can't generalize to everyone based on those states. We don't have data on who is taking CS across the U.S., due to our state-centric, decentralized model of primary and secondary school education. California and Texas are among the leaders in implementing CS education.

I recently had the opportunity to take a ride in a Waymo self-driving car in Chandler, AZ. I had been looking forward to this experience, not only to see how well the technology worked but also what the experience might be like as a passenger. Upon my arrival at the Waymo facility, I had apparently approached the side of the building where the Waymo cars go at the end of their duty cycles to be refueled and inspected. As I drove in, I was more or less surrounded by incoming Waymo vehicles. I relaxed as they navigated their way around me.

Garth gibson has spent his career pushing data storage systems to higher levels of performance, reliability, and scalability. While he was a graduate student at the University of California, Berkeley, Gibson was a part of groundbreaking research on Redundant Arrays of Inexpensive Disks (RAID). Later, as a professor at Carnegie Mellon University, he worked on projects like Network Attached Secure Disk (NASD) technology, and a clustered storage system used by Roadrunner, the world's first peta-scale supercomputer. Here, he speaks about handling failures, collaborating with industry and academia, and how deep learning has impacted systems design. When you were a graduate student at the University of California at Berkeley, you joined a computer architecture team led by David Patterson that was building a complete system based on RISC concepts.

For the running example in Figure 1, this abstraction would replace the application-specific identifiers triangle and EQUILATERAL with generic placeholders, such as VAR1 and VAR2. After this abstraction, both approaches use an RNN-based sequence-to-sequence network that predicts how to modify the abstracted code. Given the increasing interest in learning-based approaches toward software engineering problems, we will likely see more progress on learning-based repair in the coming years. Key challenges toward effective solutions include finding an appropriate representation of source code changes and obtaining large amounts of high-quality human patches as training data.