In the last couple of decades, technology has become very efficient at collecting information from the physical world, including wearable medical sensors, radar systems integrated into automobiles and satellites monitoring earth's climate--as well as from humans by monitoring the decisions they make. But that massive trove of data is mostly useless on its own; sophisticated computer algorithms are needed to find patterns, extract meaning and make predictions from the data. That's why the University of Wisconsin-Madison Department of Electrical and Computer Engineering launched the machine learning and data science option for both undergraduate electrical engineering and computer engineering majors. The option requires 18 elective credits in the 120-hour bachelor's degree consisting of courses focusing on machine learning and data science in engineering. Courses in the option cover coding for data manipulation, analysis, and visualization, and machine learning topics from applied linear algebra and probability through artificial neural networks and deep learning. When students graduate, the option is noted on their transcript, giving them a valuable credential in future employment searches.

A new machine learning (ML) platform can identify patients with colorectal cancer and helps predict their disease severity and survival, finds a new study. The non-invasive method adds to recent advances in technologies that analyse circulating tumour DNA (ctDNA) and could help spot colorectal cancers in at-risk patients at earlier stages. Like many other malignancies, colorectal cancers are most treatable if they are detected before they have metastasized to other tissues. Colonoscopies are the'gold standard' for diagnosis, but they are uncomfortable and invasive and can lead to complications, which leaves patients less willing to undergo screening. For the study, published in the journal Science Translational Medicine, lead researcher Huiyan Luo from University Cancer Center in China and colleagues leveraged machine learning techniques to develop a less invasive diagnostic method that can detect colorectal cancer in at-risk patients. Their technology works by screening for methylation markers, which are DNA modifications that are frequently found in tumors.

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – released MLPerf, a nascent benchmarking tool "for measuring the speed of machine learning software and hardware." Arrival of MLPerf follows what has been a smattering of ad hoc AI performance comparisons trickling to market. Today Intel posted a blog with data showing for select machine translation using RNNs "the Intel Xeon Scalable processor outperforms NVidia V100 by 4x on the AWS Sockeye Neural Machine Translation model." For quite some time there has been vigorous discussion around the need for meaningful AI benchmarks with proponents suggesting that the lack of meaningful benchmark tools has restrained AI adoption.

Cybercrime and state-sponsored advanced attacks continue to cling to email as a primary distribution vehicle for first-stage malware. Phishing campaigns thrive in targeted attacks, and criminals have even resuscitated old-school macro malware in attachments to gain that initial foothold on a victim's computer. Andy Wen of Google's Counter Abuse Technology group said that the new updates focus primarily on early detection of phishing and spam messages that will benefit from a dedicated machine-learning model. The model, Wen said, will delay messages (fewer than 0.05 percent of messages) in order to apply the model and analyze emails. "Machine learning helps Gmail block sneaky spam and phishing messages from showing up in your inbox with over 99.9 percent accuracy," Wen said.