Alphabet-owned DeepMind has developed a piece of artificial intelligence software that can accurately predict the structure that proteins will fold into in a matter of days, solving a 50-year-old "grand challenge" that could pave the way for better understanding of diseases and drug discovery. Every living cell has thousands of different proteins inside that keep it alive and well. Predicting the shape that a protein will fold into is important because it determines their function and nearly all diseases, including cancer and dementia, are related to how proteins function. "Proteins are the most beautiful, gorgeous structures and the ability to predict exactly how they fold up is really very, very challenging and has occupied many people over many years," Professor Dame Janet Thornton from the European Bioinformatics Institute told journalists on a call. British research lab DeepMind's "AlphaFold" AI system was entered into a competition organized by a group called CASP (Critical Assessment for Structure Prediction). It's a community experiment organization with the mission of accelerating solutions to one problem: how to compute the 3D structure of protein molecules.

Researchers developed a new app that applies facial recognition software to cows. The technology would let ranchers track cattle in the event of disease and help create a national traceability system.

Machine learning involves training a model with data so that it learns to spot or predict features. The Google team pick on the example of training a machine learning system to predict the course of a pandemic. Epidemiologists have built detailed models of the way a disease spreads from infected individuals to susceptible individuals, but not to those who have recovered and so are immune. Key factors in this spread are the rate of infection, often called R0, and length of time, D, that an infected individual is infectious. Obviously, a disease can spread more widely when it is more infectious and when people are infectious for longer.

The robot is modeled after a figure in "Mobile Suit Gundam," a Japanese cartoon first launched in the late 1970s about enormous battle robots piloted by humans. The series spawned multiple spin-offs and toys and gained a worldwide following.

A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."

When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive. Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks. As enterprises embark on major AI-powered digital transformations, those risks may become greater.

Jen Copestake looks at some of the week’s best technology news stories.

Machine learning models significantly outperformed standard models in predicting delayed cerebral ischemia and functional outcomes at 3 months after a subarachnoid hemorrhage, according to findings published in Neurology. "After subarachnoid hemorrhage (SAH), delayed cerebral ischemia (DCI) is the biggest contributor to poor functional outcomes," Jude P.J. Savarraj, PhD, a bioinformatics postdoctoral fellow in the department of neurosurgery at McGovern Medical School, and colleagues wrote. "Previous studies show that several [electronic medical record] parameters, including white blood count panel, measures of coagulation and fibrinolysis, serum glucose and sodium and vital signs (including ECG and BP) are either marginally or strongly associated with DCI and functional outcomes." The researchers hypothesized that machine learning models would be able to learn these associations and accurately predict DCI and functional outcomes and outperform standard models. To test this, Savarraj and colleagues performed a retrospective analysis of outcomes among 451 patients [women, 290; average age, 54 years; median modified Rankin Scale score (mRS) at discharge 3; median mRS at month 3 1] who had a subarachnoid hemorrhage between July 2009 and August 2016.

Researchers from Boston University and University of Virginia published a new paper in the Journal of Marketing that examines how consumers respond to AI recommenders when focused on the functional and practical aspects of a product (its utilitarian value) versus the experiential and sensory aspects of a product (its hedonic value). The study, forthcoming in the the Journal of Marketing, is titled "Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The'Word-of-Machine' Effect" and is authored by Chiara Longoni and Luca Cian. More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide recommendations to consumers. As these companies evaluate AI-based assistance, one critical question must be asked: When do consumers trust the "word of machine," and when do they resist it? A new Journal of Marketing study explores reasons behind the preference of recommendation source (AI vs. human).