Quantum Computing Is Going Commercial With the Potential to Disrupt Everything

Newsweek, 4/9/2017

IBM recently announced the IBM Q, which would be the first ever commercially available quantum computer. This is important because, if they fulfill their promise, quantum computers could potentially solve certain problems that traditional computers are simply not equipped to handle, allowing rapid developments in fields like medicine, pharmaceuticals, and transportation.

Scientists Reveal New Super-Fast Form of Computer That “Grows As It Computes”

University of Manchester, 3/1/2017

Researchers from the University of Manchester have demonstrated that it is possible to build a super-fast self-replicating computer. Because it is composed of DNA molecules rather than electrical circuitry, when presented with a choice, such a computer can replicate itself to simultaneously compute the solutions. Demonstrating that this (previously only theoretical) machine is physically possible opens up new possibilities in the future of scientific computing.

Machine-Learning Algorithms Can Predict Suicide Risk More Readily Than Clinicians, Study Finds

Newsweek, 2/27/2017

Human clinicians are known not to be very accurate at predicting suicides, so researchers are developing machine-learning algorithms that use multiple factors to identify short-term suicide risk. Data scientists trained the algorithm on data from thousands of clinical records, from both non-fatal suicide cases and random patients. Accuracy was significantly better than studying only one risk factor at a time. Using such a system could aid clinicians in targeting at-risk patients and treating them early.

AI Predicts Autism From Infant Brain Scans

IEEE Spectrum, 2/15/2017

Researchers at the University of North Carolina, Chapel Hill have applied deep-learning algorithms to brain scans of children with a high risk of autism. Algorithms used three indicators, brain surface area, volume, and the gender of the child, to determine if 6- to 12-month-old infants were likely to develop autism. The results were 81% accurate at predicting later diagnosis. This improves over a 50% prediction rate from behavioral questionnaires.

New Algorithms by University of Toronto Researchers May Revolutionize Drug Discoveries

University of Toronto News, 2/6/2017

University of Toronto researchers have developed algorithms which can efficiently generate an accurate 3D image of a protein molecule from several 2D images in just a few minutes. This advance has far-reaching implications for the medical field. For example, drug researchers will be able to use these 3D protein models to analyze the structure of disease-specific proteins and predict the way experimental medications will bind to those proteins inside the body.

China to Develop Prototype Super, Super Computer in 2017

Phys.org, 1/17/2017

China is seeking to prototype an exascale computer (capable of a billion billion calculations per second) by the end of the year, thereby ensuring the country’s position as a world leader in supercomputing. The calculation speed and data transmission efficiency of this system would have far-reaching implications in enabling big data analysis and cloud computing.

Faster Programs, Easier Programming

MIT News, 11/7/2016

Researchers from MIT’s CSAIL and Stony Brook University are researching ways to make using multi-core computers easier. They have created a method for describing, in general terms, the computation task desired, and then automatically converting this to a parallelized program. This makes it easier for domain experts (such as computational biologists or cybersecurity experts) to quickly write programs to support their research or tasks, without having to be parallel-programming experts as well.

Can We Open the Black Box of AI?

Nature International Weekly Journal of Science, 10/5/2016

Scientists attempt to understand how computers think and learn in order to verify the reliability of large scale data analysis. This article covers several efforts in the last few years to understand how deep neural nets work. If scientists can understand how computers gather and interpret data in deep learning, these techniques can be used with more confidence, in day to day applications as well as in cutting edge scientific research.