One of the most contentious debates in technology is around the question of automation and jobs. At issue is whether advances in automation, specifically with regards to artificial intelligence and robotics, will spell trouble for today's workers. This debate is played out in the media daily, and passions run deep on both sides of the issue. In the past, however, automation has created jobs and increased real wages. A widespread concern with the current scenario is that the workers most likely to be displaced by technology lack the skills needed to do the new jobs that same technology will create.

One of the most contentious debates in technology is around the question of automation and jobs. At issue is whether advances in automation, specifically with regards to artificial intelligence and robotics, will spell trouble for today's workers. This debate is played out in the media daily, and passions run deep on both sides of the issue. In the past, however, automation has created jobs and increased real wages. A widespread concern with the current scenario is that the workers most likely to be displaced by technology lack the skills needed to do the new jobs that same technology will create.

And now, an algorithm can predict not only whether they carry a genetic mutation, but which genes were mutated. The study, published Monday in Nature Medicine, is the latest from a Boston-based company called FDNA, one of a few organizations creating software that can help physicians diagnose genetic syndromes based just on a face -- and may serve an important validation of the company's technology, said Yaron Gurovich, the company's chief technology officer. "We went for this high-impact journal to prove beyond any doubt that this technology is good, it performs as we say, we can stand behind it, and now it opens a lot of doors to publish more," he said. The study itself is a collection of experiments testing how the results of algorithms -- FDNA refers to them as DeepGestalt -- stack up against clinicians' diagnoses. In one of the experiments, DeepGestalt's performance was better than random chance when picking which of five genetic mutations might be causing a condition called Noonan syndrome.

Facial recognition can help unlock your phone. Could it also be able to play a far more valuable role in people's lives by identifying whether or not a person has a rare genetic disorder, based exclusively on their facial features? DeepGestalt, an artificial intelligence built by the Boston-based tech company FDNA, suggests that the answer is a resounding "yes." The algorithm is already being used by leading geneticists at more than 2,000 sites in upward of 130 countries around the world. In a new study, published in the journal Nature Medicine, researchers show how the algorithm was able to outperform clinicians when it came to identifying diseases.

Researchers are improving the ability of algorithms to help spot the physical characteristics of conditions such as Cornelia de Lange syndrome.Credit: Michael Ares/The Palm Beach Post via ZUMA A deep-learning algorithm is helping doctors and researchers to pinpoint a range of rare genetic disorders by analysing pictures of people's faces. In a paper1 published on 7 January in Nature Medicine, researchers describe the technology behind the diagnostic aid, a smartphone app called Face2Gene. It relies on machine-learning algorithms and brain-like neural networks to classify distinctive facial features in photos of people with congenital and neurodevelopmental disorders. Using the patterns that it infers from the pictures, the model homes in on possible diagnoses and provides a list of likely options. Doctors have been using the technology as an aid, even though it's not intended to provide definitive diagnoses.

The ability to sequence genomes quickly has provided scientists with reams of data, but understanding how evolution has shaped humans is still a difficult task.Credit: Guy Tear/Wellcome Coll./CC Pinpointing where and how the human genome is evolving can be like hunting for a needle in a haystack. Each person's genome contains three billion building blocks called nucleotides, and researchers must compile data from thousands of people to discover patterns that signal how genes have been shaped by evolutionary pressures. To find these patterns, a growing number of geneticists are turning to a form of machine learning called deep learning. Proponents of the approach say that deep-learning algorithms incorporate fewer explicit assumptions about what the genetic signatures of natural selection should look like than do conventional statistical methods.

Artificial intelligence tools are helping to reveal the genetic components of autism. For geneticists, autism is a vexing challenge. Inheritance patterns suggest it has a strong genetic component. But variants in scores of genes known to play some role in autism can explain only about 20% of all cases. Finding other variants that might contribute requires looking for clues in data on the 25,000 other human genes and their surrounding DNA--an overwhelming task for human investigators.

Since the late 1980s particle physicists have used AI even as the concept of a neural network was barely in the public's consciousness. AI and particle physics go hand in hand as the experiments the physicists perform usually revolves around seeking out patterns in the data from particle detectors and AI is excellent at pattern detection. Boaz Klima, a Physicists from the Fermi National Accelerator Laboratory, also called Fermilab, says "It took us several years to convince people that this is not just some magic, hocus-pocus, black box stuff." He was amongst the first to adopt AI tools but today, it's a part of standard particle physics practices. Usually, particle physicists aim to comprehend the way the inner gears of the universe works, typically by colliding subatomic particles at hit speeds to break them down into even smaller and more unusual kinds of matter.

In a departure from using AI and machine learning tools for tasks such as automating customer service, some companies are applying the technologies to grow better corn crops and exterminate bugs and vermin. Artificial intelligence (AI) is rising in prominence with the proliferation of chatbots, virtual assistants and other conversational tools that companies are using to improve customer service, productivity and operational efficiency. But AI is also helping to automate and streamline tasks in data-intensive industries traditionally ruled by rigorous science and good old-fashioned human analysis. Seed retailers, for example, are using AI products to churn through terabytes of precision agricultural data to create the best corn crops, while pest control companies are using AI-based image-recognition technology to identify and treat various types of bugs and vermin. Such markedly different scenarios underscore how AI has evolved from science fiction to practical solutions that can potentially help companies get a leg up on their competition.

Of the many great things promised by Crispr gene editing technology, the ability to eliminate disease by modifying organisms might just top the list. But doing that requires perfecting something called a gene drive. Think of gene drives as a means of supercharging evolution to, say, give an entire population of mosquitoes a gene that kills the Zika virus. The trouble is, organisms develop resistance to gene drives, much like they eventually outwit pesticides and antibiotics. Researchers dedicate no small amount of time and thought to creating gene drives that can outsmart evolution because the potential payoffs are so great.