There's a global shortage of artificial intelligence (AI) talent; labour markets all over the world can't keep up with the demand for developers, mathematicians and scientists who can create new and innovative AI technology. There are an estimated 1,600 AI startups just in Europe, not factoring in the AI initiatives in large tech companies, so the wait for new AI graduates remains long. Microsoft has recently announced the goal of training 15,000 new AI professionals by 2022, which is a good start but not enough to fill the estimated millions of roles that are currently vacant. In a recent study, Microsoft and IDC found that the shortage of workers with AI skills has stopped companies that want to adopt AI from being able to do so. Until more highly skilled AI developers enter the workforce, organisations must find creative ways to supplement the talent they need to initiate their AI projects across industries--whether those projects involve voice, image, or pattern recognition, enabling autonomous movement or simulating realistic conversations. These innovations can underpin a new generation of healthcare tools, smart home devices or digital personal assistants.

Immigration and Customs Enforcement officials have mined state driver's license databases using facial recognition technology, analyzing millions of motorists' photos without their knowledge. In at least three states that offer driver's licenses to undocumented immigrants, ICE officials have requested to comb through state repositories of license photos, according to newly released documents. At least two of those states, Utah and Vermont, complied, searching their photos for matches, those records show. In the third state, Washington, agents authorized administrative subpoenas of the Department of Licensing to conduct a facial recognition scan of all photos of license applicants, though it was unclear whether the state carried out the searches. In Vermont, agents only had to file a paper request that was later approved by Department of Motor Vehicles employees.

Berkeley Lab researchers (from left) Vahe Tshitoyan, Anubhav Jain, Leigh Weston, and John Dagdelen used machine learning to analyze 3.3 million abstracts from materials science papers. Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A team led by Anubhav Jain, a scientist in Berkeley Lab's Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials. "Without telling it anything about materials science, it learned concepts like the periodic table and the crystal structure of metals," says Jain. "That hinted at the potential of the technique. But probably the most interesting thing we figured out is, you can use this algorithm to address gaps in materials research, things that people should study but haven't studied so far."

Machine learning (ML) is everywhere. Startups, OEMs, and industrial suppliers are investing heavily in developing technology to collect and analyze manufacturing data. Much has been written about ML in manufacturing, but it can still be difficult to understand the different approaches. In manufacturing, the application of wireless sensors and ML offers potential to reduce costs and improve efficiency across the entire organization. Predictive maintenance (PdM) has widely been seen as one of the most promising applications for ML because it gives reliability teams real-time insight into the condition of physical equipment.

Dr. Mitchell Pryor earned is BSME at Southern Methodist University in 1993. After graduating, he taught math and science courses at St. James School in St. James Maryland before returning to Texas. He completed is Masters (1999) and PhD (2002) at UT Austin with an emphasis on the modeling, simulation, and operation of redundant manipulators. Since earning his PhD, Dr. Pryor has taught graduate and undergraduate courses in the mechanical and electrical engineering departments as well as led and conducted research in the area of robotics and automation in Mechanical Engineering, Petroleum Engineering and the Nuclear Engineering Teaching Laboratory. He has worked for numerous research sponsors including, NASA, DARPA, DOE, INL, LANL, ORNL, Y-12, and many industrial partners.

Machine learning AI has recently been used to distinguish between patients who are fit for corneal refractive surgery and those who are likely to experience post-operative complications. The referral for this procedure often goes misdiagnosed, but by using AI, these researchers have potentially created an accurate screening tool for the surgery. Their work was published on June 20 in the journal npj Digital Medicine. Refractive surgery, such as LASIK, utilize lasers to reshape the cornea in treating conditions such as near and farsightedness, and astigmatism. It is essential to screen candidates for these operations to prevent adverse outcomes, but there are no existing screening methods that address the possibility of improper diagnosis.

My trip to Amazon's Staten Island center had its origins two months earlier. I was writing about a former worker named Justin Rashad Long, who contended that he had been fired for speaking out about working conditions there. Beyond the claim of retaliation, Mr. Long said laboring at Amazon had been a tremendous slog: Employees worked long shifts with few breaks. Managers held them to unreasonable goals. The time they spent waiting in line at metal detectors -- to discourage theft -- lengthened their day.

A tiny jellyfish-like robot could one day swim through the human body to deliver drugs to the right location. Metin Sitti and his colleagues at the Max Planck Institute for Intelligent Systems in Germany designed a robotic jellyfish that can swim, burrow and transport objects. It is 3 millimetres in diameter, roughly the size of a baby common jellyfish. It consists of a central body and eight bendable flaps that can beat upwards and downwards in unison. They beat roughly 150 times per minute, also similar to that of baby jellyfish, and are extended by flippers that help the robot propel through water.