Artificial intelligence is causing microcontrollers to achieve higher levels of functionality.

San Francisco (CNN Business)As wildfire season raged in California this fall, a startup a few states away used artificial intelligence to pinpoint the location of blazes there within minutes -- in some cases far faster than these fires might otherwise be noticed by firefighters or civilians. Santa Fe-based Descartes Labs, which uses AI to analyze satellite imagery, launched its US wildfire detector in July. The company's AI software pores over images coming in roughly every few minutes from two different US government weather satellites, in search of any changes -- the presence of smoke, a shift in thermal infrared data showing hot spots -- that could indicate a fire has ignited. Descartes is testing its detector by sending alerts to select forestry officials in its home state of New Mexico and told CNN Business its wildfire detector has spotted about 6,200 total thus far. The company says it can often detect these fires when they're just about 10 acres in size.

Cameras and computers together can conquer some seriously stunning feats. Giving computers vision has helped us fight wildfires in California, understand complex and treacherous roads -- and even see around corners. Specifically, seven years ago a group of MIT researchers created a new imaging system that used floors, doors, and walls as "mirrors" to understand information about scenes outside a normal line of sight. Using special lasers to produce recognizable 3D images, the work opened up a realm of possibilities in letting us better understand what we can't see. Recently, a different group of scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has built off of this work, but this time with no special equipment needed: They developed a method that can reconstruct hidden video from just the subtle shadows and reflections on an observed pile of clutter.

Cheetah uses supervised learning techniques to catch its prey. That's a bizarre, random out-of-the-blue statement you may say. A cheetah has adapted a very refined approach to hunting by honing its skills through practice, observation, experience, and computation. Much like training datasets to create a spectacular AI model. They're trained and taught continuously until they're able to operate on their own.

The past decade has seen some remarkable gains in the manufacturing industry. AI and big data have created new machine capabilities and new job opportunities for highly skilled workers. The ease of communication on a global scale has made collaborating with suppliers, producers and product development firms around the world faster and simpler than ever before. At the same time, U.S. manufacturers are facing a worrisome long-term skills shortage, while the trade war with China has made the short-term outlook for domestic companies uncertain. I've seen these changes and developments firsthand during my 37 years as an executive for global manufacturing operations -- most recently for a product design, development and manufacturing firm.

In the not too distant future, we can expect to see our skies filled with unmanned aerial vehicles (UAVs) delivering packages, maybe even people, from location to location. In such a world, there will also be a digital twin for each UAV in the fleet: a virtual model that will follow the UAV through its existence, evolving with time. "It's essential that UAVs monitor their structural health," said Karen Willcox, director of the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin (UT Austin) and an expert in computational aerospace engineering. "And it's essential that they make good decisions that result in good behavior." An invited speaker at the 2019 International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), Willcox shared the details of a project--supported primarily by the U.S. Air Force program in Dynamic Data-Driven Application Systems (DDDAS)--to develop a predictive digital twin for a custom-built UAV.

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices--or even the best doctors. But along with the benefits come new risks and regulatory challenges. In their latest article, "Algorithms on regulatory lockdown in medicine" recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center, look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML. They consider the questions: What new risks do we face as AI/ML devices are developed and implemented?

Some are starting to use facial expression technology alongside artificial intelligence to identify the best candidates in job interviews in the UK. Applicants get filmed by phone or laptop while asked a set of job-related questions. The AI technology is then used to analyze the response in terms of the language, tone and facial expressions of the applicant. AI algorithms choose the best candidate. It compares candidate performance in the video against some 25,000 pieces of facial and linguistic information that they compile from previous interviews.

Powered by artificial intelligence and machine learning, OpenPIP dramatically reduces the time and cost of integrating and quantifying mass spectrometry data while increasing the quality of output by eliminating observer-based bias. The announcement was made at BioData World Congress 2019, taking place this week in Basel, Switzerland. This AI-based software is accessed via the Google Cloud Platform and has demonstrated over 99 percent concordance with human peak selection. A publication detailing the specific neural network architecture is currently undergoing peer review. Interested scientists may access the OpenPIP platform at https://openpip.intervenn.bio.