The saying goes, "you never know when lightning will strike." That may no longer be true thanks to computer learning and a space-based lightning monitoring system. Lightning is a major threat to people and property each year, especially in the summer. There's a new tool that could revolutionize lightning safety. A machine-learning algorithm developed by John Cintineo at the University of Wisconsin uses artificial intelligence to accurately predict the location of lightning up to an hour ahead of time.

Dr Georgina Cosma and postgraduate student Kareem Ahmed of the School of Science, have designed and trained an AI model to predict building emissions rates values with 27 inputs with little loss in accuracy. The proposed AI model – which was created with the support of Cundall's head of research and innovation, Edwin Wealend, and trained using large-scale data obtained from UK government energy performance assessments – can generate a BER value almost instantly. Dr Cosma says the research "is an important first step towards the use of machine learning tools for energy prediction in the UK" and it shows how data can "improve current processes in the construction industry". In their latest paper, Dr Cosma and the team reveal their AI system can generate building emissions rates for non-domestic buildings in less than a second and with as few as 27 variables with little loss in accuracy. They used a'decision tree-based ensemble' machine algorithm and built and validated the system using 81,137 real data records that contain information for non-domestic buildings over the whole of England from 2010 to 2019.

The team of researchers then induced abnormalities in the neurons' activities during experiments, which helped provide insight into the effects on learning development and cognitive characteristics. The experiments demonstrated that generalization ability decreased in the model where heterogeneity of activity in the neural population was reduced. This suggested the formation of emotional clusters in higher-level neurons was inhibited, and it led to the neural network model having a tendency to fail in identifying the emotion of unknown facial expressions, which is also a symptom of autism spectrum disorder.

The past 5 years have seen a rapidly accelerating effort in applying machine learning to seismological problems. The serial components of earthquake monitoring workflows include: detection, arrival time measurement, phase association, location, and characterization. All of these tasks have seen rapid progress due to effective implementation of machine-learning approaches. They have proven opportune targets for machine learning in seismology mainly due to the large, labeled data sets, which are often publicly available, and that were constructed through decades of dedicated work by skilled analysts. These are the essential ingredient for building complex supervised models.

A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach of learning discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior to the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of nonlinear oscillations and the Kepler problem. In particular, the learning algorithm learns a discrete field theory from a set of data of planetary orbits similar to what Kepler inherited from Tycho Brahe in 1601, and the serving algorithm correctly predicts other planetary orbits, including parabolic and hyperbolic escaping orbits, of the solar system without learning or knowing Newton’s laws of motion and universal gravitation. The proposed algorithms are expected to be applicable when the effects of special relativity and general relativity are important.

Natural language processing continues to find its way into unexpected corners. In a small study, researchers found that they could use the deep learning language model GPT-3, along with other AI-as-a-service platforms, to significantly lower the barrier to entry for crafting spearphishing campaigns at a massive scale. Researchers have long debated whether it would be worth the effort for scammers to train machine learning algorithms that could then generate compelling phishing messages. Mass phishing messages are simple and formulaic, after all, and are already highly effective. Highly targeted and tailored "spearphishing" messages are more labor intensive to compose, though.

At the time, the technology was still proving itself. As e-commerce demand skyrocketed and labor shortages intensified, AI-powered robots went from a nice-to-have to a necessity. Covariant, one of the many startups working on developing the software to control these robots, says it's now seeing rapidly rising demand in industries like fashion, beauty, pharmaceuticals, and groceries, as is its closest competitor, Osaro. Customers once engaged in pilot programs are moving to integrate AI-powered robots permanently into their production lines. Knapp, a warehouse logistics technology company and one of Covariant's first customers, which began piloting the technology in late 2019, says it now has "a full pipeline of projects" globally, including retrofitting old warehouses and designing entirely new ones optimized to help Covariant's robot pickers work alongside humans.

But when the heartland needs tech, it still comes to Silicon Valley. On Thursday, John Deere announced that it would acquire Bear Flag Robotics, a Silicon Valley startup that makes fully autonomous tractors for farms, for $250 million. Bear Flag retrofits regular tractors with sensors, control systems, computers, and communications systems needed to operate autonomously. The company's tech lets a lone farmer remotely oversee a fleet of robot tractors autonomously tilling a field. "John Deere putting their stamp on this kind of fully autonomous technology means it's really coming," says George Kantor, a roboticist at Carnegie Mellon University who specializes in the use of robots in agriculture.

While artificial intelligence (AI) has the potential to revolutionize a number of industries, the technology isn't without its controversies. Over the past few years, researchers and developers have raised concerns around the potential impacts of widespread AI adoption--and how a lack of existing ethical frameworks may put consumers at risk. These concerns may be especially relevant to medical device manufacturers, which are increasingly using AI in new medical devices like smart monitors and health wearables. New standards and regulations on ethical AI may provide essential guidance for medical device manufacturers interested in leveraging AI. The widespread use of AI could pose a number of ethical challenges.

Concerns about ethics of AI are older than AI itself. The phrase "artificial intelligence" was first used by McCarthy and colleagues in 1955 (McCarthy et al. 1955). However, in 1920 Capek already had published his science fiction play in which robots suffering abuse rebelled against human tyranny (Capek 1920), and by 1942, Asimov had proposed his famous three "laws of robotics" about robots not harming humans, not harming other robots, and not harming themselves (Asimov 1942). During much of the last century, when AI was mostly confined to research laboratories, concerns about ethics of AI were mostly limited to futurist writers of fiction and fantasy. In this century, as AI has begun to penetrate almost all aspects of life, worries about AI ethics have started permeating mainstream media.