Just occasionally, Alán Aspuru-Guzik has a movie-star moment, when fans half his age will stop him in the street. "They say, 'Hey, we know who you are'," he laughs. "Then they tell me that they also have a quantum start-up, and would love to talk to me about it." "I don't usually have time to talk, but I'm always happy to give them some tips." That affable approach is not uncommon in the quantum-computing community, says Aspuru-Guzik, who is a computer scientist at the University of Toronto, Canada, and co-founder of quantum-computing company Zapata Computing in Cambridge, Massachusetts.

According to an analysis released by OpenAI, the demand for computing power has increased by more than 300,000 times in the six years after 2012. It grows by about factor of 10 each year, far exceeding the pace set by Moore's Law. As a latecomer to artificial intelligence (AI), Huawei boldly proposed to provide the industry with computing power that is accessible, affordable, and easy to use, to meet the exponentially increasing demand for AI computing. Now, one year after the AI strategy was proposed, has Huawei found a way to address the computing power challenges? In the late 17th century, the British mining industry, particularly the coal mine, was developed to a considerable scale.

By the end of this decade, a milestone is reached in artificial intelligence, with computers now routinely passing the Turing Test.** This test is conducted by a human judge who is made to engage in a natural language conversation with one human and one machine, each of which tries to appear human. Participants are placed in isolated locations. For several decades, information technology had seen exponential growth – leading to vast improvements in computer processing power, memory, bandwidth, voice recognition, image recognition, deep learning and other software algorithms. By the end of the 2020s, it has reached the stage where an independent judge is literally unable to tell which is the real human and which is not.* Answers to certain "obscure" questions posed by the judge may appear childlike from the AI – but they are humanlike nonetheless.*

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Although Bayesian Optimization (BO) has been employed for accelerating materials design in computational materials engineering, existing works are restricted to problems with quantitative variables. However, real designs of materials systems involve both qualitative and quantitative design variables representing material compositions, microstructure morphology, and processing conditions. For mixed-variable problems, existing Bayesian Optimization (BO) approaches represent qualitative factors by dummy variables first and then fit a standard Gaussian process (GP) model with numerical variables as the surrogate model. This approach is restrictive theoretically and fails to capture complex correlations between qualitative levels. We present in this paper the integration of a novel latent-variable (LV) approach for mixed-variable GP modeling with the BO framework for materials design. LVGP is a fundamentally different approach that maps qualitative design variables to underlying numerical LV in GP, which has strong physical justification. It provides flexible parameterization and representation of qualitative factors and shows superior modeling accuracy compared to the existing methods. We demonstrate our approach through testing with numerical examples and materials design examples. It is found that in all test examples the mapped LVs provide intuitive visualization and substantial insight into the nature and effects of the qualitative factors. Though materials designs are used as examples, the method presented is generic and can be utilized for other mixed variable design optimization problems that involve expensive physics-based simulations.

In the future, patients may no longer need to have a tube fed down their throat into their stomach. Instead they could simply swallow an ingestible device in the form of a pill. Engineers at Massachusetts Institute of Technology (MIT) have invented exactly this. It's made from a jelly-like substance: a combination of water and polymers. Inspired by the pufferfish, the team of researchers realised that in order for any edible pill to remain in the stomach once it has passed down the oesophagus, and not then pass out through the pylorus, it needed to be inflated.

Waste management is one of the world's most pressing issues, however Peruza and Dots, two Latvian companies, have created a prototype through the use of AI to increase the efficiency of plastic waste. Peruza, an equipment manufacturing and process engineering company, has collaborated with Dots, a technology company also based in Latvia, to create a prototype system which is able to recognize and collects various types of packaging materials in an effort to contribute to the EU's upcoming single plastic directive. Peruza had stated that 10 countries within Europe have already implemented deposit return schemes and that we should expect more to come in the future. This is in line with the EU's goal for 2030 for all packaging used in the market to be recyclable or reusable. Robert Dlohi, CEO of Peruza, stated, "The problem of existing devices is that they can collect individual types of packaging. They use a barcode for recognition. Our system can currently recognize, collect and sort 5.0 liter and/or 1.5 liter Pet bottles, and 5 liter plastic bottles, 1.5 liter plastic canisters, as well as aluminum cans, cardboard Tetra Pak, glass bottles and jars."

Mining companies from around the world have begun using artificial intelligence in their operations. From safety and maintenance, to exploration and autonomous vehicles, and drills, AI is being used to navigate efficiencies and speed. With this new technology, however, comes an ever-growing need for a workforce who can navigate these new systems. Thanks to a $1.25 million grant from the National Institute for Occupational Safety and Health, an interdisciplinary team at the University of Nevada, Reno, has committed to graduating six doctoral and four master's degree students who will address several challenges related to major safety and health issues in mining operations. "Future mine engineers need to understand emerging technology like AI, drones and big data," Javad Sattarvand, University College of Science assistant professor of mining engineering and the project's principal investigator, said.

Over the past few decades, the mining industry has been mired in a productivity slump of sorts. On the whole, production efficiency is down and costs are up. Mining companies have naturally looked for ways to turn this around, and digitalization has been one of the chief approaches these companies have followed. Mining companies have a lot of data at their disposal. Sensors are seemingly everywhere in their underground operations. But thus far it has been very hard for mining companies to capitalize on all their data because of the difficulty in making sense of it all.

Dr. Benji Maruyama is a Principal Materials Research Engineer in the Air Force Research Laboratory, Materials & Manufacturing Directorate. He is the Leader of the Flexible Materials and Processes Research Team, and leads research on the synthesis and processing science of carbon nanotubes. Dr. Maruyama created and is developing a new method research: Autonomous Research Systems for Materials Development. He is also the point of contact for carbon materials for the Materials and Manufacturing Directorate. His background and interests include carbon nanomaterials, energy storage, field emission, carbon, polymer and metal matrix composites, imaging of complex 3D microstructures and combinatorial experimentation.