The fourth industrial revolution is well underway with artificial intelligence (AI) at its heart powering new technologies and Internet of Things (IoT) devices from smartwatches to smart fridges, autonomous cars to home assistants, and security systems to a vast array of sensors. Using conventional computer architecture in the practical application of AI in IoTs leads to large power demands arising from the repetitive shifting of tremendous amounts of data between processors and memory units. These demands are only set to increase as AI improves and even larger amounts of data is generated. This increased power consumption comes with a potential impact on the environment via the emission of greenhouse gases through the generation of electricity through the burning of fossil fuels. The need to lower energy consumption in IoT technology has led to need for alternative, low-power alternatives that can implement AI.

Human–computer interfaces, a long sought-after goal, would open new worlds. Disabled people could regain autonomy, people could access information and operate seamlessly in a digital world. This goal is yet to be realized because training machines to follow our mental commands, such as move a cursor across the screen, is a complicated and tedious process. Now, by approaching the problem of this machine learning from a brand-new angle, researchers from the University of Helsinki are drastically improving how we can interface with machines. Rather than teaching the computer to do something when we ask it, the machine is now capable of learning what we want it to do without being told.

Artificial intelligence, or AI, is ubiquitous and integrated into almost any field or application. Progress made over the last few decades has been astounding, with achievements--such as DeepMind's AlphaGo defeating the world's foremost Go player in 2016 and the application of LinearFold to predict the secondary structure of the SARS-CoV-2 RNA sequence in just 26 seconds--demonstrating the ever-growing capabilities of these systems. There is a caveat though; running AI requires large amounts of energy and data, and computer hardware can't keep up. Integrated circuit chips are reaching capacity even as structures on the chips and circuit components become smaller. There is a limit to how far we can physically take this.

Nanoparticles could make a reliable blood test for Alzheimer's disease a reality; image credit: National Cancer Institute, Daniel Sone Using nanoparticles with different surface properties, researchers are able to detect subtle changes in the composition of proteins in the plasma years before the presentation of clinical symptoms of Alzheimer's disease, which include memory loss, confusion, and cognitive difficulties. Owing to the unique properties of nanoparticles, different proteins in biological fluids selectively stick onto their surface forming a protein corona, which was found to change during disease. Researchers from the United States and Italy identify these subtle changes in plasma protein patterns to distinguish plasma samples from healthy individuals and those diagnosed with Alzheimer's disease. "Protein corona composition is both influenced by specific health conditions as well as the chemical and physical properties of the nanoparticles themselves," says Dr. Claudia Corbo of the University of Milano-Bicocca and lead author of the study published in Advanced Healthcare Materials. "Binding of proteins to the surface of particles is very precise and dependent on the chemistry and shape of the particles and the chemistry and structure of the proteins," says senior author Professor Omid Farokhzad of Brigham and Women's Hospital and Harvard Medical School.

Researchers at Georgia Institute of Technology have demonstrated the use of artificial intelligence (AI) in obtaining valuable insights to the operation of photonic nanostructures, which manipulate light for applications such as signal processing, communications, and computing. The study was recently published in the journal Advanced Intelligent Systems. By proper selection of the geometrical features of these nanoelements, a large range of system-level functionalities (e.g., filtering, lensing, frequency conversion) can be achieved. While most reports on using AI techniques in the field of nanophotonics are focused on the design and optimization of nanostructures, such as finding the geometrical features of meta-atoms, the new approach seeks to use the "intelligence" aspects of AI to understand the physics of these nanostructures, for example, in assessing the feasibility of a response from a given nanostructure. This new approach is implemented in two steps: in the first step, the relation between input and output of the nanostructure is highly simplified by dimensionality reduction.

Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated omputing time and power requirements. The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required. The results appear in the journal Advanced Science and are a collaboration between David Smith, the James Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate," said Horstmeyer. "But that's inefficient because the computer doesn't need to'look' at an image at all." "This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn't need," added Aaron Diebold, a research assistant in Smith's lab.

Deep learning is a fascinating sub field of machine learning that creates artificially intelligent systems inspired by the structure and function of the brain. The basis of these models are bio-inspired artificial neural networks that mimic the neural connectivity of animal brains to carry out cognitive functions such as problem solving. A field with the most impressive results of neuromorphic computing is that of visual image analysis. Similar to how our brains learn to recognize objects in order to make predictions and act upon them, artificial intelligence must be shown millions of pictures before they are able to generalize them in order to make their best educated guesses for images they have never seen before. Professor Cheol Seong Hwang from the Department of Material Science and Engineering at Seoul National University and his research team have developed a method to accelerate the image recognition process by combining the inherent efficiency of resistive random access memory (ReRAM) and cross-bar array structures, two of the most commonly used hardware. Many of us have performed a reversed image search to find information based on a certain image in order to browse similar results.

Machine learning grabbed the headlines a few years ago when AlphaGo was able to defeat the reigning "Go" world champion. Since then developments have been gathering pace, with the technology finding a wide array of uses, from chemistry to facial recognition, and entertainment. In Holland, a team of researchers have demonstrated the utility of machine learning in metamaterial design. Metamaterials' properties come not as a result of the material's chemistry, but rather, their shape and structure. The team ran simulations of randomly generated designs whose geometry was defined according to seven separate variables.

Kebotix, a technology company ushering in the future of new materials discovery, came out of stealth mode with a $5 million seed round led by One Way Ventures. Investors also include Baidu Ventures, an independent venture fund with backing and resources from Baidu; Boston-based Flybridge Capital Partners; Los Angeles-based Embark Ventures; Norway-based Propagator Ventures; and New York-based WorldQuant Ventures. Developing the world's first self-driving lab for materials discovery powered by artificial intelligence (AI) and robotics, Kebotix is committed to accelerating the exploration, discovery, applications, and production of new molecules and materials. "We are building the materials company of the 21st century because how scientists discover new materials has not evolved since the 18th century," said CEO Dr. Jill S. Becker. "Being stuck in the 18th century significantly adds to the challenge of tackling climate change, antibiotic-resistant bacteria, water pollution, and other urgent problems facing the world today."

Robots perform delicate surgeries, are sent to explore and analyze Martian soil, or accompany elderly patients in their everyday life in the form of friendly baby seal pets. Advances in design and automation of robotic machines have been achieved by understanding and mimicking how living systems evolve, sense, and adapt to their environment. Yet, the range of motions, velocities, and functions of today's robots are still very far from those observed in the living animals such as cephalopods, squeezing through narrow bottlenecks in a few seconds, or even mollusks, able to grind and chew rocks. The quest, however, is not to boldly recreate synthetically living systems but to build machines with a similar level of capabilities in response to specific technological needs. Biotechnology, security or exploration may indeed require autonomous machines with not only well-defined movement accuracy, conformability, actuation speed, but also other characteristics including temperature resistance, mechanical resilience, and optical transparency which may not be found in nature.