Quantum physicists have developed an algorithm that uses a single qubit to solve a problem that had previously needed thousands of them.

The Driving Training Based Optimization (DTBO) algorithm, proposed by Mohammad Dehghani, is one of the novel metaheuristic algorithms which appeared in 2022⁸⁰. This algorithm is founded on the principle of learning to drive, which unfolds in three phases: selecting an instructor from the learners, receiving instructions from the instructor on driving techniques, and practicing newly learned techniques from the learner to enhance one’s driving abilities^81,82. In this work, DTBO algorithm is used, due to its effectiveness, which was confirmed by a comparative study⁸³ with other algorithms, including particle swarm optimization⁸⁴, Gravitational Search Algorithm (GSA)⁸⁵, teaching learning-based optimization, Gray Wolf Optimization (GWO)⁸⁶, Whale Optimization Algorithm (WOA)⁸⁷, and Reptile Search Algorithm (RSA)⁸⁸. The comparative study has been done using various kinds of benchmark functions, such as constrained, nonlinear and non-convex functions.

Lyapunov-based Model Predictive Control (LMPC) is a control approach integrating Lyapunov function as constraint in the optimization problem of MPC^89,90. This technique characterizes the region of the closed-loop stability, which makes it possible to define the operating conditions that maintain the system stability^91,92. Since its appearance, the LMPC method has been utilized extensively for controlling a various nonlinear systems, such as robotic systems⁹³, electrical systems⁹⁴, chemical processes⁹⁵, and wind power generation systems⁹⁰. In contrast to the LMPC, both the regular MPC and the NMPC lack explicit stability restrictions and can’t combine stability guarantees with interpretability, even with their increased flexibility.

The proposed method, named Lyapunov-based neural network model predictive control using metaheuristic optimization approach (LNNMPC-MOA), includes Lyapunov-based constraint in the optimization problem of the neural network model predictive control (NNMPC), which is solved by the DTBO algorithm. The suggested controller consists of two parts: the first is responsible for calculating predictions using a neural network model of the feedforward type, and the second is responsible to resolve the constrained nonlinear optimization problem using the DTBO algorithm. This technique is suggested to solve the nonlinear and non-convex optimization problem of the conventional NMPC, ensure on-line optimization in reasonable time thanks to their easy implementation and guaranty the stability using the Lyapunov function-based constraint. The efficiency of the proposed controller regarding to the accuracy, quickness and robustness is assessed by taking into account the speed control of a three-phase induction motor, and its stability is mathematically ensured using the Lyapunov function-based constraint. The acquired results are compared to those of NNMPC based on DTBO algorithm (NNMPC-DTBO), NNMPC using PSO algorithm (NNMPC-PSO), Fuzzy Logic controller optimized by TLBO (FLC-TLBO) and optimized PID controller using PSO algorithm (PID-PSO)⁹⁵.

A newly published study by Sheba Medical Center, Israel’s largest and internationally ranked hospital, shows that AI analysis of medical records as patients are admitted to the ER can accurately identify those at high risk of pulmonary embolism (PE).

A pulmonary embolism is a sudden blockage in an artery in the lung caused by a blood clot, most commonly due to a dislodged clot in the leg. They are normally diagnosed during a CT scan.

Using machine learning, the researchers trained an algorithm to detect a pulmonary embolism before a patient was hospitalized, based on existing medical records.

Unlike classical encryption, which relies on mathematical algorithms, quantum encryption assures security based on physical principles. Detection of espionage or interference is guaranteed by unavoidable alteration of the quantum states involved.

New approach creates skilled robots:

MIT researchers have developed a groundbreaking algorithm that enables robots to rapidly learn and master complex tasks.

“The moment when we wrote down the terms of this equation and saw that it all clicked together, it felt pretty incredible,” Wordsworth said. “It’s a result that finally shows us how directly the quantum mechanics links to the bigger picture.”

In some ways, he said, the calculation helps us understand climate change better than any computer model. “It just seems to be a fundamentally important thing to be able to say in a field that we can show from basic principles where everything comes from.”

Can machine learning be used to advance exoplanet science, and can this be done by non-scientists, as well? This is what Ariel Data Challenge 2024 hopes to address as participants from around the world will compete to develop machine learning algorithms designed to analyze data from space telescopes with the goal of gaining greater insight into exoplanet atmospheres. This competition will be featured at the NeurIPS 2024 machine learning conference and holds the potential to not only advance the field of exoplanets but also enable non-scientists to conduct pioneering research, as well.

“By supporting this challenge, we aim to find new ways of using AI and machine learning to develop our understanding of the universe,” said Dr. Caroline Harper, who is the Head of Space Science at the UK Space Agency. “Exoplanets are likely to be more numerous in our galaxy than the stars themselves and the techniques developed through this prestigious competition could help open new windows for us to learn about the composition of their atmospheres, and even their weather.”

Along with the UK Space Agency, other institutions supporting this challenge include the STFC DiRAC HPC Facility, European Space Agency (ESA), and STFC RAL Space. The competition is named after the ESA’s Ariel Space Mission, which is currently scheduled for launch in 2029 with the goal of using the transit method for identifying more than 1,000 exoplanets.

Artificial neural networks—algorithms inspired by biological brains—are at the center of modern artificial intelligence, behind both chatbots and image generators. But with their many neurons, they can be black boxes, their inner workings uninterpretable to users.

Researchers have now created a fundamentally new way to make neural networks that in some ways surpasses traditional systems. These new networks are more interpretable and also more accurate, proponents say, even when they’re smaller. Their developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.

Now, scientists in China have developed robots that give human-like realistic expressions.

The humanoid robot with highly expressive facial features is developed by Liu Xiaofeng, a professor at Hohai University in east China’s Jiangsu Province, and his research team.

For the development of this robot, the research team developed a new algorithm for generating facial expressions on humanoid robots.

They also found that, although the power achieved by the conventional PSO algorithm was approximately 0.15% higher than that attained by the QPSO algorithm under the same conditions, the QPSO was able to beat the conventional PSO in more challenging conditions.

“Specifically, the quantum algorithm generates 3.33% more power in higher temperature tests and 0.89% more power in partial shading tests,” they emphasized. “Additionally, the quantum algorithm displays lower duty cycles, with a reduction of 3.9% in normal operating conditions, 0.162% in high-temperature tests, and 0.54% in partial shading tests.”