The field of artificial intelligence (AI) has created computers that can drive cars, synthesize chemical compounds, fold proteins and detect high-energy particles at a superhuman level. However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation. Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.

The direct conversion of methane gas to liquid methanol at the site where it is extracted from the Earth holds enormous potential for addressing a number of significant environmental problems. Developing a catalyst for that conversion has been a critical focus for Associate Professor Heather Kulik and the lab she directs at MIT. As important as that research is, however, it is just one example of the innumerable possibilities of Kulik's work. Ultimately, her focus is far broader, the scope of her exploration infinitely more vast. "All of our research is dedicated toward the same practical goal," she says.

The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed. Machine learning potentials have previously shown great success in this domain, reaching increasingly better accuracy while maintaining computational efficiency comparable with classical force fields. In this work we propose TorchMD-NET, a novel equivariant transformer (ET) architecture, outperforming state-of-the-art on MD17, ANI-1, and many QM9 targets in both accuracy and computational efficiency. Through an extensive attention weight analysis, we gain valuable insights into the black box predictor and show differences in the learned representation of conformers versus conformations sampled from molecular dynamics or normal modes. Furthermore, we highlight the importance of datasets including off-equilibrium conformations for the evaluation of molecular potentials.

This article was written by Roger Strukhoff and Sophie Turol. The Internet of Things (IoT) is covering the gamut of industries: healthcare, aviation, automative industry, predictive maintenance, and many more. "IoT helps cities to predict accidents and crime as well as gives doctors real-time insight into information from pacemakers or biochips," said Ahmed Banafa of San Jose State University at a recent webinar. "IoT optimizes productivity across industries through on equipment and machinery, creates truly smart homes with connected appliances, and provides critical communication between self-driving cars." Most enterprises seem to be able to think of something uniquely valuable to them, as it looks like the IoT will soon be adopted by a majority of companies.

We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. Considering the dendritic-tree like plausible biological properties, BNeuralT is a single neuron neural tree model with its internal sub-trees resembling dendritic nonlinearities. BNeuralT algorithm produces an ad hoc neural tree which is trained using a stochastic gradient descent optimizer like gradient descent (GD), momentum GD, Nesterov accelerated GD, Adagrad, RMSprop, or Adam. BNeuralT training has two phases, each computed in a depth-first search manner: the forward pass computes neural tree's output in a post-order traversal, while the error backpropagation during the backward pass is performed recursively in a pre-order traversal. A BNeuralT model can be considered a minimal subset of a neural network (NN), meaning it is a "thinned" NN whose complexity is lower than an ordinary NN. Our algorithm produces high-performing and parsimonious models balancing the complexity with descriptive ability on a wide variety of machine learning problems: classification, regression, and pattern recognition.

MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection of the part of the system is important for efficient learning. In a previous study, only one particle (or vector) of each molecule was extracted as part of the system. Therefore, we investigated the effectiveness of adding information from other particles to the learning process. In the experiment of the polyethylene system, when the dynamics of three particles of each molecule were used, the diffusion was successfully predicted using one-third of the time length of the training data, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method.

The South African patent office made history in July when it issued a patent that listed an artificial intelligence system as the inventor. The patent is for a food container that uses fractal designs to create pits and bulges in its sides. Designed for the packaging industry, the new configuration allows containers to fit more tightly together so they can be transported better. The shape also makes it easier for robotic arms to pick up the containers. The patent's owner, AI pioneer Stephen L. Thaler, created the inventor, the AI system known as Dabus (device for the autonomous bootstrapping of unified sentience).

Aqueous hydrolysis is used to chemically recycle polyethylene terephthalate (PET) due to the production of high-quality terephthalic acid (TPA), the PET monomer. PET hydrolysis depends on various reaction conditions including PET size, catalyst concentration, reaction temperature, etc. So, modeling PET hydrolysis by considering the effective factors can provide useful information for material scientists to specify how to design and run these reactions. It will save time, energy, and materials by optimizing the hydrolysis conditions. Machine learning algorithms enable to design models to predict output results. For the first time, 381 experimental data were gathered to model the aqueous hydrolysis of PET. Effective reaction conditions on PET hydrolysis were connected to TPA yield. The logistic regression was applied to rank the reaction conditions. Two algorithms were proposed, artificial neural network multilayer perceptron (ANN-MLP) and adaptive network-based fuzzy inference system (ANFIS). The dataset was divided into training and testing sets to train and test the models, respectively. The models predicted TPA yield sufficiently where the ANFIS model outperformed. R-squared (R2) and Root Mean Square Error (RMSE) loss functions were employed to measure the efficiency of the models and evaluate their performance.

Scientists have created incredible robotic grippers inspired by the Japanese art of Kirigami that are delicate enough to lift a raw egg yolk without breaking it. Kirigami is a Japanese art similar to origami, except it makes use of intricate cuts to paper, rather than relying on folding alone, to create striking 3D art. The plastic grippers, created by experts at North Carolina State University, are also precise enough to lift a human hair and a live fish without hurting it. Footage shows that they lift blobs of shampoo foam and even pine nuts off the top of a raw egg yolk without puncturing it. The grippers are demonstrated in a new paper as a concept for now, but they could have applications for biomedical technologies, such as joint implants.

According to the World Health Organization (WHO), the world is going hungry. WHO data shows that in 2018, the most recent year for which data is available, 820 million people lacked enough food to eat, an increase of nine million people over the year before. Hunger kills plenty of people worldwide. It also impacts those who survive, causing serious childhood development issues like stunting, where children are too short for their age, and wasting, where they're too thin for their age. The explosion in our planet's population is a major factor in there not being enough food to go around.