Why, I wondered, was the enthusiasm so high at this week's Techonomy Climate conference in Mountain View? So I asked a smart friend why climate action suddenly commands so much passion. "The pandemic helped people realize a disaster can strike everyone on the planet all at once," they answered. "Almost none of us really thought it was possible before." It was as good an explanation as any.

We are trying to use the data that is recorded on the wind turbines to predict failures," Kalyan Veeramachaneni, principal research scientist in the Laboratory for Information and Decision Systems of the Massachusetts Institute of Technology, told DW. Ewald Hesse, CEO of Berlin-based Grid Singularity, says several countries in Africa would leapfrog the development phase of European energy systems, similar to what happened to landline phones. "In developing countries, there is no stringent regulation in the energy sector, and we don't need to convince the government of allowing a new approach to energy production and consumption. Still, local communities would benefit from one PV system in the surrounding area, which, combined with sensors to measure energy consumption, would create a localized market. "Whatever comes out in the energy field in developing countries will be by far smarter and more practical than what we have in Germany," said Hesse, adding that several companies contributed to unlocking potential markets and significant investments in developing countries.

Conductance can vary based on everything from the manufacturing of the chip to environmental factors. The variation can add up and throw neural networks off. Early approaches used analog and digital together, with digital-to-analog and analog-to-digital convertors between layers. However, this needs to be limited, as conversion is slower and more energy-intensive than staying analog. It can create its own bottleneck.

Savin Goyal is CTO and co-founder of Outerbounds, a startup building infrastructure to help teams streamline how they build machine learning applications. Prior to starting Outerbounds, Savin and team worked at Netflix, where they were instrumental in the creation and release of Metaflow, an open source Python framework that addresses some of the challenges data scientists face around scalability and version control. The machine learning universe is really fast moving. So how can we make sure that we're not making a bet, that would hinder our progress, two years or four years further down the line. Deep learning is super popular, but tomorrow there could be a new way of doing machine learning.

Although it's been around for a long time in the film industry, it's still a rather perplexing term, especially where video games are concerned. Essentially, it's a technique that makes light behave in a realistic way. The idea is to make games more realistic and immersive. Wouldn't you be spellbound by the light bouncing off of objects in a natural way? The indistinguishable line between reality and fantasy is no doubt appealing.

Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high fidelity quantum chemical properties. OrbNet-Equi models a wide spectrum of target properties with an accuracy consistently better than standard machine learning methods and a speed orders of magnitude greater than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly.

Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind farm; this arises from the fact that placing wind turbines in a limited area would hinder their productivity and therefore not be economically convenient. This naturally leads to an optimisation problem, which has three specific challenges: (1) multiple conflicting objectives (2) computationally expensive simulation models and (3) optimisation over design sets instead of design vectors. The first and second challenges can be addressed by using surrogate-assisted e.g.\ Bayesian multi-objective optimisation. However, the traditional Bayesian optimisation cannot be applied as the optimisation function in the problem relies on design sets instead of design vectors. This paper extends the applicability of Bayesian multi-objective optimisation to set based optimisation for solving the wind farm layout problem. We use a set-based kernel in Gaussian process to quantify the correlation between wind farms (with a different number of turbines). The results on the given data set of wind energy and direction clearly show the potential of using set-based Bayesian multi-objective optimisation.

Efforts to invent more practical superconductors and better batteries could be the first areas of business to get a quantum speed boost. This month IBM and Google both said they aim to commercialize quantum computers within the next few years (Google specified five), selling access to the exotic machines in a new kind of cloud service. The competitors predict a new era in which computers are immensely more powerful, with dividends including more efficient routing for logistics and mapping companies, new forms of machine learning, better product recommendations, and improved diagnostic tests. But before any of that, the first quantum computer to start paying its way with useful work in the real world will probably do so by helping chemists trying to do things like improve batteries or electronics. So far, the early, small quantum computers researchers have sketched out in most detail seem best suited to simulating molecules and reactions.

As CIOs and other executives look for ways to expand sustainability initiatives, there's a growing awareness that initiatives can't stop at the four walls of the data center or office building. Today's structures can contain hundreds of thousands of components that consume energy and add to an organization's carbon footprint. In fact, buildings consume one-third of all energy globally and produce one-quarter of all greenhouse gas emissions (GHGs), according to The World Resources Institute. What's more, business and IT leaders are often narrowly focused on improving sustainability in data centers and procuring greener computing systems. Yet they overlook critical ways that technology can shrink a carbon footprint. "There is a growing awareness that buildings and workspaces are a crucial part of sustainability initiatives," states Bryon Carlock, National Real Estate Practice Leader for consulting firm PwC.

Using algorithms and artificial intelligence, a research team led by Universidad Complutense de Madrid (UCM) has designed a tool which, in its initial trials, proved capable of predicting those areas with best access to potable groundwater in Africa, with a success rate of close to 90%. In specific terms, the papers published in Hydrology and Earth System Science and Geocarto International describe the hydrogeological mapping performed by the MLMapper software in the regions of Bamako and Koulikoro (Mali) and the region of Ouaddaï (Chad), respectively. "Ensure access to water and sanitation for all" is Sustainable Development Goal 6. In sub-Saharan Africa, groundwater plays a fundamental role in the supply of drinking water, but the percentage of wells that strike water is very often lower than 30%. "This is mainly because of a lack of hydrogeological knowledge, with the practical consequence that millions of euros of humanitarian aid are lost in fruitless drilling operations", underlines Víctor Gómez-Escalonilla Canales, a researcher at UCM's Department of Geodynamics, Stratigraphy and Palaeontology.