Goto

Collaborating Authors

Energy


PDBench: Evaluating Computational Methods for Protein Sequence Design

#artificialintelligence

Proteins perform critical processes in all living systems: converting solar energy into chemical energy, replicating DNA, as the basis of highly performant materials, sensing and much more. While an incredible range of functionality has been sampled in nature, it accounts for a tiny fraction of the possible protein universe. If we could tap into this pool of unexplored protein structures, we could search for novel proteins with useful properties that we could apply to tackle the environmental and medical challenges facing humanity. This is the purpose of protein design. Sequence design is an important aspect of protein design, and many successful methods to do this have been developed.


Emotional AI and other 'moonshot' technologies could grow to $6 trillion market by 2030, says Bank of America

#artificialintelligence

"The pace at which themes are transforming businesses is blistering, but the adoption of many technologies -- like smartphones or renewable energy -- have surpassed experts' forecasts by decades, because we often think linearly but progress occurs exponentially," say the strategists. They say a paradigm shift in the explosion of data, faster processing power and the rise of artificial intelligence will bring about the "fastest rollout of disruptive tech in history." And in the big stock universe, an increasing few are showing investors the money. "Over the past 30 years, just 1.5% of companies generated all the net wealth on the global stock market, meaning that actually only a handful of disrupters ("superstar firms") really influence long-term financial markets," says Israel and the team. Here are the 14 technologies: 6G, brain computer interfacing (BCI), emotional artificial intelligence, synthetic biology, immortality, bionic humans, eVTOL (electrical vertical takeoff and landing vehicles), wireless electricity, holograms, metaverse, next-gen batteries, oceantech (ocean energy, precision fishing, etc.), green mining and CCS (negative-emissions technology that captures and stores carbon dioxide before it can be released).


What Green AI Needs

#artificialintelligence

LONDON – Long before the real-world effects of climate change became so abundantly obvious, the data painted a bleak picture – in painful detail – of the scale of the problem. For decades, carefully collected data on weather patterns and sea temperatures were fed into models that analyzed, predicted, and explained the effects of human activities on our climate. And now that we know the alarming answer, one of the biggest questions we face in the next few decades is how data-driven approaches can be used to overcome the climate crisis. Data and technologies like artificial intelligence (AI) are expected to play a very large role. But that will happen only if we make major changes in data management.


Collaborative AI tool accelerates material discoveries

#artificialintelligence

Researchers at Liverpool University have created a collaborative AI tool that reduces the time and effort required to discover new materials. The new tool has led to the discovery of four new materials including a new family of solid-state materials that conduct lithium, an advance that is key to the development of solid-state batteries offering longer range and increased safety for electric vehicles. Further promising materials are said to be in development. The Liverpool team's findings are detailed in Nature Communications. The tool brings together AI with human knowledge to prioritise those parts of unexplored chemical space where new functional materials are most likely to be found.


The Imperative for Sustainable AI Systems

#artificialintelligence

AI systems are compute-intensive: the AI lifecycle often requires long-running training jobs, hyperparameter searches, inference jobs, and other costly computations. They also require massive amounts of data that might be moved over the wire, and require specialized hardware to operate effectively, especially large-scale AI systems. All of these activities require electricity -- which has a carbon cost. There are also carbon emissions in ancillary needs like hardware and datacenter cooling [1]. Thus, AI systems have a massive carbon footprint[2]. This carbon footprint also has consequences in terms of social justice as we will explore in this article.


Securing the energy revolution and IoT future

MIT Technology Review

In early 2021, Americans living on the East Coast got a sharp lesson on the growing importance of cybersecurity in the energy industry. A ransomware attack hit the company that operates the Colonial Pipeline--the major infrastructure artery that carries almost half of all liquid fuels from the Gulf Coast to the eastern United States. Knowing that at least some of their computer systems had been compromised, and unable to be certain about the extent of their problems, the company was forced to resort to a brute-force solution: shut down the whole pipeline. Leo Simonovich is vice president and global head of industrial cyber and digital security at Siemens Energy. The interruption of fuel delivery had huge consequences.


High-speed alloy creation might revolutionize hydrogen's future

#artificialintelligence

A Sandia National Laboratories team of materials scientists and computer scientists, with some international collaborators, have spent more than a year creating 12 new alloys -- and modeling hundreds more -- that demonstrate how machine learning can help accelerate the future of hydrogen energy by making it easier to create hydrogen infrastructure for consumers. Vitalie Stavila, Mark Allendorf, Matthew Witman and Sapan Agarwal are part of the Sandia team that published a paper detailing its approach in conjunction with researchers from Ångström Laboratory in Sweden and Nottingham University in the United Kingdom. "There is a rich history in hydrogen storage research and a database of thermodynamic values describing hydrogen interactions with different materials," Witman said. "With that existing database, an assortment of machine-learning and other computational tools, and state-of-the art experimental capabilities, we assembled an international collaboration group to join forces on this effort. We demonstrated that machine learning techniques could indeed model the physics and chemistry of complex phenomena which occur when hydrogen interacts with metals."


Artificial Intelligence Could Dramatically Speed Up Climate Action

#artificialintelligence

Sign up to receive the Green Daily newsletter in your inbox. Technological solutions to climate change can be put into two categories. Vertical solutions that tackle pollution in one sector, say low-carbon fertilizers that help reduce emissions in agriculture. Or horizontal solutions that address issues across many different industries, say lithium-ion batteries that electrify cars but also better integrate renewables in the electricity mix.


Ring Video Doorbell 4 review: pre-roll is a battery bell gamechanger

The Guardian

The latest iteration of Amazon's battery-powered Ring doorbell adds a new feature to capture the early details of events most competitors would miss without needing to be plugged in. It tops Ring's battery-powered range, which starts at £89. The look and basic function of the Doorbell 4 is very similar to Ring's older models. It has a camera with night vision, motion sensors and a large doorbell button. When someone pushes the button Ring's signature chime plays and an alert is sent to your phone. You can view a live feed and speak through the doorbell using the app from anywhere with internet.


Artificial Intelligence is Critical Enabler of the Energy Transition

#artificialintelligence

The World Economic Forum has published a new study on how artificial intelligence (AI) can be used to accelerate a more equitable energy transition and build trust for the technology throughout the industry. As the impacts of climate change become more visible worldwide, governments and industry face the urgent challenge of transitioning to a low-carbon global energy system. Digital technologies – particularly AI – are key enablers for this transition and have the potential to deliver the energy sector's climate goals more rapidly and at lower cost. Written in collaboration with BloombergNEF and Deutsche Energie-Agentur (dena) – the German Energy Agency, Harnessing Artificial Intelligence to Accelerate the Energy Transition reviews the state of play of AI adoption in the energy sector, identifies high-priority applications of AI in the energy transition, and offers a road map and practical recommendations for the energy and AI industries to maximize AI's benefits. The report finds that AI has the potential to create substantial value for the global energy transition.