Collaborating Authors


Machine learning methods provide new insights into organic-inorganic interfaces


Oliver Hofmann and his research group at the Institute of Solid State Physics at TU Graz are working on the optimization of modern electronics. A key role in their research is played by interface properties of hybrid materials consisting of organic and inorganic components, which are used, for example, in OLED displays or organic solar cells. The team simulates these interface properties with machine-learning-based methods. The results are used in the development of new materials to improve the efficiency of electronic components. The researchers have now taken up the phenomenon of long-range charge transfer.

Twins, Pirates game delayed by drone flying over Target Field

FOX News

How can teams protect players and staff? A drone flew over Target Field prior to the start of a Minnesota Twins and Pittsburgh Pirates game on Tuesday, which forced a delay. According to The Athletic, players were trying to throw baseballs at the drone, but they were unable to hit it. Eventually, it flew out of the stadium, and around one of the parking lots. The umpires made the players get off the field because the drone presents a safety issue.

The role played by Artificial Intelligence in social sector -


Artificial intelligence is already impacting our lives. And the use of AI for social functioning is on an all-time high. Be it getting riding directions through our smartphone or getting daily reminders by using our health system to extend our workouts; all these are manifestations of how artificial talent is altering the way we function. What is often much less understood is the vast function synthetic brain can play in the social sector. The Artificial Intelligence for social good can probably assist in solving some of the country's most pressing problems. As a count number of facts, it can contribute in some way or every other to tackling and addressing all of the United Nation's Sustainable Development Goals, supporting large sections of the populace in both growing and developed countries. AI is already helping in several real-life situations, from assisting blind humans in navigating and diagnosing cancer to identify sexual harassment victims and helping with catastrophe relief. Let us take a look briefly at integral social domains where AI can be carried out effectively.

AI Model Mimics Brain Neurons to Reduce Energy Costs


Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses. Artificial intelligence models continue to grow in sophistication and complexity, adding to the need for more data, computation, and energy. To help combat increasing energy costs, researchers at TU Graz's Institute of Theoretical Computer Science have developed a new algorithm, called e-propagation (e-prop for short). E-prop mimics how neurons send electrical impulses to other neurons in our brain, which massively reduces the amount of energy human brains use, in comparison to machine learning. Deployed for AI, e-prop would require only 20 watts, approximately one-millionth the energy a supercomputer uses.

Human brain has a 'limit' on how much information it can process

Daily Mail - Science & tech

The human brain has a limit on how much information it can process at once due to a finite energy supply, a new study reveals. UK neuroscientists say that energy supply to the brain remains constant and can't exceed an upper limit, however challenging a task is. But as the brain uses more energy in processing the task at hand, less energy is supplied to processing outside our immediate focus, they say. This results in what's known as'inattentional blindness' – when stimuli that's available in plain sight doesn't register, even if it's valuable to us. This can help explain why we are sometimes unable to concentrate on what our family members are telling us while we're playing video games or watching TV.

WORX Landroid M robotic mower Review : Automatic electronic yard care – IAM Network


This summer I've been testing several lawn mowers, the most unique of which is this robot from WORX. This is the WORX Landroid M robotic mower, a fully automated, cordless, rechargeable battery powered piece of equipment that'll do all your work for you. The biggest obstacle you'll face is setup, and that's pretty straightforward if you follow the directions step-by-step. The Parts Included in our review is the basic WORX Landroid M robotic mower and a few add-ons. If you're looking at the WORX website (or WORX in a store) there are at least two versions of this Landroid M, one with GPS, one without.

A Multilateral AI Strategy for Biodiversity and Restoration


Species both plants and animals on our planet are dying rapidly. It seems rather strange that we are not focusing more on this within strategies relating to artificial intelligence, however it is not strange considering the financial pull towards the few developers with the expertise to pursue development and operation of artificial intelligence. We can use artificial intelligence for analysis of biomass; mitigating impact of linear infrastructure; and marine conservation through monitoring. We have to ask ourselves the question of whether we are using artificial intelligence with the right priorities. The national AI strategy of Portugal is one of the few national strategies that speaks of: "AI for biodiversity, from forests and green economy to marine species and blue economy."

Predicting Energy Production


As created for AI4IMPACT's Deep Learning Datathon 2020, TEAM DEFAULT has created a neural-network-based deep learning model used for predicting energy production demand in France. The model was created using Smojo, on AI4IMPACT's innovative cloud-based learning and model deployment system. Our model was able to achieve a 0.131 test loss which beat persistence loss of 0.485 by a quite a fair margin. As the energy market becomes increasingly liberalized across the world, the free and open market has seen an uptick and importance for optimized energy demand. New and existing entrants turn to data and various methods to forecast energy consumption in hopes of turning over a profit.

Google's Nest devices will be the 'cornerstone' of ADT smart home security


Google has announced that it will invest $450 million in security firm ADT, forming a partnership that will allow ADT's technicians to sell and install Google's Nest family of products. At the same time, Google's Nest devices and AI technology will eventually expand ADT's home security product range and become the "cornerstone of ADT's smart home offering," Google wrote. The initial goal of Google's investment, which gives it a 6.6 percent share of ADT, will be to get its products into the hands of more consumers. "The company's network of thousands of professional technicians will be able to sell and install devices like Nest Cameras and Nest Hub Max, all powered by Google Assistant," the company said in a news release. Later on, ADT will borrow Google's tech to enhance its own capabilities.

"What's that? Reinforcement Learning in the Real-world?"


Reinforcement Learning offers a distinctive way of solving the Machine Learning puzzle. It's sequential decision-making ability, and suitability to tasks requiring a trade-off between immediate and long-term returns are some components that make it desirable in settings where supervised-learning or unsupervised learning approaches would, in comparison, not fit as well. By having agents start with zero knowledge then learn qualitatively good behaviour through interaction with the environment, it's almost fair to say Reinforcement Learning (RL) is the closest thing we have to Artificial General Intelligence yet. We can see RL being used in robotics control, treatment design in healthcare, among others; but why aren't we boasting of many RL agents being scaled up to real-world production systems? There's a reason why games, like Atari, are such nice RL benchmarks -- they let us care only about maximizing the score and not worry about designing a reward function.