An explain-able multi-attribute decision model based on argumentation.

A research team from the Technical University of Munich (TUM) and the Fritz Haber Institute in Berlin is using active machine learning in the search for suitable molecular materials for new organic semiconductors, the basis for organic field effect transistors (OFETs), light-emitting diodes (OLEDs) and organic solar cells (OPVs). To efficiently deal with the myriad of possibilities for candidate molecules, machine learning proves an invaluable tool. It is envisaged that organic semiconductors will enable important future technologies such as portable solar cells or rollable displays. For such applications, improved organic molecules – which make up these materials – need to be discovered. For material discovery tasks of this nature researchers are increasingly utilising machine learning methods, training on data from computer simulations or experiments.

Industrial companies are embracing artificial intelligence (AI) as part of the fourth digital revolution. 1 1. The first two revolutions introduced programmable logic controllers and distributed control systems, which enabled plant-wide data collection and automation. The third revolution--advanced process controls--further abstracted automation into high-level models, allowing for increasingly dynamic plant operation. For more on the latest innovations in process controls, see Stephan Görner, Andy Luse, Naman Maheshwari, Ravi Malladi, Lapo Mori, and Robert Samek, "The potential of advanced process controls in energy and materials," November 23, 2020. AI leverages big data; it promises new insights that derive from applying machine learning to datasets with more variables, longer timescales, and higher granularity than ever.

According to the researchers at Adversa, machine learning systems that process visual data account for most of the work on adversarial attacks, followed by analytics, language processing, and autonomy.

This article originally appeared on VentureBeat and is reproduced with permission. Even state-of-the-art automatic speech recognition (ASR) algorithms struggle to recognize the accents of people from certain regions of the world. That's the top-line finding of a new study published by researchers at the University of Amsterdam, the Netherlands Cancer Institute, and the Delft University of Technology, which found that an ASR system for the Dutch language recognized speakers of specific age groups, genders, and countries of origin better than others. Speech recognition has come a long way since IBM's Shoebox machine and Worlds of Wonder's Julie doll. But despite progress made possible by AI, voice recognition systems today are at best imperfect -- and at worst discriminatory.

Dementia can make it hard for a person to focus and remain alert -- two things that are really important for road safety. Being able to identify when someone is experiencing early signs of cognitive impairment could be key to saving lives on the road -- unfortunately, it's not always easy to notice these early signs. Now, machine learning could make it easier to detect dementia. The innovation: A team led by researchers at Columbia University has developed machine learning models that detect early and mild cognitive impairment in older drivers with 88% accuracy. The opportunity: By analyzing driving behavior, these machine learning algorithms can help identify when a driver might be exhibiting early indicators of dementia and mild cognitive impairment.

"AIOps," which stands for "AI for IT operations," refers to the way data and information from a dev environment is managed by an IT team -- in this case, using AI. AIOps platforms leverage big data, machine learning, and analytics to enhance IT operations via monitoring, automation, and service desk functions with proactive and personal insights, enabling the use of multiple data sources and data collection methods. In theory, AIOps can provide faster resolutions to outages and other performance problems, in the process decreasing the costs associated with IT challenges. The benefits of AIOps are driving enterprise adoption. Eighty-seven percent of respondents to a recent OpsRamp survey agree that AIOps tools are improving their data-driven collaboration, and Gartner predicts that AIOps service usage will rise from 5% in 2018 to 30% in 2023.

Most artificial intelligence systems can be advanced by adding more: more computing power, more lines of code, more analysis, more neural networks, more machine learning. And this is great if you have large amounts of power and space at your disposal, like on a car, or a rocket ship, or in a data center. But if you don't have all that? Then you have to get simple and think creatively. That's what Johannes Overvelde and his team at AMOLF, a government-funded Dutch physics research institute, did in a new study released this week in Proceedings of the National Academy of Sciences.

The advent of Transformers in 2017 completely changed the world of neural networks. Ever since, the core concept of Transformers has been remixed, repackaged, and rebundled in several models. The results have surpassed the state of the art in several machine learning benchmarks. In fact, currently all top benchmarks in the field of natural language processing are dominated by Transformer-based models. Some of the Transformer-family models are BERT, ALBERT, and the GPT series of models.

Analogies play a crucial role in commonsense reasoning. The ability to recognize analogies like "eye is to seeing what ear is to hearing," sometimes referred to as analogical proportions, shape how humans structure knowledge and understand language. In a new study that looks at whether AI models can understand analogies, researchers at Cardiff University used benchmarks from education as well as more common datasets. They found that while off-the-shelf models can identify some analogies, they sometimes struggle with complex relationships, raising questions about to what extent models capture knowledge. Large language models learn to write humanlike text by internalizing billions of examples from the public web.