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Category: robotics/AI – Page 1492

An international research team with participants from several universities including the FAU has proposed a standardized registry for artificial intelligence (AI) work in biomedicine to improve the reproducibility of results and create trust in the use of AI algorithms in biomedical research and, in the future, in everyday clinical practice. The scientists presented their proposal in the journal Nature Methods.

In the last decades, new technologies have made it possible to develop a wide variety of systems that can generate huge amounts of biomedical data, for example in cancer research. At the same time, completely new possibilities have developed for examining and evaluating this data using methods. AI algorithms in intensive care units, e.g., can predict circulatory failure at an early stage based on large amounts of data from several monitoring systems by processing a lot of complex information from different sources at the same time, which is far beyond human capabilities.

This great potential of AI systems leads to an unmanageable number of biomedical AI applications. Unfortunately, the corresponding reports and publications do not always adhere to best practices or provide only incomplete information about the algorithms used or the origin of the data. This makes assessment and comprehensive comparisons of AI models difficult. The decisions of AIs are not always comprehensible to humans and results are seldomly fully reproducible. This situation is untenable, especially in clinical research, where trust in AI models and transparent research reports are crucial to increase the acceptance of AI algorithms and to develop improved AI methods for basic biomedical research.

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Dr. Valentin Robu, Associate Professor and Academic PI of the project, says that this work was part of the NCEWS (Network Constraints Early Warning System project), a collaboration between Heriot-Watt and Scottish Power Energy Networks, part funded by InnovateUK, the United Kingdom’s applied research and innovation agency. The project’s results greatly exceeded our expectations, and it illustrates how advanced AI techniques (in this case deep learning neural networks) can address important practical challenges emerging in modern energy systems.

Power networks worldwide are faced with increasing challenges. The fast rollout of distributed renewable generation (such as rooftop solar panels or community wind turbines) can lead to considerable unpredictability. The previously used fit-and-forget mode of operating power networks is no longer adequate, and a more active management is required. Moreover, new types of demand (such as from the rollout EV charging) can also be source of unpredictability, especially if concentrated in particular areas of the distribution grid.

Network operators are required to keep power and voltage within safe operating limits at all connection points in the , as out of bounds fluctuations can damage expensive equipment and connected devices. Hence, having good estimates of which area of the network could be at risk and require interventions (such as strengthening the network, or extra storage to smoothen fluctuations) is increasingly a key requirement.

Privacy-sensitive machine learning

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AI and Machine Learning systems have proven a boon to scientific research in a variety of academic fields in recent years. They’ve assisted scientists in ripe for cutting-edge treatments, of potent and, and even. Throughout this period, however, AI/ML systems have often been relegated to simply processing large data sets and performing brute force computations, not leading the research themselves.

But Dr. Hiroaki Kitano, CEO of Sony Computer Science Laboratories, “hybrid form of science that shall bring systems biology and other sciences into the next stage,” by creating an AI that’s just as capable as today’s top scientific minds. To do so, Kitano seeks to launch the and.

“The distinct characteristic of this challenge is to field the system into an open-ended domain to explore significant discoveries rather than rediscovering what we already know or trying to mimic speculated human thought processes,” Kitano. “The vision is to reformulate scientific discovery itself and to create an alternative form of scientific discovery.”

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Hurray.

Tesla has started to hire roboticists to build its recently announced “Tesla Bot,” a humanoid robot to become a new vehicle for its AI technology.

When Elon Musk explained the rationale behind Tesla Bot, he argued that Tesla was already making most of the components needed to create a humanoid robot equipped with artificial intelligence.

The automaker’s computer vision system developed for self-driving cars could be leveraged for use in the robot, which could also use things like Tesla’s battery system and suite of sensors.

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A Tesla semi-truck with a very Tesla-worthy aesthetics highlighted by the contoured yet sharp design language that in a way reminds me of the iPhone 12!

Tesla’s visionary Semi all-electric truck powered by four independent motors on the rear is scheduled for production in 2022. The semi is touted to be the safest, most comfortable truck with an acceleration of 0–60 mph in just 20 seconds and a range of 300–500 miles. While the prototype version looks absolutely badass, how the final version will look is anybody’s guess.

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Proteins are essential to life, and understanding their 3D structure is key to unpicking their function. To date, only 17% of the human proteome is covered by an experimentally determined structure. Two papers in this week’s issue dramatically expand our structural understanding of proteins. Researchers at DeepMind, Google’s London-based sister company, present the latest version of their AlphaFold neural network. Using an entirely new architecture informed by intuitions about protein physics and geometry, it makes highly accurate structure predictions, and was recognized at the 14th Critical Assessment of Techniques for Protein Structure Prediction last December as a solution to the long-standing problem of protein-structure prediction. The team applied AlphaFold to 20,296 proteins, representing 98.5% of the human proteome.

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Determining the 3D shapes of biological molecules is one of the hardest problems in modern biology and medical discovery. Companies and research institutions often spend millions of dollars to determine a molecular structure—and even such massive efforts are frequently unsuccessful.

Using clever, new machine learning techniques, Stanford University Ph.D. students Stephan Eismann and Raphael Townshend, under the guidance of Ron Dror, associate professor of computer science, have developed an approach that overcomes this problem by predicting accurate structures computationally.

Most notably, their approach succeeds even when learning from only a few known structures, making it applicable to the types of whose structures are most difficult to determine experimentally.

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Israel-based AI healthtech company, DiA Imaging Analysis, which is using deep learning and machine learning to automate analysis of ultrasound scans, has closed a $14 million Series B round of funding.

Backers in the growth round, which comes three years after DiA last raised, include new investors Alchimia Ventures, Downing Ventures, ICON Fund, Philips and XTX Ventures — with existing investors also participating, including CE Ventures, Connecticut Innovations, Defta Partners, Mindset Ventures, and Dr Shmuel Cabilly. In total, it has taken in $25 million to date.

The latest financing will allow DiA to continue expanding its product range and go after new and expanded partnerships with ultrasound vendors, PACS/Healthcare IT companies, resellers and distributors while continuing to build out its presence across three regional markets.

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A Norwegian company called Yara International claims to have created the world’s first zero-emission ship that can also transport cargo autonomously. The Yara Birkeland electric cargo ship was first conceptualized in2017but now looks to make its first voyage with no crew members onboard later this year in Norway.

Yara International is a Norwegian company that was founded in1905to combat the rising famine in Europe at the time. The company created the world’s first nitrogen fertilizer, which remains its largest business focus today.

In addition to its perpetual battle against hunger, Yara focuses on emissions abatement and sustainable agricultural practices. While the company wants to continue finding success in feeding the planet, it believes it can also do so sustainably.

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