Lifeboat Foundation

GE Healthcare has received 510k clearance from US FDA for its Ultra Edition package on Vivid cardiovascular ultrasound systems, which come with features based on artificial intelligence (AI) that allows clinicians to get quicker and more exams repeatedly. Although methodical evaluations of heart function are necessary in echocardiography, such evaluations can be time-consuming and difficult to get. Quality acquisition of data and operator skill are essential factors to get precise and thorough exams. Given that patients undergo subsequent monitoring exams, the reproducibility of the exam evaluations is essential to monitoring improvement or progress of the disease.

Interesting Eric Klien

That prompted the researchers, who are part of the Human Brain Project, to look at two features that have become clear in experimental neuroscience data: each neuron retains a memory of previous activity in the form of molecular markers that slowly fade with time; and the brain provides top-down learning signals using things like the neurotransmitter dopamine that modulates the behavior of groups of neurons.

In a paper in Nature Communications, the Austrian team describes how they created artificial analogues of these two features to create a new learning paradigm they call e-prop. While the approach learns slower than backpropagation-based methods, it achieves comparable performance.

Continue reading “A New Brain-Inspired Learning Method for AI Saves Memory and Energy” »

The Solar Mapper uses artificial intelligence algorithms that compile data extracted from satellite images. It can estimate site solar potential and indicate the most suitable technology.

Summary: A new computational model predicts how information deep inside the brain could flow from one network to another, and how neural network clusters can self optimize over time.

Source: USC

Researchers at the Cyber-Physical Systems Group at the USC Viterbi School of Engineering, in conjunction with the University of Illinois at Urbana-Champaign, have developed a new model of how information deep in the brain could flow from one network to another and how these neuronal network clusters self-optimize over time.

Artificial intelligence has arrived in our everyday lives—from search engines to self-driving cars. This has to do with the enormous computing power that has become available in recent years. But new results from AI research now show that simpler, smaller neural networks can be used to solve certain tasks even better, more efficiently, and more reliably than ever before.

An international research team from TU Wien (Vienna), IST Austria and MIT (USA) has developed a new artificial intelligence system based on the brains of tiny animals, such as threadworms. This novel AI-system can control a vehicle with just a few artificial neurons. The team says that system has decisive advantages over previous deep learning models: It copes much better with noisy input, and, because of its simplicity, its mode of operation can be explained in detail. It does not have to be regarded as a complex “black box”, but it can be understood by humans. This new deep learning model has now been published in the journal Nature Machine Intelligence.

Humans are innately able to adapt their behavior and actions according to the movements of other humans in their surroundings. For instance, human drivers may suddenly stop, slow down, steer or start their car based on the actions of other drivers, pedestrians or cyclists, as they have a sense of which maneuvers are risky in specific scenarios.

However, developing robots and autonomous vehicles that can similarly predict human movements and assess the risk of performing different actions in a given scenario has so far proved highly challenging. This has resulted in a number of accidents, including the tragic death of a pedestrian who was struck by a self-driving Uber vehicle in March 2018.

Researchers at Stanford University and Toyota Research Institute (TRI) have recently developed a framework that could prevent these accidents in the future, increasing the safety of autonomous vehicles and other robotic systems operating in crowded environments. This framework, presented in a paper pre-published on arXiv, combines two tools, a machine learning algorithm and a technique to achieve risk-sensitive control.

I am for Ethical Robots what about you?

Every time we talk to Alexa, Siri, Google, or Cortana, we are building the brains of the robot. Human machine relationships increase and robot ethics are needed for the coming age of automation, they are simply not adequate for the nuanced capabilities and behaviors we are beginning to see in today’s devices.

The world’s small-scale farmers now can see a path to solving global hunger over the next decade, with solutions—such as adopting climate-resilient crops through improving extension services—all culled rapidly via artificial intelligence from more than 500,000 scientific research articles.

The results are synthesized in 10 new research papers—authored by 77 scientists, researchers and librarians in 23 countries—as part of Ceres2030: Sustainable Solutions to End Hunger. The project is headquartered at Cornell University, with partners from the International Food Policy Research Institute (IFPRI) and the International Institute for Sustainable Development (IISD).

The papers were published concurrently on Oct. 12 in four journals— Nature Plants, Nature Sustainability, Nature Machine Intelligence and Nature Food —and assembled in a comprehensive package online: Sustainable Solutions to End Hunger.

Over the past decade or so, the performance of batteries has skyrocketed and their cost has plummeted. Given that many experts see the electrification of everything as key to decarbonizing our energy systems, this is good news. But for researchers like Chueh, the pace of battery innovation isn’t happening fast enough. The reason is simple: batteries are extremely complex. To build a better battery means ruthlessly optimizing at every step in the production process. It’s all about using less expensive raw materials, better chemistry, more efficient manufacturing techniques. But there are a lot of parameters that can be optimized. And often an improvement in one area—say, energy density—will come at a cost of making gains in another area, like charge rate.

Improving batteries has always been hampered by slow experimentation and discovery processes. Machine learning is speeding it up by orders of magnitude.