Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 1,842

Researchers have designed a machine learning method that can predict battery health with 10x higher accuracy than current industry standard, which could aid in the development of safer and more reliable batteries for electric vehicles and consumer electronics.

The researchers, from Cambridge and Newcastle Universities, have designed a new way to monitor batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a to predict the ’s health and useful lifespan. Their method is non-invasive and is a simple add-on to any existing battery system. The results are reported in the journal Nature Communications.

Predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the big problems limiting widespread adoption of : it’s also a familiar annoyance to mobile phone users. Over time, battery performance degrades via a complex network of subtle chemical processes. Individually, each of these processes doesn’t have much of an effect on battery performance, but collectively they can severely shorten a battery’s performance and lifespan.

Read more | >

In order to better solve complex challenges at the dawn of the third decade of the 21st century, Alphabet Inc. has tapped into relics dating to the 1980s: video games.

The parent company of Google reported this week that its DeepMind Technologies Artificial Intelligence unit has successfully learned how to play 57 Atari video games. And the plays better than any human.

Atari, creator of Pong, one of the first successful video games of the 1970s, went on to popularize many of the great early classic video games into the 1990s. Video games are commonly used with AI projects because they algorithms to navigate increasingly complex paths and options, all while encountering changing scenarios, threats and rewards.

Read more | >

As of right now, Cortical’s mini-brains have less processing power than a dragonfly brain. The company is looking to get its mouse-neuron-powered chips to be capable of playing a game of “Pong,” as CEO Hon Weng Chong told Fortune, following the footsteps of AI company DeepMind, which used the game to test the power of its AI algorithms back in 2013.

“What we are trying to do is show we can shape the behavior of these neurons,” Chong told Fortune.

READ MORE: A startup is building computer chips using human neurons [Fortune].

Read more | >

The biggest change worldwide in the last decade was probably the smartphone revolution, but overall, cities themselves still look pretty much the same. In the decade ahead, cities will change a lot more. Most of our regular readers probably think I am referring to how autonomous vehicles networks will start taking over and how owning a car will start to become closer to owning a horse. However, the real answer isn’t just the autonomous vehicles on the roads — they will likely also compete with autonomous eVTOL aircraft carrying people between hubs.

Today, the European Union is moving one step closer to making this second part a reality. Together with Daedalean, an autonomous flight company we have covered in the past, EASA published a new joint report covering “The Learning Assurance for Neural Networks.”

Read more | >

This is when #ai will replace humans at creative tasks. 🧠 Credit: @worldeconomicforum… Looking for a job in AI & Machine Learning. Follow us for more updates or visit: https://aijobs.com/

#aijobs #artificialintelligence #datascience #IoT #AIoT #robot #robots #deeplearning #robotics #tecnologia #cybersecurity #aiskills #artificialintelligenceai #machinelearning #machinelearningalgorithms #futuretechnology #ML #computerengineer #codinglife #coding #programmerlife #VR #technologies #techie

Read more | >

Despite the huge contributions of deep learning to the field of artificial intelligence, there’s something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn’t emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.

Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.

In his keynote speech at the AAAI conference, computer scientist Yann LeCun discussed the limits of current deep learning techniques and presented the blueprint for “self-supervised learning,” his roadmap to solve deep learning’s data problem. LeCun is one of the godfathers of deep learning and the inventor of convolutional neural networks (CNN), one of the key elements that have spurred a revolution in artificial intelligence in the past decade.

Read more | >