Lifeboat Foundation

Artificial intelligence excels at sorting through information and detecting patterns or trends. But these machine learning algorithms need to be trained with large amounts of data first.

As researchers explore potential applications for AI, they have found scenarios where AI could be really useful—such as analyzing X-ray image data to look for evidence of rare conditions or detecting a rare fish species caught on a commercial fishing boat—but there’s not enough data to accurately train the algorithms.

Jenq-Neng Hwang, University of Washington professor of electrical and computer and engineering, specializes in these issues. For example, Hwang and his team developed a method that teaches AI to monitor how many distinct poses a baby can achieve throughout the day. There are limited training datasets of babies, which meant the researchers had to create a unique pipeline to make their algorithm accurate and useful.

Battery–electric vehicles have become ubiquitous as more people have realized how much less pollution they produce than traditional gas-powered cars. But another type of planet-friendly vehicle, the hydrogen car, has yet to catch on, for a few different reasons.

Now, a new technique developed by researchers at Rice University in Texas may provide the key to making hydrogen cars — and hydrogen fuel as a whole — more viable.

Continue reading “Scientists develop innovative technique to transform plastic waste into powerful clean fuel: ‘[It] could be produced for free’” »

Elon Musk praises the Tesla’s Full Self-Driving system for its role in aiding a Tesla owner during a mild heart attack.

A research team led by Lynden Archer, professor and dean of Cornell Engineering, has developed a new lithium battery that can charge in as little as five minutes. This could help address anxiety associated with the charging time of electric vehicles (EVs) and increase their adoption.

In their bid to reduce emissions from transportation, countries worldwide are looking to electrify various modes of transport. Road-based transport such as cars, buses, and trucks have led this transformation, aiming to even ban the sale of fossil fuel-powered cars in the next decade.

Working to promote the Apple Vision Pro as a business tool instead of solely an entertainment one, Apple has highlighted half a dozen firms from Porsche to Microsoft, who have incorporated the headset into their work.

While perhaps most attention that the Apple Vision Pro has received concerns its immersive video for entertainment, Apple has always aimed it equally at businesses. Now Apple wants to press hard on the idea of Apple Vision Pro being a tool that companies need.

“There’s tremendous opportunity for businesses to reimagine what’s possible using Apple Vision Pro at work,” Susan Prescott, Apple’s vice president of Worldwide Developer Relations and Enterprise Marketing, said in a statement. “We’re thrilled to see the innovative ways organizations are already using Apple Vision Pro, from planning fire response operations to iterating on the most intricate details of an engine design — and this is just the beginning.”

Carnegie Mellon University (CMU) researchers have developed H₂O – Human2HumanOid – a reinforcement learning-based framework that allows a full-sized humanoid robot to be teleoperated by a human in real-time using only an RGB camera. Which begs the question: will manual labor soon be performed remotely?

A teleoperated humanoid robot allows for the performance of complex tasks that are – at least at this stage – too complex for a robot to perform independently. But achieving whole-body control of human-sized humanoids to replicate our movements in real-time is a challenging task. That’s where reinforcement learning (RL) comes in.

Continue reading “How blue-collar workers will train the humanoids that take their jobs” »

Sanctuary AI announced that it will be delivering its humanoid robot to a Magna manufacturing facility. Based in Canada, with auto manufacturing facilities in Austria, Magna manufactures and assembles cars for a number of Europe’s top automakers, including Mercedes, Jaguar and BMW. As is often the nature of these deals, the parties have not disclosed how many of Sanctuary AI’s robots will be deployed.

The news follows similar deals announced by Figure and Apptronik, which are piloting their own humanoid systems with BMW and Mercedes, respectively. Agility also announced a deal with Ford at CES in January 2020, though that agreement found the American carmaker exploring the use of Digit units for last-mile deliveries. Agility has since put that functionality on the back burner, focusing on warehouse deployments through partners like Amazon.

For its part, Magna invested in Sanctuary AI back in 2021 — right around the time Elon Musk announced plans to build a humanoid robot to work in Tesla factories. The company would later dub the system “Optimus.” Vancouver-based Sanctuary unveiled its own system, Phoenix, back in May of last year. The system stands 5’7” (a pretty standard height for these machines) and weighs 155 pounds.

Tesla has been building Prefabricated Supercharger Units (PSUs) for at least a year at its Giga New York facility, and now the company released a video claiming it can deploy the pre-built EV fast chargers in just four days after getting them delivered to the installation site.

The company’s PSUs have up to four Supercharger stalls installed on a concrete base, with all the wiring in place and everything ready to be hooked up to the utilities. Up to three units–that’s 12 stalls in total–can be loaded onto the back of a truck with a crane and delivered to a new Supercharger location in the United States.

Artificial Intelligence is making its presence felt in thousands of different ways. It helps scientists make sense of vast troves of data; it helps detect financial fraud; it drives our cars; it feeds us music suggestions; its chatbots drive us crazy. And it’s only getting started.

Are we capable of understanding how quickly AI will continue to develop? And if the answer is no, does that constitute the Great Filter?

The Fermi Paradox is the discrepancy between the apparent high likelihood of advanced civilizations existing and the total lack of evidence that they do exist. Many solutions have been proposed for why the discrepancy exists. One of the ideas is the ‘Great Filter.’