Lifeboat Foundation

The Royal New Zealand Navy is currently awaiting the arrival of its latest Uncrewed Surface Vessel, the wind-powered “Bluebottle,” ahead of a 7-month sea trial.

The Royal New Zealand Navy (RNZN) will soon receive its first 22.3-foot (6.8-meter) long renewable-powered Uncrewed Surface Vessel (USV) to trial on a short-term lease, the New Zealand Defense Force has announced. Called “Bluebottle,” the USV will provide persistent surveillance around the waters of New Zealand for fishery protection, border protection, or meteorological data.

Continue reading “Kiwi Navy will test new AI, solar-powered robot boat for endless recon” »

Nolej’s cofounder, Bodo Hoenen, used AI to help design an exoskeleton for his paralysed daughter, Lorelei.

On their way to building fully autonomous vehicles, self-driving car makers are facing a tall task: training their AIs to be able to respond reliably to any and all scenarios that a car, truck or bus might encounter as well as, or hopefully better, than a human would. Today, a startup with a platform to help with that challenge is announcing a sizeable round of funding to take those strategies up a gear.

Foretellix, which builds verification and validation solutions to test the full range of driver assistance and autonomous systems that are coming out on the market, has closed its Series C at $85 million. The round includes financial investors alongside strategic backers from the automotive and chip industries, a signal of who is already doing business with Foretellix, as well as the longer business trajectory for the startup.

The round is being led by Israeli VC 83North, with Singapore’s Temasek and carmaker Isuzu investing alongside Woven Capital (Toyota’s venture fund), Nvidia, Artofin and previous backers MoreTech, Nationwide, Volvo Group VC, Jump Capital, Next Gear Ventures and OurCrowd. Foretellix may ring a bell for readers: The first close of this round was in May of this year (at $43 million).

The people who were most afraid of the risks of artificial intelligence decided they should be the ones to build it. Then distrust fueled a spiraling competition.

While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours—or even days—to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Meta, IBM and dozens of startups and researchers have launched an alliance defending a more open and collaborative method to develop artificial intelligence, setting up a clash with OpenAI and Google over the technology’s future.

The philosophical debate has become the central battleground for AI’s future, with increasing concern that Microsoft-backed OpenAI and Google will alone underpin a technology that could become increasingly crucial to our everyday lives.

“This is a pivotal moment in defining the future of AI,” said IBM CEO Arvind Krishna in the statement announcing the AI Alliance on Tuesday.

Researchers from EPFL have resolved a long-standing debate surrounding laser additive manufacturing processes with a pioneering approach to defect detection.

The progression of laser additive manufacturing —which involves 3D printing of metallic objects using powders and lasers—has often been hindered by unexpected defects. Traditional monitoring methods, such as thermal imaging and machine learning algorithms, have shown significant limitations. They often either overlook defects or misinterpret them, making precision manufacturing elusive and barring the technique from essential industries like aeronautics and automotive manufacturing.

But what if it were possible to detect defects in real-time based on the differences in the sound the printer makes during a flawless print and one with irregularities? Up until now, the prospect of detecting these defects this way was deemed unreliable. However, researchers at the Laboratory of Thermomechanical Metallurgy (LMTM) at EPFL’s School of Engineering have successfully challenged this assumption.

Recent advances allow imaging of neurons inside freely moving animals. However, to decode circuit activity, these imaged neurons must be computationally identified and tracked. This becomes particularly challenging when the brain itself moves and deforms inside an organism’s flexible body, e.g., in a worm. Until now, the scientific community has lacked the tools to address the problem.

Now, a team of scientists from EPFL and Harvard have developed a pioneering AI method to track neurons inside moving and deforming animals. The study, now published in Nature Methods, was led by Sahand Jamal Rahi at EPFL’s School of Basic Sciences.

Continue reading “Enhanced AI tracks neurons in moving animals” »

Geoffrey Miller is a professor of evolutionary psychology at the University of New Mexico, a researcher and an author.

Artificial Intelligence possesses the capability to process information thousands of times faster than humans. It’s opened up massive possibilities. But it’s also opened up huge debate about the safety of creating a machine which is more intelligent and powerful than we are. Just how legitimate are the concerns about the future of AI?