Lifeboat Foundation

Russia is amassing unprecedented military might in the Arctic and testing its newest weapons in a region freshly ice-free due to the climate emergency, in a bid to secure its northern coast and open up a key shipping route from Asia to Europe.

Weapons experts and Western officials have expressed particular concern about one Russian ‘super-weapon,’ the Poseidon 2M39 torpedo. Development of the torpedo is moving fast with Russian President Vladimir Putin requesting an update on a “key stage” of the tests in February from his defence minister Sergei Shoigu, with further tests planned this year, according to multiple reports in state media.

This unmanned stealth torpedo is powered by a nuclear reactor and intended by Russian designers to sneak past coastal defences — like those of the US — on the sea floor.

But will the effort undermine A.I. ethics?

Primer, the San Francisco A.I. company, is the latest to launch a no-code software system that lets non-experts create and train A.I. algorithms.

Chinese version of BD’s Spot. It’s no wonder that funding is so slow for robotics. As soon as Boston Dynamics finishes Atlas 100 companies will copy and pass it off as their own.

A video shared on Twitter has sparked fears of a real world robot take over. The clip shows a squadron of four-legged, canine-like robots made by a Chinese firm, all of which move in unison.

Continue reading “Chinese firm shares video of four-legged robots moving in unison” »

Commentary: We like to imagine an AI-driven future, but it’s machine learning that will actually help us to progress, argues expert Michael I. Jordan.

Machine learning (ML) models are only as good as the data you feed them. That’s true during training, but also once a model is put in production. In the real world, the data itself can change as new events occur and even small changes to how databases and APIs report and store data could have implications on how the models react. Since ML models will simply give you wrong predictions and not throw an error, it’s imperative that businesses monitor their data pipelines for these systems.

That’s where tools like Aporia come in. The Tel Aviv-based company today announced that it has raised a $5 million seed round for its monitoring platform for ML models. The investors are Vertex Ventures and TLV Partners.

For 50 years, AeroVironment has advanced UAV development. Today, the company is a Technology Solutions Provider at the intersection of four future-defining technologies: robotics, sensors, analytics and connectivity. Its culture of experimentation and R&D dates back to its founder, Dr. Paul MacCready Jr., whose achievements earned him the nickname “the father of human-powered flight”. From deploying the world’s most popular sUAS to designing the helicopter that’s en route to fly in Mars’ thin atmosphere, AeroVironment’s collective accomplishments provide a case study of imagination, innovation and collaboration — one that has and will bring effective solutions to frontlines, farms and frontiers, yesterday, today and tomorrow.

Leading the Way: UAS Capabilities – Onward and Upward

From solar-powered aircraft to the first hand-launched small UAS (sUAS) for military reconnaissance, AeroVironment literally launched the era of small aerial vehicles for environmental, commercial and defense purposes. For 35 years, the company has made its UAVs smaller and simpler, yet with multiple and ever-more-innovative functionality.

Natural language processing rivals humans’ skills.

According to OpenAI, more than 300 applications are using GPT-3, which is part of a field called natural language processing. An average of 4.5 billion words are written per day. Some say the quality of GPT-3’s text is as good as that written by humans.

What follows is GPT-3’s response to topics in general investing.

Continue reading “Artificial intelligence has advanced so much, it wrote this article” »

Do not be afraid of what you see. Even though the robotics industry has been making huge progress lately, it hasn’t quite reached the level you see here.

Fusion reactor technologies are well-positioned to contribute to our future power needs in a safe and sustainable manner. Numerical models can provide researchers with information on the behavior of the fusion plasma, as well as valuable insight on the effectiveness of reactor design and operation. However, to model the large number of plasma interactions requires a number of specialized models that are not fast enough to provide data on reactor design and operation.

Aaron Ho from the Science and Technology of Nuclear Fusion group in the department of Applied Physics at Eindhoven University of Technology has explored the use of machine learning approaches to speed up the numerical simulation of core plasma turbulent transport. Ho defended his thesis on March 17^th.

The ultimate goal of research on fusion reactors is to achieve a net power gain in an economically viable manner. To reach this goal, large intricate devices have been constructed, but as these devices become more complex, it becomes increasingly important to adopt a predict-first approach regarding its operation. This reduces operational inefficiencies and protects the device from severe damage.