Lifeboat Foundation

A former Google executive who helped pioneer the company’s foray into artificial intelligence fears the technology will be used to create “more lethal pandemics.”

Mustafa Suleyman, co-founder and former head of applied AI at Google’s DeepMind, said the use of artificial intelligence will enable humans to access information with potentially deadly consequences.

Continue reading “Ex-Google executive fears AI will be used to create ‘more lethal pandemics’” »

While Apple’s famed Vision Pro headset is not available for purchase, the company is making sure that users get plenty of apps to use when they finally get it. The company said today that iPhone and iPad apps will show up on the visionOS’ App Store on the launch day.

This means developers won’t have to do extra work to port their existing apps. Apple said that this fall, it will release a developer beta version of the visionOS including the App Store.

“By default, your iPad and/or iPhone apps will be published automatically on the App Store on Apple Vision Pro. Most frameworks available in iPadOS and iOS are also included in visionOS, which means nearly all iPad and iPhone apps can run on visionOS, unmodified. Customers will be able to use your apps on visionOS early next year when Apple Vision Pro becomes available,” the company said in a post.

University of Hawaiʻi.

To understand this discovery, let’s first lay some groundwork.

Perovskite light-emitting diode is used as the light source for a quantum random number generator used in encryption.

Encryption plays an important role in protecting information in this digital era, and a random number generator plays a vital part in this by providing keys that are used to both encrypt and unlock the information at the receiving end.

Now, a team of researchers has made use of light-emitting diodes made from the crystal-like material perovskite to devise a new type of Quantum Random Number Generator (QRNG) that can be used for encryption but also for betting and computer simulations.

It is expected to deliver performance up to eight times faster than its predecessor.

The Los Alamos National Laboratory (LANL) is in the final stages of setting up a new supercomputer dubbed Crossroads that will allow it to test the US nuclear stockpile without major tests, a press release said. The system has been supplied by Hewlett Packard and installation began in June of this year.

The Department of Energy (DoE) has been tasked with the responsibility of ensuring that the US nuclear stockpile can be relied upon if and when it needs to be used. For this purpose, the federal agency does not actually test the warheads but carries out simulations to determine the storage, maintenance, and efficacy of the weapons.

ChatGPT is not officially available in China.

China can’t buy US chips required for advanced artificial intelligence models, but that’s not stopping the country from churning out AI models. After receiving regulatory approval from the country’s internet watchdog, several Chinese tech companies launched their respective AI chatbots last week. This comes in response to OpenAI’s ChatGPT, which, since its launch, has prompted rival tech companies around the globe to launch their own chatbots.

According to a Reuters report, Baidu CEO Robin Li has claimed that over 70 large language models (LLMs) with over 1 billion parameters have been released in China.

Continue reading “China approves chatbots for public use in tech war with US” »

EU law requires manufacturers to adopt Type-C chargers in phones, tablets, etc by December 2024.

Apple will switch out its proprietary Lightning charging port in iPhones to be compatible with a USB Type-C cable instead, reported.

The EU law says that by 2024-end, all mobile phones, tablets, and cameras sold in countries under the EU will have to be equipped with a USB-C charging port. From spring 2026, the obligation will extend to laptops as well. The law’s overall purpose is to cut down on environmental waste and save consumers an estimated $247 million per annum.

A team of scientists from Ames National Laboratory has developed a new machine learning model for discovering critical-element-free permanent magnet materials. The model predicts the Curie temperature of new material combinations. It is an important first step in using artificial intelligence to predict new permanent magnet materials. This model adds to the team’s recently developed capability for discovering thermodynamically stable rare earth materials. The work is published in Chemistry of Materials.

High performance magnets are essential for technologies such as wind energy, data storage, electric vehicles, and magnetic refrigeration. These magnets contain critical materials such as cobalt and rare earth elements like neodymium and dysprosium. These materials are in high demand but have limited availability. This situation is motivating researchers to find ways to design new magnetic materials with reduced critical materials.

Machine learning (ML) is a form of artificial intelligence. It is driven by computer algorithms that use data and trial-and-error algorithms to continually improve its predictions. The team used experimental data on Curie temperatures and theoretical modeling to train the ML algorithm. Curie temperature is the maximum temperature at which a material maintains its magnetism.

In a major breakthrough, scientists have built a tool to predict the odour profile of a molecule, just based on its structure. It can identify molecules that look different but smell the same, as well as molecules that look very similar but smell totally different.

Professor Jane Parker, University of Reading, said: Vision research has wavelength, hearing research has frequency – both can be measured and assessed by instruments. But what about smell? We don’t currently have a way to measure or accurately predict the odour of a molecule, based on its molecular structure.

You can get so far with current knowledge of the molecular structure, but eventually you are faced with numerous exceptions where the odour and structure don’t match. This is what has stumped previous models of olfaction. The fantastic thing about this new ML generated model is that it correctly predicts the odour of those exceptions.