Lifeboat Foundation

An innovative new collaboration between EPFL’s HexHive Laboratory and Oracle has developed automated, far-reaching technology in the ongoing battle between IT security managers and attackers, hoping to find bugs before the hackers do.

On the 9th of December 2021 the world of IT security went into a state of shock. Before its developers even knew it, the log4j application—part of the Apache suite used on most web servers—was being exploited by hackers, allowing them to take control of servers and data centers all over the world.

The Wall Street Journal reported news that nobody wanted to hear: “U.S. officials say hundreds of millions of devices are at risk. Hackers could use the bug to steal data, install malware or take control.”

Rwanda is making strides to become a major innovation hotspot in Africa — pushing forward with digital development while also prioritizing sustainability.

Security experts from paluno, the Ruhr Institute for Software Technology at the University of Duisburg-Essen (UDE) have developed a new technique that, for the first time, enables fuzz testing of protected memory areas in modern processors. Their method revealed many vulnerabilities in security-critical software.

Intel’s “Software Guard Extension” (SGX) is a widely used technology to protect sensitive data from misuse. It helps developers in shielding a certain memory area from the rest of a computer. A password manager, for example, can be executed safely in such an enclave, even if the rest of the system is corrupted by malware.

However, it is not uncommon for errors to creep in during the programming of the enclaves. Already in 2020, the paluno team from Prof. Dr. Lucas Davi discovered and published several vulnerabilities in SGX enclaves. Now, together with partners form the CASA cluster of excellence, the researchers have achieved another breakthrough in the analysis techniques: Their latest development enables the fuzz testing of enclaves, which is much more effective than the previously used symbolic execution. The idea behind fuzz testing is to feed a large number of inputs into a program in order to gain insights into the structure of the code.

Scientists have created an artificial intelligence that is able to think and learn like a baby.

The system is able to grasp the basic common sense rules of the world in the same way as humans can, the researchers who create it say.

The breakthrough could not only help advance AI research but also the ways we understand the human mind, scientists say.

If you live near a river and want clean energy to power your home, we have great news for you. Belgian company Turbulent has created a fish-friendly whirlpool turbine that can be installed in only one week.

The innovative turbine can provide energy 24 hours a day for dozens of homes by being installed in most rivers and canals. It also delivers low-cost power as the generator just uses flowing water to produce energy.

Continue reading “This Fish-Friendly Whirlpool Turbine Can Power Up to 60 Homes” »

Modern technology allows us to measure magnetic fields created by electric signals in our brains. Now, a breakthrough makes detection 10 times better.

Meta, the Facebook parent company, has announced its new, artificial intelligence (AI)-driven language translation model, which claims to be able to translate 200 languages worldwide, in real-time. In a blog post from earlier today, Meta said that this is the first AI language translator model that brings a large number of fringe and lesser known languages from around the world — including fringe dialects from Asia and Africa.

The AI model can also carry out these translations without needing to first translate a language to English, and then translate it to the originally intended language. This, Meta said, does not only help in speeding up the translation time, but is a breakthrough of sorts since many of the 200 languages that its AI model can understand had little to no available public data for AI to train on.

The initiative is part of the company’s No Language Left Behind (NLLB) project, which it announced in February this year. The new AI model, called NLLB-200, has achieved up to 44% higher BLEU (Bilingual Evaluation Understudy) score in terms of its accuracy and quality of translation results. For Indian dialects, NLLB-200 is 70% better than existing AI models.

“We just open-sourced an AI model we built that can translate across 200 different languages — many which aren’t supported by current translation systems,” he said. “We call this project No Language Left Behind, and the AI modeling techniques we used from NLLB are helping us make high quality translations on Facebook and Instagram for languages spoken by billions of people around the world.”

Meta invests heavily in AI research, with hubs of scientists across the globe building realistic avatars for use in virtual worlds and tools to reduce hate speech across its platforms 0, among many other weird and wonderful things. This investment allows the company to ensure it stays at the cutting edge of innovation by working with the top AI researchers, while also maintaining a link with the wider research community by open-sourcing projects such as No Languages Left Behind.

The major challenge in creating a translation model that will work across rarer languages is that the researchers have a much smaller pool of data — in this case examples of sentences — to train the model versus, say, English. In many cases, they had to find people who spoke those languages to help them provide the data, and then check that the translations were correct.

It may seem like technology advances year after year, as if by magic. But behind every incremental improvement and breakthrough revolution is a team of scientists and engineers hard at work.

UC Santa Barbara Professor Ben Mazin is developing precision optical sensors for telescopes and observatories. In a paper published in Physical Review Letters, he and his team improved the spectra resolution of their superconducting sensor, a major step in their ultimate goal: analyzing the composition of exoplanets.

“We were able to roughly double the spectral resolving power of our detectors,” said first author Nicholas Zobrist, a doctoral student in the Mazin Lab.