Lifeboat Foundation

Machine learning (ML) is making incredible transformations in critical areas such as finance, healthcare, and defense, impacting nearly every aspect of our lives. Many businesses, eager to capitalize on advancements in ML, have not scrutinized the security of their ML systems. Today, along with MITRE, and contributions from 11 organizations including IBM, NVIDIA, Bosch, Microsoft is releasing the Adversarial ML Threat Matrix, an industry-focused open framework, to empower security analysts to detect, respond to, and remediate threats against ML systems.

During the last four years, Microsoft has seen a notable increase in attacks on commercial ML systems. Market reports are also bringing attention to this problem: Gartner’s Top 10 Strategic Technology Trends for 2020, published in October 2019, predicts that “Through 2022, 30% of all AI cyberattacks will leverage training-data poisoning, AI model theft, or adversarial samples to attack AI-powered systems.” Despite these compelling reasons to secure ML systems, Microsoft’s survey spanning 28 businesses found that most industry practitioners have yet to come to terms with adversarial machine learning. Twenty-five out of the 28 businesses indicated that they don’t have the right tools in place to secure their ML systems. What’s more, they are explicitly looking for guidance. We found that preparation is not just limited to smaller organizations. We spoke to Fortune 500 companies, governments, non-profits, and small and mid-sized organizations.

Our survey pointed to marked cognitive dissonance especially among security analysts who generally believe that risk to ML systems is a futuristic concern. This is a problem because cyber attacks on ML systems are now on the uptick. For instance, in 2020 we saw the first CVE for an ML component in a commercial system and SEI/CERT issued the first vuln note bringing to attention how many of the current ML systems can be subjected to arbitrary misclassification attacks assaulting the confidentiality, integrity, and availability of ML systems. The academic community has been sounding the alarm since 2004, and have routinely shown that ML systems, if not mindfully secured, can be compromised.

Just a matter of time.

In a new report, the International Energy Agency (IEA) says solar is now the cheapest form of electricity for utility companies to build. That’s thanks to risk-reducing financial policies around the world, the agency says, and it applies to locations with both the most favorable policies and the easiest access to financing. The report underlines how important these policies are to encouraging development of renewables and other environmentally forward technologies.

☀️ You love renewable energy. So do we. Let’s nerd out over it together.

The second law of thermodynamics delineates an asymmetry in how physical systems evolve over time, known as the arrow of time. In macroscopic systems, this asymmetry has a clear direction (e.g., one can easily notice if a video showing a system’s evolution over time is being played normally or backward).

In the microscopic world, however, this direction is not always apparent. In fact, fluctuations in microscopic systems can lead to clear violations of the second law of thermodynamics, causing the arrow of time to become blurry and less defined. As a result, when watching a video of a microscopic process, it can be difficult, if not impossible, to determine whether it is being played normally or backwards.

Researchers at University of Maryland developed a machine learning algorithm that can infer the direction of the thermodynamic arrow of time in both macroscopic and microscopic processes. This algorithm, presented in a paper published in Nature Physics, could ultimately help to uncover new physical principles related to thermodynamics.

Circa 2015

Blue Brain Project announces results of a decade’s work.

Circa 2013

A far-flung team is trying to build the first digital lifeform to work out the basic principles of the brain.

Continue reading “Is This Virtual Worm the First Sign of the Singularity?” »

Circa 2018

After 12 years of work, researchers at the University of Manchester in England have completed construction of a “SpiNNaker” (Spiking Neural Network Architecture) supercomputer. It can simulate the internal workings of up to a billion neurons through a whopping one million processing units.

Continue reading “Million-core neuromorphic supercomputer could simulate an entire mouse brain” »

Creating believable characters using high-quality character shader techniques, data scans of Actors, and improved geometry workflow.

Careful manufacturing yields a lithium-ion battery for compact medical devices by.

Katherine Bourzac

Sometimes science is super simple—and super tasty. A classic science experiment demonstrating how to use your microwave and a bar of chocolate to measure the speed of light is making the rounds, with easy-to-follow instructions for replicating the test at home.

🔬 You love badass science experiments. So do we. Let’s play around together.