Month: July 2021

Enlarge (credit: University of Washington)

Thanks to the development of DNA-sequencing technology, it has become trivial to obtain the sequence of bases that encode a protein and translate that to the sequence of amino acids that make up the protein. But from there, we often end up stuck. The actual function of the protein is only indirectly specified by its sequence. Instead, the sequence dictates how the amino acid chain folds and flexes in three-dimensional space, forming a specific structure. That structure is typically what dictates the function of the protein, but obtaining it can require years of lab work.

For decades, researchers have tried to develop software that can take a sequence of amino acids and accurately predict the structure it will form. Despite this being a matter of chemistry and thermodynamics, we've only had limited success—until last year. That's when Google's DeepMind AI group announced the existence of AlphaFold, which can typically predict structures with a high degree of accuracy.

At the time, DeepMind said it would give everyone the details on its breakthrough in a future peer-reviewed paper, which it finally released yesterday. In the meantime, some academic researchers got tired of waiting, took some of DeepMind's insights, and made their own. The paper describing that effort also was released yesterday.

Enlarge (credit: University of Washington)

Thanks to the development of DNA-sequencing technology, it has become trivial to obtain the sequence of bases that encode a protein and translate that to the sequence of amino acids that make up the protein. But from there, we often end up stuck. The actual function of the protein is only indirectly specified by its sequence. Instead, the sequence dictates how the amino acid chain folds and flexes in three-dimensional space, forming a specific structure. That structure is typically what dictates the function of the protein, but obtaining it can require years of lab work.

For decades, researchers have tried to develop software that can take a sequence of amino acids and accurately predict the structure it will form. Despite this being a matter of chemistry and thermodynamics, we've only had limited success—until last year. That's when Google's DeepMind AI group announced the existence of AlphaFold, which can typically predict structures with a high degree of accuracy.

At the time, DeepMind said it would give everyone the details on its breakthrough in a future peer-reviewed paper, which it finally released yesterday. In the meantime, some academic researchers got tired of waiting, took some of DeepMind's insights, and made their own. The paper describing that effort also was released yesterday.

Clearly the quickest way to bypass Microsoft facial recognition, no?

Biometric authentication is a key piece of the tech industry's plans to make the world password-less. But a new method for duping Microsoft's Windows Hello facial-recognition system shows that a little hardware fiddling can trick the system into unlocking when it shouldn't.

Services like Apple's FaceID have made facial-recognition authentication more commonplace in recent years, with Windows Hello driving adoption even farther. Apple only lets you use FaceID with the cameras embedded in recent iPhones and iPads, and it's still not supported on Macs at all. But because Windows hardware is so diverse, Hello facial recognition works with an array of third-party webcams. Where some might see ease of adoption, though, researchers from the security firm CyberArk saw potential vulnerability.

That's because you can't trust any old webcam to offer robust protections in how it collects and transmits data. Windows Hello facial recognition works only with webcams that have an infrared sensor in addition to the regular RGB sensor. But the system, it turns out, doesn't even look at RGB data. Which means that with one straight-on infrared image of a target's face and one black frame, the researchers found that they could unlock the victim's Windows Hello–protected device.