Are machine-learning algorithms biased, wrong, and racist? Essentially rule-based structures for making decisions, machine-learning algorithms play an increasingly large role in our lives. They suggest what we should read and watch, whom we should date, and whether or not we are detained while awaiting trial. Their promise is huge–they can better detect cancers. But they can also discriminate based on the color of our skin or the zip code we live in.

This week IBM, Amazon, and Microsoft all said they would halt sales of facial recognition to US police and called on Congress to impose rules on use of the technology. A police reform bill introduced in the House of Representatives Monday by prominent Democrats in response to weeks of protest over racist policing practices would do just that. But some privacy advocates say its restrictions aren't tight enough and could legitimize the way police use facial recognition today. "We're concerned," says Neema Guliani, senior legislative counsel for the ACLU in Washington, DC, citing evidence that many facial recognition algorithms are less accurate on darker skin tones. She urges a federal ban on facial recognition "unless and until it can be used in a way that respects civil liberties;" Guliani says it's not clear that that is possible.

Amazon announced Wednesday that it is pausing police use of its facial recognition software for one year following nationwide pressure on tech companies to address potential bias. While Amazon did not specify a reason for its decision, racial injustice has been at the forefront of ongoing protests in the wake of the death of George Floyd, who died May 25 after a white Minneapolis police officer pressed his knee into the handcuffed black man's neck for nearly nine minutes. "We've advocated that governments should put in place stronger regulations to govern the ethical use of facial recognition technology, and in recent days, Congress appears ready to take on this challenge," Amazon said in a statement posted to the company's blog website. Researchers have long criticized the technology for producing inaccurate results for people with darker skin, while other studies have shown technological bias against minorities and young people. Nicole Ozer, technology and civil liberties director with the American Civil Liberties Union of Northern California, said in a statement that the organization was "glad the company is finally recognizing the dangers face recognition poses to Black and Brown communities and civil rights more broadly," but that it was not enough to combat the threat to "our civil rights and civil liberties."

Microsoft will not sell facial recognition technology to U.S. police departments until there is a national law to regulate this technology. On Thursday, Microsoft president Brad Smith said on Washington Post Live the company will cease the selling of this technology until a law "grounded in human rights" is put in place. "This is a moment in time that really calls on us to listen more to learn more and, most importantly, to do more," Smith said. The move follows Amazon's Wednesday announcement to suspend police use of its facial recognition technology, Rekognition, for one year after several studies found bias in the software that disproportionately targets people of color. Similarly, in a letter to Congress, IBM CEO Arvind Krishna also said the company would not sell facial recognition services to most customers.

A team of engineers at California State University, Northridge are developing swarms of tiny, insect-sized robots that could help make exploring other planets safer and more efficient. Led by mechanical engineering professor Nhut Ho, the team was just awarded a $538,000 grant from the US Department of Defense to further develop their miniature robotic space explorers. The longterm goal is to create autonomous swarms of small robots that can move across the surface of other planets to collect samples and complete tasks that might otherwise be too complicated for a rover, or too risky for a human astronaut. 'We were inspired by the behaviors that we see in swarms of ants and bees that self-organize, create clever solutions for different tasks, work in groups of different sizes and have the ability to complete the tasks even when members fail,' Ho told CSU Northridge's news blog. Ho's team will collaborate on the project with another group from the Jet Propulsion Laboratory, which recently won a DARPA competition for autonomous robots completing reconnaissance and search and rescue operations in a simulated disaster area.

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the Quantum-assisted Variational Autoencoder has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Latent position network models are a versatile tool in network science; applications include clustering entities, controlling for causal confounders, and defining priors over unobserved graphs. Estimating each node's latent position is typically framed as a Bayesian inference problem, with Metropolis within Gibbs being the most popular tool for approximating the posterior distribution. However, it is well-known that Metropolis within Gibbs is inefficient for large networks; the acceptance ratios are expensive to compute, and the resultant posterior draws are highly correlated. In this article, we propose an alternative Markov chain Monte Carlo strategy---defined using a combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo---that leverages the posterior distribution's functional form for more efficient posterior computation. We demonstrate that these strategies outperform Metropolis within Gibbs and other algorithms on synthetic networks, as well as on real information-sharing networks of teachers and staff in a school district.

This story was published in partnership with The Moonshot Catalog. In the late 1960s, Nobel Prize-winning economist Herbert Simon posed the following thought exercise: Imagine you are an alien from Mars visiting a college on Earth, and you spend a day observing how professors teach their students. Simon argued that you would describe the process as "outrageous." "If we visited an organization responsible for designing, building and maintaining large bridges, we would expect to find employed there a number of trained and experienced professional engineers, thoroughly educated in mechanics and the other laws of nature that determine whether a bridge will stand or fall," he wrote in a 1967 issue of Education Record. "We find no one with a professional knowledge in the laws of learning, or the techniques for applying them," he wrote. Teaching at colleges is often done without any formal training. Mimicry of others who are equally untrained, instinct, and what feels right tend to provide the guidance. As a result, teaching is, to use another building metaphor, not up to code. There are widespread beliefs about the best way to teach and learn that have been proven wrong by science, yet they persist. Reading back over a textbook or taking lecture notes with a highlighter at the ready is often done by students, for instance, but these practices have proven of limited merit, and in some cases even counterproductive in aiding recall.

Video conferencing software maker Zoom shut down the account of Chinese activist Zhou Fengsuo at the behest of the Chinese government. The account was closed because Zhou, and other activists, held a digital event commemorating the Tienanmen Square Massacre. The Tienanmen Square protests were a student movement for democratic rights in the country set against mass privatisation and neoliberal globalism enacted by Deng Xiaoping, according to historian and participant in the 1989 protest Wang Hui. Thousands of people were killed and wounded when, in what came to be known as the Tienanmen Square Massacre. Zhou had paid for a Zoom account associated with the U.S. nonprofit Humanitarian China.

On Wednesday, in a brief blog post, Amazon made a surprising announcement: that it would implement a one-year moratorium on police use of its facial recognition service, Rekognition. The post did not mention the furious nationwide demand for reform in response to the killings of George Floyd, Breonna Taylor, and too many other Black people. But it did cite developments "in recent days" indicating that Congress seemed prepared to implement "stronger regulations to govern the ethical use of facial recognition technology"--regulations that Amazon claims to be advocating for and ready to help shape in the coming year. But Amazon's sudden commitment to ostensibly transformative reform should be taken with a grain of salt hefty enough to unseat a Confederate monument from its rock-solid base. Americans won't receive the privacy and civil rights protections they need because a company like Amazon decides to give them to us.