Or at least, they do in Google's latest machine learning experiment, the awe-inspiring Blob Opera, which will see a chorus of four adorable, colorful blobs serenade you with spine-tingling operatic music. Drag a blob up or down, and you'll change what pitch they sing in; drag them from side to side, and you'll change the vowel sound. Each blob will also harmonize with the others, in what can only be described as magical. The Blob Opera just sounds beautiful, with soaring harmonies ringing out from each blob. Four actual opera singers -- Christian Joel (tenor), Frederick Tong (bass), Joanna Gamble (mezzo‑soprano), and Olivia Doutney (soprano) -- recorded 16 hours of singing (Ingunn Gyda Hrafnkelsdottir and John Holland-Avery also contributed), but it's not their actual voices you're hearing when the blobs sing.

A new feature from Amazon Web Services will alert developers to potential bias in machine learning algorithms, part of a larger effort by the tech industry to keep automated predictions from discriminating against women, people of color and other underrepresented groups. The feature, SageMaker Clarify, was announced at the AWS re:Invent conference Tuesday as a new component of the AWS SageMaker machine learning platform. The technology analyzes the data used to train machine learning models for telltale signs of bias, including data sets that don't accurately reflect the larger population. It also analyzes the machine learning model itself to help ensure the accuracy of the resulting predictions. A 2018 MIT study found that the presence of a disproportionate number of white males in data sets used to train facial recognition algorithms led a larger number of errors in recognizing women and people of color.

When you are angry, do you scowl, cry or even laugh? To what extent do your facial movements depend on the situation you are in -- whether you are in a formal meeting, say, or at home with your family? And do other people around the world express anger in such situations in the same way? These questions are at the centre of a contentious scientific debate about the nature of emotion that has raged for more than a century. Writing in Nature, Cowen et al.1 enter the fray.

In addition to hoverboards, unicycles, mopeds, and dog-pulled skateboards – as well as an occasional car or bike – San Franciscans will soon be sharing the roads with driverless robocars, zipping through traffic without the added weight of human passengers. Last October Cruise LLC received a permit to test up to five vehicles at a time within City limits without a human in the driver's seat. Cruise is the fifth company allowed to conduct such field work in California. San Franciscans have seen plenty of self-driving cars, but always with human passengers. Usually identifiable by prominent logos and strangely protruding sensors, autonomous vehicles (AV) have been approved for testing on California's roadways since 2014.

Welcome to the future: Step inside this toaster. On Monday, the autonomous vehicle company Zoox--acquired by Amazon over the summer for a reported $1.2 billion--rolled out its robotaxi. The design, which has been in development for six years, may look familiar. Almost every autonomous vehicle concept revealed over the past few years--by carmakers, engineers, ride-hailers, and startups--has been a neat, rectangular box. In this case, form equals function.

Understanding how Google's search works is a key to figuring out why so many companies find it nearly impossible to compete and, in fact, go out of their way to cater to its needs. Every search request provides Google with more data to make its search algorithm smarter. Google has performed so many more searches than any other search engine that it has established a huge advantage over rivals in understanding what consumers are looking for. That lead only continues to widen, since Google has a market share of about 90 percent. Google directs billions of users to locations across the internet, and websites, hungry for that traffic, create a different set of rules for the company.

Saiph Savage, director of the human-computer interaction lab at West Virginia University, advocates for the workers who put in the time to develop training data for artificial intelligence. Many of the most successful and widely used machine-learning models are trained with the help of thousands of low-paid gig workers. Millions of people around the world earn money on platforms like Amazon Mechanical Turk, which allow companies and researchers to outsource small tasks to online crowdworkers. According to one estimate, more than a million people in the US alone earn money each month by doing work on these platforms. Around 250,000 of them earn at least three-quarters of their income this way.

Boston Dynamics is widely recognized for pioneering the field of agile robots that are inspired by animals. The legged robots are built to remain balanced as they maneuver through rocky trails, up and down staircases or through narrow passages. Several videos of the company's robotic dog Spot, cat Cheetah and humanoid Atlas have gone viral, stoking admiration -- and terror -- across the Internet.

Many of the most successful and widely used machine learning models are trained with the help of thousands of low-paid gig workers. Millions of people around the world earn money on platforms like Amazon Mechanical Turk, which allow companies and researchers to outsource small tasks to online crowdworkers. According to one estimate, more than a million people in the US alone earn money each month by doing work on these platforms. Around 250,000 of them earn at least three quarters of their income this way. But despite many working for some of the richest AI labs in the world, they are paid below minimum wage and given no opportunities to develop their skills.

Director of civil rights group Big Brother Watch, Silkie Carlo, said: "To see a supposedly ethical company secretly using rights-abusive tech like facial recognition on its customers in the UK is deeply chilling.