Computers have learned to see the world more clearly in recent years, thanks to some impressive leaps in artificial intelligence. But you might be surprised--and upset--to know what these AI algorithms really think of you. As a recent experiment demonstrated, the best AI vision system might see a picture of your face and spit out a racial slur, a gender stereotype, or a term that impugns your good character. Now the scientists who helped teach machines to see have removed some of the human prejudice lurking in the data they used during the lessons. The changes can help AI to see things more fairly, they say.

This rugged little robot takes a licking and keeps on ticking. When fretting about an imaginary robot apocalypse, it's customary to worry about large robots like Boston Dynamics' Atlas and Spot. But have you considered the potential peril of swarms of tiny, unsquishable robo-insects? The DEAnsect, a fly-swatter-defying soft robot, could inspire all sorts of sci-fi fun, but its creators foresee a helpful future where the tiny bots work together for inspections, repairs or as remote emissaries sent to study real insect colonies. A team at Ecole polytechnique fédérale de Lausanne (EPFL) in Switzerland developed the fast, agile robot. "DEAnsect is propelled by soft artificial muscles: It can be twisted, bent, squeezed, while retaining its functionality," EPFL said.

What's the first thing that comes to mind when you read the words'artificial intelligence'? Do you think of an algorithm that could solve climate change, or of HAL from 2001: A Space Odyssey? My point is: AI has become a loaded term, as has data. People are weary, even fearful, of new technology – but then, that's nothing new. According to one study, 47% of people believe the rate of technological innovation is happening too fast.

Faced with dire reports of looming global catastrophe due to the ongoing climate emergency, many of us are taking a long, hard look at the carbon footprint of our daily lives -- whether it's from the food we eat, how much we drive or how often we fly. But sometimes it's the most intangible of things that may actually be pumping out more carbon than we think -- namely, the surprisingly large carbon footprint that can be associated with creating machine learning models -- the same technology that underlies the apps on our smartphones, digital personal assistants and computers. While using such tech might not necessarily emit all that much carbon, the cause for concern lies behind the carbon impact of the computational processes that go into training AI -- and whether researchers and companies can be well-informed enough to choose less carbon-intensive options. Until now, artificial intelligence researchers have not really had an easily available method to quantify the carbon impact. But that's changing, thanks to a team from Canada's Montreal Institute for Learning Algorithms (MILA), Element AI and Polytechnique Montreal, which recently released a tool designed to help those working in the AI field estimate how much carbon is produced in training their machine learning models.

The team of researchers was composed of Zico Kolter, Shaojie Bai, Devin Wilmott, Mordechai Kornbluth, and Jonathan Mailoa, who won a machine learning Kaggle competition on Predicting Molecular Properties this past September.

PITTSBURGH/DETROIT (Reuters) - Sky's the limit optimism about self-driving cars is giving way to tougher questions about how expensive automotive artificial intelligence will ever make a profit. Those are questions the founders of Argo AI - and automaker partners Ford Motor Co and Volkswagen AG (VOWG_p.DE) - are betting they can answer by taking a different road than more highly valued rivals. The self-driving systems developer led by Bryan Salesky, who got his start developing automated vehicles for a Defense Department sponsored competition 12 years ago, is at the center of a multibillion-dollar bet by its auto giant partners that autonomous vehicle technology must be good for more than replacing taxi drivers. "I hate the word robotaxi," Salesky said in a rare interview at Argo's Pittsburgh headquarters. "There are so many applications and businesses to be built, and (try to) understand which ones are more profitable than others."

"Q: How worried do you think we humans should be that machines will take our jobs? A: It depends what role machine intelligence will play. Machine intelligence in some cases will be useful for solving problems, such as translation. But in other cases, such as in finance or medicine, it will replace people." This Q&A is taken from Tom Standage's description of how he interviewed AI (language model GPT-2) for The Economist The World in 2020. As readers of this column's annual roundup of AI predictions know, this year's first installment of 120 AI predictions for 2020 featured my interview of Amazon AI in which Alexa performed slightly better than the previous year. For the new list of 99 additional predictions, I repeated Standage's question to Alexa, and got the response "Hmm, I'm not sure." The following AI movers and shakers are a lot more confident in what the near future of machine intelligence will look like, from robotic process automation (RPA) to human intelligence augmentation (HIA) to natural language processing (NLP).

Speechly, a startup out of Helsinki that boasts an experienced team of speech recognition and "natural language understanding" experts, has raised €2 million in seed funding to make it easier for developers to add a voice UI to their products. The round is led by Berlin's Cherry Ventures, with participation from Seedcamp, Quantum Angels, Joyance Partners, Social Starts, Tiny.vc, Juha Paananen (co-founder of Nonstop Games, which exited to King), and Nicolas Dessaigne (founder of Algolia). The funding will be used by Speechly to further develop and open up its API to enable non-experts to create voice-enabled applications. "Voice has shown real promise over the past few years but a real breakthrough beyond setting kitchen timers and playing Spotify is yet to be seen," Speechly co-founder and CEO Otto Söderlund tells TechCrunch. "The current fundamental problem of voice assistant platforms is that they tend to fail with more complex user requests and needs".

The countdown to the biggest tech show on earth is on. Held annually in Las Vegas, the 53rd CES is the place to be if you are interested in next-generation innovations. They say "What happens in Vegas, stays in Vegas"- but not in this case. CES, which stands for the Consumer Electronics Show, first took place in 1967. In the 50 years since, thousands of products have been announced, including many that have transformed the lives of people around the world.

With 51 workshops, 1428 accepted papers, and 13k attendees, saying that NeurIPS is overwhelming is an understatement. I did my best to summarize the key trends I got from the conference. This post is generously edited by the wonderful Andrey Kurenkov. Disclaimer: This post doesn't reflect the view of any of the organizations I'm associated with. NeurIPS is huge with a lot to take in, so I might get something wrong.