Is it possible that lakes and forests might have rights before robots? Voters in Toledo have granted "irrevocable rights for the Lake Erie Ecosystem to exist, flourish and naturally evolve" which, according to this story, would give it legal standing to file lawsuits to protect itself from polluters (through the mouthpiece of a human guardian). It's an amazingly bold statement that is rife with thorny questions. Humans have had say over nature ever since Adam and Eve, and most political and cultural uses or abuses have been based on the shifting perspectives of their progeny. Nature is something "out there" that only gains meaning or purpose when defined by us.

Year 2020 does have a sci-fi ring to it. Perhaps that's one reason AI has so many people nervous. From your smart phone to your Google search to your Netflix and Spotify recommendations, AI shapes modern US work and life in many ways. Despite its growing ubiquity, fear of AI remains. The 2019 Artificial Intelligence--American Attitudes and Trends report from the Future of Humanity Institute at the University of Oxford found that "more Americans think that high-level machine intelligence will be harmful to humanity" than those who think it will be beneficial.

Despite their names, artificial intelligence technologies and their component systems, such as artificial neural networks, don't have much to do with real brain science. I'm a professor of bioengineering and neurosciences interested in understanding how the brain works as a system – and how we can use that knowledge to design and engineer new machine learning models. In recent decades, brain researchers have learned a huge amount about the physical connections in the brain and about how the nervous system routes information and processes it. But there is still a vast amount yet to be discovered. At the same time, computer algorithms, software and hardware advances have brought machine learning to previously unimagined levels of achievement.

EU presidency extends access to free AI course across EU Jan Petter Myklebust 13 December 2019 European Union employment ministers have endorsed a proposal from Finland's Presidency of the Council of the EU to provide European citizens with free access to a successful online course on basic artificial intelligence (AI), developed and run by the University of Helsinki in partnership with private firm Reaktor. To achieve this, the course on "Elements of AI" will be made available in all official EU languages. The Finnish government will fund the project with €1.7 million (US$1.9 million) from Finland's Ministry of Economic Affairs and Employment as part of the EU Council presidency's effort to democratise awareness of AI and develop people's skills for jobs of the future. At the launch of the initiative in Brussels on 10 December, Finland's Minister of Employment Timo Harakka said: "Our investment has three goals: we want to equip EU citizens with digital skills for the future; we wish to increase practical understanding of what artificial intelligence is; and by doing so, we want to give a boost to the digital leadership of Europe." "As our presidency ends, we want to offer something concrete. It's about one of the most pressing challenges facing Europe and Finland today: how to develop our digital literacy," Harakka said.

The most exciting thing is that we are able to predict risk of new dementia diagnosis up to eight years in advance. BOSTON – A team of scientists from Massachusetts General Hospital (MGH) has developed a software-based method of scanning electronic health records (EHRs) to estimate the risk that a healthy person will receive a dementia diagnosis in the future. Their algorithm uses machine learning to first build a list of key clinical terms associated with cognitive symptoms identified by clinical experts. Next, they used national language processing (NLP) to comb through EHRs looking for those terms. Finally, they used those results to estimate patients' risk of developing dementia.

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.