The 'Doughnut' of social and planetary boundaries has emerged as a popular framework for assessing environmental and social sustainability. Here, we provide a proof-of-concept analysis that shows how machine learning (ML) methods can be applied to a simple macroeconomic model of the Doughnut. First, we show how ML methods can be used to find policy parameters that are consistent with 'living within the Doughnut'. Second, we show how a reinforcement learning agent can identify the optimal trajectory towards desired policies in the parameter space. The approaches we test, which include a Random Forest Classifier and $Q$-learning, are frugal ML methods that are able to find policy parameter combinations that achieve both environmental and social sustainability. The next step is the application of these methods to a more complex ecological macroeconomic model.

Breakthroughs, discoveries, and DIY tips sent every weekday. Billions of light-years away, a cosmic jet bearing a striking resemblance to the eye of Sauron from the Lord of the Rings is swirling at the heart of a very active galaxy. The unique cosmic body was spotted thanks to 15 years of observations using the Earth-based Very Long Baseline Ar-ray and is helping scientists better understand the anatomy of cosmic jets,powerful beams of plasma and energy that come from black holes, neutron stars, and other celestial bodies. The unique attributes of this "Eye of Sauron" cosmic jet is detailed in a study published August 12 in the journal Astronomy & Astrophysics. "When we reconstructed the image, it looked absolutely stunning," Yuri Kovalev, study co-author and astrophysicist at the Max Planck Institute for Radio Astronomy, said in a statement.

The Trump administration is mobilizing 700 Marines to respond to protests triggered by Immigration and Customs Enforcement raids in Los Angeles. Aerial footage of protests downtown on Saturday, Sunday, and Monday evening seemed to show crowds of a few hundred people, while another pro-immigration rally earlier on Monday reportedly drew thousands. With 4,000 members of the National Guard already deployed to the city, in addition to ICE and local police, armed law-enforcement officers appear to outnumber actual protesters, who have remained largely nonviolent (aside from the ones who set several robotic taxicabs on fire). What follows is a speculative attempt to convey the emotional truth of what these troops might encounter. We woke up at dawn, heads pounding, in a hut lit by a single bulb. We were two clicks outside the perimeter and three clicks from the nearest Lamill or Blue Bottle--a desperate goddamn distance, no no no this can't be happening.

Fair decision making has largely been studied with respect to a single decision. In this paper we investigate the notion of fairness in the context of sequential decision making where multiple stakeholders can be affected by the outcomes of decisions, and where decision making may be informed by additional constraints and criteria beyond the requirement of fairness. In this setting, we observe that fairness often depends on the history of the sequential decision-making process and not just on the current state. To advance our understanding of this class of fairness problems, we define the notion of non-Markovian fairness in the context of sequential decision making. We identify properties of non-Markovian fairness, including notions of long-term, anytime, periodic, and bounded fairness. We further explore the interplay between non-Markovian fairness and memory, and how this can support construction of fair policies in sequential decision-making settings.

Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities. However, previous work does little to explore what environment state information is provided to LLM actors via language. Exhaustively describing high-dimensional states can impair performance and raise inference costs for LLM actors. Previous LLM actors avoid the issue by relying on hand-engineered, task-specific protocols to determine which features to communicate about a state and which to leave out. In this work, we propose Brief Language INputs for DEcision-making Responses (BLINDER), a method for automatically selecting concise state descriptions by learning a value function for task-conditioned state descriptions. We evaluate BLINDER on the challenging video game NetHack and a robotic manipulation task. Our method improves task success rate, reduces input size and compute costs, and generalizes between LLM actors.

Researchers have gained a first insight into how the brain structures higher-level information. By extracting and analysing data from a neural network of grid cells, they found that the collective neural activity is shaped like the surface of a doughnut. The study, from the Norwegian University of Science and Technology's (NTNU) Kavli Institute for Systems Neuroscience and collaborators, is published in Nature, High-level brain functions result from the orchestration of activity between many thousands of neurons in neural networks. For grid cells, these neural network conversations result in our understanding of location, our capacity to navigate, and our mental maps. "This discovery provides one of the first insights into how brain cells operate collectively, as a society. It provides an unprecedented glimpse into how large networks of neurons produce properties that cannot be inferred from the activities of single cells. These collective codes are the clue to all high-level cognitive functions of the brain," said Edvard Moser, a professor of neuroscience and co-director of the Norwegian University of Science and Technology's(NTNU)Kavli Institute for Systems Neuroscience.

When prompted to generate "a mural of a blue pumpkin on the side of a building," OpenAI's new deep ... [ ] learning model DALL-E produces this series of original images. OpenAI has done it again. Earlier this month, OpenAI--the research organization behind last summer's much-hyped language model GPT-3--released a new AI model named DALL-E. While it has generated less buzz than GPT-3 did, DALL-E has even more profound implications for the future of AI. In a nutshell, DALL-E takes text captions as input and produces original images as output. For instance, when fed phrases as diverse as "a pentagonal green clock," "a sphere made of fire" or "a mural of a blue pumpkin on the side of a building," DALL-E is able to generate shockingly accurate visual renderings.

With a tank-like continuous track and an angular arm reminiscent of the Pixar lamp, the lightweight PackBot robot was designed to seek out, defuse and dispose of the improvised explosive devices, or IEDs, that killed and injured thousands of coalition soldiers during the wars in Iraq and Afghanistan. Bomb disposal was and is highly dangerous work, but the robot could take on the riskiest parts while its human team controlled it remotely from a safer distance. US Army explosive ordinance disposal technician Phillip Herndon was assigned a PackBot during his first tour in Iraq. Herndon's team named their robot Duncan, after a mission when the robot glitched and began spinning in circles, or doughnuts (doughnuts led to Dunkin Donuts, hence Duncan). His fellow bomb disposal techs named theirs too, and snapped photos of themselves next to robots holding Xbox controllers, dressed in improvised costumes or posing with a drink in their claws.

Is the art world ready for AI? Christie's held the first-ever auction of art created by artificial intelligence. By Thomas Mulier Four months ago, Christie's said it held the first-ever auction of art created by artificial intelligence. The $432,500 sale sparked a controversy among critics over whether it's really AI-generated if a human was involved in making the portrait. Next month, a new Sotheby's sale in London may end the dispute and could even presage a boom in AI-generated art, which until now has been relatively scarce. The firm will take bids for a piece made by German computer scientist Mario Klingemann on March 6 in London.

Many of us have already decided that things will be different in 2018. We'll eat better, get more exercise, save more money or finally get around to decluttering those closets. But by the time February rolls around, most of us – perhaps as many as 80 percent of the Americans who make New Year's resolutions – will have already given up. Why does our self-control falter, so often leaving us to revert to our old ways? The answer to this question has consequences beyond our waistlines and bank balances.