Although yoga is a multi-component practice to hone the body and mind and be known to reduce anxiety and depression, there is still a gap in understanding people's emotional state related to yoga in social media. In this study, we investigate the causal relationship between practicing yoga and being happy by incorporating textual and temporal information of users using Granger causality. To find out causal features from the text, we measure two variables (i) Yoga activity level based on content analysis and (ii) Happiness level based on emotional state. To understand users' yoga activity, we propose a joint embedding model based on the fusion of neural networks with attention mechanism by leveraging users' social and textual information. For measuring the emotional state of yoga users (target domain), we suggest a transfer learning approach to transfer knowledge from an attention-based neural network model trained on a source domain. Our experiment on Twitter dataset demonstrates that there are 1447 users where "yoga Granger-causes happiness".

Project Loon is using balloons such as this to set up an aerial wireless network for telecommunications.Credit: Loon The goal of an autonomous machine is to achieve an objective by making decisions while negotiating a dynamic environment. Given complete knowledge of a system's current state, artificial intelligence and machine learning can excel at this, and even outperform humans at certain tasks -- for example, when playing arcade and turn-based board games1. But beyond the idealized world of games, real-world deployment of automated machines is hampered by environments that can be noisy and chaotic, and which are not adequately observed. The difficulty of devising long-term strategies from incomplete data can also hinder the operation of independent AI agents in real-world challenges. Writing in Nature, Bellemare et al.2 describe a way forward by demonstrating that stratospheric balloons, guided by AI, can pursue a long-term strategy for positioning themselves about a location on the Equator, even when precise knowledge of buffeting winds is not known.

Machine learning involves training a model with data so that it learns to spot or predict features. The Google team pick on the example of training a machine learning system to predict the course of a pandemic. Epidemiologists have built detailed models of the way a disease spreads from infected individuals to susceptible individuals, but not to those who have recovered and so are immune. Key factors in this spread are the rate of infection, often called R0, and length of time, D, that an infected individual is infectious. Obviously, a disease can spread more widely when it is more infectious and when people are infectious for longer.

If DeepMind's methods can be refined, he and other researchers said, they could speed the development of new drugs as well as efforts to apply existing medications to new viruses and diseases. The breakthrough arrives too late to make a significant impact on the coronavirus. But researchers believe DeepMind's methods could accelerate the response to future pandemics. Some believe it could also help scientists gain a better understanding of genetic diseases along the lines of Alzheimer's or cystic fibrosis. Still, experts cautioned that this technology would affect only a small part of the long process by which scientists identify new medicines and analyze disease.

A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."

Social Networking Sites (SNS) are one of the most important ways of communication. In particular, microblogging sites are being used as analysis avenues due to their peculiarities (promptness, short texts...). There are countless researches that use SNS in novel manners, but machine learning (ML) has focused mainly in classification performance rather than interpretability and/or other goodness metrics. Thus, state-of-the-art models are black boxes that should not be used to solve problems that may have a social impact. When the problem requires transparency, it is necessary to build interpretable pipelines. Arguably, the most decisive component in the pipeline is the classifier, but it is not the only thing that we need to consider. Despite that the classifier may be interpretable, resulting models are too complex to be considered comprehensible, making it impossible for humans to comprehend the actual decisions. The purpose of this paper is to present a feature selection mechanism (the first step in the pipeline) that is able to improve comprehensibility by using less but more meaningful features while achieving a good performance in microblogging contexts where interpretability is mandatory. Moreover, we present a ranking method to evaluate features in terms of statistical relevance and bias. We conducted exhaustive tests with five different datasets in order to evaluate classification performance, generalisation capacity and actual interpretability of the model. Our results shows that our proposal is better and, by far, the most stable in terms of accuracy, generalisation and comprehensibility.

How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.

TLDR generates one-sentence summaries of computer-science papers on the scientific search engine Semantic Scholar.Credit: Agnese Abrusci/Nature The creators of a scientific search engine have unveiled software that automatically generates one-sentence summaries of research papers, which they say could help scientists to skim-read papers faster. The free tool, which creates what the team call TLDRs (the common Internet acronym for'Too long, didn't read'), was activated this week for search results at Semantic Scholar, a search engine created by the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington. For the moment, the software generates sentences only for the ten million computer-science papers covered by Semantic Scholar, but papers from other disciplines should be getting summaries in the next month or so, once the software has been fine-tuned, says Dan Weld, who manages the Semantic Scholar group at AI2 and led the work. Preliminary testing suggests that the tool helps readers to sort through search results faster than viewing titles and abstracts, especially on mobile phones, he says. "People seem to really like it."

On a summer night in Dallas in 2016, a bomb-handling robot made technological history. Police officers had attached roughly a pound of C-4 explosive to it, steered the device up to a wall near an active shooter and detonated the charge. In the explosion, the assailant, Micah Xavier Johnson, became the first person in the United States to be killed by a police robot. Afterward, then-Dallas Police Chief David Brown called the decision sound. Before the robot attacked, Mr. Johnson had shot five officers dead, wounded nine others and hit two civilians, and negotiations had stalled.

This post is dedicated to John Horton Conway and Tom Fawcett, who recently passed away, for their noted contributions to the field of cellular automata and machine learning. With the advent of fast data streams, real-time machine learning has become a challenging task. They can be affected by the concept drift effect, by which stream learning methods have to detect changes and adapt to these evolving conditions. Several emerging paradigms such as the so-called "Smart Dust", "Utility Fog", "TinyML" or "Swarm Robotics" are in need for efficient and scalable solutions in real-time scenarios. Cellular Automata (CA), as low-bias and robust-to-noise pattern recognition methods with competitive classification performances, meet the requirements imposed by the aforementioned paradigms mainly due to their simplicity and parallel nature.