The world's food system is in disarray. One in ten people is undernourished. One in four is overweight. More than one-third of the world's population cannot afford a healthy diet. Food supplies are disrupted by heatwaves, floods, droughts and wars.

Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains. These approaches circumvent the need for manually annotated training data from the new domain and, instead, generate synthetic question-answer pairs that are used for training. However, existing methods for question generation rely on large amounts of synthetically generated datasets and costly computational resources, which render these techniques widely inaccessible when the text corpora is of limited size. This is problematic as many niche domains rely on small text corpora, which naturally restricts the amount of synthetic data that can be generated. In this paper, we propose a novel framework for domain adaptation called contrastive domain adaptation for QA (CAQA). Specifically, CAQA combines techniques from question generation and domain-invariant learning to answer out-of-domain questions in settings with limited text corpora. Here, we train a QA system on both source data and generated data from the target domain with a contrastive adaptation loss that is incorporated in the training objective. By combining techniques from question generation and domain-invariant learning, our model achieved considerable improvements compared to state-of-the-art baselines.

The "Artificial Intelligence Global Market Report 2021: COVID-19 Growth and Change to 2030" report has been added to ResearchAndMarkets.com's offering. This report provides strategists, marketers and senior management with the critical information they need to assess the global artificial intelligence market. This report focuses on the artificial intelligence market which is experiencing strong growth. The report gives a guide to the artificial intelligence market which will be shaping and changing our lives over the next ten years and beyond, including the markets response to the challenge of the global pandemic. The global artificial intelligence market is expected to grow from $40.17 billion in 2020 to $51.56 billion in 2021 at a compound annual growth rate (CAGR) of 28.4%.

"Voice skins" have become a very popular feature for AI-based voice assistants, to help personalize some of the more anodyne aspects of helpful, yet also kind of bland and robotic, speaking voices you get on services like Alexa. Now a startup that is building voice skins for different companies to use across their services, and for third parties to create and apply as well, is raising some funding to fuel its growth. LOVO, the Berkeley, California-based artificial intelligence (AI) voice & synthetic speech tool developer, this week closed a $4.5 million pre-Series A round led by South Korean Kakao Entertainment along with Kakao Investment and LG CNS, an IT solution affiliate of LG Group. Its previous investor SkyDeck Fund and a private investor, vice president of finance at DoorDash, Michael Kim, also joined the funding. The proceeds will be used to propel its research and development in artificial intelligence and synthetic speech and grow the team.

Law enforcement agencies and government organizations from 24 countries outside the United States used a controversial facial recognition technology called Clearview AI, according to internal company data reviewed by BuzzFeed News. That data, which runs up until February 2020, shows that police departments, prosecutors' offices, universities, and interior ministries from around the world ran nearly 14,000 searches with Clearview AI's software. At many law enforcement agencies from Canada to Finland, officers used the software without their higher-ups' knowledge or permission. After receiving questions from BuzzFeed News, some organizations admitted that the technology had been used without leadership oversight. In March, a BuzzFeed News investigation based on Clearview AI's own internal data showed how the New York–based startup distributed its facial recognition tool, by marketing free trials for its mobile app or desktop software, to thousands of officers and employees at more than 1,800 US taxpayer-funded entities.

Trajectory data represent a trace of an object that changes its position in space over time. This kind of data is complex to handle and analyze, since it is generally produced in huge quantities, often prone to errors generated by the geolocation device, human mishandling, or area coverage limitation. Therefore, there is a need for software specifically tailored to preprocess trajectory data. In this work we propose PTRAIL, a python package offering several trajectory preprocessing steps, including filtering, feature extraction, and interpolation. PTRAIL uses parallel computation and vectorization, being suitable for large datasets and fast compared to other python libraries. BSD 3-Clause "New" or "Revised" License C5 Code versioning system used The traces of moving objects are generally called trajectories and can be informally defined as a temporal sequence of geo-locations of a moving object.

Several metrics have been proposed for assessing the similarity of (abstract) meaning representations (AMRs), but little is known about how they relate to human similarity ratings. Moreover, the current metrics have complementary strengths and weaknesses: some emphasize speed, while others make the alignment of graph structures explicit, at the price of a costly alignment step. In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses. Specifically, our new metrics are able to match contextualized substructures and induce n:m alignments between their nodes. Furthermore, we introduce a Benchmark for AMR Metrics based on Overt Objectives (BAMBOO), the first benchmark to support empirical assessment of graph-based MR similarity metrics. BAMBOO maximizes the interpretability of results by defining multiple overt objectives that range from sentence similarity objectives to stress tests that probe a metric's robustness against meaning-altering and meaning-preserving graph transformations. We show the benefits of BAMBOO by profiling previous metrics and our own metrics. Results indicate that our novel metrics may serve as a strong baseline for future work.

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On a recent afternoon in Maracaibo, Venezuela, Alexander Marinez, who has short-cropped black hair and three-to-four-day stubble, sat in front of his computer tracking herbiboars in the mushroom forests on Fossil Island. He pressed down on his glowing mouse, the newest addition to his otherwise timeworn gaming setup. The pixelated character on his computer screen followed the tracks of a hedgehoglike creature with triangular tusks and herbs growing out of its back. Outside Marinez's one-story house, the sun bore down on the dirt road. His home lies about six miles away from the strait that connects the Caribbean Sea with Lake Maracaibo, one of the world's richest sources of oil. The character inspected a tunnel. Suddenly, the herbiboar appeared, and the character attacked, stunning it.

With AI taking the world by storm, it can be said that sci-fi novels and movies weren't wrong about its adoption and the subsequent rise. What if there were other books and films that showcased a vision of artificial intelligence under the guise of a different genre? But first, "The content expressed here are my opinions and not hard facts." Now that's out of the way; I can share my controversial opinion in a risk-free and carefree manner. Could I please get a drum roll first?