One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 77.24% in F1-score and 67.43% in Exact Match on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model based on the transformer architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further explore the Vietnamese machine reading comprehension task and related tasks such as question answering, question generation, and natural language inference.

Ukraine's defense ministry on Saturday began using Clearview AI's facial recognition technology, the company's chief executive said after the US startup offered to uncover Russian assailants, combat misinformation, and identify the dead. Ukraine is receiving free access to Clearview AI's powerful search engine for faces, letting authorities potentially vet people of interest at checkpoints, among other uses, added Lee Wolosky, an adviser to Clearview and former diplomat under US presidents Barack Obama and Joe Biden. The plans started forming after Russia invaded Ukraine and Clearview Chief Executive Hoan Ton-That sent a letter to Kyiv offering assistance, according to a copy seen by Reuters. Clearview said it had not offered the technology to Russia, which calls its actions in Ukraine a "special operation". Ukraine's Ministry of Defense did not reply to requests for comment.

Football clubs in Australia will use artificial intelligence to combat online … will use machine-learning based software to monitor social media …

The construction and theoretical analysis of the most popular universally consistent nonparametric density estimators hinge on one functional property: smoothness. In this paper we investigate the theoretical implications of incorporating a multi-view latent variable model, a type of low-rank model, into nonparametric density estimation. To do this we perform extensive analysis on histogram-style estimators that integrate a multi-view model. Our analysis culminates in showing that there exists a universally consistent histogram-style estimator that converges to any multi-view model with a finite number of Lipschitz continuous components at a rate of $\widetilde{O}(1/\sqrt[3]{n})$ in $L^1$ error. In contrast, the standard histogram estimator can converge at a rate slower than $1/\sqrt[d]{n}$ on the same class of densities. We also introduce a new nonparametric latent variable model based on the Tucker decomposition. A rudimentary implementation of our estimators experimentally demonstrates a considerable performance improvement over the standard histogram estimator. We also provide a thorough analysis of the sample complexity of our Tucker decomposition-based model and a variety of other results. Thus, our paper provides solid theoretical foundations for extending low-rank techniques to the nonparametric setting

The following is the "100 most noteworthy artificial intelligence companies" compiled by the AI generation (tencentAI) (in alphabetical order by company name): Inspired by recent discoveries about the way the brain processes information, Cortical.io's Retina engine converts language into semantic fingerprints, and then compares the semantic relatedness of any two texts by comparing the degree of overlap of the fingerprints. CrowdFlower is a human intervention training platform for data science teams that helps clients generate high-quality custom training data. The CrowdFlower platform supports a range of use cases including self-driving cars, personal assistants, medical image tagging, content classification, social data analysis, CRM data improvement, product classification and search relevance, and more. Headquartered in San Francisco, CrowdFlower's clients include Fortune 500 and data-driven companies.

The main contribution of this paper is a novel sensory feedback control law for an octopus arm. The control law is inspired by, and helps integrate, several observations made by biologists. The proposed control law is distinct from prior work which has mainly focused on open-loop control strategies. Several analytical results are described including characterization of the equilibrium and its stability analysis. Numerical simulations demonstrate life-like motion of the soft octopus arm, qualitatively matching behavioral experiments. Quantitative comparison with bend propagation experiments helps provide the first explanation of such canonical motion using a sensory feedback control law. Several remarks are included that help draw parallels with natural pursuit strategies such as motion camouflage or classical pursuit.

As advances in computer graphics and natural language AI have made it easier to create realistic-seeming virtual humans, some companies are attempting to turn these artificial personas into influencers that can work on behalf of brands and cultivate a fan base. One such company is New Zealand-based startup Uneeq, maker of a digital human called Sophie, who has partnered with brands like BMW, Deutsche Telekom and IBM. Uneeq recently launched a new collection of nonfungible token (NFT) art as a way for Sophie to build her profile and better integrate her into the constellation of emerging technologies known as Web3. Sophie isn't the first AI persona to sell NFTs; that distinction likely belongs to similarly named Sophia the robot, who managed to sell a "self-portrait" for nearly $700,000 last year. But Uneeq CEO Danny Tomsett said Sophie's NFTs aren't necessarily as much about raising revenue as building a fan community around Sophie and boosting her status as a tech-savvy influencer.

In December 2021 and January 2022, we asked recipients of our Data and AI Newsletters to participate in our annual survey on AI adoption. We were particularly interested in what, if anything, has changed since last year. Are companies farther along in AI adoption? Do they have working applications in production? Are they using tools like AutoML to generate models, and other tools to streamline AI deployment? We also wanted to get a sense of where AI is headed. The hype has clearly moved on to blockchains and NFTs. AI is in the news often enough, but the steady drumbeat of new advances and techniques has gotten a lot quieter. Compared to last year, significantly fewer people responded. This year's survey ran during the holiday season (December 8, 2021, to January 19, 2022, though we received very few responses in the new year); last year's ran from January 27, 2021, to February 12, 2021. Pandemic or not, holiday schedules no doubt limited the number of respondents.

The thriving industry of Data Science is continuously evolving with the technological advancements in Machine Learning and Artificial intelligence. This has opened up whole new avenues for Data Scientists worldwide. Professionals who can handle Big Data and have the necessary knowledge required for understanding, analysing and processing data are in high demand in the job market. However, there is one important thing that also needs to be addressed is the raging problem caused by the gender gap in this sector. As per the statistical report from the Boston Consulting Group, only 15 to 22 per cent of the Data Science-related professional roles are occupied by women.

Previous partial permutation synchronization (PPS) algorithms, which are commonly used for multi-object matching, often involve computation-intensive and memory-demanding matrix operations. These operations become intractable for large scale structure-from-motion datasets. For pure permutation synchronization, the recent Cycle-Edge Message Passing (CEMP) framework suggests a memory-efficient and fast solution. Here we overcome the restriction of CEMP to compact groups and propose an improved algorithm, CEMP-Partial, for estimating the corruption levels of the observed partial permutations. It allows us to subsequently implement a nonconvex weighted projected power method without the need of spectral initialization. The resulting new PPS algorithm, MatchFAME (Fast, Accurate and Memory-Efficient Matching), only involves sparse matrix operations, and thus enjoys lower time and space complexities in comparison to previous PPS algorithms. We prove that under adversarial corruption, though without additive noise and with certain assumptions, CEMP-Partial is able to exactly classify corrupted and clean partial permutations. We demonstrate the state-of-the-art accuracy, speed and memory efficiency of our method on both synthetic and real datasets.