The development and adoption of advanced technologies including smart automation and artificial intelligence has the potential not only to raise productivity and GDP growth but also to improve well-being more broadly, including through healthier life and longevity and more leisure. Alongside such benefits, these technologies also have the potential to reduce disruption and the potentially destabilizing effects on society arising from their adoption. Tech for Good: Smoothing disruption, improving well-being (PDF–1MB) examines the factors that can help society achieve such benefits and makes a first attempt to calculate the impact of technology adoption on welfare growth beyond GDP. Our modeling suggests that good outcomes for the economy overall and for individual well-being come about when technology adoption is focused on innovation-led growth rather than purely on labor reduction and cost savings through automation. This needs to be accompanied by proactive transition management that increases labor market fluidity and equips workers with new skills. Technology for centuries has both excited the human imagination and prompted fears about its effects. Today's technology cycle is no different, provoking a broad spectrum of hopes and fears.

Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as constrained as games like chess or poker. Instead, word game strategy is defined by the players' understanding of the way words relate to each other. The word game Codenames provides a unique opportunity to investigate common sense understanding of relationships between words, an important open challenge. We propose an algorithm that can generate Codenames clues from the language graph BabelNet or from any of several embedding methods - word2vec, GloVe, fastText or BERT. We introduce a new scoring function that measures the quality of clues, and we propose a weighting term called DETECT that incorporates dictionary-based word representations and document frequency to improve clue selection. We develop BabelNet-Word Selection Framework (BabelNet-WSF) to improve BabelNet clue quality and overcome the computational barriers that previously prevented leveraging language graphs for Codenames. Extensive experiments with human evaluators demonstrate that our proposed innovations yield state-of-the-art performance, with up to 102.8% improvement in precision@2 in some cases.

The business use cases for chatbots are nearly endless, but there are also interesting and impactful ways that chatbots can be used for social good around the world. In particular, our favourite technology is a fantastic medium for improving access to much-needed education in emerging international markets. In many emerging markets access to a smartphone is much more common than access to a laptop or desktop computer. And while there are still gaps in ownership between the women and men of some emerging markets, many people will share a smartphone in order to access the apps and information they require. In order to improve access to education and support services, the smartphone will play a crucial role.

U.S. Sens. Marco Rubio (R-FL), Maria Cantwell (D-WA), Marsha Blackburn (R-TN), and Jacky Rosen (D-NV) introduced legislation Thursday that would create a U.S.-Israel Artificial Intelligence Research and Development Center to further collaborate in AI and contribute to the field's advancement. Specifically, the bill directs the U.S. Secretary of State to establish a joint U.S.-Israel AI Center in the United States to serve as a hub for research and development in AI across the public, private, and education sectors in both nations. "America, and the world, benefit immensely when we engage in joint cooperation and partnerships with Israel, a global technology leader and our most important ally in the Middle East," said Rubio, the Vice Chairman of the Senate Select Committee on Intelligence, and a member of the Senate Committee on Foreign Relations. "I'm proud to lead this legislation to build on current, highly successful bilateral research ties between the U.S. and Israel, as well as help both nations stay ahead of China's ever-growing technology threat." The Senators said the bill would enable America to maintain its technological edge and enhance its competitiveness while leveraging the innovation advantages of its allies.

Self-training is an effective approach to semi-supervised learning. The key idea is to let the learner itself iteratively generate "pseudo-supervision" for unlabeled instances based on its current hypothesis. In combination with consistency regularization, pseudo-labeling has shown promising performance in various domains, for example in computer vision. To account for the hypothetical nature of the pseudo-labels, these are commonly provided in the form of probability distributions. Still, one may argue that even a probability distribution represents an excessive level of informedness, as it suggests that the learner precisely knows the ground-truth conditional probabilities. In our approach, we therefore allow the learner to label instances in the form of credal sets, that is, sets of (candidate) probability distributions. Thanks to this increased expressiveness, the learner is able to represent uncertainty and a lack of knowledge in a more flexible and more faithful manner. To learn from weakly labeled data of that kind, we leverage methods that have recently been proposed in the realm of so-called superset learning. In an exhaustive empirical evaluation, we compare our methodology to state-of-the-art self-supervision approaches, showing competitive to superior performance especially in low-label scenarios incorporating a high degree of uncertainty.

Differential equations in general and neural ODEs in particular are an essential technique in continuous-time system identification. While many deterministic learning algorithms have been designed based on numerical integration via the adjoint method, many downstream tasks such as active learning, exploration in reinforcement learning, robust control, or filtering require accurate estimates of predictive uncertainties. In this work, we propose a novel approach towards estimating epistemically uncertain neural ODEs, avoiding the numerical integration bottleneck. Instead of modeling uncertainty in the ODE parameters, we directly model uncertainties in the state space. Our algorithm - distributional gradient matching (DGM) - jointly trains a smoother and a dynamics model and matches their gradients via minimizing a Wasserstein loss. Our experiments show that, compared to traditional approximate inference methods based on numerical integration, our approach is faster to train, faster at predicting previously unseen trajectories, and in the context of neural ODEs, significantly more accurate.

Africa Data School is a practical Data school in Africa that offers training in Data science, Machine learning, Artificial intelligence and Natural Language Processing. The Africa Data School community invites you to our virtual Open day Wednesday 23rd June 2021 from 4:00 pm to 5:00 pm EAT. Don't miss out on the opportunity to hangout with Africa Data School as they give you first hand experience of the realm of Data Science.

Data Bridge Market Research published a new report, titled, "Artificial intelligence in medical imaging Market". The report offers an extensive analysis of key growth strategies, drivers, opportunities, key segments, and competitive landscape. This study is a helpful source of information for market players, investors, VPs, stakeholders, and new entrants to gain a thorough understanding of the industry and determine steps to be taken to gain a competitive advantage. Businesses can bring about an absolute knowhow of general market conditions and tendencies with the information and data covered in the large scale Artificial intelligence in medical imaging market survey report. To get knowledge of all the above things, this market report is made transparent, wide-ranging and supreme in quality.

Welcome back to our "Life in 2050" series! In previous installments, we looked at how accelerating change and environmental issues will affect the future of warfare, economy, education, everyday living, and space exploration (in two installments). Today, we look at how people will get from A to B by mid-century, whether it's across town, from one city to the next, or one continent to the next. Transportation is another sector that is expected to undergo a major revolution in the coming decades. In several respects, this revolution is already underway thanks to the introduction of autonomous vehicles, the wide-scale adoption of electric vehicles, the growth of renewable energy, and the advent of commercial spaceflight. Between now and 2050, these technologies and trends will accelerate and lead to the creation of new transportation infrastructure, radically different from what we know today. Of course, the infrastructure of tomorrow will be built on existing transportation networks.

Artificial Intelligence in Fintech Market Size and Forecast 2021-2028 by Verified Market Research specialize in market strategy, market direction, expert opinions, and knowledgeable insight into the global market. The report is a combination of critical information including the competitive landscape; global, regional, and country-specific market size; Market participants; Market growth analysis; Market share; Analysis of opportunities, recent developments, and growth in segmentation. The report also provides other information and thoughtful facts such as historical data, sales, revenue and global market share of Artificial Intelligence in Fintech, product scope, market overview, opportunities, driving force and market share of Artificial Intelligence in Fintech. One of the important factors that make this report interesting is its comprehensive overview of the industry's competitive landscape. The report includes upstream raw materials and downstream needs analyses.