In addition to moving the vast majority of its infrastructure to AWS, the National Football League (NFL) team will use the breadth and depth of AWS's services, including compute, storage, database, analytics, and ML to drive deep analysis of game footage to inform game strategy, improve operational efficiencies, and accelerate decision-making to advance team performance game-to-game. The Seahawks will combine the weekly NFL Next Gen Stats player tracking data, which tracks the position of the ball and every player 10 times per second, with its own player and club data to develop custom analytics and proprietary statistics. The Seattle Seahawks are relying on AWS's unmatched portfolio of services to discover actionable outcomes from its vast amount of player, team, and business data, enabling them to continue to compete at a championship caliber level. The Seahawks are building a data lake on Amazon Simple Storage Service (Amazon S3) that will combine team stats and NFL data, such as Next Gen Stats player tracking, player health and wellness data, and scouting information to provide deeper visibility into player capabilities, as well as give the coaching staff a single, real-time view of player and team performance. By applying AWS analytics services to the data, the Seahawks will be able to quickly uncover insights to better evaluate talent and develop game plans that take advantage of the team's strengths.

Marketresearch.biz points out that the competitive landscape in the global Mobile Artificial Intelligence market is fairly consolidated. "If you are involved in the Mobile Artificial Intelligence industry or intend to be, then this study will provide you a comprehensive outlook. It's vital information to keep your market knowledge up to date." Mobile Artificial Intelligence Market 2019 report gives key quantification available status of the Mobile Artificial Intelligence Manufacturers and is a consequential wellspring of direction and bearing for organizations and people inspired by the Mobile Artificial Intelligence Industry. In the Mobile Artificial Intelligence Market report, there is an area for rivalry scenes of the ecumenical Mobile Artificial Intelligence Industry.

In addition to moving the vast majority of its infrastructure to AWS, the National Football League (NFL) team will use the breadth and depth of AWS's services, including compute, storage, database, analytics, and ML to drive deep analysis of game footage to inform game strategy, improve operational efficiencies, and accelerate decision-making to advance team performance game-to-game. The Seahawks will combine the weekly NFL Next Gen Stats player tracking data, which tracks the position of the ball and every player 10 times per second, with its own player and club data to develop custom analytics and proprietary statistics. The Seattle Seahawks are relying on AWS's unmatched portfolio of services to discover actionable outcomes from its vast amount of player, team, and business data, enabling them to continue to compete at a championship caliber level. The Seahawks are building a data lake on Amazon Simple Storage Service (Amazon S3) that will combine team stats and NFL data, such as Next Gen Stats player tracking, player health and wellness data, and scouting information to provide deeper visibility into player capabilities, as well as give the coaching staff a single, real-time view of player and team performance. By applying AWS analytics services to the data, the Seahawks will be able to quickly uncover insights to better evaluate talent and develop game plans that take advantage of the team's strengths.

In Japan, one restaurant is exploring artificial intelligence (AI) robotics technology to enable paralyzed employees to remotely pilot robotic waiters. JPMorgan Chase, Microsoft and Ford are hosting virtual career fairs tailored to the needs of neurodiverse candidates. Enterprise Rent-A-Car integrated braille-reader technology into its reservations system for blind employees. Using AI to increase accessibility at work is one of the Gartner Top 10 strategic predictions for 2020 and beyond. The predictions examine how technology is changing the definition of what it means to be human, and IT leaders must be prepared to adapt in a changing environment.

The use of artificial intelligence (AI) and machine learning is on the rise but it's important to know which reputation management processes should and shouldn't be automated. AI, using natural language processing (NLP) models, allows computers to understand and decipher what a human is saying. This is starting to be used by companies in South Africa responding to customers through online channels. It also being employed increasingly in the reputation marketing sector. The main reasons organisations turn to these technologies is that they dramatically improve efficiency, can reduce errors and they save time.

Human brains are singularly special in the animal kingdom, writes Andrew Quixley, Data Science and AI Sales Lead, IBM South Africa. We are the curious, communicative collaborators who rose from a simple foraging existence on the savannahs to build structures of incredible complexity. Sure, other animals are curious, communicative and collaborative too. An octopus will investigate and solve problems; elephants use infrasound to communicate over vast distances; termites collaborate to build structures that are millions of times larger than any individual; but none of these feats comes close to the scale of human complexity. And the key to this complexity is our ability to generate language. There are 7 099 official languages on Earth.

On 17 November, the first edition of the Mint Visionaries series, which seeks to delve into the minds of people inspiring a new future, was kicked off with entrepreneur-philanthropist Bill Gates, who is also the co-chair of the Bill and Melinda Gates Foundation, sharing his thoughts with Wipro Ltd chairman Rishad Premji. The two discussed the challenges of mitigating climate change, eliminating malnutrition, and improving the health and education infrastructure, besides the role of technology, such as artificial intelligence, for social inclusion, something Gates considers a mission statement. Rishad Premji: Climate change will be one of the defining challenges of the 21st century--the impact of weather events, rising sea level, islands getting flooded. It will affect the way people live and potentially impact health and mortality. There is a huge implication of climate change. I know you personally and the Gates Foundation is spending a lot on mitigation--on how to reduce carbon emission. I know you are spending time on breakthrough energy ventures in your personal capacity, investing in technology that can pay off, as well as around adaptation. What are you personally, and through Gates Foundation, doing in these areas? And, what can we do to learn how to leverage science and technology, as governments and as citizens, to be more informed about climate change and its impact, considering that we often have this debate on whether it is real. And, what can come out of it? Bill Gates: I am actually writing a book about climate change.

Bengaluru: Akshaya Patra provides mid-day meals to 1.8 million school children across India. The NGO came to Accenture a couple of years ago with a simple query: how do we feed more children? The consultant looked at the supply chain and then worked with three startups from different domains for a solution. One startup used data from IoT sensors to streamline cooking processes and monitor the quality of food. Another one used machine learning and artificial intelligence (AI) to predict the demand for food. And a third startup used blockchain to put feedback from schools on a distributed ledger in a tamper-proof manner.

Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics. Graph matching problems can be cast as two types of quadratic assignment problems (QAPs): Koopmans-Beckmann's QAP or Lawler's QAP. In our paper, we provide a unifying view for these two problems by introducing new rules for array operations in Hilbert spaces. Consequently, Lawler's QAP can be considered as the Koopmans-Beckmann's alignment between two arrays in reproducing kernel Hilbert spaces (RKHS), making it possible to efficiently solve the problem without computing a huge affinity matrix. Furthermore, we develop the entropy-regularized Frank-Wolfe (EnFW) algorithm for optimizing QAPs, which has the same convergence rate as the original FW algorithm while dramatically reducing the computational burden for each outer iteration. We conduct extensive experiments to evaluate our approach, and show that our algorithm significantly outperforms the state-of-the-art in both matching accuracy and scalability.

Words embedding (distributed word vector representations) have become an essential component of many natural language processing (NLP) tasks such as machine translation, sentiment analysis, word analogy, named entity recognition and word similarity. Despite this, the only work that provides word vectors for Hausa language is that of Bojanowski et al. [1] trained using fastText, consisting of only a few words vectors. This work presents words embedding models using Word2Vec's Continuous Bag of Words (CBoW) and Skip Gram (SG) models. The models, hauWE (Hausa Words Embedding), are bigger and better than the only previous model, making them more useful in NLP tasks. To compare the models, they were used to predict the 10 most similar words to 30 randomly selected Hausa words. hauWE CBoW's 88.7% and hauWE SG's 79.3% prediction accuracy greatly outperformed Bojanowski et al. [1]'s 22.3%.