Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach requires specific assumptions for modelling uncertainty. We remove this constraint by presenting a purely data-driven framework for modelling probabilistic drift in flexible environments. Using ocean circulation model simulations, we generate probabilistic trajectories of object location by simulating uncertainty in the initial object position. We train an emulator of probabilistic drift over one day given perfectly known velocities and observe good agreement with numerical simulations. Several loss functions are tested. Then, we strain our framework by training models where the input information is imperfect. On these harder scenarios, we observe reasonable predictions although the effects of data drift become noticeable when evaluating the models against unseen flow scenarios.

Accenture Completes Acquisition of Mudano, Enhancing Its Analytics and Data Transformation Services to Financial Services Firms in the UK LONDON; Feb. 28, 2020 – Accenture (NYSE: ACN) has completed its acquisition of Mudano, a strategic data consultancy to U.K. financial services firms. The acquisition enhances Accenture's analytics, data and artificial intelligence (AI) transformation capabilities. Terms of the transaction were not disclosed. Mudano's team of industry-focused data professionals will join Accenture Applied Intelligence. Mudano was founded in 2014 and is headquartered in London, with a presence in Edinburgh, Scotland. About Accenture Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations.

Accenture will soon acquire Mudano, a strategic data consultancy to UK financial services firms. While the terms of the transaction have not been disclosed, the intent of the acquisition is to enhance Accenture's analytics, data, and artificial intelligence (AI) transformation capabilities. After the acquisition, Mudano's team of data professionals will join Accenture Applied Intelligence, which employs more than 20,000 professionals worldwide who help clients scale artificial intelligence, including 6,000 data scientists, data engineers, and artificial intelligence professionals. Ed Broussard, Mudano CEO, said, "Accenture's reputation for excellence and large-scale delivery will enable us to help clients realize the benefits of data transformation -- from setting the strategy and building the culture to leveraging the game-changing insights that data analytics can bring. We are excited to become part of one of the world's leading companies and look forward to the opportunities this will bring for our employees and clients."

Break through the barriers to scaling AI and follow our roadmap to maximise the ROI of your AI projects. I bet you thought the holidays were over, but don't be too blue friends, I've got exciting news! We combine recommendations based our own empirical insights from doing this work with clients, along with learnings from our latest research, AI: Built To Scale. If you work with me as a client or a colleague, you know the subject of scaling AI is important to me and you'll have long heard me talk (or perhaps preach!?) about the importance of getting to production and avoiding the continuous loop of proof of concept. It feels timely for us to talk about this now given the surge in conversations around scaling AI from other industry leaders and analysts.

Failure to Scale Artificial Intelligence Could Put 75% of Organizations Out of Business, Accenture Study Shows Companies that shift from AI experimentation to execution achieve lasting ROI and competitive agility NEW YORK; Nov. 14, 2019 – Three-quarters of C-level executives believe if they don't move beyond experimentation to aggressively deploy artificial intelligence (AI) across their organizations they risk going out of business by 2025, according to a newly released study from Accenture (NYSE: ACN). The report, titled "AI: Built to Scale" and produced by Accenture Strategy and Accenture Applied Intelligence, is based on a global survey of 1,500 C-level executives across 16 industries designed to understand how companies are implementing AI across their organizations. The research found 84% of C-level executives believe they won't achieve their business strategy without scaling AI, yet only 16% have made the shift from mere experimentation to creating an organization powered by robust AI capabilities. As a result, this small group of top performers is achieving nearly three times the return from AI investments as their lower-performing counterparts. The report reveals the secret to success for these top performers centers around three key elements: a strong data foundation; multiple dedicated AI teams; and a C-suite-led commitment to strategic, organization-wide AI deployment.

The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018. This report summarizes the The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018.  IEA/AIE 2018 continued the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation a robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions.

Artificial intelligence (AI) is poised to impact nearly every industry, and businesses that don't take immediate steps to upskill their workforces to collaborate with machines will miss out on revenue, according to a recent report from Accenture Strategy. If businesses invest in AI and human-machine collaboration at the same rate as top-performing companies, they could boost revenues by 38% by 2022, the report found, lifting profits by $4.8 trillion globally. These businesses could also raise employment levels by 10% in that timeframe. Business leaders are optimistic about the changes that AI can bring to their organization and workforce, according to the 1,200 senior executives surveyed for the report: 72% said that intelligent technology will be critical to their organization's market differentiation. Further, 61% said the share of roles requiring collaboration with AI will rise in the next three years. And 69% of the 14,000 workers surveyed said that it was important to develop skills to work with these intelligent systems.

In this letter, a novel weighted ensemble classifier is proposed that improves classification accuracy and minimizes the number of classifiers. Ensemble weight finding problem is modeled as a cost function with following terms: (a) a data fidelity term aiming to decrease misclassification rate, (b) a sparsity term aiming to decrease the number of classifiers, and (c) a non-negativity constraint on the weights of the classifiers. The proposed cost function is a non-convex and hard to solve; thus, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. The proposed method achieves better or similar performance compared to state-of-the art classifier ensemble methods, while using lower number of classifiers.