Across industries and in nearly every vertical, AI is driving digital transformation. In sales, for example, 70% of US-based professionals are now using some form of AI at work. In marketing, platforms like Acoustic, Salesforce Commerce Cloud, and Pega's Unified DMP are bringing brands closer than ever before to their customers. Project scheduling optimizers for engineering, telecommunications AI that recognize early signs of churn, and personalized gaming experiences based on in-game data are just a few of the ways AI is disrupting major industries today. AI is no longer the exclusive domain of massive enterprise with equally outsized R&D budgets, either.

Despite some hiccups, the use of artificial intelligence (AI) and big data in marketing will continue to flourish as companies look for new ways to influence consumer behavior, said the head of a Morristown-based digital marketing firm at a recent tech conference. Nick Kraus, founder and CEO of Kraus Marketing, said that today's marketing strategies, dominated by the likes of digital giants Google, Facebook, Instagram and Twitter, have become smarter and more pervasive as the result of AI and data analytics providing deeper insights into consumers' buying habits. Kraus was one of several speakers who presented their views on the rapid growth of digital marketing at the DigiCON 2019 conference, held on October 11 at the County College of Morris, in Randolph. The Morris County Chamber of Commerce hosted the event. The use of AI and big data for marketing purposes has been around for awhile, but many businesses, especially smaller ones, may not know how to apply these technologies to various marketing tools, such as social media, email, search engine optimization and websites.

The real threat is much more obvious and interesting. We've all heard the prophetic apocalyptic predictions for AI's future. Elon Musk has said that it's our "biggest existential threat" and has likened it to "summoning the demon." Other great minds are similarly vocal about their fears. The late Stephen Hawking said that AI could wipe out human race.

Most of us have heard of phishing, we may get an email supposedly from the CEO of the company we work for demanding we transfer some money. As it's the boss, and we are human, and don't always react calmly when our boss aggressively demands we do something, we may well comply. But these days, more people are aware of the danger -- and are likely to check the authenticity of the email. Suppose, however, we get a phone call apparently from the boss, complete with the cadences of the boss's voice that we are familiar with -- we are far less likely to be suspicious. But now, in a variation of deepfake, it has been reported that AI has been used to scam an organisation out of money by impersonating the voice of a company's chief executive.

NEW DELHI: Germany and India are likely to sign agreements including a partnership on the use of artificial intelligence in farming during a three-day visit to New Delhi by Chancellor Angela Merkel that begins on Thursday, the German ambassador said. Merkel will be accompanied by several cabinet colleagues and a business delegation, ambassador Walter J. Lindner told reporters. Merkel and Indian Prime Minister Narendra Modi are expected to discuss trade, investment, regional security and climate change. Both countries could sign agreements in areas such as artificial intelligence and green urban mobility, Lindner said. "This time, the focus will be on economic and trade relations, innovation and digitalisation, and climate protection and sustainable development," Merkel said in a message ahead of the visit released by the Indian embassy in Berlin.

That would improve health alerts for people at heightened risk of developing problems because of high ozone levels. Yunsoo Choi, associate professor in the Department of Earth and Atmospheric Sciences and corresponding author for a paper explaining the work, said they built an artificially intelligent model using a convolutional neural network, which is able to take information from current conditions and accurately predict ozone levels for the next day. The work was published in the journal Neural Networks. "If we know the conditions of today, we can predict the conditions of tomorrow," Choi said. Ozone is an unstable gas, formed by a chemical reaction when sunlight combines with nitrogen oxides (NOx) and volatile organic compounds, both of which are found in automobile and industrial emissions.

In federated distributed learning, the goal is to optimize a global training objective defined over distributed devices, where the data shard at each device is sampled from a possibly different distribution (a.k.a., heterogeneous or non i.i.d. data samples). In this paper, we generalize the local stochastic and full gradient descent with periodic averaging-- originally designed for homogeneous distributed optimization, to solve nonconvex optimization problems in federated learning. Although scant research is available on the effectiveness of local SGD in reducing the number of communication rounds in homogeneous setting, its convergence and communication complexity in heterogeneous setting is mostly demonstrated empirically and lacks through theoretical understating. To bridge this gap, we demonstrate that by properly analyzing the effect of unbiased gradients and sampling schema in federated setting, under mild assumptions, the implicit variance reduction feature of local distributed methods generalize to heterogeneous data shards and exhibits the best known convergence rates of homogeneous setting both in general nonconvex and under {\pl}~ condition (generalization of strong-convexity). Our theoretical results complement the recent empirical studies that demonstrate the applicability of local GD/SGD to federated learning. We also specialize the proposed local method for networked distributed optimization. To the best of our knowledge, the obtained convergence rates are the sharpest known to date on the convergence of local decant methods with periodic averaging for solving nonconvex federated optimization in both centralized and networked distributed optimization.

While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of reward sparsity. This is especially true for tasks such as training an agent to play StarCraft II, a real-time strategy game where reward is only given at the end of a game which is usually very long. While this problem can be addressed through reward shaping, such approaches typically require a human expert with specialized knowledge. Inspired by the vision of enabling reward shaping through the more-accessible paradigm of natural-language narration, we develop a technique that can provide the benefits of reward shaping using natural language commands. Our narration-guided RL agent projects sequences of natural-language commands into the same high-dimensional representation space as corresponding goal states. We show that we can get improved performance with our method compared to traditional reward-shaping approaches. Additionally, we demonstrate the ability of our method to generalize to unseen natural-language commands.

We address the problem of continual learning in multi-task Gaussian process (GP) models for handling sequential input-output observations. Our approach extends the existing prior-posterior recursion of online Bayesian inference, i.e.\ past posterior discoveries become future prior beliefs, to the infinite functional space setting of GP. For a reason of scalability, we introduce variational inference together with an sparse approximation based on inducing inputs. As a consequence, we obtain tractable continual lower-bounds where two novel Kullback-Leibler (KL) divergences intervene in a natural way. The key technical property of our method is the recursive reconstruction of conditional GP priors conditioned on the variational parameters learned so far. To achieve this goal, we introduce a novel factorization of past variational distributions, where the predictive GP equation propagates the posterior uncertainty forward. We then demonstrate that it is possible to derive GP models over many types of sequential observations, either discrete or continuous and amenable to stochastic optimization. The continual inference approach is also applicable to scenarios where potential multi-channel or heterogeneous observations might appear. Extensive experiments demonstrate that the method is fully scalable, shows a reliable performance and is robust to uncertainty error propagation over a plenty of synthetic and real-world datasets.

We present a new feature extraction method for complex and large datasets, based on the concept of transport operators on graphs. The proposed approach generalizes and extends the many existing data representation methodologies built upon diffusion processes, to a new domain where dynamical systems play a key role. The main advantage of this approach comes from the ability to exploit different relationships than those arising in the context of e.g., Graph Laplacians. Fundamental properties of the transport operators are proved. We demonstrate the flexibility of the method by introducing several diverse examples of transformations. We close the paper with a series of computational experiments and applications to the problem of classification of hyperspectral satellite imagery, to illustrate the practical implications of our algorithm and its ability to quantify new aspects of relationships within complicated datasets.