What if there was a way you could wish your best, who a Harry Potter fan, in the voice of Alan Rickman on her birthday or congratulate your baby brother on his promotion in the voice of billionaire Bill Gates or Mark Zuckerberg or surprise your sister on her baby shower by wishing her in the voice of Betty White? Though it seems impossible, it isn't. Vocode is a software powered by artificial intelligence (AI) that generates voices of dozens of celebrities, politicians and tech entrepreneurs. The list includes names such as Mark Zuckerberg, Bill Gates, Alan Rickman, Betty White and Christopher Lee among others. Using Vocode is extremely simple.

Artificial intelligence (AI) has reached new heights. The mix of the innovation, information, and talent that make intelligent frameworks possible has arrived at a critical stage, driving phenomenal development in AI investment. The time of AI has arrived. Established organizations are now moving past experimentation. Money is flowing into AI advances and applications at large organizations.

The former top executive of Google described artificial intelligence as a linchpin in global power struggles and warned the U.S. could fall behind rivals like China unless it elevates AI as a cornerstone of national security. Eric Schmidt, who led the tech giant for a decade and was executive chairman of parent company Alphabet Inc., urged the public and private sectors to bolster the country's AI capabilities at a virtual event hosted by the Brookings Institution on Wednesday. Innovation and talent development will be particularly...

Southern Africa has experienced drought-flood cycles for the past decade that strain the ability of any country to properly manage water resources. This dynamic is exacerbated by human drivers such as the heavy reliance of sectors such as mining and agriculture on groundwater and surface water, as well as subsistence agriculture in rural areas along rivers. These factors have progressively depleted natural freshwater systems and contributed to an accumulation of sediment in river systems. In a region where two or more countries share many of the groundwater and surface resources, water security cuts across the socioeconomic divide and is both a rural and urban issue. For example, the City of Cape Town had to heavily ration all water uses in 2017 and 2018, as its dams were drying up.

To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, Ph.D., an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning--an approach first implemented by Google for keyboards' autocorrect functionality--trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.

New York – Critical digital rights battles over privacy, free speech and anonymity are increasingly being fought in video games, a growing market that is becoming a "new political arena," experts and insiders said on Thursday. With the industry set to more than double annual revenues to $300 billion by 2025, questions about how video game operators, designers and governments handle sensitive issues take on added urgency, said participants at RightsCon, a virtual digital rights conference. In recent months, a Hong Kong activist staged a protest against Beijing's rule inside a popular social simulator game called Animal Crossing, and a member of the U.S. Congress, Alexandria Ocasio-Cortez, campaigned in the game as well. The game Minecraft, meanwhile, has been used to circumvent censorship, with groups using it to create digital libraries and smuggle banned texts into repressive countries. "Video games have become this new political arena," said Micaela Mantegna, founder of GeekyLegal, an Argentinian group that focuses on tech policy.

Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but adoption in clinical practice is still limited. Lack of transparency is identified as one of the main barriers to implementation, as clinicians should be confident the AI system can be trusted. Explainable AI has the potential to overcome this issue and can be a step towards trustworthy AI. In this paper we review the recent literature to provide guidance to researchers and practitioners on the design of explainable AI systems for the health-care domain and contribute to formalization of the field of explainable AI. We argue the reason to demand explainability determines what should be explained as this determines the relative importance of the properties of explainability (i.e. interpretability and fidelity). Based on this, we give concrete recommendations to choose between classes of explainable AI methods (explainable modelling versus post-hoc explanation; model-based, attribution-based, or example-based explanations; global and local explanations). Furthermore, we find that quantitative evaluation metrics, which are important for objective standardized evaluation, are still lacking for some properties (e.g. clarity) and types of explanators (e.g. example-based methods). We conclude that explainable modelling can contribute to trustworthy AI, but recognize that complementary measures might be needed to create trustworthy AI (e.g. reporting data quality, performing extensive (external) validation, and regulation).

Abstract--Bayesian networks are powerful statistical models to study the probabilistic relationships among set random variables with major applications in disease modeling and prediction. Here, we propose a continuous time Bayesian network with conditional dependencies, represented as Poisson regression, to model the impact of exogenous variables on the conditional dependencies of the network. We also propose an adaptive regularization method with an intuitive early stopping feature based on density based clustering for efficient learning of the structure and parameters of the proposed network. Using a dataset of patients with multiple chronic conditions extracted from electronic health records of the Department of Veterans Affairs we compare the performance of the proposed approach with some of the existing methods in the literature for both short-term (one-year ahead) and long-term (multi-year ahead) predictions. The proposed approach provides a sparse intuitive representation of the complex functional relationships between multiple chronic conditions. It also provides the capability of analyzing multiple disease trajectories over time given any combination of prior conditions.

In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinforcement learning appears ideal for learning how to optimally make these sequential treatment decisions. However, significant challenges must be overcome before reinforcement learning can be effectively deployed in a mobile healthcare setting. In this work we are concerned with the following challenges: 1) individuals who are in the same context can exhibit differential response to treatments 2) only a limited amount of data is available for learning on any one individual, and 3) non-stationary responses to treatment. To address these challenges we generalize Thompson-Sampling bandit algorithms to develop IntelligentPooling. IntelligentPooling learns personalized treatment policies thus addressing challenge one. To address the second challenge, IntelligentPooling updates each user's degree of personalization while making use of available data on other users to speed up learning. Lastly, IntelligentPooling allows responsivity to vary as a function of a user's time since beginning treatment, thus addressing challenge three. We show that IntelligentPooling achieves an average of 26% lower regret than state-of-the-art. We demonstrate the promise of this approach and its ability to learn from even a small group of users in a live clinical trial.

Networks and graphs, which consist of objects of interest and a vast array of possible relationships between them, arise very naturally in fields as diverse as political science (party affiliations among voters); bioinformatics (gene interactions); physics (dimer systems); and sociology (social network analysis), to name but a few. As such, they are a useful data structure for modeling complex interactions between different experimental entities. Network data, however, is qualitatively distinct from more traditional Euclidean data, and statistical inference on networks is a comparatively new discipline, one that has seen explosive growth over the last two decades. While there is a significant literature devoted to the rigorous statistical study of single networks, multiple network inference-- the analogue of the classical problem of multiple-sample Euclidean inference--is still relatively nascent. Much recent progress in network inference has relied on extracting Euclidean representations of networks, and popular methods include spectral embeddings of network adjacency [3] or Laplacian [45] matrices, representation learning [20, 44], or Bayesian hierarchical methods [16].