In this paper, we study a class of online optimization problems with long-term budget constraints where the objective functions are not necessarily concave (nor convex) but they instead satisfy the Diminishing Returns (DR) property. Specifically, a sequence of monotone DR-submodular objective functions $\{f_t(x)\}_{t=1}^T$ and monotone linear budget functions $\{\langle p_t,x \rangle \}_{t=1}^T$ arrive over time and assuming a total targeted budget $B_T$, the goal is to choose points $x_t$ at each time $t\in\{1,\dots,T\}$, without knowing $f_t$ and $p_t$ on that step, to achieve sub-linear regret bound while the total budget violation $\sum_{t=1}^T \langle p_t,x_t \rangle -B_T$ is sub-linear as well. Prior work has shown that achieving sub-linear regret is impossible if the budget functions are chosen adversarially. Therefore, we modify the notion of regret by comparing the agent against a $(1-\frac{1}{e})$-approximation to the best fixed decision in hindsight which satisfies the budget constraint proportionally over any window of length $W$. We propose the Online Saddle Point Hybrid Gradient (OSPHG) algorithm to solve this class of online problems. For $W=T$, we recover the aforementioned impossibility result. However, when $W=o(T)$, we show that it is possible to obtain sub-linear bounds for both the $(1-\frac{1}{e})$-regret and the total budget violation.

Retaining premium players is key to the success of free-to-play games, but most of them do not start purchasing right after joining the game. By exploiting the exceptionally rich datasets recorded by modern video games--which provide information on the individual behavior of each and every player--survival analysis techniques can be used to predict what players are more likely to become paying (or even premium) users and when, both in terms of time and game level, the conversion will take place. Here we show that a traditional semi-parametric model (Cox regression), a random survival forest (RSF) technique and a method based on conditional inference survival ensembles all yield very promising results. However, the last approach has the advantage of being able to correct the inherent bias in RSF models by dividing the procedure into two steps: first selecting the best predictor to perform the splitting and then the best split point for that covariate. The proposed conditional inference survival ensembles method could be readily used in operational environments for early identification of premium players and the parts of the game that may prompt them to become paying users. Such knowledge would allow developers to induce their conversion and, more generally, to better understand the needs of their players and provide them with a personalized experience, thereby increasing their engagement and paving the way to higher monetization.

In this paper, the performance of three deep learning methods for predicting short-term evolution and reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory (RNN-LSTM). This Lorenz system has three tiers of nonlinearly interacting variables representing slow/large-scale ($X$), intermediate ($Y$), and fast/small-scale ($Z$) processes. For training or testing, only $X$ is available; $Y$ and $Z$ are never known/used. It is shown that RC-ESN substantially outperforms ANN and RNN-LSTM for short-term prediction, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps, equivalent to several Lyapunov timescales. RNN-LSTM and ANN show some prediction skills as well; RNN-LSTM bests ANN. Furthermore, even after losing the trajectory, data predicted by RC-ESN and RNN-LSTM have probability density functions (PDFs) that closely match the true PDF, even at the tails. PDF of the ANN data deviates from the true PDF. Implications, caveats, and applications to data-driven and inexact, data-assisted surrogate modeling of complex dynamical systems such as weather/climate are discussed.

Being a complex subject of major importance in AI Safety research, value alignment has been studied from various perspectives in the last years. However, no final consensus on the design of ethical utility functions facilitating AI value alignment has been achieved yet. Given the urgency to identify systematic solutions, we postulate that it might be useful to start with the simple fact that for the utility function of an AI not to violate human ethical intuitions, it trivially has to be a model of these intuitions and reflect their variety $ - $ whereby the most accurate models pertaining to human entities being biological organisms equipped with a brain constructing concepts like moral judgements, are scientific models. Thus, in order to better assess the variety of human morality, we perform a transdisciplinary analysis applying a security mindset to the issue and summarizing variety-relevant background knowledge from neuroscience and psychology. We complement this information by linking it to augmented utilitarianism as a suitable ethical framework. Based on that, we propose first practical guidelines for the design of approximate ethical goal functions that might better capture the variety of human moral judgements. Finally, we conclude and address future possible challenges.

Recently, machine learning has prevailed in many academia and industrial applications. At the same time, quantum computing, once seen as not realizable, has been brought to markets by several tech giants. However, these machines are not fault-tolerant and can not execute very deep circuits. Therefore, it is urgent to design suitable algorithms and applications implementable on these machines. In this work, we demonstrate a novel approach which applies variational quantum circuits to deep reinforcement learning. With the proposed method, we can implement famous deep reinforcement learning algorithms such as experience replay and target network with variational quantum circuits. In this framework, with appropriate information encoding scheme, the possible quantum advantage is the number of circuit parameters with $poly(\log{} N)$ compared to $poly(N)$ in conventional neural network where $N$ is the dimension of input vectors. Such an approach can be deployed on near-term noisy intermediate-scale quantum machines.

We present a new approach for improving the friendliness and warmth of a virtual agent in an AR environment by generating appropriate movement characteristics. Our algorithm is based on a novel data-driven friendliness model that is computed using a user-study and psychological characteristics. We use our model to control the movements corresponding to the gaits, gestures, and gazing of friendly virtual agents (FVAs) as they interact with the user's avatar and other agents in the environment. We have integrated FVA agents with an AR environment using with a Microsoft HoloLens. Our algorithm can generate plausible movements at interactive rates to increase the social presence. We also investigate the perception of a user in an AR setting and observe that an FVA has a statistically significant improvement in terms of the perceived friendliness and social presence of a user compared to an agent without the friendliness modeling. We observe an increment of 5.71% in the mean responses to a friendliness measure and an improvement of 4.03% in the mean responses to a social presence measure.

When I went on a break to take care of my children, I was in marketing. When I decided to come back, the work itself had changed to digital marketing," says Franky Aggarwal, a 40-year-old working mother in Pune. Aggarwal, after doing a one-year digital marketing certification course, is now working for a US-based personal care brand through FlexiBees, a platform that reemploys female professionals part-time or on a work-fromhome arrangement. Women are leaving work as young mothers or caregivers, resulting in a leaky talent pipeline across sectors. Even as the pool of second-career women -- those returning to work after a break -- is growing, the tech and digital disruption that is changing the way India Inc works is making it increasingly difficult for them to come back. In fact, technology-led disruption is the newest gender-diversity challenge in corporate India. Companies such as IBM, Microsoft and Ingersoll Rand are rolling out programmes to deal with this. In December 2018, the World Economic Forum's "The Global Gender Gap Report" noted that the increasing expansion of artificial intelligence was creating demand for a range of new skills, among them neural networks, deep learning, machine learning and tools. It said: "Only 22% AI professionals globally are female, compared to 78% who are male.

A former patient of the University of Chicago Medical Center is suing the institution amid claims it violated patients' privacy rights. The class-action lawsuit claims records containing identifiable patient information were shared as a result of a partnership between Google and the University of Chicago. All three institutions are named as defendants in the suit, which was filed Wednesday in the Northern District of Illinois by Matt Dinerstein, who received treatment at the medical center during two hospital stays in 2015. The collaboration between Google and the University of Chicago was launched in 2017 to study electronic health records and develop new machine-learning techniques to create predictive models that could prevent unplanned hospital readmissions, avoid costly complications and save lives, according to a 2017 news release from the university. The tech giant has similar partnerships with Stanford University and the University of California-San Francisco.

The Daily Journal has recognized Morgan Lewis partner David Sanker among the Top 20 Artificial Intelligence (AI) Lawyers in California for 2019. David works with clients to build strong patent portfolios in a variety of areas, including AI, machine learning, natural language processing, data visualization software, large-scale database architecture and storage infrastructure, data analytics software, and touch screen technology. In a profile of the honorees, the Daily Journal highlighted David's unique background in software development. Earning a Ph.D. in mathematics, he worked as a software engineer developing large-scale data processing applications for over 10 years.

When the US Library of Congress ranked history's most important innovations, it gave a foremost place to the printing press. While the mechanics behind the printing press weren't far more sophisticated than the other machines of its era, the consequences of its invention were world changing; finally, mankind had a means for the mass distribution of information, improving literacy and changing every industry in the world. Technologies such as these are known as foundational technologies, inventions that can be applied to solve a multitude of problems across a vast number of industries. More contemporary examples include the internet which is now used nearly constantly in all industries and in our personal lives and smartphones, which are so completely integrated with our lives it seems impossible to live without them. As we peer into the near future, we can already see some of the next great potential foundational technologies arising.