Frisco Public Library is named the Top Innovator in Customer Experience in the nation for the Library's artificial intelligence project. The annual award by the Urban Libraries Council recognizes innovative programs and practices from libraries across North America. "We celebrate Frisco Public Library for presenting a groundbreaking initiative that is sure to transform the community and inspire libraries across North America," said Urban Libraries Council President and CEO Susan Benton. The Urban Libraries Council is an innovation and impact tank of North America's leading public library systems. The winning project combines A.I. kits, coding, and classes making artificial intelligence accessible to entrepreneurs, students, and anyone looking to increase their skill set.

OK, let's start from the beginning: I just read that in 2018 the US government announced a new security clearance program - including for individuals in civilian roles - which would run "continuous evaluations" of all applicants, thanks to machine learning technology. The article itself highlights the obvious risks of such a system "going off the rails", but the really interesting questions here are: At least in certain cases, we may never know the answer to the first question because, as the article says, certain systems "offer little to no insight as to how their highly accurate predictions are actually made". But hey, we are only talking of national security, no big deal right? So let's focus on the second question. The article does correctly acknowledge my first thought when I read its title: "if the system works, it might actually generate deeper problems still".

I want to start off with a quick question – can you recognize the two personalities in the below image? I'm certain you got one right. For most of us early age math enthusiasts, the movie "A Beautiful Mind" is inextricably embedded into our memory. Russell Crowe plays the role of John Nash in the movie, a Nobel prize winner for economics (and the person on the left-hand side above). Now, you would remember the iconic scene often regarded as: "Don't go after the blonde". "….the best outcome would come when everyone in the group is doing what's best for himself and the group."

We have already established the fact that AI will certainly play a key role in transforming the future of Indian financial services. With various fintech business models in place, BFSI industry is now adopting the AI-based fintech solutions at a much larger scale than ever. "Most corporates, and increasingly governments as well, are experimenting with how to use AI to improve their processes and outcomes. The fintech industry is no exception and is, in fact, one of the leaders in its adoption," said Gaurav Jalan, Founder and CEO, mPokket. And this is not just one opinion.

This paper is concerned with inverse problems where we intend to match certain given data to data predicted by a (discrete or continuous) mathematical model, often called the forward model.

Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of translation and rotational steps from a given start location to the destination goal location. The goal of the route planning problem is to determine the most optimal route avoiding any collisions with the obstacles present in the environment. Route planning is an NP-hard optimization problem. In this paper, a newly proposed Salp Swarm Algorithm (SSA) is used, and its performance is compared with deterministic and other Nature-Inspired Algorithms (NIAs). The results illustrate that SSA outperforms all the other meta-heuristic algorithms in route planning for multiple UAVs in a 3D environment. The proposed approach improves the average cost and overall time by 1.25% and 6.035% respectively when compared to recently reported data. Route planning is involved in many real-life applications like robot navigation, self-driving car, autonomous UAV for search and rescue operations in dangerous ground-zero situations, civilian surveillance, military combat and even commercial services like package delivery by drones.

As deep learning techniques advance more than ever, hyper-parameter optimization is the new major workload in deep learning clusters. Although hyper-parameter optimization is crucial in training deep learning models for high model performance, effectively executing such a computation-heavy workload still remains a challenge. We observe that numerous trials issued from existing hyper-parameter optimization algorithms share common hyper-parameter sequence prefixes, which implies that there are redundant computations from training the same hyper-parameter sequence multiple times. We propose a stage-based execution strategy for efficient execution of hyper-parameter optimization algorithms. Our strategy removes redundancy in the training process by splitting the hyper-parameter sequences of trials into homogeneous stages, and generating a tree of stages by merging the common prefixes. Our preliminary experiment results show that applying stage-based execution to hyper-parameter optimization algorithms outperforms the original trial-based method, saving required GPU-hours and end-to-end training time by up to 6.60 times and 4.13 times, respectively.

Lung cancer is one of the death threatening diseases among human beings. Early and accurate detection of lung cancer can increase the survival rate from lung cancer. Computed Tomography (CT) images are commonly used for detecting the lung cancer.Using a data set of thousands of high-resolution lung scans collected from Kaggle competition [1], we will develop algorithms that accurately determine in the lungs are cancerous or not. The proposed system promises better result than the existing systems, which would be beneficial for the radiologist for the accurate and early detection of cancer. The method has been tested on 198 slices of CT images of various stages of cancer obtained from Kaggle dataset[1] and is found satisfactory results. The accuracy of the proposed method in this dataset is 72.2%

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered great success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Though empirically successful, theoretical foundations for MARL are relatively lacking in the literature. In this chapter, we provide a selective overview of MARL, with focus on algorithms backed by theoretical analysis. More specifically, we review the theoretical results of MARL algorithms mainly within two representative frameworks, Markov/stochastic games and extensive-form games, in accordance with the types of tasks they address, i.e., fully cooperative, fully competitive, and a mix of the two. We also introduce several significant but challenging applications of these algorithms. Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc. Some of the new angles extrapolate from our own research endeavors and interests. Our overall goal with this chapter is, beyond providing an assessment of the current state of the field on the mark, to identify fruitful future research directions on theoretical studies of MARL. We expect this chapter to serve as continuing stimulus for researchers interested in working on this exciting while challenging topic.

Deep reinforcement learning for high dimensional, hierarchical control tasks usually requires the use of complex neural networks as functional approximators, which can lead to inefficiency, instability and even divergence in the training process. Here, we introduce stacked deep Q learning (SDQL), a flexible modularized deep reinforcement learning architecture, that can enable finding of optimal control policy of control tasks consisting of multiple linear stages in a stable and efficient way. SDQL exploits the linear stage structure by approximating the Q function via a collection of deep Q sub-networks stacking along an axis marking the stage-wise progress of the whole task. By back-propagating the learned state values from later stages to earlier stages, all sub-networks co-adapt to maximize the total reward of the whole task, although each sub-network is responsible for learning optimal control policy for its own stage. This modularized architecture offers considerable flexibility in terms of environment and policy modeling, as it allows choices of different state spaces, action spaces, reward structures, and Q networks for each stage, Further, the backward stage-wise training procedure of SDQL can offers additional transparency, stability, and flexibility to the training process, thus facilitating model fine-tuning and hyper-parameter search. We demonstrate that SDQL is capable of learning competitive strategies for problems with characteristics of high-dimensional state space, heterogeneous action space(both discrete and continuous), multiple scales, and sparse and delayed rewards.