AI is within the minds of businesses worldwide and has been experimenting and innovating to gain a competitive advantage. Here's what business professionals have said--- Business leaders believed that they would double the rate of innovation with AI. There are significant advancements in AI for a business that are expecting further modifications to create a more impactful digital workforce. Employee expectations are fixed from the beginning before the employee joins the organization, which is made to enter the workplace. For this, HR can effectively map AI with an employee's onboarding program, which gives an open communication platform where new employees can fearlessly discuss with the management. For instance, if a person works with the data team but later sometimes faces an IT-related issue, the employee knows where to reach.

Wireless communications are often subject to fading conditions. Various models have been proposed to capture the inherent randomness of fading in wireless channels, and conventional model-based receiver methods rely on accurate knowledge of this underlying distribution, which in practice may be complex and intractable. In this work we propose a collaborative neural network-based symbol detection mechanism for downlink fading channels, referred to as FedRec, which is based on the maximum a-posteriori probability (MAP) detector. To facilitate training using a limited number of pilots, while capturing a diverse ensemble of fading realizations, we propose a federated training scheme in which multiple users collaborate to jointly learn a universal data-driven detector. The performance of the resulting FedRec receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics, while inducing a substantially reduced communication overhead in its training procedure compared to training in a centralized fashion.

In this paper, we study a distributed privacy-preserving learning problem in general social networks. Specifically, we consider a very general problem setting where the agents in a given multi-hop social network are required to make sequential decisions to choose among a set of options featured by unknown stochastic quality signals. Each agent is allowed to interact with its peers through multi-hop communications but with its privacy preserved. To serve the above goals, we propose a four-staged distributed social learning algorithm. In a nutshell, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to its peers' perturbed latest adoptions, and iv) decides whether or not to adopt the selected option according to its latest quality signal. By our solid theoretical analysis, we provide answers to two fundamental algorithmic questions about the performance of our four-staged algorithm: on one hand, we illustrate the convergence of our algorithm when there are a sufficient number of agents in the social network, each of which are with incomplete and perturbed knowledge as input; on the other hand, we reveal the quantitative trade-off between the privacy loss and the communication overhead towards the convergence. We also perform extensive simulations to validate our theoretical analysis and to verify the efficacy of our algorithm.

Many real-world multi-agent interactions consider multiple distinct criteria, i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may lead to different utilities for each participant. Therefore, it is essential for an agent to learn about the behaviour of other agents in the system. In this work, we present the first study of the effects of such opponent modelling on multi-objective multi-agent interactions with non-linear utilities. Specifically, we consider two-player multi-objective normal form games with non-linear utility functions under the scalarised expected returns optimisation criterion. We contribute novel actor-critic and policy gradient formulations to allow reinforcement learning of mixed strategies in this setting, along with extensions that incorporate opponent policy reconstruction and learning with opponent learning awareness (i.e., learning while considering the impact of one's policy when anticipating the opponent's learning step). Empirical results in five different MONFGs demonstrate that opponent learning awareness and modelling can drastically alter the learning dynamics in this setting. When equilibria are present, opponent modelling can confer significant benefits on agents that implement it. When there are no Nash equilibria, opponent learning awareness and modelling allows agents to still converge to meaningful solutions that approximate equilibria.

We adapt previous research on topological unsupervised learning to develop a unified functorial perspective on manifold learning and clustering. We first introduce overlapping hierachical clustering algorithms as functors and demonstrate that the maximal and single linkage clustering algorithms factor through an adaptation of the singular set functor. Next, we characterize manifold learning algorithms as functors that map uber-metric spaces to optimization objectives and factor through hierachical clustering functors. We use this characterization to prove refinement bounds on manifold learning loss functions and construct a hierarchy of manifold learning algorithms based on their invariants. We express several state of the art manifold learning algorithms as functors at different levels of this hierarchy, including Laplacian Eigenmaps, Metric Multidimensional Scaling, and UMAP. Finally, we experimentally demonstrate that this perspective enables us to derive and analyze novel manifold learning algorithms.

This textbook mainly addresses beginners and readers with a basic knowledge of object-oriented programming languages like Java or C#, but with little or no modeling or software engineering experience â€" thus reflecting the majority of students in introductory courses at universities. Using UML, it introduces basic modeling concepts in a highly precise manner, while refraining from the interpretation of rare special cases. After a brief explanation of why modeling is an indispensable part of software development, the authors introduce the individual diagram types of UML (the class and object diagram, the sequence diagram, the state machine diagram, the activity diagram, and the use case diagram), as well as their interrelationships, in a step-by-step manner. The topics covered include not only the syntax and the semantics of the individual language elements, but also pragmatic aspects, i.e., how to use them wisely at various stages in the software development process. To this end, the work is complemented with examples that were carefully selected for their educational and illustrative value.

Typical school science fair projects include a soda volcano, graphite circuit or a potato battery, but one 12-year-old from New York went beyond with a project that uses an optical illusion to understand our cognitive process. Ishana Kumar, from Chappaqua, submitted the project - called Color Is in the Eye of the Beholder: The Role of Retinal Fatigue on Imaginary Fechner Colors –for this year's Society for Science and Public's Broadcom MASTERS (Math, Applied Science, Technology and Engineering for Rising Stars). Kumar was awarded the Samueli Foundation Prize for her research, as well leadership, collaboration and critical thinking skills – all of which earned her a prize of $25,000. Her project investigated whether retinal fatigue changes our perception of'imaginary colors,' an illusion of color most commonly seen from a spinning black and white disk, called a Benham's disk. Ishana Kumar, from Chappaqua, submitted the project - called Color Is in the Eye of the Beholder: The Role of Retinal Fatigue on Imaginary Fechner Colors –for this year's Society for Science and Public's Broadcom MASTERS) 'When they called my name I was in complete disbelief,' Kumar said after hearing the results.

The age of artificial intelligence (AI) has arrived, changing the world around us in exciting and unpredictable ways. We are getting accustomed to AI and our children will be highly dependent on it. AI helps bring about new careers, discover new drugs, augment our senses, and influence both our interaction with the world and our understanding of it. One day, it may help us eradicate war, disease, and poverty. According to Max Tegmark, the President of the Future of Life Institute, AI systems could potentially trigger an intelligence explosion, leaving humans far behind.

Any credible list of influential books about tech from the last decade would include AI Superpowers: China, Silicon Valley and the New World Order by Kai-Fu Lee. Considered the world's foremost authority on artificial intelligence, Taipei-born Lee got an early start, writing a pioneering speech-recognition program while a student at Carnegie Mellon in the 1980s. He later had a career in China and the U.S. at Apple, Microsoft, Silicon Graphics and Google, where he was president of Google China. Now based in Beijing, Lee runs a venture capital firm called Sinovation, which focuses on AI investments. The interview with Lee took place (virtually) in early October.

The age of artificial intelligence (AI) has arrived, changing the world around us in exciting and unpredictable ways. We are getting accustomed to AI and our children will be highly dependent on it. AI helps bring about new careers, discover new drugs, augment our senses, and influence both our interaction with the world and our understanding of it. One day, it may help us eradicate war, disease, and poverty. According to Max Tegmark, the President of the Future of Life Institute, AI systems could potentially trigger an intelligence explosion, leaving humans far behind.