This is an age of artificial intelligence (AI) driven automation and autonomous machines. The increasing ubiquity and rapidly expanding potential of self-improving, self-replicating, autonomous intelligent machines has spurred a massive automation driven transformation of human ecosystems in cyberspace, geospace and space (CGS). As seen across nations, there is already a growing trend towards increasingly entrusting complex decision processes to these rapidly evolving AI systems. From granting parole to diagnosing diseases, college admissions to job interviews, managing trades to granting credits, autonomous vehicles to autonomous weapons, the rapidly evolving AI systems are increasingly being adopted by individuals and entities across nations: its government, industries, organizations and academia (NGIOA). Individually and collectively, the promise and perils of these evolving AI systems are raising serious concerns for the accuracy, fairness, transparency, trust, ethics, privacy and security of the future of humanity -- prompting calls for regulation of artificial intelligence design, development and deployment.

Take, for example, EVA, a "digital voice assistant" created by Silicon Valley startup Voicea. The AI agent can automatically read users' calendars, dial itself into their meetings, and use natural-language processing algorithms to create real-time transcripts of what's said. As a meeting progresses, EVA can also respond to voice commands ("EVA, add that to my to-do list") and "trigger" words ("that's a good point") to highlight what's most important. In an interview, Voicea CEO Omar Tawakol described the technology as a way to help the masses employ the same listening and learning skills as top executives. "Really good CEOs are 100 percent focused on their conversations, not looking at a screen," Tawakol said.

AI For Everyone will launch on February 28th! This non-technical course will teach you the language of AI, how to plan and execute successful AI projects, and how to drive AI adoption in your company. This course is taught by deeplearning.ai

Machine learning is a branch of artificial intelligence that helps enterprises to discover hidden insights from large amounts of data and run predictions. Machine learning algorithms are written by data scientists to understand data trends and provide predictions beyond simple analysis. Python is a popular programming language that is used extensively to write machine learning algorithms due to its simplicity and applicability. Many packages are written in Python that can help data scientists to perform data analysis, data visualization, data preprocessing, feature extraction, model building, training, evaluation, and model deployment of machine learning algorithms. This tutorial describes the installation and configuration of Python-based ecosystem of machine learning packages on IBM AIX .

Last year we took our annual data science survey to the next level by turning over the results to YOU through an open-ended Kernel competition. We were overwhelmed by the response and quality of kernels submitted. Not only are Kagglers amazing data scientists, but they're incredible storytellers as well! Mhamed Jabri was one of those skillful enough to take our data and shape it into something meaningful-- not just for Kaggle, but for the data science community at large. We hope you enjoy getting to know him as much as we did.

Mark Cahill, managing director for the ManpowerGroup, UK, commented that companies were deploying a myriad of approaches to upskill their existing workforce and build talent further, with many employers turning to long-term training courses. Online learning management systems are a popular channel for organizations to use, providing mass content which is especially useful for onboarding, compliance and cybersecurity training. Companies need to promote a culture of learning, provide career guidance, and offer short, focused upskilling opportunities. People need to know how to prepare for high growth roles of the future and that their employer supports their learning. As well as providing internal in-person and online training, companies can tap into external resources by partnering with organizations such as schools, universities and industry bodies to build communities of talent." The report also found that demand for IT skills is growing significantly: 16% of employers expect to increase headcount in IT, five times more than those expecting a decrease. The vast majority of employers in the U.S plan to increase or maintain headcount as a result of automation. Upskilling is on the rise, with 76% of companies planning to upskill their workforce by 2020, up from 28% in 2011. In the UK, 95% of employers are planning to increase or maintain headcount as a result of automation, according to the report. The research found that companies that are digitalizing are growing and this growth is producing more and new kinds of jobs. Cahill argued that the narrative around automation and AI "stealing our jobs" couldn't be further from the truth. As robots enter the workforce, they are transforming jobs but equally creating more employment opportunities as well. Every industry needs to accept this revolution is here to stay. Employers need to work out how to manage the shift and get humans to collaborate with machines."

Though businesses are adopting exponential technologies for automating redundant operations and efficiently utilizing enterprise data, HR (Human Resources) has been one of the most important support functions that was not known for espousing much of artificial intelligence, machine learning, deep learning, reinforcement learning, robotics, virtual reality, augmented reality and other powerful tools until now. The impact of the new wave of change brought about by the fourth industrial revolution has been propelling and profound in HR practice too. For more than a decade, I have been involved in talent acquisition, skill building, corporate training and appraisal processes from technology as well as project management side. Though HR has always been an intriguing art for many, the science of it has been a forerunner in captivating my interest. Earlier when enterprises found their HR databases getting inundated with data, making it next to impossible to derive valuable insights necessary for business decisions, enterprises resorted to analytics.

Fifteen years ago I came to Bangalore, India's Silicon Valley, to do a documentary on outsourcing. One of our first stops was a company called 24/7 whose main business was answering customer service calls and selling products, like credit cards, for U.S. companies half a world away. The beating heart of 24/7 back then was a vast floor of young phone operators, most with only high school degrees, save for a small pool of techies who provided "help desk" advice. These young Indians spoke in the best American English, perfected in a class that we filmed, where everyone had to practice enunciating "Peter Piper picked a peck of pickled peppers" -- and make it sound like they were from Kansas not Kolkata. The operations floor was so noisy from hundreds of simultaneous phone conversations that 24/7 installed a white-noise machine to muffle the din, but even then you could still occasionally hear piercing through the cacophony some techie saying to someone in America, the likes of: "What, Ma'am? Your computer is on fire?"

We consider the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE). While the millimeter-wave band might offer higher data rate, there is a significant probability of outage during which the communication should be carried on the (more reliable) centimeter-wave band. We consider two variations of the BA problem, one-shot and sequential BA. For the former the BS uses only the currently observed information to decide whether to switch to the other frequency band, for the sequential BA, the BS uses a window of previously observed information to predict the best band for a future time step. We provide two approaches to solve the BA problem, (i) a deep learning approach that is based on Long Short Term Memory and/or multi-layer Neural Networks, and (ii) a Gaussian Process based approach, which relies on the assumption that the channel states are jointly Gaussian. We compare the achieved performances to several benchmarks in two environments: (i) a stochastic environment, and (ii) microcellular outdoor channels obtained by ray-tracing. In general, the deep learning solution shows superior performance in both environments.

Methods for learning and planning in sequential decision problems often assume the learner is aware of all possible states and actions in advance. This assumption is sometimes untenable. In this paper, we give a method to learn factored markov decision problems from both domain exploration and expert assistance, which guarantees convergence to near-optimal behaviour, even when the agent begins unaware of factors critical to success. Our experiments show our agent learns optimal behaviour on small and large problems, and that conserving information on discovering new possibilities results in faster convergence.