Welcome to our HR Modernization Playbook: Tomorrow's people – Why HR matters more than ever in the age of artificial intelligence. Digital transformation is happening faster than ever. The adoption of artificial intelligence (AI) and automation will redefine jobs, enhance employee productivity and accelerate workforce development. In fact, skills and culture – not technology – are the biggest barriers to business growth in the AI era. This means CEOs are looking to their CHRO to lead culture change, manage talent and drive down costs.

But concerns over growing inequality and the lack of opportunity for many in the labor force--serious matters linked to a variety of structural changes in the economy–are well-founded and need to be addressed, four scholars on artificial intelligence and the economy recently told an audience at Stanford Graduate School of Business. That's not to say that artificial intelligence isn't having a profound effect on many areas of the economy. But understanding the link between the two trends is difficult and it's easy to make misleading assumptions about the kinds of jobs that are in danger of becoming obsolete. "Most jobs are more complex than [many people] realize," said Google's chief economist, Hal Varian, during a forum on the future of work, which was sponsored by the Stanford Institute for Human-Centered Artificial Intelligence. Today's workforce is sharply divided by levels of education, and those who have not gone beyond high school are affected the most by long-term changes in the economy, says David Autor, professor of economics at the Massachusetts Institute of Technology.

If you too want to start a career in AI and DL, check out our "PG Certificate Program in Artificial Intelligence & Deep Learning" course http://bit.ly/2F42DeK AI and Deep learning have shown promising growth in recent years and in the near future can change the way companies operate. After completing the Deep Learning and Artificial Intelligence online course, you'll be able to: - Use Tensorflow, Scikit Learn library, Keras and other machine learning and deep learning tools.

So you're the CEO of a clothing retailer, a rental car agency, or a payroll processing company, and you hear that artificial intelligence is changing the world. What are you supposed to do? The short answer, says Paul Oyer at Stanford Graduate School of Business, is to start learning fast. "Artificial intelligence will affect every industry, whether it's clothing or shipping," says Oyer, a professor of economics and the codirector of a new multidisciplinary course on AI for senior executives. "We need to find a complementary relationship between those who deal with the technology of AI and the managers who understand what drives their companies. Managers don't need to learn all the technical details, but they do need to understand the implications for their business."

Machine learning is becoming an essential part of developing solutions for many industrial applications, but the lack of interpretability hinders wide industry adoption to rapidly build, test, deploy and validate machine learning models, in the sense that the insight of developing machine learning solutions are not structurally encoded, justified and transferred. In this paper we describe Maana Meta-learning Service, an interpretable and interactive automated machine learning service residing in Maana Knowledge Platform that performs machine-guided, user assisted pipeline search and hyper-parameter tuning and generates structured knowledge about decisions for pipeline profiling and selection. The service is shipped with Maana Knowledge Platform and is validated using benchmark dataset. Furthermore, its capability of deriving knowledge from pipeline search facilitates various inference tasks and transferring to similar data science projects.

In the continual learning setting, tasks are encountered sequentially. The goal is to learn whilst i) avoiding catastrophic forgetting, ii) efficiently using model capacity, and iii) employing forward and backward transfer learning. In this paper, we explore how the Variational Continual Learning (VCL) framework achieves these desiderata on two benchmarks in continual learning: split MNIST and permuted MNIST. We first report significantly improved results on what was already a competitive approach. The improvements are achieved by establishing a new best practice approach to mean-field variational Bayesian neural networks. We then look at the solutions in detail. This allows us to obtain an understanding of why VCL performs as it does, and we compare the solution to what an `ideal' continual learning solution might be.

Coursera Machine Learning Course is offered by Stanford University with a rating of 4.9 out of 5. More than 2.2 million students are already enrolled in this course. This online course has over 25K reviews. After doing this course, 40% started a new career and 37% got a tangible career benefit from this course. You can complete this course 100% online with your flexible schedule.

The size of the datasets available today leads to distribute Machine Learning (ML) tasks. An SGD--based optimization is for instance typically carried out by two categories of participants: parameter servers and workers. Some of these nodes can sometimes behave arbitrarily (called \emph{Byzantine} and caused by corrupt/bogus data/machines), impacting the accuracy of the entire learning activity. Several approaches recently studied how to tolerate Byzantine workers, while assuming honest and trusted parameter servers. In order to achieve total ML robustness, we introduce GuanYu, the first algorithm (to the best of our knowledge) to handle Byzantine parameter servers as well as Byzantine workers. We prove that GuanYu ensures convergence against $\frac{1}{3}$ Byzantine parameter servers and $\frac{1}{3}$ Byzantine workers, which is optimal in asynchronous networks (GuanYu does also tolerate unbounded communication delays, i.e.\ asynchrony). To prove the Byzantine resilience of GuanYu, we use a contraction argument, leveraging geometric properties of the median in high dimensional spaces to prevent (with probability 1) any drift on the models within each of the non-Byzantine servers. % To convey its practicality, we implemented GuanYu using the low-level TensorFlow APIs and deployed it in a distributed setup using the CIFAR-10 dataset. The overhead of tolerating Byzantine participants, compared to a vanilla TensorFlow deployment that is vulnerable to a single Byzantine participant, is around 30\% in terms of throughput (model updates per second) - while maintaining the same convergence rate (model updates required to reach some accuracy).

There are many resources, free and paid, for learning data science. However, most of them are not really complete, and they have the wrong focus. The modern data scientist needs to be able to combine various skills, which not many courses take into account. First of all, a complete data scientist needs to know both machine learning and statistics. Also, familiarity with at least either R or Python (ideally both) is a must.

We study online reinforcement learning for finite-horizon deterministic control systems with {\it arbitrary} state and action spaces. Suppose that the transition dynamics and reward function is unknown, but the state and action space is endowed with a metric that characterizes the proximity between different states and actions. We provide a surprisingly simple upper-confidence reinforcement learning algorithm that uses a function approximation oracle to estimate optimistic Q functions from experiences. We show that the regret of the algorithm after $K$ episodes is $O(HL(KH)^{\frac{d-1}{d}}) $ where $L$ is a smoothness parameter, and $d$ is the doubling dimension of the state-action space with respect to the given metric. We also establish a near-matching regret lower bound. The proposed method can be adapted to work for more structured transition systems, including the finite-state case and the case where value functions are linear combinations of features, where the method also achieve the optimal regret.