Academic programs are one way for professionals to stay current with today's most in-demand skills. With the skills shortage increasing and competition for talent raging through industry and among start-ups, training has become a priority. Many aspiring data professionals are sharpening their skills through online courses or attending industry conferences. However, sometimes you just want to go back to school, at least for a visit. Try the University of Washington or the University of California, Irvine.

Inspired by the matching of supply to demand in logistical problems, the optimal transport (or Monge--Kantorovich) problem involves the matching of probability distributions defined over a geometric domain such as a surface or manifold. In its most obvious discretization, optimal transport becomes a large-scale linear program, which typically is infeasible to solve efficiently on triangle meshes, graphs, point clouds, and other domains encountered in graphics and machine learning. Recent breakthroughs in numerical optimal transport, however, enable scalability to orders-of-magnitude larger problems, solvable in a fraction of a second. Here, we discuss advances in numerical optimal transport that leverage understanding of both discrete and smooth aspects of the problem. State-of-the-art techniques in discrete optimal transport combine insight from partial differential equations (PDE) with convex analysis to reformulate, discretize, and optimize transportation problems. The end result is a set of theoretically-justified models suitable for domains with thousands or millions of vertices. Since numerical optimal transport is a relatively new discipline, special emphasis is placed on identifying and explaining open problems in need of mathematical insight and additional research.

Kimera Systems, the creator of the world's first functional artificial general intelligence, is going to be sharing its special quantum-based method with the world in excruciating detail on May 3. Kimera started off not by following the crowd and trying to optimize neuroscience-based approaches to make AI mimic the human brain, but instead worked backward from a new definition of intelligence that it determined from quantum mechanics theory. This yielded a new breed of AI that was incredibly versatile, allowing the company to create the first functional, consumer-facing AGI program, Nigel AGI. The fruits of those labors are available on the Play Store right now, but those who want to dive in and see how it's all done from the ground up will have to wait for the webinar. The first thing the webinar will cover is Kimera's unique quantum approach to the definition of intelligence, the foundation of its AGI work that eventually resulted in the creation of Nigel and the AGI framework behind it. From there, the webinar will go over the architecture of the Nigel AGI specifically, showing how AGI can be crafted to learn and grow over time, but cannot be created with a specific problem in mind like contemporary AI programs.

In current times, AI or machine learning is implemented in our life. Every teacher in the world uses either a computer or a laptop in order to improve the educational experience and boost productivity. This is the main benefit of AI. In current times, every student has a sterling opportunity to receive a personalized schedule. AI tools allow teachers to evaluate the skills and knowledge of their students and identify their merits and flaws.

By Rachel Thomas, Co-founder at fast.ai Q: What 3 things would you most like the general public to know about AI?

Agenda • "Ready" - Introduction to AI/Machine Learning – April 29 Boaz Ziniman • "Set" - What is ML for developers?

Artificial intelligence is steadily making its way into the traditional classroom setting, but what about in corporate America? Employees are constantly giving continuing education lectures, seminars, and classes. At the moment, it seems like few of them take advantage of the artificial intelligence platforms that could better engage workers. Don't be surprised to find that this technological advancement is going to play a key role in the future of corporate education. On a recent study from the Boston Consulting Group and MIT Sloan Management Review, 83 percent of executives believe that artificial intelligence is a strategic priority for their business. If you aren't certain what artificial intelligence can offer a contemporary workplace, it's time to find out why upper management might decide to make the switch.

Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms. This course begins by taking you through videos on linear models; with scikit-learn, you will take a machine learning approach to linear regression. As you progress, you will explore logistic regression. Then you will build models with distance metrics, including clustering. You will also look at cross-validation and post-model workflows, where you will see how to select a model that predicts well.

Journal of Parallel and Distributed Computing 104, 19-35.

Data Science is an inter-disciplinary field, which deals with algorithms, processes, systems and is used to extract insights from huge amounts of data and improve understanding. Data mined can be in any form – structured or unstructured. Data Science utilizes numerous theories & techniques that are part of other fields such as Mathematics, Statistics, Computer Science, Information Science, and Chemometrics. The emergence of Data Science has been primarily due to the burgeoning growth of data across companies, internet, raising computer power etc. For instance, today an estimated 2.5 quintillion bytes of data is created daily.