On screen, the virtual character sits in a comfortable purple chair. She wears plain pants, a turquoise shirt, and a slim jacket with the sleeves rolled up past her elbows. Her short dark hair is swept to one side and her ethnicity is intentionally ambiguous, according to her developers, a team of researchers with the University of Southern California (USC) Institute for Creative Technologies. Some of the people who have interacted with her assume she is Asian; others conclude she has a completely different ethnicity. "People have come up and said that they're so thankful we paired them with someone of their race because it helped them connect," recalls Gale Lucas, a research assistant professor at USC.

Most people believe that lifelong learning is critical for staying prepared as artificial intelligence transforms the job market worldwide. Yet, higher education, business and government are all failing to provide solutions.

A technology recruiting firm is having such a hard time finding artificial-intelligence professionals that it is offering a training program that covers the subject, with tuition costs covered if they agree to work for a client company. Modis Inc., a subsidiary of Switzerland-based global staffing company Adecco Group AG, this spring began testing a program in the U.S. that offers courses on topics including machine learning, some of which are open to people with little or no background in computer science.

Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner. We introduce the Google Research F ootball Environment, a new reinforcement learning environment where agents are trained to play football in an advanced, physics-based 3D simulator. The resulting environment is challenging, easy to use and customize, and it is available under a permissive open-source license. In addition, it provides support for multiplayer and multi-agent experiments. We propose three full-game scenarios of varying difficulty with the F ootball Benchmarks and report baseline results for three commonly used reinforcement algorithms (IMP ALA, PPO, and Ape-X DQN). We also provide a diverse set of simpler scenarios with the F ootball Academy and showcase several promising research directions.

The ability of deep learning models to generalize well across different scenarios depends primarily on the quality and quantity of annotated data. Labeling large amounts of data for all possible scenarios that a model may encounter would not be feasible; if even possible. We propose a framework to deal with limited labeled training data and demonstrate it on the application of vision-based vehicle control. We show how limited steering angle data available for only one condition can be transferred to multiple different weather scenarios. This is done by leveraging unlabeled images in a teacher-student learning paradigm complemented with an image-to-image translation network. The translation network transfers the images to a new domain, whereas the teacher provides soft supervised targets to train the student on this domain. Furthermore, we demonstrate how utilization of auxiliary networks can reduce the size of a model at inference time, without affecting the accuracy. The experiments show that our approach generalizes well across multiple different weather conditions using only ground truth labels from one domain.

Springboard runs an online, self-paced, Machine Learning Engineering Career Track in which participants learn with the help of a curated curriculum and 1-1 guidance from an expert mentor. Our mentor community - the biggest strength of our programs - comprises experts from the best organizations in the world. Our mentors range from engineers and researchers at premier companies (Netflix, Pandora, LinkedIn, Apple) to a wide variety of top-notch startups and research institutes. If you are as passionate about mentoring as you are about machine learning, and can give a few hours per week in return for an honorarium, we would love to hear from you. This 6-month course is primarily designed for Software Engineers who want to become Machine Learning Engineers. As part of the course, students go through an intensive curriculum that's based on the way real-world applications are created.

This is the third blog in a four-part series detailing the components necessary for AI success. You can read my earlier posts about cultural willingness, and data and infrastructure readiness to get caught up. Look for the final post in this series coming soon, covering ethics, risk and compliance planning. Organizations face a daunting task in today's digital era: to identify, organize and analyze the hordes of data that continue to grow in complexity, scope, and size. While Artificial Intelligence (AI) can automate basic tasks, there still remains the challenge of freeing employees up for analytical and creative thinking, to develop the skills needed to successfully implement AI, and to benefit from its power.

Artificial Intelligence (AI) today has occupied centre stage, we hear about it all the time and here's what it is all about. It has been an intriguing topic since the late 90s for the common man, but only in the context of sci-fi Hollywood movies like Star Trek or Star Wars. Thanks to the immense progress in technology, AI is now being used by many of us in our day to day life. For example, today many homes are using Amazon's Alexa, an AI-based assistant. In fact, many of the support queries by airlines, banks, food delivery apps, etc., are being handled by smart AI is driven chatbots.

Machine learning is an area of artificial intelligence and computer science that includes the development of software and algorithms that can make predictions based on data. The software can make decisions and follow a path that is not specifically programmed. Machine learning is used within the field of data analytics to make predictions based on trends and insights in the data.

We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.