Popular online learning service Udacity already trains engineers for work in the fast-growing autonomous vehicles field, but now the company is ready to harness all that talent and launch its own self-driving taxi company. Led by CEO (and former Udacity Vice President) Oliver Cameron, the new spin-off company will be called Voyage and has given itself the goal of getting autonomous taxis to "real users" in less than five years. As Cameron noted on Twitter, he thinks Voyage can hit that goal thanks to a "maturing" ecosystem that will allow the company to add autonomous functions to existing vehicles without needing to build a new self-driving car from the ground up. According to Business Insider, Voyage plans to differentiate itself from the competition at Uber and Lyft by allowing riders to control the experience with voice commands that set destination, add additional stops or simply control music playback. Although the company didn't specify which markets it would enter first, Voyage is aiming to start test rides with real passengers "very soon" -- possibly in the next few months.

Reiterating its commitment to boost adoption of artificial intelligence (AI), Intel India today announced a developer community initiative – AI Developer Education Programme, aimed at educating 15,000 scientists, developers, analysts, and engineers. The educational programme is also aimed at deep learning and machine learning, the tech major said in a statement. The programme was announced at the first AI Day held in Bengaluru where thought-leaders from government, industry, and the academia congregated and discussed the potential of accelerating the AI revolution in the country. Under the programme, Intel will run 60 programmes across the year, ranging from workshops, roadshows, user group and senior technology leader round-tables. Announcing the programme, Intel South Asia managing director Prakash Mallya said data center and the intelligence behind the data collected can enable government and industry to make effective decisions based on algorithms.

What are the best sources to study machine learning and artificial intelligence? You're in luck - now is better than ever before to start studying machine learning and artificial intelligence. The field has evolved rapidly and grown tremendously in recent years. Experts have released and polished high quality open source software tools and libraries. New online courses and blog posts emerge every day.

If you're interested in large scale machine learning, then this book is for you. This book doesn't provide deep coverage of theoretical foundations of machine learning (I would recommend to look to other books, like Introduction to Machine Learning (Adaptive Computation and Machine Learning series),Machine Learning in Action or Programming Collective Intelligence: Building Smart Web 2.0 Applications, etc., if you want to get more background), but concentrates on explanation on how to use Apache Mahout ([...]) to solve some of machine learning problems: making recommendations, data clustering & classification. For each of class of these problems, description starts with base things, and continues with more complex examples, including complete solutions, that could be easily adapted for your machine learning problems. All examples that come with book were checked with actual release of Apache Mahout (version 0.5). Book is written in succinct, but understandable language and provides many code snippets that make understanding of topics much easier.

This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catastrophic forgetting: as they tend to adapt to the most recently seen task, they lose performance on the tasks that were learned previously. Our method aims at preserving the knowledge of the previous tasks while learning a new one by using autoencoders. For each task, an under-complete autoencoder is learned, capturing the features that are crucial for its achievement. When a new task is presented to the system, we prevent the reconstructions of the features with these autoencoders from changing, which has the effect of preserving the information on which the previous tasks are mainly relying. At the same time, the features are given space to adjust to the most recent environment as only their projection into a low dimension submanifold is controlled. The proposed system is evaluated on image classification tasks and shows a reduction of forgetting over the state-of-the-art

According to the National Center for Education Statistics, 42 percent of students who are bullied report that it happens in school hallways and stairwells. Thirty-four percent say they;re bullied in the classroom, right under their teachers; noses, yet the bullying problem continues in both public and private schools. In this episode, Mark shares three easy hacks to stop bullying at your school. Check out the show notes at http://hacklearning.org/bullying.

The 78-video playlist above comes from a course called Neural Networks for Machine Learning, taught by Geoffrey Hinton, a computer science professor at the University of Toronto. The videos were created for a larger course taught on Coursera, which gets re-offered on a fairly regularly basis. Neural Networks for Machine Learning will teach you about "artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc." The courses emphasizes " both the basic algorithms and the practical tricks needed to get them to work well." It's geared for an intermediate level learner – comfortable with calculus and with experience programming (Python).

Graham Johnson is an artist with a curious muse: the human cell. Twenty years ago he graduated from a quiet corner of Johns Hopkins where students draw cadavers instead of cutting them up. At first, Johnson stuck to the medical illustrator canon, animating cells in a classic, cartoonish style. But he dreamed of constructing three-dimensional, data-driven models that could capture all their beautiful complexity. For that, he'd need computers, lots of them.

Developing training programmes for a large group of sales or technical or services personnel is a challenging task as the programme is meant for a diverse group, and has to be engaging and meaningful for the participants. The programmes are mostly delivered at multiple locations, they have to be updated from time to time and at times, also require to be culturally sensitive to remain relevant as well as contemporary. Effective assessment strategy is also important to ensure the programmes meet the stated business objectives. In the digital era, there is a plethora of content available on the internet. A lot of it is free of cost via options such as MOOCs, Course Era, You Tube and others.

Neural Networks for Machine Learning will teach you about "artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc." The courses emphasizes " both the basic algorithms and the practical tricks needed to get them to work well." It's geared for an intermediate level learner – comfortable with calculus and with experience programming (Python).