Educational, training institutions and eLearning content publishers must adapt themselves to the new technological landscape in order to keep their courses and learning materials relevant to today's learners. The development of educational mobile apps provides exciting new ways to develop educational courses that are both effective in reaching educational objectives for teachers and rewarding to the online learner. This presentation will serve as a guide for managers at learning organizations into ways to adapt courses for the multi-screen and multi-device app based environment that today's learners engage in. Mobile devices are outpacing traditional desktop environments when it comes to accessing the web. In fact, 60% of search queries are now done through mobile devices (source: SearchEngineLand).

Legal services are notoriously complicated, not to mention costly for those who need to access them. However, Chrissie Lightfoot, a leading futurist, entrepreneur and lawyer, has come up with an innovative solution. Instead of forking out for a human advisor, clients can now use Robot Lawyer LISA, an impartial Legal Intelligence Support Assistant, to draw up Non-Disclosure Agreements. The artificially intelligent platform can create legally binding documents in under seven minutes at absolutely no cost to the user. Allowing individuals and businesses to use LISA to protect themselves without any prior legal knowledge. LISA is also the first law robot to provide unbiased and objective assistance to both parties, allowing users to avoid having to engage traditional human lawyers on either side.

Artificial intelligence is gaining widespread momentum at a rapid rate. Machines recently attained voice and image recognition. Think Apple's Siri or Amazon's Alexa as well as Facebook and Google accounts. AI can be broadly-described as a computer system designed to interact with the world through capabilities and intelligent behaviors. AI's developing usefulness is striking, especially in light of some recent successes.

Artificial intelligence is one of the heralds of a smarter tech world -- but could it help us be a more compassionate one, as well? The University of Southern California thinks so. Last fall, it christened its Center on Artificial Intelligence in Society, dedicated to deploying AI toward humanitarian ends. In particular, it's aiming toward two social entrepreneurship challenges: the Grand Challenges for Social Work initiative from the American Academy of Social Work and Social Welfare and the United Nations' Sustainable Development Goals. How can AI help these noble projects?

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