Software has lately become much, much better at understanding images. Last year Microsoft and Google showed off systems more accurate than humans at recognizing objects in photos, as judged by the standard benchmark researchers use. That became possible thanks to a technique called deep learning, which involves passing data through networks of roughly simulated neurons to train them to filter future data (see "Teaching Machines to Understand Us"). Deep learning is why you can search images stored in Google Photos using keywords, and why Facebook recognizes your friends in photos before you've tagged them. Using deep learning on images is also making robots and self-driving cars more practical, and it could revolutionize medicine.

Hitachi recently announced the development of "EMIEW3," a humanoid robot, and its "remote brain" robotics IT platform. This platform was developed to provide necessary services and guidance in stores and public facilities. Enhanced by the "remote brain" consisting of a robotics IT platform connected to cloud-based intelligent processing systems and a remote operation system to monitor and control multiple robots at various locations, EMIEW3 is able to provide high quality services. Since the announcement of "EMIEW" in 2005, Hitachi has continued to develop human symbiotic robots that can safely co-exist with humans, providing robot-based services with advanced communication capabilities. Using EMIEW2, first announced in 2007, Hitachi developed functions necessary for customer and guidance services, and demonstrated capabilities which include autonomous mobility at a brisk human walking pace, isolation of human voice from background noise, accessing information from the Web to identify objects and using indoor network cameras as "eyes" to locate objects. More recently, artificial intelligence technology was applied for functions requiring advanced intelligent processing such as for dialogue with appropriate response to questions posed in different forms and predictive function to avoid collision with moving objects which may suddenly appear from blind angles.

For the past few decades, analytics for risk management has meant business intelligence (BI) and predictive analytics, which for the current purpose I will also take to include optimisation, a.k.a. Now the buzz around artificial intelligence, enhanced by popular myths around robots (thanks, Hollywood!), has created some confusion. Does AI replaces previous technologies, or work with them? How will AI help us in risk management? There is no denying that AI and machine-learning technologies have piqued public interest.

Chat applications are becoming a mainstream trend and our preferred way of interacting with colleagues, friends and family. From the early days of SMS to the favorite snaps of our children, real-time online conversations are everywhere and here to stay. The acquisition of WhatAapp by Facebook in 2014 for a hefty 22 Billion price tag made it clear and promising asTechCrunch noticed it one year later. But although TechCrunch saw messaging apps as the future of mobile portal, they remained more or less next to the Internet, without a direct impact, except their increasing audience. The recent surge of interest in Bots and AI is changing the game and we'll be witnessing the second major fragmentation of the Internet.

Campaigning is often based on feeling or experience by marketeers, but the speed of digital developments are forcing businesses to rapidly adapt to the new needs of consumers created by digitization. Of course, marketeers can't leave their data out of the equation nowadays, however this remains a struggle as this often calls for allocating expensive resources such as data scientists. Luckily, technology is catching up on the smart brains of data scientists. So what does this mean for the future of campaigning? And how can big data, machine learning and artificial intelligence be used to improve campaigns?

H2O is feature-rich open source machine learning platform known for its R and Spark integration and it's ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things. H2O is an Open Source machine learning platform for smarter applications. At the Data Science for IoT course, we have been following H2O for features such as Open Source, R integration, Spark integration, Deep Learning and it's ease of use. This blog is authored by Sibanjan Das and Ajit Jaokar as part of our work at the Data Science for IoT course exploring H2O Deep Learning for Internet of Things.

From undergrads in music and social work, to PhDs in philosophy, to those who never graduated high school -- I've had quite a variety of co-workers during the ten years I've been in tech. Of course, in every software company you'll find your traditional computer science and engineering graduates as well. However, there's a significant and growing population of developers who took a different path to learn to code and are building a profession out of it. I hold a degree in linguistics, which in most universities is not a computational program, but an anthropological one. Required coursework includes topics such as historical and cultural language studies.

Predictive analytics applications use machine learning to build predictive models for applications including price prediction, risk assessment, and predicting customer behaviour. Based on the trainers' book, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies" (www.machinelearningbook.com) this course presents a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. This course has been designed to guide delegates through the most important topics in machine learning, and how they should be applied to build real-world relevant predictive analytics models.

In this tutorial, you'll be briefly introduced to machine learning with Python (2.x) and Weka, a data processing and machine learning tool. The activity is to build a simple spam filter for emails and learn machine learning concepts. This article is written by the Codementor team and is based on a Codementor Office Hour by Codementor Benjamin Cohen, a Data Scientist with a focus in Natural Language Processing. In a nutshell, machine learning is basically learning from data. Way back when before access to data was plentiful and access to computing power was plentiful, people tried to hand-write rules to solve a lot of problems. E.g., if you see {{some word}}, it's probably spam. That worked all right, but as problems get more and more complicated, the combinations of rules start to grow out of hand, both in terms of writing them and in terms of taking them up and processing them. The number of techniques to do this all fall under the umbrella of machine learning.

Chat applications are becoming a mainstream trend and our preferred way of interacting with colleagues, friends and family. From the early days of SMS to the favorite snaps of our children, real-time online conversations are everywhere and here to stay. The acquisition of WhatAapp by Facebook in 2014 for a hefty 22 Billion price tag made it clear and promising as TechCrunch noticed it one year later. But although TechCrunch saw messaging apps as the future of mobile portal, they remained more or less next to the Internet, without a direct impact, except their increasing audience. The recent surge of interest in Bots and AI is changing the game and we'll be witnessing the second major fragmentation of the Internet.