Data Scientists live and breathe data. We choose the best tools for that task from a vast array of ever changing tools. We turn data into information, information into discovered knowledge, and through wisdom the discovered knowledge is turned into intelligent action. Our tool suites include many open source software products. In this session we will review the foundations for today's Data Scientist's skill set, introducing the concepts behind machine learning, data mining, analytics and data science and the open source tool suite that has served us well over the past two decades.

Chatbots are ready to succeed. If you think you've to hack days or even weeks to create a chatbot, you might be wrong. You don't have to be aware of any coding skills. Immediately after big players like Facebook Messenger or Skype have opened their platform for programmers many tools emerged. With this article I want to give you an introduction to mockup and overview of different tools to build your first chatbot. You want to show your use case?

BT has announced that it will be replacing its famous and much-revered red boxes with new kiosks. Those kiosks won't allow people to hide in them, but will otherwise offer the modern version of phonebox technology – super fast Wi-Fi, free calls and chargers for phones, instead of traditional handsets. The new kiosks will start opening up on major high streets in London from next year. They'll eventually roll out to the rest of the country – though there'll be fewer of them than there are traditional phone boxes. Amy Rimmer, Research Engineer at Jaguar Land Rover, demonstrates the car manufacturer's Advanced Highway Assist in a Range Rover, which drives the vehicle, overtakes and can detect vehicles in the blind spot, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire Chris Burbridge, Autonomous Driving Software Engineer for Tata Motors European Technical Centre, demonstrates the car manufacturer's GLOSA V2X functionality, which is connected to the traffic lights and shares information with the driver, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module'Kibo' of the International Space Station (ISS) and develop launch vehicles The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight.

Corporate giants like Google, Facebook, and IBM are collectively investing billions of dollars in artificial intelligence (AI), bringing together some of the world's brightest minds who claim that new techniques can create machines that think independently and creatively. So with all this effort, money, and hype, where are the smart robots in our society? Where are the AI assistants, co-workers, and companions? Where are all the cool droids and humanoids that science fiction promised us? In order to build AIs with human-like intelligence--AIs who can interact socially, who are able to work with us to achieve goals, and who are behaviorally and intellectually similar to beloved characters from Star Trek and Star Wars--we must first create one fundamental feature almost entirely missing from their current design.

Abstract: A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several... Read More

Analysts have estimated that one third of Amazon's sales come via their recommendation system. Where do you think they got this recommendation system?? From visualization to data preparation, from the creation of machine learning models to putting these models in production -- all of this generates a lot of plumbing to connect all the parts. These pioneers broke the CLIENT-PROVIDER RELATIONSHIP that exists INSIDE companies between the business, IT and analytics teams. They found a way to create a predictive application "dream team" with all the key players focused on building the best product possible.

When the term "artificial intelligence" – better known as "AI" – was initially coined, it was thought that humans (carbon based life forms) had "real" intelligence while the best a machine's intelligence (to the extent they had any) could get was "artificial". As I work with companies that are leading major machine learning, algorithms, and AI initiatives I'm convinced that we're ushering in a new golden age of AI but one that might need some terminology refinements. IBM, one of the leaders in AI's new golden age, talks about how cognitive computing will take us to the promised land where machines aren't just augmenting our calculation skills but really recognize patterns without being taught by humans, sift through data without us teaching them, digitize our experiences without our involvement, strike up conversations, drive our cars, and make decisions just like humans do. With the rapid progress that we've been making, much of which I'll get to see first-hand when I attend the next week, I've been wondering whether we should move away from the term artificial intelligence to just silicon intelligence. We're carbon-based so our intelligence could be called carbon Intelligence (CI) not "real intelligence".

This book includes a selection of research papers in robot control applications, and presents several tools and mathematical concepts that allow the development and operation of robotic systems. The description of projects using robotic systems in areas such as vision, navigation, path planning, trajectories, non-holonomic systems, mobile robotics, robot control with very specific structures, as well as artificial intelligence systems is pointed out. The development of different ideas in control systems that are useful and hopefully enriching for the reader are also presented in this book.

In his prescient work on investigating the potential use of information technology in the legal domain, Lawlor surmised that computers would one day become able to analyse and predict the outcomes of judicial decisions (Lawlor, 1963). According to Lawlor, reliable prediction of the activity of judges would depend on a scientific understanding of the ways that the law and the facts impact on the relevant decision-makers, i.e., the judges. More than fifty years later, the advances in Natural Language Processing (NLP) and Machine Learning (ML) provide us with the tools to automatically analyse legal materials, so as to build successful predictive models of judicial outcomes. In this paper, our particular focus is on the automatic analysis of cases of the European Court of Human Rights (ECtHR or Court). The ECtHR is an international court that rules on individual or, much more rarely, State applications alleging violations by some State Party of the civil and political rights set out in the European Convention on Human Rights (ECHR or Convention).

Artificial intelligence, such as neural networks, deep learning and predictive analytics, has the potential to transform radiology, by enhancing the productivity of radiologists and helping them to make better diagnoses. This short report from Signify Research presents 5 reasons why artificial intelligence will increasingly be used in radiology in the coming years and concludes with a list of the barriers that will first need to be overcome before mainstream adoption will occur.