Perceiving the surrounding environment in terms of objects is useful for any general purpose intelligent agent. In this paper, we investigate a fundamental mechanism making object perception possible, namely the identification of spatio-temporally invariant structures in the sensorimotor experience of an agent. We take inspiration from the Sensorimotor Contingencies Theory to define a computational model of this mechanism through a sensorimotor, unsupervised and predictive approach. Our model is based on processing the unsupervised interaction of an artificial agent with its environment. We show how spatio-temporally invariant structures in the environment induce regularities in the sensorimotor experience of an agent, and how this agent, while building a predictive model of its sensorimotor experience, can capture them as densely connected subgraphs in a graph of sensory states connected by motor commands. Our approach is focused on elementary mechanisms, and is illustrated with a set of simple experiments in which an agent interacts with an environment. We show how the agent can build an internal model of moving but spatio-temporally invariant structures by performing a Spectral Clustering of the graph modeling its overall sensorimotor experiences. We systematically examine properties of the model, shedding light more globally on the specificities of the paradigm with respect to methods based on the supervised processing of collections of static images.

Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning). Automated algorithm configuration methods have recently received much attention in the AI community since they replace tedious, irreproducible and error-prone manual parameter tuning and can lead to new state-of-the-art performance. However, practical applications of algorithm configuration are prone to several (often subtle) pitfalls in the experimental design that can render the procedure ineffective. We identify several common issues and propose best practices for avoiding them. As one possibility for automatically handling as many of these as possible, we also propose a tool called GenericWrapper4AC.

As Innovation Lead for Precision Medicine at Innovate UK I am sometimes asked about the best STEM subjects to study, usually by parents wanting to help their children select the best university courses. Something they're really interested in, I have tended to say, but now add that something involving AI (Artificial Intelligence) might be a very wise choice. AI's nothing new, but now seems on the verge of making a big impact in clinical settings, reflected in our competition applications in the area of precision medicine. There are many ways AI can play a role in the medical arena, where being able to find patterns and associations in large data sets is fundamental to developing new technologies and services. These large data sets include disparate patient information, such as the increasing levels of genetic information we will have about patients, and linking it to phenotypic information (observable physical properties e.g.

Leading Valamis' product development, our Chief Technology Officer Dmitry "Dima" Kudinov has spent the past six years researching AI and the best applications to support lifelong learning. With years of research under his belt, Dima talks about the power of AI to personalize learning, the benefits of AI supported lifelong learning, and what this will mean for the future of Valamis product development. First of all, I'm very excited about the progress made in Natural Language Understanding. Of course, this topic is nothing new, but in recent time there has been significant progress made thanks to the accessibility of greater computing power, richer data sets for training, and the creation of more sophisticated algorithms. This improvement with text-based input has allowed a new way of interaction between people and systems in the form of chatbots to emerge. Backed by an even more exciting progress in Speech to Text and Text to Speech conversions, chatbots now have personalities, and they can engage in voice dialog with people.

User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on pre- dicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.

In this, the first in a two-part series, you will learn about artificial intelligence and machine learning, common missteps, success criteria, and how to take advantage of these new capabilities. A few years ago, machine learning was virtually unheard of outside the geek press; now it's blasted past cutting-edge to the top of the strategic agenda. In fact, in a recent study by SAP and the Economist Intelligence Unit, "Making the Most of Machine Learning: 5 Lessons from Fast Learners," 68% of companies surveyed are using machine learning in some form; among procurement companies, it is about 65%. These companies are on a path toward automation. Social psychologist and Harvard professor Shoshana Zuboff said, "Everything that can be automated should be automated."

H2O.ai, the open source leader in AI, today announced the latest additions to the speaker lineup for H2O AI World London, a two-day interactive event featuring deep-dive technical sessions, talks on real-world business use cases and hands-on training. With speakers from PwC, Barclays, NVIDIA, IBM, Citi and more, the conference will bring together data scientists, business analysts and executives across multiple industries to discuss the latest trends in artificial intelligence, machine learning and data science, important use cases and the biggest challenges currently facing the industry. Join H2O.ai in London to connect with the community and learn how to harness the full value of AI, ML, deep learning and data science from industry-recognized speakers and hands-on training sessions. Register here to secure your spot. On day one of the conference, sessions will focus on hands-on technical training for H2O.ai's groundbreaking products, H2O Driverless AI, H2O-3 and Sparkling Water, to empower data scientists and analysts of all levels to work on projects faster and more efficiently through automation and state-of-the-art computing power.

Working alongside robots will be part of a new university course aimed at students entering careers as carers, therapists and social workers. The new university programme is designed to help people get comfortable working with'social robots' that will be their colleagues in the future, researchers say. A recent survey found that almost 40 per cent of people are afraid that robots will steal their jobs. Earlier this year NHS officials announced that robots will carry out dementia care within 20 years. Working alongside robots will be part of a new university courses aimed at students entering careers as carers. Scientists from Sligo Institute of Technology in Ireland are testing a'Paro' robot seal that reacts to petting and conversation.

It's August here in Arizona, and despite the occasional monsoon thunderstorm (and accompanying dust storm), the heat is on! While we stay huddled in our air-conditioned homes, cars and offices, we decided to turn up the heat on some of this year's hottest SEO trends. And who better to talk to about the latest SEO happenings than our very own Brandon Alisoglu, Digital Current's newest SEO Strategist? Brandon gave us his perspective on some of the industry's leading topics and cutting-edge updates. SEOs and digital content creators have to think about how search engines, devices and humans will be accessing their content in the future (and today).

Do you have an idea that could make you South Africa's most successful fintech entrepreneur? Are you looking to acquire the skills to launch a data-driven fintech business? A new one-year data science and business skills programme aims to assist aspirant SA fintech entrepreneurs. In an announcement today, the Explore Data Science Academy (Edsa) and Rand Merchant Investments' (RMI) fintech division, AlphaCode, said their Explore 10X programme would assist 20 aspiring SA future fintech entrepreneurs. Successful candidates will go through an intensive six-month data science-training programme, where they will learn how to design a fast-growing business along with the core digital skills needed to build a fintech organisation.