Way back in 2013, Toyota showed off its self-driving testbed for autonomous vehicle features like safety braking and automatic lane control. Now, the company is pulling the dust sheets off the second-generation of the ride, which is designed to do a whole lot more. The Toyota Research Institute 2.0 Advanced Safety Research is the first that the company can say was built entirely in-house. The technology has been built on top of a current-generation Lexus LS600hL, kitted out with layered and overlapping LIDAR, Radar and camera sensors. This, paired with machine vision and machine learning, should reduce the car's dependency on high-definition maps that may not yet exist.

Marketing and sales seemed to be much easier in the past. Customers simply visited a retail shop, where they could ask a knowledgeable salesperson about a product they discovered in a local newspaper. In recent years, the ubiquity of the internet and a state-of-the-art technology changed everything. Customers became prosumers, well informed about the product before the purchase. And what is even more important, customers frequently use a variety of channels: online and traditional stores, mobile apps, online auctions, price comparison websites, social media and more. Today, in spite of all the available technologies, life is more challenging for both marketers and salespeople.

The Applied AI Conference is a must-attend event for people who are working, researching, building, and investing in Applied Artificial Intelligence technologies and products. The event is focused on practical applications and current commercialization of AI technologies across industries such as Transportation & Logistics, Internet of Things (IoT), Future of Work (FoW), Financial Technologies (FinTech), CyberSecurity, and Healthcare Technologies (HealthTech). It also explores how AI is impacting society, the enterprise and you! The 2017 conference agenda will provide insights into the present and future impact of AI on your organization, as well as in your daily life. It will also feature concrete ways, tools, and methods to prepare, organize, and tap AI's transformative power.

Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications.

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model equipped by renewable sources and an energy storage system, taking into account the economical profit in energy trading with the main-grid. In particular, this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform decision making in the microgrid. The HGA rationale focuses on a particular encoding scheme, based on control genes and parametric genes applied to the optimization of the FIS parameters, allowing to perform a reduction in the structural complexity of the RB. This approach will be referred in the following as fuzzy-HGA. Results are compared with a simpler approach based on a classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned, while the number of fuzzy rules is fixed in advance. Experiments shows how the fuzzy-HGA approach adopted for the synthesis of the proposed controller outperforms the classic fuzzy-GA scheme, increasing the accounting profit by 67\% in the considered energy trading problem yielding at the same time a simpler RB.

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

The UK government is planning to announce new measures to help artificial intelligence (AI) and robotics researchers to commercialise their breakthroughs. The Department for Culture, Media, and Sport (DCMS) announced on Monday that it will include a number of AI-related proposals in its upcoming Digital Strategy document, which will be unveiled in Parliament on Wednesday. As part of the Digital Strategy, DCMS said it expects to announce an AI review that will be led Southampton University professor Wendy Hall and ex-IBM scientist Jérôme Pesenti, who is now the CEO of London healthcare startup Benevolent.AI. The government is also expected to announce a £17.3 million investment into robotics and AI that will be given to UK universities via the Engineering and Physical Sciences Research Council (EPSRC). "There has been a lot of unwarranted negative hype around AI but it has the ability to drive enormous growth for the UK economy, create jobs, foster new skills, positively transform every industry and retain Britain's status as a world leader in innovative technology," said Hall in a statement.

We have just taken another momentous step in the journey to unveil the hidden wonders of our own planet! Since the launch of the Shell Ocean Discovery XPRIZE at the American Geophysical Union Fall Meeting in San Francisco in December 2015, individuals from around the world have been racing to form Teams and develop a range of groundbreaking technologies to access the deep-sea. Registration closed at the end of September 2016 with 32 bold Teams stepping forward to take on the challenge of mapping and imaging our ocean as never before. Today, we announce the 21 semi-finalists Teams advancing in the Ocean Discovery XPRIZE. These innovative semi-finalist Teams, consisting of almost 350 individuals from 25 countries, represent a broad, impressive diversity of backgrounds and expertise, including middle and high school students, university students, maker-movement enthusiasts, and water and ocean industry professionals.

HIMSS 2017 buzz centered on health data cybersecurity, but that hot topic of recent years' gatherings of the health IT universe simmered alongside emerging trends such as patient engagement and artificial intelligence and machine learning. The progression of healthcare IoT, or the Internet of Medical Things, is not without its challenges. Download a PDF of this exclusive guide now and learn how to overcome the obstacles: security, data overload, regulations, and more. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

Are you willing to learn about Deep Learning for Cyber Security? Join the webinar to learn more! In this webinar, Steven Hutt, Consultant in Deep Learning and Financial Risk, will provide an overview of network anomaly detection. This webinar will be of interest to Data Scientists, Software Engineers and Entrepreneurs in the areas of Connected Cars, Internet of Things/Industrial Internet, Medical Devices, Financial Technology (blockchain) and predictive apps/APIs of all sorts. Steven Hutt is a consultant in Deep Learning and Financial Risk, currently working in Cyber Security and Algorithmic Trading.