Key Points: – Chatbots will save businesses more than $8 billion per year by 2022, a huge increase from the $20 million estimated for this year. In banking, this will climb to 90% in 2022. A new study releases this week by UK-based Juniper Research supports our prediction that chatbots will redefine the customer service industry, with healthcare and banking industries expected to benefit the most. The new report titled "Chatbots: Retail, eCommerce, Banking & Healthcare 2017-2022," estimates that chatbots will help businesses save more than $8 billion per year by 2022, which is a huge increase from the $20 million estimated for this year. Call centers and customer service departments should already be investing in these new conversational technologies if they want to stay competitive and cost-effective as companies across industries grow their investment in building chatbots to help service customers faster, across any channel, device or platform, 24 7. Advancements in technology continue to transform customer service interactions.

A study from Redwood Software and Sapio Research released October 4th revealed that IT leaders believe automation could impact 60% of businesses by 2022 and threaten jobs in the process. Now, a new, separate report from PwC, the second biggest professional services firm worldwide, suggests a similar timeline; one in which people may need to practice and learn new skills -- or be left behind as automation takes over. The report, titled Workforce of the Future, surveyed 10,000 people across China, India, Germany, the UK, and the U.S. to "better understand the future of work." Of those, nearly 37% think artificial intelligence and robotics will put their jobs at risk; in 2014, 33% had a similar concern. A startling scenario the report envisions for the future is one in which "typical" jobs -- jobs people can steadily advance in through promotions -- no longer exist, prompting the aforementioned move to develop new skills.

The self-driving vehicle has become a prominent part of the new wave of automotive advancement. Various automakers are now looking at diving in headfirst to get a piece of the pie while improving their cars. A lot of research has gone into perfecting the self-driving vehicle to make sure it passes all regulations and doesn't harm those who get behind the wheel. Several automotive brands are starting to roll out vehicles with these features, and it's illustrating the power of technology in the modern age. The idea of a self-driving vehicle was often spoken of as a goldmine and an automaker's dream.

Attendance: Participation is by invitation due to space constraints. Participants will include government delegates from a range of domains including digital economy ministries, labour ministries, space agency representatives, research ministries, data protection authorities and consumer protection agencies. Contact: For further information, please contact AI@oecd.org. Webcast: You will be able to follow the event live on this page. The conference is being organised by the OECD with support from the Ministry of Internal Affairs and Communications of Japan (MIC).

The second problem in this report is how those trained PhDs actually create value and to what extent that value will accrue to the UK economy over the next few years. The biggest commercial AI centres in the UK are owned by American firms (Google DeepMind, Microsoft Research, and so on). And those with AI PhDs will be in high demand in those firms, as well as in high demand in nations which welcome migrating talent. As it is many of the UK AI teams I have met are made up of broad swathes of European talent, whose status in the UK post-Brexit is not guaranteed by the Government. How will the UK retain people with some of the most desirable technical skills in the world?

As Google nears 200 M&A deals since its YouTube acquisition back in 2006, we visualize the tech giant's top acquisitions. Eleven years ago, tech giant Google announced its largest acquisition since it incorporated in a Menlo Park garage, paying $1.7B for YouTube, a video platform that at the time had fewer than 100 employees. Since then, Google's checkbook has opened wide (as we highlighted in our deep dive into Google's M&A strategy), with close to 200 M&A transactions announced over the past decade. This includes six $1B acquisitions, such as marketing solutions provider DoubleClick ($3.1B, 2007) and navigation app Waze ($1.15B, 2013). More recently, the company made a big push into AI, acquiring UK-based DeepMind ($650M, 2014).

You know a technology is reaching mainstream adoption when the government is commissioning reports into growing it as a sector. That is exactly what has just happened for AI. A new independent report looking at the state of AI in the UK, and what we need to do to remain world-leaders, has just been released. It is no surprise that the focus is on growing the talent pool & making more data readily available for training. A couple of years ago, start-ups using AI were sexy and all the pitches we saw majored on how they were adopting this emerging technology. Just like cloud computing before it, AI is in the transition from cutting edge new technology to just another tool in the start-up toolbox.

As many in the enterprise IT community will remember, technology suppliers succeeded in roundly confusing buyers in the early part of the millennium by "greenwashing" their products and services – or in other words, exaggerating the true extent of their environmentally-friendly credentials – thereby shooting themselves in the foot and, arguably, putting the brakes on the market. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit. Please provide a Corporate E-mail Address.

The characteristics (or numerical patterns) of a feature vector in the transform domain of a perturbation model differ significantly from those of its corresponding feature vector in the input domain. These differences - caused by the perturbation techniques used for the transformation of feature patterns - degrade the performance of machine learning techniques in the transform domain. In this paper, we proposed a nonlinear parametric perturbation model that transforms the input feature patterns to a set of elliptical patterns, and studied the performance degradation issues associated with random forest classification technique using both the input and transform domain features. Compared with the linear transformation such as Principal Component Analysis (PCA), the proposed method requires less statistical assumptions and is highly suitable for the applications such as data privacy and security due to the difficulty of inverting the elliptical patterns from the transform domain to the input domain. In addition, we adopted a flexible block-wise dimensionality reduction step in the proposed method to accommodate the possible high-dimensional data in modern applications. We evaluated the empirical performance of the proposed method on a network intrusion data set and a biological data set, and compared the results with PCA in terms of classification performance and data privacy protection (measured by the blind source separation attack and signal interference ratio). Both results confirmed the superior performance of the proposed elliptical transformation. 1 1. INTRODUCTION Feature vectors carry useful numerical patterns that characterize the original domain (or a sub original domain - input domain) formed by the feature vectors themselves. Machine learning algorithms generally utilize these patterns to generate classifiers, that can help make decisions from data, by using supervised or unsupervised learning techniques (Suthaharan, 2015).

Last June Volume, a leading magazine on architecture and design, published an article on the GoogleUrbanism project. Conceived at a renowned design institute in Moscow, the project charts a plausible urban future based on cities acting as important sites for "data extractivism" – the conversion of data harvested from individuals into artificial intelligence technologies, allowing companies such as Alphabet, Google's parent company, to act as providers of sophisticated and comprehensive services. The cities themselves, the project insisted, would get a share of revenue from the data. The company does take cities seriously. Its executives have floated the idea of taking some struggling city – Detroit?