Artificial intelligence (AI) is powering the next wave of customer experience. Virtual buying assistants, chatbots and voice-activated apps all are changing the way customers interact with brands. The future of e-commerce will look entirely different, and brands are taking steps now -- big or small -- to make this vision a reality. This is the sexy side of AI, the stuff of headlines mixed in with sci-fi fanfare. It's exciting for sure, but the love story between customer and brand, quietly driven by AI for years -- and just skimming the surface of what's possible with access to more data today -- is what gets our heart rate up at Boxever.

Oxford Winter Intelligence - Abstract: In this paper we will address an important issue of reward function integrity in artificially intelligent systems. Throughout the paper, we will analyze historical examples of wireheading in man and machine and evaluate a number of approaches proposed for dealing with reward-function corruption. While simplistic optimizers driven to maximize a proxy measure for a particular goal will always be a subject to corruption, sufficiently rational self-improving machines are believed by many to be safe from wireheading problems. Claims are often made that such machines will know that their true goals are different from the proxy measures, utilized to represent the progress towards goal achievement in their fitness functions, and will choose not to modify their reward functions in a way which does not improve chances for the true goal achievement. Likewise, supposedly such advanced machines will choose to avoid corrupting other system components such as input sensors, memory, internal and external communication channels, CPU architecture and software modules.

For those out there who know me, it'll be no surprise to learn that I'm going long on the transformative power of artificial intelligence (AI). Since 2013, I've spent most of my energy studying, researching, investing (e.g. Mapillary, Numerai, Ravelin) and building AI communities (AI Summit 2015and 2016, LondonAI meetup), with a mission to accelerate its real-world applications. I am passionate about seeking out and bringing technology advancements to markets that can enable us to solve the high-value (and often complex) problems we face in business and society. Importantly, this includes ones that were previously intractable from either a technical or commercial standpoint.

Chatbots are appearing on Facebook, Slack, consumer retail sites, you name it and they're getting really easy to build and with some systems, really smart. At the low end where serious smarts aren't as important, chatbots are becoming projects that non-programmers can build with ease and at the top end, while we're not at HAL 9000 level yet, chatbots that use machine learning and understanding techniques are getting more powerful all the time. So, if you're looking for new, shiny ways to interact with your customers or you want to add slick support for your staff on Slack, this collection of chatbot platforms is what you need to get started. We've got 10 chatbots to consider that range from "throw it together in an afternoon" for fast, simple results, to systems that require a CS degree and deliver impressive results. Let the chatbot takeover begin!

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. There are many reasons why the mathematics of Machine Learning is important and I'll highlight some of them below: The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques.

The primary application of artificial intelligence in retail is customer service chatbots, intelligent search tools, and personalisation. Despite knowing where to put their money, only a handful of retailers in the UK have trialled AI due to it being prohibitively expensive. Most AI investment in 2017 will be targeted towards e-commerce -- though Amazon is something of an exception. Amazon intends to use AI to replace in store cashiers in its Amazon Go stores to detect products shoppers have picked up. Over the next five years a growing number of retailers will buy into AI as it becomes more affordable. Here's where some big players are putting their money: Most retailers use algorithms to suggest similar items or items bought by other customers already, but in 2016 John Lewis was one of the first retailers in the UK to implement an artificially intelligent visual search tool for its iPad app.

Microsoft CEO Satya Nadella spoke at a public event in India on Monday, stressing upon the immense potential of artificial intelligence (AI), calling it the "ultimate breakthrough" in technology. "Because for all the advances in computer interface, there is nothing to beat language [the ability to do human-level speech recognition]," he said during a fireside chat with Nandan Nilekani -- India's premier technocrat and the brain behind the Aadhaar identification system. The chat was streamed live on the Microsoft Developer page on Facebook. Nadella and Nilekani were later joined by Binny Bansal, CEO of Flipkart, India's largest e-commerce company that announced a cloud partnership with Microsoft's Azure. Calling AI "the third run time", Nadella said, "If the operating system was the first run time, the second run time you could say was the browser, and the third run time can actually be the agent. Because in some sense, the agent knows you, your work context, and knows the work. And that's how we are building Cortana. We are giving it a really natural language understanding."

BENGALURU: Microsoft chief executive officer Satya Nadella said "an enlightened immigration policy" has enabled him to live the American dream, even as he emphasised that governments had the right to determine immigration and trade policies. "American values have always been about inclusion and diversity. It's a land of immigrants," said Nadella, who was born in Hyderabad and completed his bachelor's degree in engineering from the Manipal Institute of Technology, before moving to the US in 1988. "American technology reaching me in India is what made it even possible to dream the dream. And then, the enlightened American immigration policy is what led me to live the dream. And I think those are things we will always advocate for... the American dream and the American enlightened immigration policy, especially for high-skilled workers, is something that I'm optimistic about," Nadella told ETin an interview.

Investment in seafood and aquaculture technology increased 271% compared to the $52 million raised across both 2014 and 2015, but there is still a huge need for technology to bring efficiency and sustainability to the industry.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.