Technologies such as robotics, 3D printing and artificial intelligence are poised to reshape where we live in the coming years. Augmented reality could beam a'holographic' Gordon Ramsay into your kitchen, to offer cooking tips as you fire up the induction hob. Every surface in the home could be transformed into a touchscreen that operates different tasks, walls can turn into windows on demand and your house could double as a food-growing farm. Augmented reality could put a virtual chef in your kitchen, talking you through meal plans. Walls, floors and ceilings might be able to transform themselves in response to voice commands, with nanotechnology turning walls solid or translucent or into a giant TV screen.

And on today's episode, we have two fantastic guests from Bsquare. The first is Ralph Derrickson, the President and CEO and Matthew Inglis, the Vice President of Engineering. For those of you may not be familiar with Bsquare, they are designing intelligent, secure software to help companies really realize the full potential of a connected world and kind of how that all plays into their business and the business of their customers.

Wearing a t-shirt, slub jeans, and a new pair of sneakers, he said it first: "If content is king, context is god." While I'd love to have been the first to utter those words, Gary Vaynerchuck beat me to it. And why will context marketing revolutionize business in the next decade? Context is the reason why a person takes action. Let's be honest, no consumer ever said, "I want more branded content!" No, people engage with our content because it helps them achieve a goal in a moment.

Markov decision processes (MDPs) are the defacto frame-work for sequential decision making in the presence ofstochastic uncertainty. A classical optimization criterion forMDPs is to maximize the expected discounted-sum pay-off, which ignores low probability catastrophic events withhighly negative impact on the system. On the other hand,risk-averse policies require the probability of undesirableevents to be below a given threshold, but they do not accountfor optimization of the expected payoff. We consider MDPswith discounted-sum payoff with failure states which repre-sent catastrophic outcomes. The objective of risk-constrainedplanning is to maximize the expected discounted-sum payoffamong risk-averse policies that ensure the probability to en-counter a failure state is below a desired threshold. Our maincontribution is an efficient risk-constrained planning algo-rithm that combines UCT-like search with a predictor learnedthrough interaction with the MDP (in the style of AlphaZero)and with a risk-constrained action selection via linear pro-gramming. We demonstrate the effectiveness of our approachwith experiments on classical MDPs from the literature, in-cluding benchmarks with an order of 10^6 states.

Even if you don't have a chatbox on your website, you have definitely encountered one before. Facebook, eBay, Domino's Pizza, and Universal Studios are some of the big names that have their own chatbots. With the advancements in AI, mundane tasks like customer service can easily be handled by a chatbot. Some people reject the merit of chatbots stating that communicating with them feels very impersonal. According to the 2018 State of Chatbots Report by Salesforce, 69% of consumers said they preferred communicating with chatbots.

Dating back to the 1960s, before the internet, MIT Professor Joseph Weizenbaum created the first ever chatbot. He designed a programme to copy human conversation by pairing scripted responses to text users entered into a computer and thus the chatbot was born. Becoming more conversational since the early days, Chatbots have revolutionised the customer experience with instant, personalised responses. Although they still lack some human aspects and the responses are not always perfect, we are able to see how beneficial they can be. In the past few years we've seen an increase in Artificial Intelligence chatbots across social platforms such as Facebook, when it launched bots for messenger in 2016, to voice experiences with smart speakers, such as the Amazon Alexa.

The system consists of an overall agent architecture and five components within the architecture. The five components are (1) goaldirected learning (GDL), a decisiontheoretic method for selecting learning goals; (2) probabilistic bias evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals; (3) uniquely predictive theories (UPTs) and probability computation using independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories; (4) a probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories; and (5) a decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA's initial learning goal is just An autonomous agent must be able to select biases (Mitchell 1980) for new learning tasks as they arise. PBE uses probabilistic background knowledge and a model of the system's expected learning performance to compute the expected value of learning biases for each learning goal. The resulting expected discounted future accuracy is used as the expected value of the bias.

That book was made by Mr. Mark Twain, and he told the truth, mainly. There were things which he stretched, but mainly he told the truth." I haven't even gotten my reading light adjusted, and already I am stuck in the conundrum that is present on every page of David Lodge's generous novel. More importantly, whom should I believe? Mark Twain" disguised as Huck?

From virtual assistants to driverless cars, technology imitating human intelligence is on the rise. But at what ethical cost and how do boards future-proof their organisations in the face of rapid change? Earlier this year, a Japanese insurance company made headlines for doing something that company executives and directors around the world have been anticipating - and fearing - for years. Fukoku Mutual Life Insurance made 34 of its staff redundant and replaced them with artificial intelligence (AI) system IBM Watson. Japanese newspaper The Mainichi reported the company will be using Watson to determine payout amounts and check customer cases against their insurance contracts. Other Japanese insurance companies have announced they are looking at or are already using AI for similar purposes and The Japan Times reported in April that the country's Ministry of Economy, Trade and Industry was planning to trial AI to help government workers write draft answers for questions put to Cabinet ministers.

Most artificial intelligence is actually not human-shaped robots or talking computers. The risk of artificial intelligence to jobs should be considered by a Government working group, a law firm and a business organisation say. In a call to action paper, the Institute of Directors and law firm Chapman Tripp have highlighted the risks, opportunities and challenges that artificial intelligence presents. Institute chief executive Simon Arcus said artificial intelligence had the greatest potential to affect people's jobs. "What we don't want to have is a whole lot of efficiencies created by artificial intelligence that displaces people and leaves people with no jobs and no future."