Today on In Case You Missed It: MIT researchers made a deep learning vision system watch TV and it learned to predict when people are going to kiss, shake hands, high five or hug. Georgia Tech scientists teamed up with others to figure out how to create 3D images of microscopic cells, by giving them a selfie mirror. To check out the WalMart fireworks video in full, click here. As always, please share any interesting tech or science videos you find by using the #ICYMI hashtag on Twitter for @mskerryd.

High-tech entrepreneur Elon Musk has his sights set on building robots that can do housework, have conversations and play games. In working on these different robotic abilities, Musk, the CEO of both SpaceX and Tesla Motors, said he hopes to advance the artificial intelligence algorithms that will be needed to create them. "A significant fraction of our research bandwidth is being spent on fundamental research," wrote Musk, along with Ilya Sutskever, Greg Brockman and Sam Altman, who also are working with OpenAI, an open-source A.I. research company, in a blog post Monday. "A significant fraction of our research bandwidth is being spent on fundamental research. We'll always be developing and testing new ideas... This is important -- our current ideas will not be enough to achieve our long-term goal."

Using cognitive science theories, Forbus and his collaborators have developed a model that could give computers the ability to reason more like humans and even make moral decisions. Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. "In terms of thinking like humans, analogies are where it's at," said Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science in Northwestern's McCormick School of Engineering. "Humans use relational statements fluidly to describe things, solve problems, indicate causality, and weigh moral dilemmas." The theory underlying the model is psychologist Dedre Gentner's structure-mapping theory of analogy and similarity, which has been used to explain and predict many psychology phenomena.

History evidences the fact that every overhaul or step forward in technology has brought a questionable bag of user data integrity. A majority of the scenarios involve critical information being analyzed on-the-wire, which means everything is happening anonymously to improve services or in some cases, data is immediately dismissed from the servers as soon as the required result is achieved. Privacy has been always the core concern of any developing technology primarily because these advancements demand user patterns for improving and sustain better overall outcomes and additionally, due to a sudden increase in digital thefts recently. Google, at their I/O event this year, mentioned that privacy violation has been kept minimal with new and modern products by not individualizing the data collected. That basically denotes that any third party or Google itself won't be able to track down particular user with specific content.

Google is worried about artificial intelligence. No, not that it will become sentient and take over the world, but that, say, a helpful house robot might accidentally skewer its owner with a knife. The company's latest AI research paper delves into this issue under the title "Concrete Problems In AI Safety." Really, though, that's just a fancy way of saying "How Are We Going To Stop These Terror-Bots Killing Us All In Our Sleep." To answer this brain-tickler, Google's researchers have landed on five "practical research problems" -- key issues that programmers will need to consider before they start creating the next Johnny Five.

Artificial intelligence is the branch of computer science concerned with making computers behave like humans. As much freaky as it sounds, Artificial Intelligence (AI) is no longer a work of science fiction where machines would take over the world a la The Matrix. AI has been around for decades, and it has proved its superiority over human intelligence several times. For instance, when IBM's supercomputer Deep Blue beat the world chess champion Garry Kasparov in 1997. And, that was almost 20 years ago! But, the history of AI can be traced back to as early as 1950, when the world's first computer scientist Alan Turing pioneered the idea of a'thinking machine' and said these famous words, "A computer would deserve to be called intelligent if it could deceive a human into believing that it was human".

Paris, France/Vianen, Netherlands, June 22, 2016 --The post-app era has started including a shift from apps to conversation, from apps to messaging platforms and from app-stores to bot-stores, all triggered by machine intelligence, also known as artificial intelligence. The end of apps is the beginning of doing everything on messaging platforms. This is described in the new report from SogetiLabs, The Bot Effect: Friending a Brand. "The fact that machines are now (more and more) capable of understanding natural language like voice and speech, and talk back is something that we've seen happening in science fiction movies. Now fiction becomes a fact. Due to better hardware, new neuromorphic chips and Big Data, these machines are able to learn and improve. The impact of this shift in possibilities has implications for businesses and human beings, for efficiency and psychology", concludes Menno van Doorn, Director of SogetiLabs, and co-author of the new report on the topic of Machine Intelligence.

Complex interdependencies between applications and computing environments can make whitelist-based security nearly impossible to enforce in practice, a Cisco executive has argued as the company debuts a data-centre analytics platform that utilises machine-learning techniques to model and predict the security impact of configuration and application changes. Those changes can wreak havoc with conventional security models built around blacklists that are difficult to keep up to date as the environment changes, vice president of product marketing Rajeev Bhardwaj told CSO Australia as the company debuted a Tetration Analytics platform that surveils the data centre to monitor data flows and application interactions. While many data-centre operators preferred to operate on a zero-trust, whitelist-based security model, that had been difficult to accomplish because of changing interdependencies within the "black box" of the data centre, Bhardwaj said. "The data centre of today has multiple layers of complexity from compute, network, and storage infrastructure as well as virtualisation, firewalls, load balancers and customer applications," he explained. "If something goes wrong it's extremely hard to find out what happened: you look at the black box and don't know which application is talking with which applications, which ports are open, which applications are protected with a firewall."

Building such a robot isn't just a way of getting rid of household chores, according to a blog entry posted by the nonprofit research group. It would also be a neat way of testing whether or not its work in artificial intelligence is progressing in the right way. There are already ways of creating a robot that can carry out specific tasks, the researchers note. The difference is that Musk's team hopes to create "learning algorithms" that would allow the creation to serve as a "general purpose" robot – meaning that it can be left around the home and be clever enough to work out what it needs to do to clean.

Looking back is much easier than looking forward - when Management Today published its first issue in 1966, Britain was debating decimalisation, the average weekly wage was 14 and workers were enjoying a'golden age' of employment and easy-to-find jobs. As our Future of Work special report has demonstrated, we've come a long way since then, but it's safe to say that the changes over the next 50 years will be ever more profound. Of course, no one can predict them with any accuracy - the future is not a mere extrapolation of the past. But each year CEBR, one of the UK's leading economics consultancies, predicts where the British economy will rank in 15 years' time. As things stand, it forecasts that in 2031 the UK will be the world's sixth biggest economy with a GDP of 4.7bn (compared to 3bn now), and that it could even overtake the German and Japanese economies during the 2040s.