As advancements in Quantum Computing and Artificial Intelligence are always popular media topics, I thought it'd be interesting to take a look at how the two are converging and what such a convergence might mean for cybersecurity. Today's traditional computers operate by processing information as "bits." A bit can be either a "1" or a "0." Even massive data-crunching super computers such as the US Department of Energy's Titan Cray XK7 operate in this fashion. These computers explore potential solutions to problems in a methodical, sequential manner.

Security is a broad term, and in industry and government there are a myriad of "security" contexts on a variety of levels – from the individual to nation-wide. Artificial intelligence and machine learning technologies are being applied and developed across this spectrum. While many of these technologies have the potential and have greatly benefited society (helping reduce credit card fraud, for example), the evolving social contexts and applications of these technologies often leave more questions than answers – in terms of rules, regulations and moral judgments – in their wake. Artificial intelligence and security were – in many ways – made for each other, and the modern approaches of machine learning seem to be arriving just in time to fill in the gaps of previous rule-based data security systems. The purpose of this article is to shed light on current trends and applications, in industry and government, at the intersection of artificial intelligence and the security field.

We are dealing with plethora of data and information in the world today and expectation is to predict and forecast how we can gain competitive advantage based on the information that we have, to act in advance. We look forward to define and furnish various methods based on our gut feel, past historical data, simple mathematical averages, and many more to get an incredibly precise prediction. With advanced analytics and data science, we develop "always-on" forecasting models which enable our clients to take their decisions effectively. From intuition to traditional algorithms to machine learning, phases have been evolving over a period. Processes 2 & 3 are iterative and 6 & 7 are iterative. We try to look for answers to various questions in the process – is the goal / business objective descriptive or predictive in nature?

Machine learning is breaking down language barriers, fuelling – and fighting – cybercrime, and can even recognise emotions, but the complex processes behind these breakthroughs are often a mystery. Startup Graphcore wants to change this. The Bristol-based firm has created a series of'AI brain scans', using its development chip and software, to produce Petri dish-style images that reveal what happens as processes run. Most machine learning programs – including Google's own systems, and open source frameworks – work by training AI on computational graphs. Put very simply, machine learning systems go through a construction phase, during which a graph showing all the computations needed is created. This is followed by an execution phase where the machine uses the computations (or steps) highlighted in the graph to run through its training processes.

Everybody seems to want to get in on the machine learning hype these days. According to the head of Google's DeepMind team, however, "There's only a few hundred people in the world that can do that really well." No wonder that Gartner has machine learning at the absolute apex of its 2016 Hype Cycle. Nothing else promises to do so much to transform humanity...with such an anemic record to show for itself. This isn't to suggest that artificial intelligence has a bleak future, but rather that we're getting way ahead of ourselves in terms of what it's delivering today.

All of us that work with social data know the signal to noise ratio is ever increasing, and not in our favor. As the irrelevant baby picture or the 1000th Trump meme pops up in our social, that micro-dopamine hit of ingesting content from social media may not fire like it used to, and you lose interest. In fact, it is this increasing noise that can cause businesses to become disillusioned or skeptical that they can use social data to achieve their goals. As these noise levels increase, it's not hard to recognize that businesses need ever more powerful tools to find those sparkly signal-diamonds in the social rough. The basics of attributing sentiment scores and broad topic categories are outgrowing their usefulness and competitive advantages to drive significant results.

What exactly are Cognitive Services and what are they for? Cognitive Services are a set of machine learning algorithms that Microsoft has developed to solve problems in the field of Artificial Intelligence (AI). The goal of Cognitive Services is to democratize AI by packaging it into discrete components that are easy for developers to use in their own apps. Web and Universal Windows Platform developers can consume these algorithms through standard REST calls over the Internet to the Cognitive Services APIs. So why is it worthwhile to provide easy access to AI? Anyone watching tech trends realizes we are in the middle of a period of huge AI breakthroughs right now with computers beating chess champions, go masters and Turing tests.

His session, "IA for AI: New Methods for Designing a New Class of Products," will explore how cognitive design can help measure and optimize the performance of artificial intelligence systems. In it, he will be introducing new biometric discoveries about our emotional responses to Artificial Intelligence systems by measuring heart rate, skin conductance and facial expressions. In the past two years, artificial intelligence (AI) has forayed into our daily lives through systems like Siri, Cortana, Alexa and Google Assistant. John, a PhD in Cognitive Science with more than 15 years of experience in user-centered design, will delve into how we can optimally architect AI experiences by analyzing how humans structure their commands to AI systems and react to AI responses. Do we prefer systems that give better answers, or ones that feel more human?

EU-wide rules are needed for the fast-evolving field of robotics, e.g. to enforce ethical standards or establish liability for accidents involving driverless cars, say MEPs in a resolution voted on Thursday. MEPs ask the EU Commission to propose rules on robotics and artificial intelligence, in order to fully exploit their economic potential and to guarantee a standard level of safety and security. They note that regulatory standards for robots are being planned in several countries, and point out that the EU needs to take the lead on setting these standards, so as not to be forced to follow those set by third countries. Rapporteur Mady Delvaux (S&D, LU) said "Although I am pleased that the plenary adopted my report on robotics, I am also disappointed that the right-wing coalition of ALDE, EPP and ECR refused to take account of possible negative consequences on the job market. They rejected an open-minded and forward-looking debate and thus disregarded the concerns of our citizens."

Renowned Apple analyst Ming-Chi Kuo of KGI Securities predicted that iPhone 8 would supplant Touch ID with advanced sensors capable of recognizing one's face with very high accuracy. JPMorgan analyst Rod Hall corroborated Kuo's report, speculating that facial recognition would utilize a front-facing 3D sensor. For many people, this has been one of the tougher iPhone 8 rumors to swallow. Touch ID lets you unlock your iPhone without looking whereas facial recognition would require you to face the sensor, a major inconvenience. A few reports have attempted to shed some light on the subject, explaining the basics of 3D motion and object scanning and how Apple might leverage 3D sensors in iPhone 8.