Artificial intelligence (AI) has continued to gain prominence in 2017 as one of the biggest upcoming technologies. It is beginning to have more of an influence on companies' strategies and is predicted to drive significant change for organisations. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

In 2017, the predictive ability of artificial intelligence (AI) powered many new tools and platforms. So what does 2018 have in store for AI? I asked some marketers to find out. Gregg Johnson, CEO of Invoca, a call tracking and analytics service, says that 2018 will be "the year the voice trend becomes undeniable." "As people increasingly trade typing for talking, we'll see more companies invest in developing for voice interfaces," Johnson said.

Nigel is CEO and Founder of When Labs: artificial intelligence for augmenting management, driving compliance, employee engagement, retention, and productivity. Nigel's passion for HR and management comes from over two decades of building and managing teams from two to thousands around the globe, and led to his acquisition of Kenexa, a human capital management company, the 6th largest acquisition IBM had ever made. Nigel is a proven leader and innovator. As founding CTO of Footprint Software, a fintech startup, he engineered the largest retail banking system of its kind, building the fastest growing startup in Canada at the time, which sold to IBM. There, he architected IBM's entry into Open Source software, making IBM the first major corporation to embrace Linux and Apache, and was founding product line manager for their most successful organic software product of the last two decades, WebSphere.

If nothing else, you can this much for artificial intelligence: They're rarely afraid to look stupid. If a learning A.I. encounters something outside its preprogrammed knowledge, it will not typically be shy in asking the person with whom it is speaking to clarify. This can, however, make for rather monotonous conversation for the human involved in talking to the chatbot, voice assistant, or generally conversant robot: "What's an apple?" "What's tiramisu?" "What's cured meat?" "What's do you know literally anything about food you stupid recipe chatbot?" You get the idea, and as researchers from Japan's Osaka University point out in a recent spotlight on their work, that last line is indicative of the real problem facing A.I.: Asking questions might be the best way for them to learn, but that doesn't count for much if the barrage of questions is so irritating or tedious that the human wanders off.

Artificial Intelligence, Machine Learning and Deep Learning are all the rage in the press these days, and if you want to be a good Data Scientist you're going to need more than just a passing understanding of what they are and what you can do with them. There are loads of different methodologies, but for me I would always suggest Artificial Neural Networks as the first AI to learn - but then I've always had a soft spot for ANNs since I did my PhD on them. They've been around since the 1970s, and until recently have only really been used as research tools in medicine and engineering. Google, Facebook and a few others, though, have realised that there are commercial uses for ANNs, and so everyone is interested in them again. When it comes to algorithms used in AI, Machine Learning and Deep Learning, there are 3 types of learning process (aka'training').

About a week ago, Stanford University researchers posted online a study on the latest dystopian AI: They'd made a machine learning algorithm that essentially works as gaydar. After training it with tens of thousands of photographs from dating sites, the algorithm could perform better than a human judge in specific instances. For example, when given photographs of a gay white man and a straight white man taken from dating sites, the algorithm could guess which one was gay more accurately than actual people participating in the study.* They wanted to protect gay people. "[Our] findings expose a threat to the privacy and safety of gay men and women," wrote Michal Kosinski and Yilun Wang in the paper.

This piece was written by PI Technologist Dr Richard Tynan. Tech firms and governments are keen to use algorithms and AI, everywhere. We urgently need to understand what algorithms, intelligence, and machine learning actually are so that we can disentangle the optimism from the hype. It will also ensure that we come up with meaningful responses and ultimately protections and safeguards. Many technologists emerge from University, College or graduate courses with the impression that technology is neutral and believe that all systems they apply their expertise in developing will also be completely neutral.

In the not so distant future, we will have machines capable not only of storing data, but of thinking, feeling, and being as intelligent as the human being. Following the new trends in the information technology market, such as tracking digital transformation, is a key factor for organizations seeking to remain competitive with the great competition in the market. With increasingly advanced and sophisticated resources, technological innovations have computer machines that promise to facilitate the routine of companies, where intelligent machines can perform their activities in an optimized way. We are talking about the trend of the future, a revolutionary technology – Artificial Intelligence (AI). Artificial Intelligence is already part of our everyday life, but many times we do not even notice it.

Could robots change the way we think? While that might seem the stuff of dark science fiction, New Zealand artificial intelligence (AI) experts say there's real fear that computer algorithms could hijack our language, and ultimately influence our views on products or politics. "I would compare the situation with the subliminal advertising that was outlawed in the 1970s," said Associate Professor Christoph Bartneck, of Canterbury University's Human Interface Technology Laboratory, or HIT Lab. "We are in a danger of repeating the exact same issue with the use of our language." Bartneck has been working in the area with colleague Jurgen Brandstetter and other experts at the New Zealand Institute of Language Brain and Behaviour and Northwestern University in the United States.

Artificial intelligence and big data have formed a truly symbiotic relationship, and they need each other to bring to fruition what both are promising. "Throughout the business world, every company these days is basically in the data business, and they're going to need AI to civilize and digest big data and make sense out of it." "In the past, AI's growth was stunted due to limited data sets, representative samples of data rather than real-time, real-life data and the inability to analyze massive amounts of data in seconds. Today, there's real-time, always-available access to the data and tools that enable rapid analysis. This has propelled AI and machine learning and allowed the transition to a data-first approach.