What do you think of when someone says "AI" or "Artificial Intelligence"? For most of us, it conjures up an image of the future. It doesn't much evoke the here and now. Artificial intelligence is already out of the box. And while it might not be as slick as the movies, it has vast applications in almost every field, from business to medicine, traffic jams to Facebook photos. Most of us use or benefit from artificial intelligence every day.

Bee Gees singer Barry Gibb and Beatles drummer Ringo Starr have been knighted, and Strictly judge Darcey Bussell made a dame, in the New Year Honours. Ex-Deputy PM Nick Clegg and War Horse novelist Michael Morpurgo also receive knighthoods, and author Jilly Cooper and TV chef Rick Stein become CBEs. Among five honours for the World Cup-winning England Women cricket team is an OBE for captain Heather Knight. Ex-astronaut Helen Sharman joins the Order of St Michael and St George. Alexandra Shulman, who recently stood down as editor of British Vogue after 25 years; actors Hugh Laurie and Susan Hampshire, and leading artificial intelligence researcher Demis Hassabis are made CBEs.

One of the biggest problems for businesses today is sorting through data to find customer insights. In fact, most companies today collect mass amounts of data--probably too much, in my opinion. Mining through it all can be difficult and time-consuming for sales teams hoping to learn more about their prospects and improve their chance of a sale. Thankfully, machine learning can help. Using artificial intelligence (AI) and as-a-Service (aaS) MarTech software, companies can better identify and interact with customers--without ever personally setting foot in the quagmire of dumped data.

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.

Machine learning already plays a big part in your everyday life, and its role is only going to grow. Google searches and muttered requests to Amazon's Alexa may tap into a veiled world of clever algorithms, but these techniques teeter on something much larger: a world of self-developing artificial intelligence. Deep learning, and the neural networks that do the thinking, is becoming an integral seam to digital technology. By extension, artificial intelligence is having a growing effect on our experience of the world and, as an artist, it is a material that can't be ignored. That is the thinking of Anna Ridler, who is building a name for herself with works that hoist machine-learning techniques into the gallery.

One company pioneering technological innovations in travel is KAYAK, and the company is taking a self-declared bullish approach to using artificial intelligence to forward the next phase of travel planning. For example, KAYAK recently was the first in the travel industry to launch of a hands-free booking capabilities on Alexa for hotels. KAYAK assistants currently are also found on platforms like Facebook Messenger, Microsoft Teams, Apple TV, Google Home, and Slack. To understand how travel bookings and planning are changing, Digital Journal caught up with Matthias Keller, the Chief Scientist at KAYAK. Digital Journal: What are the main challenges facing the travel sector in terms of AI? Matthias Keller: One challenge we're facing is that voice transactions are brand new and people are not yet used to spending money on these platforms.

Enjoy some of the most interesting and thought-provoking opinion pieces on all matters fintech published by Banking Technology in 2017. Faure to fintech – what music can teach us about communicating the benefits of tech By Caroline Page, director, Chilli Communication On the surface Handel or Shostakovich seem a world away from the world of finance. It's time to pull the brake on Bitcoin By Daniel Döderlein, founder and CEO of Auka Strong opinions yielded strong feedback. Ten questions for, and about, AI By Roger Schank, founder and CEO at Socratic Arts "I have had it with the stream of articles about what artificial intelligence (AI) can do." I know what I am doing!

This is an excerpt from a long interview between an anonymous data scientist and Logic Magazine about AI, deep learning, FinTech, and the future, conducted in November 2016. LOGIC: Alright, let's get started with the basics. What is a data scientist? Do you self-identify as one? DATA SCIENTIST: I would say the people who are the most confident about self-identifying as data scientists are almost unilaterally frauds.

What can we expect from Artificial Intelligence in 2018? Read on for my predictions for the artificial intelligence (AI) trends we'll see in 2018. The hype that has surrounded artificial intelligence for so long is being recognized for what it is, allowing us to accept and even embrace certain realities of working with AI-based technologies. In 2018, I believe that organizations will understand that automatic learning IS NOT REAL. Training a machine learning-based system requires an investment of resources, time and the patience to endure the many reiterations required of any system that has to "learn."

AI autopilots in commercial airlines is a surprisingly early use of AI technology that dates as far back as 1914, depending on how loosely you define autopilot. The New York Times reports that the average flight of a Boeing plane involves only seven minutes of human-steered flight, which is typically reserved only for takeoff and landing. Many high school and college students are familiar with services like Turnitin, a popular tool used by instructors to analyze students' writing for plagiarism. While Turnitin doesn't reveal precisely how it detects plagiarism, research demonstrates how ML can be used to develop a plagiarism detector. Historically, plagiarism detection for regular text (essays, books, etc.) relies on a having a massive database of reference materials to compare to the student text; however, ML can help detect the plagiarizing of sources that are not located within the database, such as sources in foreign languages or older sources that have not been digitized.