One of the big topics that was discussed in financial technology (fintech) circles at length in 2016 ― and will continue to be discussed at even greater length as we enter 2017 ― is artificial intelligence (AI). Machine learning is emerging as the field within AI that is seeing the most amount of real-world applications and use cases among financial institutions, especially in the area of fraud. Take, for example, a historic event that unfolded in March 2016 that demonstrated the power of machine learning: the victory of program AlphaGo of Google DeepMind over professional gamer Lee Se-dol. This exciting technological breakthrough demonstrates how far AI has come, even in just the past year, and how it is now able to catch humans out. It's true, though, that the idea of computers learning autonomously has been around for decades.

Technology has transformed the way we communicate. From the early telegrams through to email, social media and smart phones, it has been an area of life that has been constantly evolving. Now, a Catz Fellow is pioneering a new method of interaction in the form of semi-autonomous telepresence robots. Shimon Whiteson, Associate Professor, Fellow and Tutor in Computer Science is the scientific coordinator for the TERESA project which has developed'socially intelligent' telepresence robots, which allow people to interact with others without being there physically. In telepresence systems, a human controller remotely interacts with people by guiding a remotely located robot, allowing the controller to be more physically present than with standard teleconferencing.

Earlier this morning, our CEO - Josh Ziegler, spoke with Dan Murphy on CNBC about artificial intelligence, chatbots, and IBM Watson in relation to how ZUMATA is using these technologies to assist our clients and partners like Expedia, Amadeus, DHISCO, and Paytm.

Smart machines and digitalization will boost rather than cut employment -- but people must get used to constant retraining to keep pace with change, the development chief of German software giant SAP tells Handelsblatt. SAP is the world's biggest supplier of business software and is shifting its business model towards cloud computing services. People are too negative about digitalization and artificial intelligence and should embrace new technologies as job creators rather than job killers, Bernd Leukert, the head of development at German business software giant SAP told Handelsblatt at the Davos World Economic Forum last week. Mr. Leukert, 49, who has worked at SAP since 1994 and joined the management board in 2014, said the German education system, the government and firms weren't doing enough to prepare people for a future in which there will be a constant need for new job training. "I think there's there's too much talk of negative scenarios in the discussion about digitalization and new technologies," he said.

Announced at the Consumer Electronics Show (CES) in Las Vegas, BelleFox (www.bellefox.ai) is poised to revolutionize how families interact in this constantly connected world. The BelleFox Wi-Fi router is a unique technology that delivers not only reliability and fast speeds, but a host of features to help parents see and understand their children's online behavior -- and even to learn more about who their children are as individuals. The system uses Big Data and AI (artificial intelligence) to deliver its powerful, useful insights. "Children today are online natives, which can be extremely stressful for parents," stated Lily Li, co-founder of BelleFox. "Until now, family Wi-Fi systems simply offered ways to limit children's time and access to specific sites.

We were all fascinated by Siri when she first burst onto the scene five years ago. The world was captivated, the talking computer had finally arrived and was easily available. Since then, more fascinating examples of artificial intelligence (AI) have emerged. While other tech companies have been introducing flashy new examplesof AI at play, it appeared that Apple (NASDAQ: AAPL) had lost its advantage and was falling behind in the pursuit of this nascent technology. While some thought Apple had languished, it turns out it has been quietly integrating AI into many everyday features of its iconic iPhone.

In 1872 a British general named Alexander Cunningham, excavating an area in what was then British-controlled northern India, came across something peculiar. Buried in some ruins, he uncovered a small, one inch by one inch square piece of what he described as smooth, black, unpolished stone engraved with strange symbols -- lines, interlocking ovals, something resembling a fish -- and what looked like a bull etched underneath. The general, not recognizing the symbols and finding the bull to be unlike other Indian animals, assumed the artifact wasn't Indian at all but some misplaced foreign token. The stone, along with similar ones found over the next few years, ended up in the British Museum. In the 1920s many more of these artifacts, by then known as seals, were found and identified as evidence of a 4,000-year-old culture now known as the Indus Valley Civilization, the oldest known Indian civilization to date. Since then, thousands more of these tiny seals have been uncovered.

We've traditionally seen machine learning interview questions pop up in several categories. The first really has to do with the algorithms and theory behind machine learning. You'll have to show an understanding of how algorithms compare with one another and how to measure their efficacy and accuracy in the right way. The second category has to do with your programming skills and your ability to execute on top of those algorithms and the theory. The third has to do with your general interest in machine learning: you'll be asked about what's going on in the industry and how you keep up with the latest machine learning trends. Finally, there are company or industry-specific questions that test your ability to take your general machine learning knowledge and turn it into actionable points to drive the bottom line forward. We've divided this guide to machine learning interview questions into the categories we mentioned above so that you can more easily get to the information you need when it comes to machine learning interview questions. These algorithms questions will test your grasp of the theory behind machine learning.

Business intelligence (BI) tools first appeared on the enterprise technology scene several decades ago, at birth clumsy and difficult to use but ultimately improving the flow of data through organizations from their operational systems to decision support. Data warehousing cut the time it took to access data, but even at their full maturity, BI systems could do little more than produce data and reports in a traditional organized way. But with the advancement of artificial intelligence and--more importantly--machine learning, true business intelligence is actually on its way to the enterprise. Such self-learning software will run on servers, be built into bots, drive decision-making systems, be embedded into cars or aircraft, and become the beating heart of mobile devices. Increased data-processing power, the availability of big data, the Internet of Things, and improvements in algorithms are converging to power this actual business intelligence.