AI has been talked about since the very early days of computing and has attained mainstream use in recent years with the likes of Amazon's Alexa and Apple's Siri. "Just as in the last 40 years, computation has enabled us to change the way we do business and create new products, AI will help us to make better decisions," Carlos Kuchovsky, chief of technology and R&D at BBVA, tells Finextra. "We are now looking at the ways in which it can help us change the way we operate and bring value." The Bank of England has recently reported that machine learning tools are in use at two thirds of UK financial firms, with the average company using it two business areas, which is expected to double in the next three years. It may be through interoperation with cloud and blockchain technology that AI's capabilities will be fully harnessed. AI Utilisation of machine learning and artificial intelligence has become commonplace in everyday life, whether it be in search engines, music streaming services or internet shopping.

Swipe right for "would like to meet", left for "wouldn't". Seven years after Tinder made choosing a date as simple as flicking your thumb across a smartphone screen, it is by far the most-used dating app in the UK and the US. Downloaded 300m times and with more than 5 million paying subscribers, it is the highest-grossing app of any kind in the world, according to the analysts App Annie. For Americans, apps and online dating are the most common way to meet a partner. "It's an amazing responsibility, and an amazing privilege," says Elie Seidman, Tinder's 45-year-old chief executive.

Keep your Amazon Echo close to your bed for when you really need it. When you wake up feeling groggy and sick, the last thing you want to do is get out of bed and go see the doctor. Fortunately, if you've got your Amazon Echo ($70 at Amazon) at your side (or even the Alexa app), you can get diagnosed right from your comfy zone. While Alexa isn't a doctor and can't physically examine you, it can use the web and its smarts to help give you a diagnosis based on the condition you've described. Not to mention, you can avoid that dreaded copay and doctor bill.

Google held its big annual hardware event Tuesday in New York to unveil the Pixel 4, Nest Mini, Pixelbook Go, Nest Wifi, and Pixel Buds. It was mostly predictable because details about virtually every piece of hardware the company revealed at the event were leaked months in advance, but if Google's biggest hardware event of the year had an overarching theme, it was the many applications of on-device machine learning. Most of the hardware Google introduced includes a dedicated chip for running AI, continuing an industry-wide trend to power services consumers will no doubt enjoy, but there can be privacy implications too. The new Nest Mini's on-device machine learning recognizes your most commonly used voice commands to quicken Google Assistant response time compared to the first-generation Home Mini. In Pixel Buds, due out next year, machine learning helps recognize ambient sound levels and increase or decrease sound the same way your smartphone dims or brightens when it's in sunlight or shade.

We propose a novel learning framework for recommendation systems, assisting collaborative filtering with a synthetic feedback loop. The proposed framework consists of a "recommender" and a "virtual user." The recommender is formulizd as a collaborative-filtering method, recommending items according to observed user behavior. The virtual user estimates rewards from the recommended items and generates the influence of the rewards on observed user behavior. The recommender connected with the virtual user constructs a closed loop, that recommends users with items and imitates the unobserved feedback of the users to the recommended items. The synthetic feedback is used to augment observed user behavior and improve recommendation results. Such a model can be interpreted as the inverse reinforcement learning, which can be learned effectively via rollout (simulation). Experimental results show that the proposed framework is able to boost the performance of existing collaborative filtering methods on multiple datasets.

Communications has become the buzz word of the century. From cellphones to Facebook pages, people are in almost constant contact--or at least trying to be in contact. The IoT (Internet of Things) adds a layer to the communications concept, bringing everyday "things" into contact with everyday people. Amazon's Alexa, Google's Assistant, and Apple's Siri are all talking to us, in home and out, and now more and more houses are talking back. For example, all SheaConnect homes, by Shea Homes, include a standard set of smart home features, with additional options available in select communities.

One of the technologies we are seeing being trialled and deployed in airports is robotic assistants. The humanoid robots are positioned around the airport terminal assisting passengers with queries and information. By making use of Artificial Intelligence (AI) and Machine Learning, the robots can process large amounts of data, with real-time updates to enable them to provide the latest information to passengers. This technology is starting to be used in some select airports but for different functions. Munich Airport in Germany is using robotic assistants primarily for information.

Each of these brand-name voice assistants speaks when spoken to, turning on a smart speaker or other voice-activated device that can answer questions. Voice is rapidly emerging as a hands-free medium consumers use for, well, just about anything -- music, news feeds, hints on removing stains, instructions for mixing cocktails. In homes, people use voice commands to adjust interconnected lights and thermostats, and search for -- and even buy -- products and services. Think of cave dwellers huddled around a fire pit enraptured by hunting tales, or a 1940s nuclear family gathered in front of a radio for the next episode of The Lone Ranger. And until recently, forms of entertainment and media that relied solely on voice seemed to be on the decline.

Two important problems in preference elicitation are rank aggregation and label ranking. Rank aggregation consists of finding a ranking that best summarizes a collection of preferences of some agents. The latter, label ranking, aims at learning a mapping between data instances and rankings defined over a finite set of categories or labels. This problem can effectively model many real application scenarios such as recommender systems. However, even when the preferences of populations usually change over time, related literature has so far addressed both problems over non-evolving preferences. This work deals with the problems of rank aggregation and label ranking over non-stationary data streams. In this context, there is a set of $n$ items and $m$ agents which provide their votes by casting a ranking of the $n$ items. The rankings are noisy realizations of an unknown probability distribution that changes over time. Our goal is to learn, in an online manner, the current ground truth distribution of rankings. We begin by proposing an aggregation function called Forgetful Borda (FBorda) that, using a forgetting mechanism, gives more importance to recently observed preferences. We prove that FBorda is a consistent estimator of the Kemeny ranking and lower bound the number of samples needed to learn the distribution while guaranteeing a certain level of confidence. Then, we develop a $k$-nearest neighbor classifier based on the proposed FBorda aggregation algorithm for the label ranking problem and demonstrate its accuracy in several scenarios of label ranking problem over evolving preferences.

In the next 10 minutes, you'll possibly be amazed, amused, blown away, frightened, or lost in thought. As this is the beginning of a new year and we'd all rather feel joyful – let's focus on the amusing part. Here are 8 mind-boggling technology acceleration outcomes awaiting for us in the (near) future. Artificial Intelligence (AI) by definition is an artificially created ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. AI could carry out a complete simulation of the human brain and even exceed it.