Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms.

Like most organizations, you want to embed analytics insights in your operational processes and promote a culture of analytical decision making. You want to use machine learning, deep learning, and related technologies to automate decision making when and where it makes sense. These goals might seem both realistic and attainable. After all, software and cloud vendors are pitching you easy-to-use, quasi-automated, self-service tools and consultants promise to help you bridge the gap between the skills you have and the skills they say you'll need. Far from it, says Mark Madsen, a research analyst with information management consultancy Third Nature.

The researchers add, "[I]t is possible that in the future algorithms will write many if not most algorithms." That future looks pretty close. MIT Technology Review rounds up several recent milestones in machine learning software that is making machine learning software. There are lots of implications here, but one obvious one is that some of the hottest tech job categories of 2017 may be at just as much risk of being automated away as truck drivers. "If self-starting AI techniques become practical, they could increase the pace at which machine-learning software is implemented across the economy," writes Tom Simonite, the magazine's San Francisco Bureau Chief. "Companies must currently pay a premium for machine-learning experts, who are in short supply."

Sony's latest 4K panels boast powerful processors that improve the look of just about any video you throw onto the screen. But the chips inside these TVs don't stop at making movies look better: Like giant Pixel phones, the TVs will have Google's voice-controlled Assistant built into them. According to Sony, Google Assistant will be added to their new sets via a firmware update later this year. The sooner they can do it, the better. Tens of millions of people are expected to decide it's finally time to buy a 4K TV this year, and the deals are delicious at every point of the pricing spectrum. Variety abounds too, as the industry's big brands try to solve different pieces of the picture-quality puzzle.

Technology often hits the market at the speed of innovation and we play catch up with its impacts. We craft policy after the fact, write standards for safety and market growth and, perhaps, ponder how the latest technology will shape our behavior and impact society at large. Fortunately, this somewhat ad hoc, imperfect approach has worked well enough in a rough-and-tumble world. But sometimes entirely new, potentially far-reaching, high-impact areas of innovation come along that demand vigorous consideration upfront before opportunities for unintended consequences become manifest. Such is the case, I believe, with artificial intelligence, or AI.

Slush, Europe's leading startup event, took place in Helsinki from November 30th to December 1st. Thousands of attendees including startups, investors, tech companies, and researchers came together to get a glimpse of the latest developments in a massive range of fields. Futurice was there in force: our Futucafe went on tour, providing good coffee and a meeting space for attendees, and we launched the Chilicorn Fund as a way to make the world a slightly better place. Our data science team went along too, and I'm going to talk about some of the trends we saw there. Machine learning, artificial intelligence, analytics, data science: these terms were on the lips of companies and speakers throughout Slush.

One of the most common analyses we perform is to look for patterns in data. What market segments can we divide our customers into? How do we find clusters of individuals in a network of users? It's possible to answer these questions with Machine Learning. Even when you don't know which specific segments to look for, or have unstructured data, you can use a variety of techniques to algorithmically find emergent patterns in your data and properly segment or classify outcomes.

Illustrious physicists including Stephen Hawking have warned against the dangers of artificial intelligence and its unchecked growth in the last century. Could it one day outgrow us, and make us extinct? How would it do this? A massive thank you to our super fans who have supported us on our Patreon page. Follow us on Twitter - http://bit.ly/MNqFgY

The new year has arrived and it's going to be one big year for wearable tech. Evey year I look into my connected crystal ball and peek into the future to predict what might happen in the world of wearables. Here is how I think 2017 will shake down for the wearable world. The biggest buzzword of 2017 is AI, and it's set to be the saving grace of IoT and wearables. Deep learning is a massive missing piece when it comes to our connected world -- wearable tech included -- as cognitive computing and machine learning will make use of big data and connect the dots for users.

Ever since Facebook expanded access to its Messenger service in April 2016, giving businesses the ability to reach customers through APIs, "Chatbot" has become the buzzword in developer communities across the globe. Here is a short piece on the essentials for investing resources in chatbots that may be helpful to startups and established businesses alike. There is a certain'wow factor' when someone books a cab or orders a bouquet of flowers simply through a conversation. It's a never-seen-before phenomenon where computing intelligence uses natural human language to deliver simplified consumer experiences. Chatbots have the advantage of enabling users to access business services while on chat platforms like Facebook Messenger and Telegram.