Synxi, a ManyWorlds brand, discovers content and expertise across your organization most relevant to you. Delivered as apps for collaborative platforms including Microsoft SharePoint, Yammer and Tibbr, Synxi uses patented machine learning and behavioral inferencing technologies to anticipate and adapt to your context and needs.

Others have a more positive approach and say that AI is just an extension of human thinking that can make intelligent decisions faster than us regular people. Just some days ago, Tieto nominated an AI member on the leadership team of a business unit, called Alicia T. And most of us are already familiar with personal AI assistants such as Siri, Cortana, Alexa, and Google Now. IT security has often been described as a game of cat and mouse. The bad guys try to devise attacks that cannot yet be detected, while the good guys try to figure out beforehand where the next attack could be coming from and hitting to. This is also a playing field where AI is going to play an important role.

BOT TO THE FUTURE: Mastercard wants to make commerce'more conversational' Mastercard has unveiled plans to launch artificial intelligence (AI) bots for its merchant and bank partners, allowing consumers to use chat, messaging and natural language interfaces to shop and manage their finances. Mastercard KAI, the payments giant's bot for banks, will allow consumers to ask the bot questions about their accounts, review purchase history, monitor spending levels and receive contextual offers. Meanwhile, the Mastercard Bot for Merchants will allow consumer to shop and transact on messaging platforms and then check out with the Masterpass global digital payment service. According to research firm Gartner, nearly US$2bn in online sales will be performed exclusively through mobile digital assistants by the end of 2016. Kiki Del Valle, SVP at Mastercard, explained to NFC World the company was aiming to make commerce "more conversational by combining secure digital payments and artificial intelligence technology".

As of last month, Google, Facebook, Amazon, IBM and Microsoft are all joining forces to create a new AI partnership called the Partnership on Artificial Intelligence to Benefit People and Society. The collaboration, aside from having a lengthy name, will advance public understanding of AI, and agree on a list of standards for future development. Although there's currently a clear domestic trend, AI has uses that stretch beyond analysing the traffic. A number of companies involved with AI research are looking well beyond family applications. Right now, though, the immediate aim seems to be to get consumers to adopt AI as part of their everyday lives.

For many people, the first name that comes to mind when you say "Artificial intelligence" still isn't Siri, Alexa or Google. They all talk like us, or look like us. We just can't resist defining AI, an abstract and potentially revolutionary concept, in terms of ourselves. All we can see and hear is the interface -- the way we interact with AI is what makes it intelligent to us. Not what it knows, what it can do or the choices it makes.

As Amazon's Alexa voice assistant becomes smarter each day, it's also showing up in more form factors, with the latest being a small home robot courtesy of, well, a smartwatch brand. Omate's Yumi is, in many ways, a mini version of the ASUS Zenbo: Its head features a 5-inch 720p touchscreen, it runs on wheels, it's powered by Android and it even shares a similar appearance in white. Much like Omate's Rise 3G smartwatch, the Yumi supports Alexa out of the box, so it's effectively an Echo Dot with wheels plus a cute face -- look closer and you'll see the same smile in Amazon's logo. When you do get bored of that look, just pick another face. With a starting price of $349, the 11.7-inch tall Yumi is bound to miss out on some of the bells and whistles found on the $599 Zenbo. For one, Yumi lacks object avoidance and drop avoidance sensors, so you'll have to be careful when it's placed on the table or countertop.

Artificial intelligence is the next big thing in the world of computing, and it is already there. Google's Pixel smartphones, which come with an inbuilt Google Assistant that can get you anything from the daily dose of news to meditation tips, has already started selling in India. Siri is more intelligent than before; it lets you chat her up to find a good place to eat nearby and even draft an email for you. But the biggest use of artificial intelligence might be in bringing down the bar to access for millions of people who find all the text and foreign languages on smartphones hard to comprehend. That is exactly what companies like Karbonn, who think more mass than niche, are working on.

Move over desktop: Mobile is the future of online purchases and commerce, thanks to its ability to connect with location-based and personalized data. Forrester's new report "Predictions 2017: Mobile is the Face of Digital," scheduled to be publicly released Tuesday, explains that mobile has become the new pathway for consumers find brands -- and it is moving past the traditional social media app. Advertisers will increasingly use mobile to connect next year using chatbots, other artificial intelligence-enabled platforms like Apple's Siri or Amazon's Alexa and messaging apps, the report said. The company previously said that people spend more than two hours a day on mobile, and that by 2019 the majority of our billion websites will be on mobile. "The magic of mobile is the immediacy," said Julie Ask, principal analyst at Forrester and co-author of the report.

This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms? Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab: These algorithms are described in the article What you wont learn in statistics classes.