There is an urgent need to expand the AI epistemic community beyond the specific geographies in which it is currently clustered. Artificial Intelligence (AI) is no longer the subject of science fiction and is profoundly transforming our daily lives. While computers have already been mimicking human intelligence for some decades now using logic and if-then kind of rules, massive increases in computational power are now facilitating the creation of'deep learning' machines i.e. algorithms that permit software to train itself to recognise patterns and perform tasks, like speech and image recognition, through exposure to vast amounts of data. These deep learning algorithms are everywhere, shaping our preferences and behaviour. Facebook uses a set of algorithms to tailor what news stories an individual user sees and in what order.

In the future, Artificial Intelligence can be utilized to eliminate inherent human biases that often influence important decisions surrounding employment, government policy, and even policing. At the event, Professor Iris Bohnet stated that every person has biases that inform their decisions. These biases can affect whether a candidate for a job is chosen or not. As a result, Bohnet suggested that by using algorithms, employers could choose the best candidates by using AI to focus on the candidate's qualifications rather than by basing decisions on gender, race, age or other variables. However, the panel also discussed the fact that even algorithms can have bias.

Element AI has acquired the entire team at MLDB.ai, an open source machine learning database. The acquisition includes all staff, MLDB.ai's complete product line, and customer base. The company will continue to be developed as an open-source project and leverage Element AI's resources. The Pro version of MLDB.ai will be open-sourced, as will some plugins associated with the processing of LiDAR datasets. The company said in a blog post that it will be winding down support contracts over the next six months, and replacing it with an expanded presence on free, community-based support channels.

IBM (NYSE: IBM) today announced IBM Machine Learning, the first cognitive platform for continuously creating, training and deploying a high volume of analytic models in the private cloud at the source of vast corporate data stores. Even using the most advanced techniques, data scientists – in shortest supply among today's IT skills1 – might spend days or weeks developing, testing and retooling even a single analytic model one step at a time. IBM has extracted the core machine learning technology from IBM Watson and will initially make it available where much of the world's enterprise data resides: the z System mainframe, the operational core of global organizations where billions of daily transactions are processed by banks, retailers, insurers, transportation firms and governments. IBM Machine Learning also for the first time deploys Cognitive Automation for Data Scientists from IBM Research to assist data scientists in choosing the right algorithm for the data by scoring their data against the available algorithms and providing the best match for their needs. The service also considers various circumstances – such as what the algorithm is needed to do and how fast it needs to produce results. Clients are beginning to see the value in IBM Machine Learning for z/OS.

AI is about to go mainstream. It will show up in the connected home, in your car, and everywhere else. While not as glamorous as sentient beings that turn on us in futuristic theme parks, the use of AI in fraud detection holds major promise. Keeping fraud at bay is an ever-evolving battle where both sides, good and bad, are adapting as quickly as possible to determine how to best use AI to their advantage. There are currently three major ways that AI that is used to fight fraud, corresponding to how AI developed as a field.

I'm really excited about artificial intelligence. It's going to be awesome… right up until the revolution, that is.

AI is everywhere at the moment, and it's responsible for everything from the virtual assistants on our smartphones to the self-driving cars soon to be filling our roads to the cutting-edge image recognition systems reported on by yours truly. Unless you've been living under a rock for the past decade, there's good a chance you've heard of it before -- and probably even used it. Right now, artificial intelligence is to Silicon Valley what One Direction is to 13-year-old girls: an omnipresent source of obsession to throw all your cash at, while daydreaming about getting married whenever Harry Styles is finally ready to settle down. But what exactly is AI? -- and can terms like "machine learning," "artificial neural networks," "artificial intelligence" and "Zayn Malik" (we're still working on that analogy…) be used interchangeably? To help you make sense of some of the buzzwords and jargon you'll hear when people talk about AI, we put together this simple guide help you wrap your head around all the different flavors of artificial intelligence -- If only so that you don't make any faux pas when the machines finally take over.

Apple's Siri voice assistant picks up a few new tricks each year, but in 2017, it may get a lot brainier. The Cupertino, California-based tech titan plans to enhance Siri with artificial intelligence, DigiTimes reports. We're hoping Siri keeps getting smarter as time passes. That fits with other rumors that the next iPhone, possibly called the iPhone 8, will mark its 10-year anniversary with big changes like an edge-to-edge display and glass body. A much more proactive Siri that can learn your behavior would be a welcome addition to this list. Artificial intelligence, or AI, is a budding trend among phones for 2017.

Facebook's news feed has recently been taken to task for the explosion of "fake news" that can have real consequences. Music curation has moved from record labels and radio stations to increasingly AI-based algorithms. We are all accustomed to this intrusion of machine intelligence into our lives over the past decade -- systems watching our behavior and trying to infer what we want, and then combining such information with that from other sources so as to create a complex consumer profile that is valuable to advertisers. What happens when the next generation of intelligent systems mediate our environments completely? Or, more to the point, what happens when we delegate the curation of all aspects of our lives, as well as the actual physical appearance of the world, to a pervasive network that learns about us and continually adapts to our needs and desires?