In future, machine learning could improve transport, security, healthcare and revolutionise industry. But despite its reach, this powerful technology remains mysterious to most. Join our panel of speakers, chaired by Marcus du Sautoy, to discuss what we mean by machine learning and discover some of the exciting current and future uses of this technology. We'll have presentations from the Head of Microsoft Research, Chris Bishop and robotics researcher Sabine Hauert. You'll also be able to take part in an interactive exhibition where machine learning developers and researchers will showcase examples of the technology in action.

At the recent RSA Conference it was virtually impossible to find a vendor that was not claiming to use machine learning. Both new and established companies are now touting "machine learning" as a major component of the data science being used in their products. What the heck is machine learning anyway? And is it really going to reshape cyber security in 2016? For brevity's sake, I'll define machine learning as the science of getting computers to act without being explicitly programmed.

Following in the wake of the recent trouncing of humans by artificial intelligence platform AlphaGo, Google has announced the launch of a cloud-based machine learning platform. The search giant's new large-scale platform will be able to learn and make predictions'across a whole variety of scenarios', and is reminiscent of the fictional Skynet service from Terminator. A limited preview of the service is now available for users to build their own machine-learning models'that work on any type of data, of any size'. Following in the wake of the recent trouncing of humans by artificial intelligence platform AlphaGo, Google has announced the launch of a cloud-based machine learning platform. The search giant's new large-scale platform will be able to learn and make predictions'across a whole variety of scenarios' A limited preview of the service is now available for users to build their own machine-learning models'that work on any type of data, of any size'.

In big-data discussions, the value of data sometimes refers to the predictive capability of a given data model and other times to the discovery of hidden insights that appear when rigorous analytical methods are applied to the data itself. From a cybersecurity point of view, I believe the value of data refers first to the "nature" of the data itself. Positive data, i.e. malicious network traffic data from malware and cyberattacks, have much more value than some other data science problems. To better understand this, let's start to discuss how a wealth of network traffic data can be used to build network security models through the use of machine learning techniques. Machine learning, together with data science and big data, is gaining a lot of popularity due to its widespread use in many tech companies around the world.

Google had an announcement that means strictly business for its push to be known as a leader in cloud services. "Today [Wednesday], "we've taken a major stride forward with the announcement of a new product family: Cloud Machine Learning." The move is all about taking Cloud Machine Learning mainstream, "giving data scientists and developers a way to build a new class of intelligent applications," according to a post from Fausto Ibarra, director, product management, Google Cloud Platform. Blair Hanley Frank of IDG News Service said in so doing, that "Google is making it easier for businesses to take advantage of the machine learning revolution with a new product for building models that predict the future." Cade Metz in Wired said "the company unveiled a new family of cloud computing services that allow any developer or business to use the machine learning technologies that power some of Google's most powerful services." Basically, as Robert Hof in SiliconANGLE put it, "Now that Google has infused the brand of artificial intelligence known as machine learning into everything from search to speech recognition, it's tossing the technology it views as tech's next big wave into the public domain." The announcement was part of events at a two day conference in San Francisco, namely the Google Cloud Platform (GCP) Next 2016. The company said the Cloud Machine Learning provides machine learning services, with pre-trained models and platform so that a person can generate his or her own tailored models. Major Google applications use Cloud Machine Learning, including Photos (image search), the Google app (voice search), Translate, and Inbox (Smart Reply)--but now their platform is available as a cloud service for business applications. Google is not shy about blowing its own horn when it comes to machine learning technology: Compared to other large scale deep learning systems, said Google, "Our neural net-based ML platform has better training performance and increased accuracy." This announcement may be filed under strategy. Robert Hof made the observation: "Google made it clear that it intends to pitch the Cloud Machine Learning services announcement as a key differentiator.

Digital marketers, accustomed to using software that helps them think about marketing, are now transitioning to a time when software will do much of the thinking. It's called cognitive marketing, and research firm IDC expects that half of all companies will use this emerging generation of computer intelligence for their marketing and sales efforts by 2020. IDC Research Manager Gerry Murray (who is speaking at our MarTech conference next week) predicts that just a year from now, the current group of several dozen applications employing cognitive computing will become "hundreds." Such applications, he told me, utilize "processes akin to the human brain, [by] taking signals and drawing conclusions." The converging drivers -- massive computing power, massive amounts of data and a booming population of connected devices and sensors -- are of course instrumental in delivering this new kind of marketing.

World Go champion Lee Se-dol has beaten AlphaGo, an AI program developed by Google's DeepMind unit this weekend, though he still trails the program 3-1 in the series. Google's publicity stunt highlights the progress which has been made in the world of artificial intelligence and machine learning, as commentators predicted a run-away victory for Se-dol. DeepMind founder Demis Hassabis commented on Twitter "Lee Sedol is playing brilliantly! We are in trouble now…" allowing Se-dol to win the fourth game in the five game series. While the stunt demonstrates the potential of machine learning, Se-dol's consolation victory proves that the technology is still capable of making mistakes.

Artificial Intelligence became a hot topic earlier this year when suddenly all the heads of big tech firms were voicing their opinions around it. A movie called Ex-Machina helped contribute to the debate around AI as it demonstrated how a robot could gain so much human intelligence that it was able to manipulate humans and escape from their control. Understandably it prompted both excitement and concern. Similarly the Matrix films portrayed humans as believing they exist while actually living in an artificial or simulated world. It is thanks to popular science fiction films that our understanding of AI has grown.

Join us live at Entrepreneur's Accelerate Your Business event series in Chicago or Denver. Technology is changing at an incredible pace, so fast in fact that many industries and individuals are having a hard time keeping up. Whether you're an entrepreneur, an investor, or just plain curious, these are the fields to keep an eye on in the next decade, and the ones most likely to mint a new generation of millionaires and billionaires. Google's AI program AlphaGo recently beat a world class human player at the game of Go, which many experts believed was years away from being possible. As of last year, Google's Cloud Vision AI was as good as or better than a human at recognizing the contents of images (so was Microsoft's AI for that matter).

Microsoft's attempt at engaging millennials with artificial intelligence has backfired hours into its launch, with waggish Twitter users teaching its chatbot how to be racist. The company launched a verified Twitter account for "Tay" – billed as its "AI fam from the internet that's got zero chill" – early on Wednesday. The chatbot, targeted at 18- to 24-year-olds in the US, was developed by Microsoft's technology and research and Bing teams to "experiment with and conduct research on conversational understanding". "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation," Microsoft said. "The more you chat with Tay the smarter she gets."