It is a good idea to have small well understood datasets when getting started in machine learning and learning a new tool. The Weka machine learning workbench provides a directory of small well understood datasets in the installed directory. In this post you will discover some of these small well understood datasets distributed with Weka, their details and where to learn more about them. We will focus on a handful of datasets of differing types. Standard Machine Learning Datasets Used For Practice in Weka Photo by Marvin Foushee, some rights reserved.

We know you, dear readers, have been tracking the megatrend of artificial intelligence. There are many issues in this trend that should inform your day-to-day decision-making (we examine AI issues as part of our CAMBRIC construct to help put the trend in the context of other major thrusts in the tech world). Most AI solutions today are fielded by the big players in IT. For example, Apple's Siri or the capabilities they embedded directly in iOS9, or Google's many savvy search solutions or Amazon's very smart recommendations. Amazon's Echo is also, like Siri, connecting to a very smart cloud capability that takes advantage of AI.

The next step in achieving human-level ai is creating intelligent--but not autonomous--machines. What are some of the strategies you think will help mitigate the potential existential risks of artificial intelligence? Earlier this year, the korean Go champion Lee Sedol played a historic five-game match against Google's AlphaGo, an artificially intelligent computer program. As with nuclear technology, the worst-case scenario for strong AI--malevolent superintelligence turns on humanity and tries to kill it--would be globally devastating.

From Jarvis, Mark Zuckerberg's Iron Man-inspired assistant, to Microsoft's rather unfortunate Tay experience, chatbots have been making the news this year – not least in how they are playing an ever bigger role in customer service. While there has been a lot of speculation about how chatbots are going to replace human agents in contact centers, we are still a long way from that scenario. Rather, they are freeing up humans and, somewhat counterintuitively, helping to deliver a more personalized experience. Automation in customer experience is all about making things faster, easier and more streamlined for customers – so we don't have to repeat ourselves multiple times, and explain our problems to different agents every time we contact an organization. Pretty much any organization today has some sort of customer experience process in place, and that process has evolved along with technology.

A recent announcement out of the company reports, "XPRIZE, the global leader in incentivized prize competitions, today announced that registration is now open and guidelines are available for the 5 million IBM Watson AI XPRIZE, a four-year global competition challenging teams to develop and demonstrate how humans can collaborate with powerful artificial intelligence (AI) technologies to tackle the world's greatest challenges. The AI competition is XPRIZE's first open challenge where teams will define their own goals and create AI applications that solve some of humanity's most pressing challenges in areas such as healthcare, education, energy & environment, global development and exploration." Marcus Shingles, CEO of XPRIZE, commented, "In the coming decade, as XPRIZE strives to achieve its impact mission through incentive competitions and crowd-sourcing, we see tremendous opportunity in this emerging generation of problem solvers to use AI to solve humanity's grandest challenges… The IBM Watson AI XPRIZE is intended to promote and progress the notion of'AI for impact' among the global bold innovator crowd, both the established community of practitioners, as well as encourage newcomers to experiment and ultimately demonstrate how AI can be used as a tool for good." The article adds, "Teams have until December 1, 2016 to register through the XPRIZE website for the four-year competition, and then will have until March 1, 2017 to submit a detailed development and testing plan for their proposed solution. There will be three subsequent rounds of selection each fall during which a panel of expert judges will choose the top 10 teams that will advance to compete at IBM Watson sponsored events where they can receive Milestone Prizes."

Once again, thousands of people from various corners of the marketing industry and the world descended on the French Riviera for the Cannes Lions International Festival of Creativity. Some things haven't changed: the celebrity appearances, the late nights on the Carlton terrace that invariably lead to a regrettable hangover, the gigantic Daily Mail Yacht and the endlessly flowing rose. But this year also brought some surprises, including the Brexit decision at the end of the week as well as some unexpected Lions winners. Here are some other takeaways from adland's biggest event. The Maison Samsung featured a VR surfing experience on the roof and a super-secret "second-floor experience."

An'Airbnb of dog sitting and walking', Rover.com is available in the US and offers a more affordable and flexible option than traditional services like kennels. But matching local pet owners with appropriate sitters is a surprisingly complex big data challenge, which is where the developers and data scientists that create and test Rover.com's Founded in 2011 by Greg Gottesman (Founder and Board Member) and Philip Kimmey (Co-Founder and Director of Software Development), "Rover.com is a dog lover's other best friend -- second only, of course, to your dog," says the website. When his nine-year-old daughter said she would have actually paid to take care of someone else's dog, Gottesman saw a business. He and his team, including Phillip Kimmey, pitched the idea for Rover.com at the 2011 Startup Weekend in Seattle and won top prize.

Here's a task for you: Type "I want to get started with machine learning" into your favourite search engine. I get back a whole list of options from Coursera, Azure, Amazon, kdnuggets, reddit,… and I could continue on and on. So where should one get started? In this post, I want share the experiences of UK Technical Evangelist and ML Expert Amy Nicholson. Amy will share her experience of using the Cortana Intelligence Gallery in conjunction with the Azure ML Studio, and how that combination helped her break down the otherwise high barriers into popular ML techniques and start building her own ML models as well as her knowledge about this space. Amy is a graduate from The University of Sheffield in the UK, where she studied Computer Science.

Software agents are nothing new, but we're now entering a new wave of innovation relating to bots and artificial intelligence that has the potential to impact our lives at home and at work, and change how businesses operate. New applications for'intelligent' automation are springing up everywhere, with the potential to affect all of the processes and engagements that make organisations work… but where does it make sense to prioritise your efforts? From work automation to augmentation, what approaches are right for your use cases today? On Thursday July 21 we're running a free webinar where you'll learn about the factors driving the explosive growth of machine learning, how to build up a big picture of the opportunities and challenges, and how to give your business an edge. If you can't make it for the live webinar, we'll be recording the session and will make the recording available to view on The Advisor.

Twitter (NYSE:TWTR) recently acquired Magic Pony technologies, a machine learning startup focusing on deep learning in computer vision. In my continuing coverage of the AI market, I want to briefly comment on this transaction. First, another contributor has already commented and described the company's product (without technical details). At its core, Magic Pony uses machine learning to enhance low resolution images and videos. It is quite remarkable when you think about it.