Las Vegas: Marvin is just a few inches tall, but he has a big mouth. "What do you think of that, human?" he sneers as he ties the scores 1-1. "Is that the best you can do?" he jeers as he takes a 2-1 lead. "Oh no, IBM will fire me if I lose," he says, as the score is tied 2-2. The decisive move comes up, and Marvin is beaten.

There is a lot of buzz these days on artificial intelligence (AI) in business intelligence products. At a recent tweetchat of my #BIWisdom group of users, vendors and consultants, a participant asked: Is AI becoming a part of everything we do, the way analytics have been added to apps of all shapes and sizes across the business and personal space? Although companies tend to overreact to emerging technologies, AI is not a new technology. One of the tweetchat participants commented that AI is crossing a chasm, either in awareness or approach and that perhaps its evolution to this point is due to the prevalence of data scientists. Another member of the group agreed, tweeting that the data scientists craze has helped more organizations embrace machine learning and AI to get deeper insights.

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The radiology world has been abuzz with discussions of machine learning and what artificial intelligence may mean for the future of the field. The goal is for the technology to quickly scan medical images and prioritize abnormal results, allowing doctors to spend their time on the more difficult cases. The machines would also provide a check on human error. Companies are jumping on board. IBM Watson Health, which acquired enterprise imaging software company Merge Healthcare in 2015, plans to put its Watson supercomputer to work analyzing medical images.

Humanity's relentless march towards a future where machines rule everything gained some ground today, as Google revealed that one of its fancy artificial brains is now better at diagnosing some medical conditions than human doctors are. This should prove extremely valuable for whenever the inevitable robot uprising results in a swift demise for the human race. Google's announced its findings in the Journal of the American Medical Association in a paper detailing the company's work with deep learning algorithms and how they can be utilized for medical purposes. In this particular case, Google set its sights on diagnosing diabetic retinopathy via retina photographs. The neural network Google used for the project was fed over 128,000 images to train it in detecting the condition.

The State of European Tech report points to the success of tech-based businesses or acquisitions across the continent, but at the core of this success is deep tech. When it comes to advances in AI, machine learning, VR and AR, drones, robotics and 3D printing, Europe is either leading the way, or is on par with the likes of the US. Since the start of 2015, $2.3 billion has been invested into deep tech in Europe and 2016 is on track for $1 billion – four times the amount in 2011. Even Norway, the lowest ranking country of those studied, has received $56 million in the past five years. These figures also don't represent the recent acquisition of Skyscanner by China's Ctrip travel firm.

As a smart technology entrepreneur with a machine intelligence research background and passionate about advancing the state-of-the-art in machine or artificial intelligence (AI) to help solve real-world problems, it is very encouraging and exciting to see the AI buzz in the tech industry right now, the progress made in the field to create an even stronger intelligence toolbox, and the ever increasing practical applications in all industries and enterprise functions. Machine intelligence is not only changing the way we use our computers and smartphones but the way we interact with the real world. It is also one of the key exponential technologies in the Fourth Industrial Revolution. Given how all the major technology companies are embracing machine intelligence as a core part of their business and the multitude of startups building their business on this technology, AI is clearly not a passing fad, but being pushed across the rest of the tech world too. In this post I'm not only addressing some key topics about the current and future state of machine intelligence, but also practical steps we are taking here in Africa to not only use smart technology to solve problems, but also make a contribution towards advancing the-state-of-the-art in machine intelligence.

Volume; ever increasing volume which breaks down traditional data-holding capacity Variety; more and more heterogeneous data from many formats and types are bombarding the data environment Velocity; more and more data is time sensitive now; frequent updates are taking place instead of relying on historical old data and data in real time is being generated now by the internet of things, amongst others. Veracity; how valid and reliable is the data? Since now we have so much data, any point of view can be supported by selective adaption of data. Velocity; more and more data is time sensitive now; frequent updates are taking place instead of relying on historical old data and data in real time is being generated now by the internet of things, amongst others. Veracity; how valid and reliable is the data?

Google researchers got an eye-scanning algorithm to figure out on its own how to detect a common form of blindness, showing the potential for artificial intelligence to transform medicine remarkably soon. The algorithm can look at retinal images and detect diabetic retinopathy--which affects almost a third of diabetes patients--as well as a highly trained ophthalmologist can. It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual deterioration of vision. If caught early it can be treated, but a sufferer may experience no symptoms early on, making screening vital.

Three years ago, I decided to learn how to code. A large part of the reason why I decided to embark on a career in tech was to empower myself with the ability to create an application, thereby providing value to society. I was reminded once again why I chose to go down this route during IBM Chairman, President and CEO Ginni Rometty's opening presentation at the Watson Developer Conference in San Francisco this November. Rometty invited Joshua Browder, a 19 year-old student at Stanford and co-founder of DoNotPay, and Ashok Goel, professor of computer science at Georgia Institute of Technology, on stage with her. Browder was there to talk about the DoNotPay application he created.