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.

In 2011, cybersecurity researcher and entrepreneur Stuart McClure spent his last year working at McAfee, as the company's global chief technology officer, apologizing a lot. McClure said hackers were slipping into McAfee customer networks and each subsequent breach seemed worse than the last. McClure would have to meet with each of the big McAfee corporate customers to explain why the software failed and at the end of each meeting someone would ask McClure the same question: "'What type of security software do you use on your machine to prevent cyber attacks?'" The customers would then wait, pens poised above a piece of paper to jot down the long list of layer after layer of high-end software that the global CTO of a multimillion-dollar security company would surely recommend. "I would tell them I only trust my brain and my hand, because there are no new ways to breach a network," says McClure of his former employer, which is now owned by Intel.

Auto parts and electronics maker Delphi Corp. has signed a deal with Intel to buy high-powered computer processors for Delphi's future autonomous vehicle systems. Delphi says Intel's added computing capacity will be needed as autonomous car systems gather and store more and more information while expanding their ability to deal with situations on real roads. In August, Delphi announced that it had joined with Israeli software maker Mobileye to develop the building blocks for a fully autonomous car in about two years. Intel Corp. will supply Delphi with high-capacity computers needed to process input from radar, cameras and laser sensors as well as maps of roadside landmarks. Glen De Vos, vice president of Delphi's business-services unit, says the Intel deal gives the company everything it needs to develop an autonomous-driving package to sell to automakers. Delphi makes its own radar and laser sensors and uses Mobileye's cameras and software.

Incase you missed it, here are the prequels to this article about the Mobile Vision API. The first post was on the Face Detection API while the second was on the Barcode Detection API. According to the overview, the Text Detection API allows for detecting text in images and videos and it breaks down those texts into blocks (paragraphs/columns), lines (sets of words on the same vertical axis) and words (set of alphanumeric characters on the same vertical axis). The API recognizes text in various Latin based languages. I'll write about what's possible with this API before I go ahead to explain how to use it .

Let us narrow down the phrase "implementing from scratch" a bit further in context of the 6 points I mentioned above. When we talk about "implementing from scratch," we need to narrow down the scope to make this question really tangible. Let's talk about a particular algorithm, simple logistic regression, to address the different points using concrete examples. I'd claim that logistic regression has been implemented more than thousand times. One reason why we'd still want to implement logistic regression from scratch could be that we don't have the impression that we fully understand how it works; we read a bunch of papers, and kind of understood the core concept though.

They say what happens in Vegas stays in Vegas. But that would be a wild mistake if applied to this circumstance. If you're a business leader or entrepreneur intent on staying competitive in the years to come, you'd best pay close attention. Last month, IBM hosted the World of Watson conference in Las Vegas aimed at raising awareness and educating participants about advances in computing over the last decade. But how did we get to this point?