Drones and airports usually go together like oil and water, but you can't say that about Atlanta's air hub. The city has formed a partnership with 3DR, Autodesk and engineering firm Atkins that has drones mapping Hartsfield-Jackson International Airport as part of a planned expansion. The key to making it work was Site Scan, 3DR's autonomous data capturing tech. The drones could capture 2D mosaics and 3D point scans while staying well away from the airliners -- no mean feat when they're flying between runways at the busiest airport in the world (over 100 million passengers per year). If anything, the biggest challenge was getting the green light from extra-wary FAA regulators.

Machine learning, artificial intelligence, deep learning… Unless you've been living under a rock, chances are you've heard these terms before. Indeed, they seem to have become a must for market researchers. Unfortunately, so many precise terms have never meant so little! For computer scientists these terms entail highly technical algorithms and mathematical frameworks; to the layman they are synonyms; but as far as most of us should be concerned, increasingly, they are meaningless. My engineers would severely chastise me if I used these words incorrectly--an easy mistake to make since there is technically no correct or incorrect way to use these terms, only strict and less strict definitions.

The concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy. Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as Big Data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information.

Artificial intelligence is one of the hottest areas of growth and transformation we have ever seen. The next step is integrating itself and working with wireless and wireline networks. This is a huge growth opportunity and the sky looks bright for the investor, worker, corporate customer, end user and governments worldwide. However, there are plenty of challenges we must deal with. I continually get asked about the other side of the AI coin.

Researchers at MIT have developed a new approach to training speech recognition systems that does not depend on transcriptions – as is the current model. Instead, their system analyses correspondences between images and spoken descriptions of those images, as captured in a large collection of audio recordings. The system then learns a mapping between acoustic features of the recordings correlated with image characteristics. Traditionally speech recognition systems such as those that convert speech to text on smartphones are the result of machine learning systems that go over many thousands of utterances and their transcriptions to learn a mapping between acoustic features and words. While this method works quite well, the requirement of professional grade transcription is costly and time-consuming.

Dlib is a modern C toolkit containing machine learning algorithms and tools for creating complex software in C to solve real world problems. That will build all the examples. Before you can run the Python example programs you must compile dlib. Note that on windows your compiler might put the test executable in a subfolder called Release. If that's the case then you have to go to that folder before running the test.

DeepMind is a British AI startup which was relatively unknown until it was bought by Google for around $600 million in 2014. Since then DeepMind has continued to refine its neural-network driven technology which has broken new frontiers with machine learning, particularly deep learning. Perhaps DeepMind's most famous accomplishment so far is being the brains behind AlphaGo, the first computer program to beat a professional human player of the board game Go. AlphaGo was developed by feeding DeepMind's machine learning algorithms with 30 million moves from historical tournament data, and then having it play against itself and learn from each defeat or victory. DeepMind's work is based on a solid grounding in neuroscience.

This is an image of the Zaamenkomst panel: one of the best remaining exemplars of rock art from the San people of Southern Africa. As soon as you see it, you are inevitably herded, like the eland in the scene, through a series of thoughts. Does it have a meaning? Why are the eland running? What do the white lines coming from the mouths of the humans and animals signify? What event is unfolding in this scene?

Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks which are capable of learning unsupervised from data that is unstructured or unlabeled. Also known as Deep Neural Learning or Deep Neural Network. The digital era has brought about an explosion of data in all forms and from every region of the world. This data, known simply as Big Data, is gotten from sources like social media, internet search engines, e-commerce platforms, online cinemas, etc. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.

How would you feel about getting therapy from a robot? Emotionally intelligent machines may not be as far away as it seems. Over the last few decades, artificial intelligence (AI) have got increasingly good at reading emotional reactions in humans. If AI cannot experience emotions themselves, can they ever truly understand us? And, if not, is there a risk that we ascribe robots properties they don't have? The latest generation of AI's have come about thanks to an increase in data available for computers to learn from, as well as their improved processing power.