Artificial intelligence is usually associated with futuristic sci-fi movies and the rise of the machines against humanity. What many people don't realise is that it's commonplace today, and something you've probably not noticed has become a critical part of your life. The intelligence Facebook uses to make friends suggestions or how Google photos recognises faces and places are both examples of AI, and machine learning, in action. AI is also being used in organisations worldwide to augment and assist human employees to do their jobs better and smarter. In South Africa, Stellenbosch-based CLEVVA has developed an AI platform that enables companies to rapidly and easily deploy Virtual Advisors across every aspect of their businesses.

Chatbots have been around for a while. Yet, now that Facebook will allow businesses to deliver automated customer support and interactive experiences through bots, they will be everywhere soon. Brands might feel that they should replace their human employees with chatbots now, but they should think hard before shaking up the status quo. Ideally, humans and chatbots will work together to do the tasks they are best equipped to handle in order for both parties to be most effective. While bots are being touted by some as the next big thing for handling customer service needs, chatbots are not sufficiently equipped to serve as the complete answer to a brand's growing requirements.

The media interest surrounding deep learning has grown exponentially in the last few years. But what does it actually mean, and how will it change business and society? Deep learning is a subset of machine learning that refers to mapping artificial neural networks to recreate some of the same processes that the human brain performs, and using algorithms with speech, images and text, to recognise, identify and understand patterns in the data. Although this sounds simple, it involves complex processes and functions - but once trained, the application of deep learning algorithms could be world changing. For instance, a machine that learns like a human, but can rapidly process thousands of images and recognise patterns, is already showing promise for applying deep learning to medical imaging.

Monday 18th July 2016, Whitley: Sir Ben Ainslie has hailed Land Rover's artificial intelligence (AI) as a'game changer' ahead of the British America's Cup team's home event in Portsmouth (22-24 July). Land Rover, Title and Exclusive Innovation Partner to Land Rover BAR, is applying its big data processing power and machine learning expertise to help co-engineer the fastest boat in America's Cup history and bring the world's oldest sporting trophy to the UK for the first time. Land Rover engineers, embedded into the team for over a year, are using artificial intelligence to explore and find patterns in sailing performance data to help'make the boat go faster'. When testing, the sailing team receives over 16 GB of uncompressed data per day from sensors on the boat - the equivalent of filling an iPhone's memory. The ability to process and make sense of this volume of data is unprecedented in sailing.

Major landmarks around the world are Lighting It Up Blue on April 1 and 2 to raise awareness about Autism Spectrum Disorders (ASD) for World Autism Awareness Day at Forte Sangallo on April 02, 2016 in Nettuno, Italy. In recent months, artificial intelligence (AI) has been making its presence known in different fields of sciences. In fact, AI is deemed as a valuable asset in precision medicine. But now, a team of researchers is exploring the possibilities if machine learning could play a vital part in autism screening, diagnostics and intervention. Before delving deeper into the latest research on the importance of artificial intelligence in autism screening and diagnostics, let's first define the two most relevant subjects on the study - autism and machine learning. According to Autism Speaks, autism refers to the "general term used for group of complex disorders of brain development," which are marked by social interaction, verbal and nonverbal communication difficulties, as well as repetitive behaviors.

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Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. Before applying the machine learning models, the data must be converted to a tabular form.

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Computers learn differently: their hardware remains always constant, and never reconfigures itself. What changes is the software; the data, or the program, or both. A human mind is a work-in-progress, as the brain evolves and constantly changes. A computer mind does not evolve, or change its structure, or hardware; it becomes better by accumulating and storing more data, by creating more connections between the data, and by running logical programs of better quality faster. This subtle difference between computers and biological brains is what makes humans have subjective experiences, as opposed to computers that cannot.

I think I read about activation functions "squashing inputs into outputs" five or six times before I finally started getting the gist of them. This was no fault of the material I was reading, they were just this abstract concept that I just accepted and shoved my inputs into when I was building a neural network. However, I finally feel that I have a much clearer picture of why they are used and how well each one performs for certain tasks. I hope somehow I can squash this concept into your head by the end of this article so something useful can come out in the future! I'm sure you will catch on much faster than I did.