You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around 1000/GB; by 2014 that cost had plummeted to 0.03/GB (2). With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics. As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses.

Thanks to Hollywood, much of what we perceive regarding Artificial Intelligence is rooted in robots taking over the world. And because it is largely misunderstood, many people fear even considering adopting the technology. But ignoring it won't make it go away any more than refusing to use Facebook prevented it from becoming a multi-billion-dollar industry that changed the face of business. AI technology is improving all the time, and while it may be a while before it becomes part of mainstream customer service, it is probably going to change the way we operate. Wouldn't it be better to be prepared and know enough to embrace the technology when it rolls out?

I never thought I'd write "Microsoft" and "exciting" in the same sentence. I started my data career using Excel, which is a great tool for prototyping and sharing data quickly, and has been used by many people as a gateway to learning programming through VBA, but has none of the features someone serious about working with data could get behind: version control, testing, code review,cross-platform compatibility, or more than 1 million rows. Oh, also it crashes all the time. Then there all of the wrong Microsoft has made on the developer side, from making C# proprietary, to forcing IE 6 compatibility, to the awfulness that is the Windows command prompt. Needless to say, as soon as I learned enough to know better, I switched to Mac and never looked back.

Deals and dollars to startups using AI algorithms reached record levels in 2016. The trend is revolutionizing several industries including healthcare, business intelligence, and advertising. Particularly, startups using machine learning and natural language processing to solve problems in the financial sector have piqued investors' interest in recent years. As we mentioned in our AI heatmap, deals to AI in the financial services area have seen an uptick since 2014. The competition in the space is heating up as several companies focused on core AI technologies, like Sentient Technologies and Ayasdi, are bringing their products to fintech, for applications ranging from quantitative trading and sentiment analysis to threat detection and risk analytics.

'Executives who don't use AI will become jobless'

New research by Narrative Science, predicted that 62% of organisations will be using artificial intelligence solutions by 2018. The concept of AI may seem revolutionary, but it is something which is already present in our day-to-day lives. Apple's Siri will soon be upgraded, enabling iPhone users to interact with a number of third party apps such as message bots, photo search, personal payments and more. Other examples of everyday use of AI include the smart home. Smart thermostats now include the ability to learn your behavioural patterns, learning your daily routine, saving you energy and money on your monthly bill.

As we are half past 2016, all eyes are locked on forthcoming and future accomplishments. One particular tech trend that's making a lot of noise these days is Deep Learning, which is a popular branch of machine learning. Deep learning is expected to influence a number of sectors and industries in the near future. As we know that computers these days are getting better and better with groundbreaking innovations such as cloud computing, image recognition, 3D printing, language translation, etc., whereas a decade ago, things were complete different. Of all the technological innovations, Deep Learning is one such tech trend that has been making consistent and quick advances since its inception.

Tamr, a machine learning-driven company for procurement analytics and media analytics, has won financial backing from big guys such as HP Ventures and Google Ventures. The company applies machine learning technologies to traditional areas of the enterprise such as procurement. The company's founders, Michael Stonebraker and Andy Palmer, previously started database company Vertica, which HP acquired in 2011. Tamr customers include Cisco, GE, Toyota, and Thomson Reuters.

The DGX-1 is a huge advance," OpenAI Research Scientist Ilya Sutskever said. "It will allow us to explore problems that were completely unexplored before, and it will allow us to achieve levels of performance that weren't achievable." OpenAI is a non-profit artificial intelligence research company with a goal "to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. "Artificial intelligence has the potential to be the most positive technology that humans ever create," said OpenAI Chief Technology Officer Greg Brockman. "It has the potential to unlock the solutions to problems that have really plagued us for a very long time."

Start-ups are hoping new artificial intelligence (AI) techniques that make searching the web more like talking to a friend will help them take on Google. Companies are developing search software that understands natural language, something that has typically been difficult for computers to do. Israel-based start-up Twiggle has come up with a solution specifically for e-commerce. The company, backed by Chinese online shopping giant Alibaba, is trying to help make searching sites for items a lot easier. "We are trying to bring the search experience as close as possible to the positive aspect of the in-store experience," Amir Konigsberg, chief executive of Twiggle, told CNBC in a phone interview on Wednesday.