The world of today runs on oil – heating and cooling our homes, and powering almost all forms of transportation. Try and drive a car today without oil-based products, and you won't get far. Without oil, a car engine overheats, pistons and rings fuse to the cylinder walls, the engine block cracks and, of course, there is no gas. You could say oil is the key technology that allowed the automotive world we know today. But that's all about to change.

Let's punch through the noise around machine learning. The tech ecosystem is well acquainted with buzzwords. From "Web 2.0" to "cloud computing" to "mobile first" to "on-demand," it seems as though each passing year heralds the advent and popularization of new catchphrases to which fledgling companies attach themselves. But while the trends these phrases represent are real, and category-defining companies will inevitably give weight to newly coined buzzwords, so too will derivative startups seek to take advantage of concepts that remain ill-defined by experts and little-understood by everyone else. "It's clear that 9 of 10 investors have very little idea what AI is so if you're a founder raising money, you should sprinkle some AI into your pitch deck. Use of'artificial intelligence,' 'AI,' 'chatbot,' or'bot' are winners right now and might get you a little valuation bump or get the process to move quicker. If you want to drive home that you're all about that AI, use terms like machine learning, neural networks, image recognition, deep learning, and NLP. Then sit back and watch the funding roll in."

Artificial intelligence is the yin and blockchains are the yang of digital business. Artificial intelligence is the yin and blockchains are the yang of digital business. While AI helps us assess, understand, recognize and decide, blockchains can help us verify, execute and record. While the machine learning methods that are a part of AI help us find opportunity and improve decision making, smart contracts and blockchains can automate verification of the transactional parts of the process. AI and blockchains in that sense are complementary and synergistic.

What if a computer could predict who's a criminal and who isn't just by scanning their face? It sounds like creepy science fiction from a film like Minority Report, but a pair of researchers in China have been busy testing that exact theory out. Xiaolin Wu and Xi Zhang from China's Shanghai Jiao Tong University published a study called " Automated Inference on Criminality using Face Images ." They fed the faces of 1,856 people (half of which were convicted violent criminals) into a computer and set about analysing them. The photos were all of Chinese men aged between 18 and 55, with no beards, scars or other distinguishing features.

Artificial Intelligence (AI) is rapidly creeping into our everyday lives; it is no longer something we associate with solely appearing in sci-fi films. Not only is AI transforming online services, such as Apple Siri voice recognition or Facebook chatbot API for Messenger, but it is also transforming how organisations are using and understanding data to provide customers with personalised experiences. Some companies within the banking, automotive, insurance and telecom industries are already using AI to provide customers with real-life contextual experiences based on data. Swedish retail bank Swedbank has integrated the technology tool Nina, an intelligent virtual assistant, into its customer service strategy. Nina delivers automated customer service via the brand's website in a conversational manner enabling self-service capabilities to Swedbank customers.

When an app claims to be powered by "artificial intelligence" it feels like you're in the future. What does that really mean, though? We're taking a look at what buzzwords like AI, machine learning, and neural networks really mean and whether they actually help improve your apps. Just recently, Google and Microsoft both added neural network learning to their translation apps. Google said it's using machine learning to suggest playlists. Todoist says it's using AI to suggest when you should finish a task.

Intel CEO, Brian Krzanich, announced that Intel Capital will invest $250m in the next two years in the autonomous vehicle (AV) ecosystem, focused on problems in connectivity, communication, context awareness, deep learning, security and safety. When viewed in the context of the fund's short-lived intention to sell $1bn worth of portfolio holdings in March this year (it was cancelled in May), I think this shows Intel is serious on going long with AI. Indeed, the company purchased recently Nervana Systems and Movidius, which could help it's larger AV program and the race against NVIDIA. Nauto, the startup offering a direct to consumer network of cloud-connected dashboard cameras applied to car insurance, inked a data sharing agreement and investment from Toyota Research Institute, BMW iVentures and Allianz Ventures (thanks Moritz for sharing!). One of the reasons for the immense progress in AI is data crowdsourcing.

Having a smart speaker like Amazon Echo or Google Home in the house can feel like you've stepped into the future. It can also feel creepy. All these impressions coursed through me over the last several days as I tested Echo and Home. I'm still not sure I'd want one in my house, but I can certainly understand their appeal. Home debuted last month as Google's answer to Echo, which Amazon first released two years ago.

A team of researchers at Oxford University have coaxed an artificial intelligence program into an impressive leap forward and towards our own obsolescence. The program, known as LipNet, is showing particularly promising ability to read lips in video clips, thanks to machine learning and a novel way of approaching the data. The key difference is that rather than try to teach the AI the mouth shapes of single words and phonemes, the LipNet is asked to interpret whole sentences. Using GRID, a huge bank of 3 second videos featuring brightly lit forward facing speakers, LipNet has learned to translate speech to text with a 93.4% accuracy rate. Compare that to humans' 52.3%.

The use of Machine Learning is growing widespread. Organizations are embracing it in various ways to improve their businesses. The rise of Machine Learning is coupled with the advance of open source big data processing frameworks which makes it possible to analyze tons of data in a cost-effective and efficient way. Apache Spark is one of those frameworks which have grown as a blue eyed technology in this Big Data processing world. Apache Spark provides two libraries- MLlib and ML Pipeline for implementing machine learning.