To process an image, for example, the lowest layer is fed the raw images. It notes things like the brightness and colours of individual pixels, and how those properties are distributed across the image. The next layer combines these observations into more abstract categories, identifying edges, shadows and the like. The layer after that will analyse those edges and shadows in turn, looking for combinations that signify features such as eyes, lips and ears. And these can then be combined into a representation of a face--and indeed not just any face, but even a new image of a particular face that the network has seen before.

Those amazing, lifelike robots, reports Jefferson Graham on #TalkingTech. LAS VEGAS --The one, coolest thing from this year's 2017 CES is an easy pick -- those amazing robots. We saw robots to make your morning coffee, pour candy, fold your clothes, turn on and off your lights, project a movie on the wall, handle your daily chores and most impressively, look just like a human, or in this case, legendary scientist Albert Einstein, with facial expressions and movement. Why did robots dominate CES? You can thank the popularity of Amazon's Alexa for showcasing the technology of a voice-activated personal assistant.

In Pressed Data, my Forbes.com In 2016, I covered artificial intelligence--the 60-year-old new new thing, big data--the most recent hottest trend and a catalyst for the new-found popularity of the new one, the fading away of former tech leaders, a number of startups, and a number of influential business and tech innovators. When Artificial Intelligence Started To'Change The World' A review of ENIAC in Action: Making and Remaking the Modern Computer, "a nuanced, engaging and thoroughly researched account of the early days of computers, the people who built and operated them, and their old and new applications," contrasting it with "history as hype, offering a distorted view of the past, sometimes through the tinted lenses of contemporary fads and preoccupations." Hype is on full display in "The Human Face of Big Data" of which I wrote: "…in our technology-obsessed world, new technologies and new technology applications tend sometimes to become buzzwords that are hyped, celebrated and often discussed irresponsibly by technology vendors and the media. Unfortunately, 'The Human Face of Big Data' by and large falls into this trap, the fascination (self-delusion?) with the idea of we are living a momentous time in history thanks to technology."

Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim is to highlight three I find useful. To set the problem up let's assume we have a census dataset containing age, education, employment status and so on. Given all this information we want to see if we can predict whether a person earns more than $50k per year. How can tree methods help us?

A robot is invading a popular reality TV show in China that tests people's brainpower. The smart, AI-powered bot, Xiaodu, will take on human competitors in complex trials involving face and voice recognition. The AI robot built by search engine giant Baidu is one of the contestants, facing off against four people and other clever computer programs. Baidu first showed off a small version of Xiaodu in 2015. After working on the AI in the interim – and building a giant version just for TV – the gadget now faces its biggest ever battle.

"Artificial intelligence isn't the cataclysmic force of destruction that Hollywood or marketing technicians sometimes fear it be" – Kyle Harper. Harper believes the fair of AI stems from popular media, and that a lot of the conversations regarding the technology today, often takes a paranoid tone. It does not help that famous scientists such as Stephen Hawking and Elon Musk raises their concern, but Harper thinks it is time to change how we think of AI. "There's a fear that as we improve efficiency, we may also lose touch of some of the human factors in the marketing process; that machine learning's data-driven approach may not perfectly hit the mark for understanding people, that automation could threaten to displace too many people from much-needed jobs", Harper writes. However, Harper urges the readers to look at it from a different perspective – from the view of a company leadership. He poses the scenario of a small company that develops a product that surprisingly turns into a success overnight.

David Freedman is the author of an excellent book: "Statistical Models: Theory and Practice" which discusses the issue of causation. It's a very unique stat book in that it really gets into the issue of model assumptions. It claims to be introductory but I believe that a semester or two of math stat as a pre-req would be helpful. In the time series context, you can run a VAR and then do tests for Granger Causality to see if one variable is really "causing" the other where "causing" is defined by Granger. R has a nice package called vars which makes building VAR models and doing testing extremely straightforward.

Branch of AI: Artificial intelligence is the study and development by which a computer and its systems are given the ability to successfully accomplish tasks that would typically require a human's intelligent behavior. Supervised learning: in this type of learning, the correct outcome for each data point is explicitly labeled when training the model. In a classification context, the learning algorithm could be, for example, fed with historic credit card transactions each labeled as safe or suspicious. Machine learning is used to find meaningful relations and to predict outcomes while data experts serve as translators to make sense of why the relation exists.

He's deeply concerned; he has an illness that the doctor doesn't recognise. After some research online and a discussion with her colleagues at the practise, it turns out that it's a rare complaint. The doctor then starts looking at possible medications, comparing drug side effects that might react adversely with the patient's current prescriptions. But what if the doctor had a powerful resource at her disposal: a repository of medical information and insights? What if she also had access to smart, accurate clinical decision support and the kind of intelligent predictive analytics that could cut down the time spent looking for answers and help her get straight to diagnosing and treating the patient's problem…what if they had the help of Artificial Intelligence? Artificial Intelligence (AI) as a concept has actually existed since the '80s, but it's only now that data, processing and storage have become abundant that there's been resurgence in interest and investment.

At Inbound 2016, HubSpot's co-founders Brian Halligan and Dharmesh Shah entertained 19,000 attendees with their take on the past and future of marketing. Here's what I learned from their keynote presentation and a brief interview. So predicts Halligan, adding "in five years, you will do a lot less navigating through apps and more just asking questions and chatting back and forth with bots… the next thing you know, we like it and it's easier and more efficient than waiting for the sales rep to call you back." Shah notes that businesses started building websites in the 1990s so they can answer customer questions 24/7. "Soon," he says, "they will start building bots. They won't replace the websites, but they will power them. The shortest time between a customer question and the answer will be a bot. It's not human vs. bot, it's human to the bot powered."