"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."

"If a machine can think, it might think more intelligently than we do, and then where should we be? Even if we could keep the machines in a subservient position, for instance by turning off the power at strategic moments, we should, as a species, feel greatly humbled." IBM's artificial intelligence (AI) platform, Watson, is loquacious; it can tell jokes, answer questions and write songs. Google's AI can now read lips better than a professional and can master video games within hours. MIT's AI can predict action on video two seconds before it begins. All seem to propel us closer to Turing's world of machines with more intelligence than humans. If Turing's words now ring true, should we feel humbled or anxious? For many marketers, the anxiety and existential fear has given way to hope and excitement for a new tomorrow. Dome, who works as a marketing consultant and adjunct professor at University of Chicago's Graham School, grows excited as he talks about the possibility of AI: the time it could save marketers, how it can bring companies closer to consumers and its potential to catch customers in stride, saving effort on the business and consumer side.

Emotion-recognition technology is emerging as one of the top AI trends this year, enabling brands to build on consumers' emotions to deliver customised services and products, according to a report publishd by Zenith. According to the report, Artificial Intelligence: Powering the Consumer Journey, the mood-sensing devices that consumers carry in their pockets--smartphones--and the continuous progression of customised triggers enabled by emotion-detection tech will allow brands to make more emotionally relevant recommendations and enhance programmatic advertising. Bentley has used such technology in the Bentley Inspirator, an app that concocts a Bentayga SUV for order based on a consumer's reaction to a film. The report also cites the ability of IOT household devices (such as Amazon's Alexa) to deliver services and recommendations tailored to consumers' emotions. "Human decisions are heavily influenced by emotion," said Hugo Pinto, IBM Interactive Experience's innovation officer, in an interview published in the Zenith report.

The whole technology landscape is changing and is ushering in new ways to put it to use. The way things have been run in the past 20 years no longer holds water and we need to change to adapt and be relevant. At the Bengaluru ITE.BIZ 2016, Bhaskar Ghosh, Group Chief Executive, Accenture Technology Services, shed light on what the next wave in the IT sector would be. "We went through several waves - IT services, e-commerce, cloud, artificial intelligence (AI) and quantum computing. New tech is maturing faster and the time to learn and execute in the market is short. We need to learn to master the speed. Every business is a software business. Even manufacturing is driven by software," he explained.

Carnival will give its cruise ship guests the Ocean Medallion so they can access their personalized cruise experience. Carnival Cruise line is betting that a whole host of intelligent, connected services for guests will turn the cruise experience from a "supposedly fun thing" to a truly personal and compelling vacation. "Our sole purpose is... to make our guests happy, to transport them," Carnival Corporation CEO Arnold Donald said at CES 2017 in Las Vegas Thursday. Donald's speech -- the first keynote address on the first official day of the 50th annual CES -- illustrated how technology has become an integral part of just about every industry, with much of it powered by machine learning. The cruise line is partnering with Accenture to deliver a range of new guest services, including an IoT network, a digital "experience" portal and even a new wearable.

Everyone today talks about AI, big data and machine learning, yet most do not delve into the fundamental properties of how they will operate and how they might be an actual threat to asset managers. Some view technological methods as tools to assist them instead of being such a threat, and it would help provide both perspectives of the argument. Deep learning is a branch of machine learning that uses particular architectures of neural networks. These are artificial networks that attempt to actually replicate how the neural structures in human brains operate. Such methods have successfully been applied to areas such as computer vision – i.e. image processing and classification – as well as speech recognition. The techniques are readily available to any undergraduate student willing to learn the process.