DeepMind has surpassed the human mind on the Go board. Watson has crushed America's trivia gods on Jeopardy. But ask DeepMind to play Monopoly or Watson to play Family Feud, and they won't even know where to start. Because these artificial intelligence engines weren't specifically designed to play these games and aren't smart enough to figure them out by themselves, they'll give nonsensical answers. They'll struggle greatly, and humans will outperform them--by a lot. Assaf Baciu is co-founder and senior vice president of Persado, a cognitive content-generation company in New York.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data. There are whole swaths (not swatches) of techniques that have been developed over the years like Linear Regression, K-means, Decision Trees, Random Forest, PCA, SVM and finally Artificial Neural Networks (ANN).

Not too long ago, robots were a far-off reality, a science fiction fantasy. Today artificial intelligence (AI) is at the fingertips of every iPhone owner in the form of Siri. AI dominates the popular landscape -- IBM's Watson computer crushed longstanding Jeopardy!

From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.

Some of the questions you ask your human resources department could soon be answered by, well, non-humans. That's the concept behind Talla, a Boston-area start-up that has developed a chatbot to do some of the more mundane tasks that HR departments carry out on a daily basis. That includes explaining company policy, surveying employees, collecting information or training new hires. The Talla bot operates inside enterprise group messaging software, such as Slack, HipChat or Microsoft Teams, which has increasingly become an alternative to email as a method of digital communication within companies. Employees send messages to the chatbot just as they would a human, and it uses language processing software to understand the message and respond accordingly.

DEDHAM, MA--(Marketwired - Dec 7, 2016) - RAGE Frameworks, a provider of artificial intelligence (AI) for the Enterprise, today announced that a leading diversified investment and financial services company has deployed RAGE LiveSpread to automate the extraction, interpretation and processing of financial statements and other documents needed for credit analysis. RAGE LiveSpread is a contextual, traceable machine learning solution built on the RAGE-AI platform. Dealing with variations in form (electronic files, pdfs, and paper statements), format, language, accounting standards across countries, and data delivery methods makes financial statement automation a major challenge. Currently, this is a manual, error-prone, and non-scalable process at every major bank around the world. At this leading investment and financial services company, RAGE's solution completely automates the ingestion, extraction, interpretation of investment reports, bank statements and Income Tax returns.

If you asked the average person what they know about artificial intelligence (AI), they would probably launch into stories about intelligent computers taking over the world and rebellious robots running amok. While the misconception the movies have created may be wildly wide of the mark, AI is an area of technological development having a massive impact in all corners of our lives for generations to come. That's why Stanford University has launched a long-term project to study the impact of AI on society. A study that's not necessarily going to offer solutions, but will promote a dialogue about AI to guide us through the ethical, legal, and technological challenges machine intelligence might bring. I think that's a pretty cool undertaking.

"We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances." "When you have two competing theories that make exactly the same predictions, the simpler one is the better." One famous example of Occam's Razor in action is found in conspiracy theories surrounding the NASA moon landings. Many conspiracy theorists believe that the first Moon Landing was staged and filmed in a studio, part of an elaborate hoax. Their justification relies upon many twisted and convoluted theories, whereas the NASA argument is fairly straightforward.

A/B Testing - In marketing, A/B testing is a simple randomized experiment with two variants, A and B, which are the control and treatment in the controlled experiment. It is a form of statistical hypothesis testing. Other names include randomized controlled experiments, online controlled experiments, and split testing. In online settings, such as web design (especially user experience design), the goal is to identify changes to web pages that increase or maximize an outcome of interest (e.g., click-through rate for a banner advertisement). Adaptive Boosting (AdaBoost) - AdaBoost, short for "Adaptive Boosting", is a machine learning meta-algorithm formulated by Yoav Freund and Robert Schapire who won the prestigious "Gödel Prize" in 2003 for their work.

A knowledge of algorithms (maths and stats) is the main differentiator between traditional programming and analytics -based programming. Having said that, it helps to start with programming and approach the maths (initially) through APIs and libraries. I find that this technique works better because more people are familiar with programming than with maths. Techniques used in Data Science such as Data transformations, Exploratory data analysis, Feature engineering, Ensemble strategies, and Visualization (story telling) all involve maths and stats. Future versions of this tutorial will elaborate on this.