Personal touch: "If you have a friend you've known for a long time and they know things about you that the general public doesn't know you'll keep them around in your life for a long time," USAA's Patrick Kelly says in describing the building of so-called digital empathy. "The same is true of your [financial] institution." USAA is using virtual agents, personalization and artificial intelligence to forge what it calls a "digital empathy" with its members – conversations that feel human and personal, with an understanding of each customer's behavior, likes and dislikes built in. If empathy – the feeling that you understand and share another person's experiences and emotions – sounds like a tall order for a financial services company that almost never interacts with customers in person, it is. But it's a worthwhile goal for USAA and would be a smart objective for any financial institution.

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As artificial intelligence (AI) continues to move from the summer of hype to the fall tech conference news cycle, mass confusion has begun on what AI can be used for. From fears of SKYNET, to hopes for the computer in StarTrek and Jarvis in Iron Man, the value will come from defining the proper outcomes. AI is more than just a fad. With a market size of $100B by 2025, Constellation sees the AI subsets of machine learning, deep learning, natural language processing, and cognitive computing taking the market by storm (see Figure 1). The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity.

More and more self-driving vehicles are making their debut, raising the question of who should be held accountable if, or perhaps when, they cause accidents. Following American and German automakers Tesla Motors Inc. and Mercedes-Benz, Nissan Motor Co. released a minivan model with self-driving functions in the Serena family in August at a time when the government and automakers in Japan are looking to have autonomous vehicles in regular use by 2020. In Japan, autonomous vehicles are now sold with the understanding that drivers are responsible for maintaining control of their vehicles. Drivers are required to stay behind the steering wheel even when self-driving functions are in operation, and they are held accountable for accidents. The autonomous Serena model is designed for expressway use in single-lane traffic.

The result of standardization (or Z-score normalization) is that the features will be rescaled so that they'll have the properties of a standard normal distribution with Standardizing the features so that they are centered around 0 with a standard deviation of 1 is not only important if we are comparing measurements that have different units, but it is also a general requirement for many machine learning algorithms. Intuitively, we can think of gradient descent as a prominent example (an optimization algorithm often used in logistic regression, SVMs, perceptrons, neural networks etc.); with features being on different scales, certain weights may update faster than others since the feature values play a role in the weight updates Other intuitive examples include K-Nearest Neighbor algorithms and clustering algorithms that use, for example, Euclidean distance measures – in fact, tree-based classifier are probably the only classifiers where feature scaling doesn't make a difference. In fact, the only family of algorithms that I could think of being scale-invariant are tree-based methods. Let's take the general CART decision tree algorithm. Without going into much depth regarding information gain and impurity measures, we can think of the decision as "is feature x_i some_val?"

But you probably know this already, if only from the preponderance of conference presentations, blogs and trade articles on the topic. Yes, text analytics are all the rage these days. You may feel under the gun to catch up, but if you're late to the game, you may be comforted to know that for many people, text analytics aren't living up to the hype. Nearly every researcher I come in contact with at conferences and through my professional network is at least actively investigating text analysis if they haven't already adopted a solution. And in either case, they're frequently underwhelmed. It's my experience that there are two primary reasons for this: How Do I Know Before I Buy?

Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car. Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) -- even though they appear in new contexts such as IoT or machine to machine communication -- still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others. Some are new, such as indexation algorithms to automate digital publishing, improve search engines, or create and manage large catalogs such as Amazon's product listing. As a result, many deep learning practitioners call themselves data scientist, computer scientist, statistician, or sometimes engineer.

Artificial intelligence (AI) is already becoming entrenched in many facets of everyday life, and is being tapped for a growing array of core business applications, including predicting market and customer behavior, automating repetitive tasks and providing alerts when things go awry. As technology becomes more sophisticated, the use of AI will continue to grow quickly in the coming years, as explored in a new eMarketer report, "Artificial Intelligence 2016: What's Now, What's New and What's Next" (eMarketer PRO customers only). In its most widely understood definition, AI involves the ability of machines to emulate human thinking, reasoning and decision-making. A May 2015 survey of US business executives by Narrative Science found that 31% of respondents believed AI was "technology that thinks and acts like humans." Other conceptions included "technology that can learn to do things better over time," "technology that can understand language" and "technology that can answer questions for me."

Chatbots are the new black in UX design. Facebook already has over 11,000 chatbots since the official announcement of chatbot support made earlier this year. The question is: Should you build a chatbot for your business? Yes, if your goal is to create a more personalized experience with your customers without hiring additional staff. You shouldn't be perceived just as "faceless" website or a product page.

For example, if you visit the landing page for the Poncho weatherbot, it clearly states how to use it -- as a virtual weather agent.