It's no secret that brands have been trying to make business-to-consumer interactions more personal, and it's also no secret that the B2C relationship is far from monogamous. In order to facilitate choice, convenience and control, brands have used data to categorize (or "bucketize") customers in any number of segments and act accordingly. For example, looking at a laundry list of factors and behaviors, "Joe's characteristic make-up is A, B, C and his behavior patterns are predominantly G, P, X and Y, so we know that we'll have pretty good luck engaging him in these ways…" But business analytics and, specifically, machine learning are turning the proverbial "dating" game between businesses and consumers on its head. Instead of assembling interactions based on preconceived notions about how consumers want to be treated, machine learning enables companies to deliver truly personalized communication based on up-to-the-moment data: "Joe is uniquely Joe, and right now, he needs 1, 2 and 3." Smart, fast and personal. Data big and small has already been transforming the way brands interact with customers (or prospective customers) – whether it's remarketing ads for the shoes we abandoned in our online carts, curated playlists based on our listening preferences, or automatic notifications to refill our prescriptions based on our typical use.

Perhaps the coolest thing about IBM's 9th "Five Innovations that will Help Change our Lives within Five Years" predictions is that none of them sound like science fiction. "With advances in artificial intelligence and nanotechnology, we aim to invent a new generation of scientific instruments that will make the complex invisible systems in our world today visible over the next five years," said Dario Gil, vice president of science & solutions at IBM Research in a statement. More on Network World: IBM says soon you won't need passwords; mind reading will be routine; the so-called digital divide will cease to exist and junk mail will become important Among the five areas IBM sees as being key in the next five years include artificial intelligence, hyperimaging and small sensors. In five years, what we say and write will be used as indicators of our mental health and physical wellbeing. Patterns in our speech and writing analyzed by new cognitive systems will provide tell-tale signs of early-stage mental and neurological diseases that can help doctors and patients better predict, monitor and track these diseases.

The partnership between car brands and their timepiece counterparts extends longer than you and I can even imagine. We've seen these partnerships manifest themselves in one form or another, with some even taking the form of obscenely expensive, limited edition watches. If there ever was a new frontier for the partnership between these two industries, Swiss watchmaker Swatch may have opened that door. A report from watch-dedicated website TwentyTwoTen revealed Read More ... Places: Americas North America United States The two models posed near their flashy cars as they reunited for the sexy photo op. Both Chyna and Amber posted the three photos to their Instagram accounts as they showed off their curves and cars.

With the majority of consumers spending significant time on various messaging platforms, brands are turning to these messaging platforms to better interact with consumers. The increase in private messaging between customers and brands is driving companies to turn to chatbots for improved social customer care. The Watson Conversation Service offers a simple, scalable and science-driven solution for developers to build powerful chat bots to address the needs of various brands and companies. As developers leverage Watson Conversation to build cognitive solutions for various, one recurring question is: "How much time should I plan to train my solution" or "How do I know when my model is trained sufficiently well"? While the answer depends greatly on the problem being solved and the data powering the solution, in this blog we offer a common methodology for training the machine learning (ML) models powering your chat bot solution.

Artificial neural networks are a fascinating area of study, although they can be intimidating when just getting started. There are a lot of specialized terminology used when describing the data structures and algorithms used in the field. In this post you will get a crash course in the terminology and processes used in the field of multi-layer perceptron artificial neural networks. Crash Course In Neural Networks Photo by Joe Stump, some rights reserved. We are going to cover a lot of ground very quickly in this post.

Summary: Are there large, sustainable career opportunities in AI and if so where? Do they lie in the current technologies of Deep Learning and Reinforcement Learning or should you focus your career on the next wave of AI? If you're a data scientist thinking about expanding your career options into AI you've got a forest and trees problem. There's a lot going on in deep learning and reinforcement learning but do these areas hold the best future job prospects or do we need to be looking a little further forward? To try to answer that question we'll have to get out of the weeds of current development and get a higher level perspective about where this is all headed. The roots of AI are actually in the behavioral sciences migrating eventually into biology and neurology.

Summary: IBM's Watson as it exists today is as close as we've come to a single integrated platform for AI. It contains all the capabilities for image and video, natural language speech and text input and output, and the most comprehensive knowledge recovery module yet combined together. If you want to exploit the advances we've made in AI you need to understand where Watson is today and where it's heading. Recently we wrote about how the'popular' Watson of Jeopardy fame still lingers in the memories of our non-data scientist colleagues and perhaps misleads them about the capabilities of AI. It's time we got in tune with the modern Watson, or more correctly IBM's Watson Group and its Watson platform and took a look at all there is to offer. There are three broad capabilities in today's AI and they are: Image and video processing: Largely driven by Convolutional Neural Nets (CNNs) this field has been getting most of the press with capabilities like facial and object recognition.

Machine learning systems seem a little bit like a math problem. Figure out the algorithm, pop in the data, and answers come out. When you're trying to predict what movies or books people like, that can be extremely important, the difference between a boost in a revenue and a reputation hit that appears on mediabuzz.com. Yet testing is rarely at the top of our minds as we try to develop and deploy systems based on machine learning algorithms. Simply building a good set of algorithms that model the problem space is difficult enough. But testing is a part of the software development and deployment process, and we need to look seriously at how these systems will be tested.

For the last few years, our digital presence showed us what an environment where all our actions somehow influence our further actions (or actions of others through algorithms) might look like. This is one of the major trends that keeps accelerating us to a state where we hardly even have to make a decision before executing. Take the on-demand economy (Uber, Postmates, etc.) as an example. Machines already tell us where to go, when to go, how much to pay and even what to eat. Companies like Amazon have started recommending products to buy and Facebook suggests individuals to add as friends.

Lantern Pharma Inc., is a privately held, global biotech company pioneering the field of precision oncology. Lantern's proprietary approach to drug development is driven by advanced genomics and machine learning-based artificial intelligence (AI), which when combined, are advancing a new wave of precision drug therapies that significantly reduce the time to market and overall risk associated with drug development. Lantern has reached an agreement to collaborate with India-based AI and data analytics company, Intuition Systems. Intuition will work closely with Lantern's existing computational team to bring additional AI, big data analysis, cloud support and infrastructure to support drug development and biomarker identification. The treatment of cancer represents a large market with many underserved areas where precision therapies will be highly valued.