Summary: Whether you are a startup person or data science-minded executive in a larger organization what logic can you apply to spot the most compelling opportunities for AI in your organization. In 2014 Kevin Kelly, founder of Wired magazine and prolific futurist famously said, "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Kevin you were clearly right. The Silicon Valley and every other tech startup haven are awash in companies trying to fulfill his vision. However, whether you are a startup person or data science-minded executive in a larger organization that's not enough information to spot compelling opportunities. What are the rules or guidelines you can apply to identify these transformative applications?

Eno is the first natural language SMS chatbot from a U.S. bank. And we launched Eno at SXSW on March 10, open as a pilot to Capital One customers. When it came to giving this new SMS chatbot a name, we looked for a gender-neutral name with human-like qualities consistent with Capital One's mission of bringing humanity to banking. We chose the name Eno because it's "one" (as in Capital One) spelled backwards. And we also liked that when we asked Google for the definition of Eno, the first result returned said "Awesome, cool." After deciding to create a gender-neutral character, we worked to define Eno's character traits and develop its backstory.

The basic Dexter interface isn't dissimilar to Wordpress or any other blogging platform you might have tried. There's a large main composing window with some toolbars up top and off to the sides. To get started, you type in an example of what the user says to the bot -- to keep it simple, you can just start with "hello," or a variety of salutations (hello, hi, sup). Then, you just decide what the bot will say in response -- this case, I went with the Lionel Ritchie classic "is it me you're looking for?" There's a window on the right that you can test the code in and make sure you're getting the responses you wanted, and then it's just a matter of hitting publish.

As artificial intelligence increasingly takes center stage in computer technology, large-scale orders are heading to Japan's supercomputer makers from research entities in need of the massive processing power those machines provide. Fujitsu is assembling a dedicated AI-use supercomputer for the Institute of Physical and Chemical Research, better known as Riken, which plans to start using it in April. Tokyo Institute of Technology, also known as Tokyo Tech, has ordered a large-scale system for AI-education purposes from SGI Japan, another supercomputer maker. The institute plans to introduce it in August. And industry insiders are looking forward to an even bigger opportunity: a huge AI-use supercomputer the National Institute of Advanced Industrial Science and Technology, or AIST, wants to start using in 2018.

Many high-performance organizations remain passionate about Vilfredo Pareto, the incisive Italian engineer and economist. They continue to be inspired by his 80/20 principle, the idea that 80% of effects (sales, revenue, etc.) come from 20% of causes (products, employees, etc). As machine learning and AI algorithmic innovation transform analytics, I'm betting that next-generation algorithms will supercharge Pareto's empirically provocative paradigm. Here are three important ways that AI and machine learning will redefine how organizations use the Pareto principle to digitally drive profitable innovation to levels beyond conventional analytics. First, greater volumes and variety of data guarantee that algorithms get the training they need to get smarter.

The advent of easily accessible, mondo computing power could open up lots of possibilities in the arts, not just in the startup and tech world. Brooklyn startup Paperspace has a new post out about how to use its product (which is sort of like AWS for the computational power required of machine learning) to make art. The post centers on "style transfer," which is taking the style of one image and imparting it onto the content of another. "The central problem of style transfer revolves around our ability to come up with a clear way of computing the'content' of an image as distinct from computing the'style' of an image," the post reads. "Before deep learning arrived at the scene, researchers had been handcrafting methods to extract the content and texture of images, merge them and see if the results were interesting or garbage."

An article in Wired, "Google, Facebook and Microsoft are remaking themselves around AI", highlights a topic I've been discussing for a long time now: the efforts technology giants are making to educate their employees at all levels by incorporating talent and developing new products based on machine learning and artificial intelligence in order to change how they organize and operate their businesses. In a very short time, we have gone from the AI Winter and seeing these types of technologies as something out of science fiction to finding ourselves in the middle of a rapidly changing reality with advances appearing each day to the point that the development of this type of technology and its application to more and more areas is beginning to create a digital divide between the companies that have it and those that do not. From the viewpoint of a business school, the phenomenon can be clearly seen: the demand for executives able to understand this area is increasing, reflected by increased demand for courses related to the subject from year to year, while such content is incorporated into virtually all programs at all levels, from a simple understanding of the concepts to analysis at the highest level. Several of the entrants in this field seem to have a reasonably clear strategy to develop machine learning platforms and to offer them to third parties through Machine Learning as a Service (MLaaS). In this context, which I follow with great interest given my work as a strategic advisor at BigML, are large companies like Amazon (its traditional strategy, develop any service, be it logistics, cloud computing or whatever, and open it to others), Microsoft, IBM, Facebook and Google, to smaller companies that are usually faster and more agile at product development.

The matrix of anatomy, pathology and image observations on multiple modalities is huge. The supervised learning algorithms are hampered by the lack of annotated data sets for each cell in that matrix. The unsupervised learning methods will be hampered by the few examples of many of the rare cells of the matrix. Algorithms are typically developed one by one, i.e., this image observation in this disease setting (possibly in this population). Even after an algorithm is developed, it has to be'perfected' to high sensitivity and specificity because humans are very good at this task.

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Project failures are very common in IT. This risk is higher if you are adopting a new technology and which is unfamiliar to your organization. Machine learning is not at all new to the world but development and awareness have now reached a point at which its benefits are becoming attractive for business. Though machine learning has a huge potential of reducing costs and finding new revenues by applying new technology aptly but if not implemented properly there could be many pitfalls. There is a lot to do for developers in machine learning as it offers the promise of applying business critical analytics to any applications.