As enterprise enters further into the digital age, data has become the strategic asset that knowledge workers, small or large, rely on to guide their decisions. However, managing such large volumes of data has exposed some unprecedented challenges for the enterprises. Enterprises have learned that the data that they hold, comes in a variety of formats, resides in different and distributed systems and is specific to the organization and its domain. Setting these challenges as the backdrop, IBM's Watson division has built solutions that not only allow for data connectivity but also the analysis of unstructured data and its customization to an enterprise domain. IBM Watson Explorer is Watson's flagship product for text analytics and discovery.

Donald Trump's presidential victory defied just about everything supposedly smart people knew about politics and winning the White House. He prevailed by tapping a force that was far more powerful than the strongest debate performance, the most attention-grabbing TV spot, the savviest turnout operation or the highest-profile surrogates, from the White House down. He tapped into seething anger and voters' ravenous desire for change. If people get mad enough, they will storm the polls without prodding -- and without, apparently, the need to confide in opinion pollsters, who largely missed the huge outpouring of Americans displaced by decades of economic restructuring and unsettled by the country's changing complexion and shifting cultural mores. If people get mad enough, they will look past a candidate's overt prejudice, his coarse put-downs of women, his mockery of a disabled journalist, his taunting of a Gold Star family.

California voters have approved a significant change of the rules in how proposed laws are approved by the Legislature, overwhelmingly supporting a new mandate for public review of legislation before any final vote. Proposition 54, which will impose a three-day waiting period before lawmakers can take action on the final version of bills, appeared headed for an easy victory on election night. As of early Wednesday, it was winning with 64% of the vote. The change in legislative rules was long discussed in the state Capitol but failed to gain momentum until the initiative written by a former GOP legislator and bankrolled by a wealthy Bay Area activist. In addition to the three-day delay for public review of most bills, Proposition 54 will also impose new rules requiring that video of legislative hearings and debates be posted online.

AI and machine learning changes the software paradigm computers have been based on for many decades. In the traditional computing domain, providing an input, we feed it into an algorithm to produce the desired output. This is the rule-based frameworkthe majority of the systems around us still work with. We set up our thermostat to a desire temperature (input) and a rule based programming (algorithm) will take care of reading a sensor and activating heating or AC machines to get to the room temperature we want (output). The industry has been working relentlessly for many years developing better hardware, software and apps to solve a gazillion problems and use cases around us with programmable solutions.

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization's business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what's on the public's mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don't expect to see machines displacing knowledge workers anytime soon -- they expect computing technology to augment rather than replace the work of humans.

Westbrook added six rebounds and five assists for the Thunder, who surged in the final minutes of a tight meeting between Western Conference contenders. Although he wasn't near a triple-double for the first time this season, the ferocious point guard carried the Thunder down the stretch, scoring their final six points and making a key hustle play in the last minute.

But, this story is real. About 15 years ago, in Walmart, a sales guy made efforts to boost sales in his store.

In this paper we propose a novel approach for learning from data using rule based fuzzy inference systems where the model parameters are estimated using Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques. We show the applicability of the method for regression and classification tasks using synthetic data-sets and also a real world example in the financial services industry. Then we demonstrate how the method can be extended for knowledge extraction to select the individual rules in a Bayesian way which best explains the given data. Finally we discuss the advantages and pitfalls of using this method over state-of-the-art techniques and highlight the specific class of problems where this would be useful.

Machine learning is a branch of AI. Other tools for reaching AI include rule-based engines, evolutionary algorithms, and Bayesian statistics. While many early AI programs, like IBM's Deep Blue, which defeated Garry Kasparov in chess in 1997, were rule-based and dependent on human programming, machine learning is a tool through which computers have the ability to teach themselves, and set their own rules. In 2016, Google's DeepMind, beat the world champion in Go by using machine learning--training itself on a large data set of expert moves. In supervised learning, the "trainer" will present the computer with certain rules that connect an input (an object's feature, like "smooth," for example) with an output (the object itself, like a marble). In unsupervised learning, the computer is given inputs and is left alone to discover patterns. In reinforcement learning, a computer system receives input continuously (in the case of a driverless car receiving input about the road, for example) and constantly is improving. A massive amount of data is required to train algorithms for machine learning. First, the "training data" must be labeled (for instance: a GPS location attached to a photo).

For consumer companies, large-scale leveraging of customer and behavioral data to drive personalized customer experiences is turning into a virtual arms race. Marketing technology platforms of the last 10 years were built around campaign process that were still highly manual, requiring marketing execs to do all the testing, optimization and which makes the cycle time for learning and actually influencing marketing very slow. Now more and more marketers recognize the need to deploy advanced personalization capabilities that make the use of machine-learned optimization. And, Matt Fleckenstein, Chief Product Officer at Amplero, helps marketers achieve just that. With a track record for conceiving, building, and launching martech products and services it comes easy to him.