In an industry known for its love of buzzwords and hype, artificial intelligence (AI) has become marketing's new'big data'. But where big data ultimately led to new layers of complexity, AI promises the opposite. Big data forced marketers to become data scientists (or hire them, if they could be found), but AI holds out the hope that marketers may get to go back to doing what they signed up for the in the first place. Recent months have seen technology providers such as Salesforce, Oracle and Microsoft bring new AI-based technologies to market, promising to derive insights and improve conversions by mimicking the processes of the human brain in software. Salesforce, for example, is rolling out its Einstein AI technology to provide functions such as product recommendations within the Commerce Cloud, email content recommendations within its Marketing Cloud, and predictive forecasting tools for sales managers with its Sales Cloud.

At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the upfront effort required in interpreting the model, or the effort required to make predictions about a model's behavior. One approach to interpretable machine learning is designing inherently interpretable models. Visualizations of these models usually have perfect coverage, but there is a tradeoff between the accuracy of the model and the effort required to comprehend it - especially in complex domains like text and images, where the input space is very large, and accuracy is usually sacrificed for models that are compact enough to be comprehensible by humans. Experiments usually involve showing humans these visualizations, and measuring human precision when predicting the model's behavior on random instances, and the time (effort) required to make those predictions [7, 8, 9]. Model-agnostic explanations [12] avoid the need to trade off accuracy by treating the model as a black box. Explanations such as sparse linear models [11] (henceforth called linear LIME) or gradients [2, 10] can still exhibit high precision and low effort (which are de-facto requirements, as there is little point in explaining a model if explanations lead to poor understanding or are too complex) even for very complex models by providing explanations that are local in their scope (i.e.

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.

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).