True story: one of my social media connections asked for recommendations for reliable new sources and got a few outlets named, though some of us -- myself included -- said that you simply cannot rely wholly on any single source and have to check through multiple sources to be sure you get the full picture of the facts in context to find where the truth lies. But not everyone is sophisticated enough to be aware that reports they see -- even from outlets with solid reputations -- need to be taken with a grain of salt. That's why Valentinos Tzekas founded FightHoax, the creator of an AI-powered algorithm that empowers anyone to ascertain if an article is fake or not in just seconds without Googling the story. Described with the tagline, "Fighting against the mass misinformation spread," FightHoax claims an accuracy rate of 89% based on the most recent and most user-requested 172 fact-checked articles). It is still in private beta and not fully public.

There is a lot of debate about whether data scientists will or won't be automated out of a job. It turns out that machines are far better at doing rote tasks faster and more reliably than humans, such as data wrangling. "The automation of data science will likely be widely adopted and speak to this issue of the shortage of data scientists, so I think in the near term this could have a lot of impact," said David Schatsky, managing director at Deloitte and one of the authors of Deloitte's new report. Industry analysts are bullish about the prospect of automating data science tasks, since data scientists can spend an inordinate amount of time collecting data and preparing it ready for analysis. For example, Gartner estimates that 40% of a data scientist's job will be automated by 2020.

That phrase became the popular song What's the Frequency, Kenneth? (Rather was mugged by a disturbed man who, thinking CBS was sending radio messages to his mind, referred to Rather as "Kenneth" while asking what "the frequency" was.) Some people even consider the phrase as exclamatory slang for something insane that happens. Creativity has certainly been applied to data, at least from what I have seen during my analytics career. That part has not changed. But the quality of its information has changed -- due to creative observations that digitally represents the activity of people, products, and services.

Get up to speed with emerging analytics technologies including Natural Language Processing, Edge Analytics, Machine Learning, Real-time Analytics, and Augmented Analytics. These expert-led sessions are for analytics leaders, professionals and business users.

But suppose every piece is the same shape and is small enough to make images confusing at first look. You'd take a guess at how they fit, right? Data can be that way. Fortunately, analysts are finding many advanced ways to bring data together. One technique receiving attention these days is clustering, an unsupervised machine learning method that calculates how unlabeled data should be grouped.

Artificial intelligence and machine learning have hit the mainstream, spanning consumer devices to enterprise initiatives. Experts in the field say change is coming fast. But just where are we? If you don't have your plan started, are you behind the curve? AllAnalytics reached out to several thought leaders in the areas of AI, machine learning, and advanced analytics to get their perspective. Their answers to the four questions we posed will help prepare you and your organization for the coming wave of AI and machine learning.

We asked Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton, what machine learning technologies he's watching. He focused his reply on applications, not algorithms. "When I think about what's new and coming up, I don't expect that it's the mathematics that will be changing but the types of things we can do with the math," he says. With that in mind, consider these three applications of analytics that Borne is watching. Graph analysis is what we used to refer to as social network analysis, but then Facebook and Twitter came along and the term "social networks" took on a whole new meaning.

The Unilever brand is associated with things like soap and deodorant. But technology does play a central role in its business operations, and the UK-based company sought to tap into its power to improve hiring. With the goal of diversifying its pool of entry-level candidates, Unilever experimented with replacing its standard approach to recruiting with algorithms and targeted mobile ads. As described in a recent Wall Street Journal article, Unilever's recruiting in the past had centered primarily around eight college campuses and the usual resume collection. But the company wanted to try a different approach that would reach more people and filter through applications without overwhelming the human resources in place. The solution was setting up a hiring process based on algorithms.

Artificial intelligence and all the advanced analytics technologies that go with it are driving a new wave of innovation when it comes to analytics. The possibilities, opportunities, and use cases that surround machine learning and natural language processing and capturing the imagination of organizations and even the public. Self-driving cars and chatbots that mimic real customer service representatives may be some of the more well-known use cases today. But other technologies are out there that can diagnose health conditions, advance treatment for cancer, and improve the outcomes for cardiac patients. By applying thes technologies, organizations can realize better efficiency and lower costs.

The city of Chicago is using an algorithm to predict whihch individuals are likely to be the victim or perpetrator of a crime. It sounds like the premise behind the TV show Person of Interest. Chicago, a city that has seen its crime rate surge, is using this algorithm in an attempt to help get crime under control. That's a good thing if it can help reduce crime. But there are ethical concerns about using data in this way. Do we want to predict the likelihood of someone committing a crime, like in the film Minority Report, and incarcerate them before the crime even happens?