Over the past four years, the role of chief marketing officers has changed. Once, CMOs led small silos that focused on growing business impact across paid media channels. Now, CMOs are increasingly being asked to manage company impact and growth across an array of owned digital media and customer relationship channels -- with robust budgets to match. While the breadth of technology continues to grow, separate tools themselves do not necessarily equate success. Budgets for marketers are slowly climbing, and responsibilities are broadening, according to Gartner's 2016-2017 CMO Spend Survey.

This post is part of the Learning Machine Learning series. It's based on Chapter 1 and 2 of Python Machine Learning. Machine learning can be intimidating for a newcomer. The concept of a machine learning things alone is quite abstract. How does that work in practice?

It wasn't long ago that we hosted a webinar for our clients that focused on emerging messaging channels for customer service. The shiny new object in the room by far was SMS chatbots. Using SMS as a support channel was cool, but the real goal of the pilot was to implement chatbots and see how many customer interactions could be automated. All of the wind went out of the sails when we realized that thousands of interactions would be required to train the bot and our client's volume was somewhere in the low hundreds. The pilot was abandoned shortly thereafter and the search for other technology that increase customer service efficiency continued.

Signature and behavioral based anti-malware are no match for next generation adversaries who utilize mutating hashes, sophisticated obfuscation mechanisms, self-propagating malware, and intelligent malware components. In this analysis, entitled "Signature Based Malware Detection is Dead," the Institute for Critical Infrastructure Technology provides a thought-provoking analysis of the necessity for critical infrastructure sectors to adopt advanced machine learning and artificial intelligence based solutions to defend against a hyper-evolving adversary. This analysis was authored by James Scott, Sr. Fellow, ICIT

Artificial intelligence is the hottest arena in the tech world today, but the complexities of developing practical applications from the technology have made it slow to impact people's everyday lives. That may be starting to change. Leading stun gun and body camera manufacturer TASER International (NASDAQ: TASR) announced on Thursday that it has acquired two companies that are creating artificial intelligence technology. It's a bold move for the company, but it could make both its Axon cameras and the Evidence.com The purpose of an A.I. service from TASER would be to help sift through the enormous quantity of video and data law enforcement agencies are storing every single day.

Guest blog by Kevin Gray.. Kevin is president of Cannon Gray, a marketing science and analytics consultancy. Regression is arguably the workhorse of statistics. Despite its popularity, however, it may also be the most misunderstood. The answer might surprise you: There is no such thing as Regression. The Dependent Variable is something you want to predict or explain.

Ford Motor said Friday it will invest $1 billion over the next five years in a Pittsburgh-based artificial intelligence company, Argo AI, to help put fully autonomous vehicles on the road by 2021 and to potentially license self-driving car technology to other companies. That in itself is pretty big news: major auto company plunks down huge money for robotics geeks to help it across the finish line on ambitious self-driving car project. But there's another very interesting aspect to the deal that shows Ford is serious about leading in the race to develop self-driving cars. With its $1 billion investment, Ford will become majority stakeholder in Argo AI, but the rest will be owned by Argo AI's cofounders, Bryan Salesky and Peter Rander, and their team, including a contingent of Ford software engineers who will leave Ford and become Argo AI employees. Giving those workers a piece of the company as a sweetener is seen as critical to attracting and retaining talent in the competition between traditional automakers and Silicon Valley to develop cars of the future.

This post is the second part in a series where we will build a real-time example for analysis and monitoring of Uber car GPS trip data. If you have not already read the first part of this series, you should read that first. The first post discussed creating a machine learning model using Apache Spark's K-means algorithm to cluster Uber data based on location. This second post will discuss using the saved K-means model with streaming data to do real-time analysis of where and when Uber cars are clustered. The example data set is Uber trip data, which you can read more about in part 1 of this series.

One goal of machine learning algorithms is to use past information to predict the future. The advantage of machine learning over traditional analytics is the ability for the machine-learning algorithm to automatically build a good model, saving time, preventing overfitting, and generally being more robust. To do this the algorithm builds a model, calculates the error rate of the model, adjusts parameters to lower the error rate, and iterates again, 'learning' from its mistakes. There's a step in between: calculating the error rate requires us to split our dataset into a training and test dataset, in which we train the model on the training dataset, and calculate the error rate on the test dataset. We need to do this because if we calculate the error rate while training on the entire dataset we would get a low error rate since the model is trained on that specific data, and this would be misleading for predicting future, unknown data.

We have seen a fast growing interest in current activities around AI startups and research in the last couple of months. Headlines like "2016 was the year AI came of age", "AI was everywhere in 2016", and "The Great A.I. Awakening" were all over the media in the ending weeks of 2016 and we are curious about what 2017 will bring. I found particularly interesting that the current applications, future potential, and possible risks even attracted interest beyond the tech community through TV shows like Westworld, coverage on traditional media and even Obama's farewell address. Sadly, for many of us tech enthusiasts here in Europe, we sometimes feel like there is way less movement on this side of the Atlantic than in the Silicon Valley. However, with major acquisitions like DeepMind, Magic Pony Technology, Movidius, Vision Factory, and Dark Blue Labs, Europe has shown that it is actually leading the way in AI and machine learning.