There are two kinds of office work: the uninspiring, but necessary stuff that takes long hours to do and requires little in terms of judgement or emotional intelligence; and the creative part, which is challenging and flexes the human mind. Software bots have already started to take on some of the routine tasks. We believe that within a decade, Artificial Intelligence will take care of all the repetitive office work so that we can focus our minds on higher value and rewarding activity. AI goes way beyond automation; it brings intelligence to the job at hand, with a truly transformational impact. The news that chatbots are coming to Facebook Messenger made quite a few waves.

Priyadharshini writes on Project Management, IT, Six Sigma, & e-Learning. With a penchant for writing and a passion for professional education & development, she is adept at penning educative articles. She was previously associated with Oxford University Press and Pearson Education, India.

Google acquires another machine learning startup, predictive analytics and wearables are driving the continued adoption of electronic health records (EHR), millennials are believers in the value of data-driven projects, Alteryx has released a new set of capabilities for its analytics platform, and H2O.ai has released an updated API for Apache Spark. The self-service data analytics company has added a new set of enhancements to the Alteryx Analytics platform. Among them is the ability to create models that progress from descriptive to predictive to prescriptive analytics. Alteryx said that this lets users evaluate possible alternatives and predict outcomes through simulation analysis. Other new capabilities include predictive modeling support for users working inside Teradata data warehouses with in-database analytics.

Having installed the public beta of macOS Sierra, I am staggered by this - it's not image recognition of a photo that finds a building in it, or something like that. I have some 27,000 photos and videos in my library - I was curious to see if it would find images of birds - so that's what I typed in to the search box. While I was doing that it made two suggestions - birds and birdhouse. Out of curiosity I clicked the birdhouse one. There are two things in there - a photo and a video.

Artificial intelligence research continues to accelerate as human and machines collaborate to solve more complex problems. A new survey by the National Academy of Science identifies the frontiers of AI research that include "augmented cognition" along with "integrative" AI. In a workshop report on IT innovation just released by the National Academy's science, engineering and medicine branches, a section on "Developing Smart Machines" describes ongoing AI research efforts along with machine learning, a discipline some experts consider a subfield of AI. "Machine learning is what lets computers discover patterns within data and then use those patterns to make useful, and ideally correct, predictions," the report states. The authors quotes Jaime Carbonell, a professor of computer science at Carnegie Mellon University, as noting" "Machine learning essentially is the engine that is driving modern artificial intelligence." Machine learning "often deals with unbalanced data sets in which the ultimate focus of decision making is precisely the outlier cases," that is, the extreme cases that contrast sharply with typical data and provide opportunities for the "most important learning opportunities."

Elsewhere, I have suggested that there are three junctures at which any data mining project may go the most wrong: 1. problem definition, 2. data acquisition and 3. model validation (see the Data Mining and Predictive Analytics Web log). Data acquisition is a superset of statistical sampling, and the text by Lohr is highly recommended for this topic. Model validation is well explained in the literature: see, for instance, Weiss and Kulikowski. Problem definition involves understanding the business problem and mapping an appropriate technical solution to it. This may not be as simple as it sounds, and it is easy to be naïve about the best way to construct a technical solution which most naturally solves the given problem.

This episode of Fresh Machine Learning is all about a relatively new concept called a Generative Adversarial Network. A model continuously tries to fool another model, until it can do so with ease. At that point, it can generate novel, authentic looking data! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

Everyone who gets going in Machine Learning (and Deep Learning) gets overwhelmed by the plethora of MOOCs available. Here, I try to give a comprehensive survey of such courses available freely on the internet. You can take this post as an complementary to this and this previous posts. I will try to highlight some important pointers such as the difficulty of the courses, the correct order in which these should to be completed, the right audience for these courses. You will get a feel of how these courses give you a stack of skills in your arsenal and how you can use them to develop practical machine learning systems.

Today eBay announced it will acquire SalesPredict, an Israel-based company that leverages advanced analytics to predict customer buying behavior and sales conversion. SalesPredict is eBay's latest acquisition that will support its artificial intelligence, machine learning and data science efforts. It follows eBay's recent acquisition of Expertmaker, in order to further bolster our structured data efforts. Financial terms of the deal were not disclosed. Upon the close of the transaction, a number of SalesPredict's employees will join eBay's structured data organization, working from eBay's Israeli Development Center in Netanya.

We were all quite pumped a few weeks ago when Anki announced its plan to move beyond smartphone controlled cars into emotionally intelligent robots. At the time of the announcement Anki indicated that it would release an SDK with the inclusion of Cozmo, but until now details on what that SDK would be were sparse. Now we know, and the SDK is so jam-packed with goodies it could turn Cozmo into the Commodore 64 of robotics--a device that changes how people interact with abstruse technology. The Commodore 64 changed the way we thought about computers. Before that ugly box got plugged into TVs across the United States computers were still for the very rich, the very smart, and the ladies programming the IBM beasts that took up entire floors of buildings.