Summary: What are the real threats of job loss from real and AI enhanced virtual robots? How do we position ourselves and our children to succeed in this new environment?
Added by William Vorhies on April 25, 2017 at 8:09am — No Comments
Summary: We are swept up by the rapid advances in AI and deep learning, and tend to laugh off AI’s failures as good fodder for YouTube videos. But those failures are starting to add up. It’s time to take a hard look at the weaknesses in AI and where that’s leading us.
Added by William Vorhies on April 18, 2017 at 8:04am — No Comments
Summary: The argument in the popular press about robots taking our jobs fails in the most fundamental way to differentiate between robots and AI. Here we try to identify how each contributes to job loss and what the future of AI Enhanced Robots means for employment. …Continue
Summary: DataOps is a series of principles and practices that promises to bring together the conflicting goals of the different data tribes in the organization, data science, BI, line of business, operations, and IT. What has been a growing body of best practices is now becoming the basis for a new category of data access, blending, and deployment platforms that may solve data conflicts in your organization.
Summary: Autonomous Vehicles (AVs) are supposed to be just around the corner but the anecdotal evidence is that their claims to safety are way out ahead of reality. The solution may be in a shared segment of on-board telematics, part of the SLAM group (simultaneous localization and mapping) and sharing some of that data car-to-car.
Added by William Vorhies on March 28, 2017 at 8:54am — No Comments
Summary: Some observations about new major trends and directions in data science drawn from the Strata+Hadoop conference in San Jose last week.
Added by William Vorhies on March 20, 2017 at 4:48pm — No Comments
Summary: Whether you are a startup person or data science-minded executive in a larger organization what logic can you apply to spot the most compelling opportunities for AI in your organization.
Added by William Vorhies on March 12, 2017 at 9:00am — No Comments
Summary: IBM’s Watson now to do your taxes at H&R Block? This is a good opportunity to explore the differences between Question Answering Machines (Watson) and Expert Systems.
Added by William Vorhies on March 7, 2017 at 9:19am — No Comments
Summary: Count yourself lucky if you’re not in one of the regulated industries where regulation requires you to value interpretability over accuracy. This has been a serious financial weight on the economy but innovations in Deep Learning point a way out.
Added by William Vorhies on February 28, 2017 at 9:21am — No Comments
Summary: Looking beyond today’s commercial applications of AI, where and how far will we progress toward an Artificial Intelligence with truly human-like reasoning and capability? This is about the pursuit of Artificial General Intelligence (AGI).
There is no question that we’re making a lot of progress in artificial intelligence (AI). So much so that we are rapidly approaching or have already arrived at a plateau in development where more effort is…Continue
Added by William Vorhies on February 21, 2017 at 8:30am — No Comments
Summary: In our recent article on “5 Types of Recommenders” we failed to mention Indicator-Based Recommenders. These have some unique features and ease of implementation that may be important in your selection of a recommender strategy.
A few weeks ago in the midst of our series on recommenders we published an article “5 Types of Recommenders” in which…Continue
Summary: For those of you traditional data scientist who are interested in AI but still haven’t given it a deep dive, here’s a high level overview of the data science technologies that combine into what the popular press calls artificial intelligence (AI).
Added by William Vorhies on February 7, 2017 at 7:02am — No Comments
Summary: In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance.
Added by William Vorhies on January 31, 2017 at 9:54am — No Comments
Summary: There are many sources of packaged recommenders including the more comprehensive Digital Personalization platforms. It’s also possible to code your own. Here are a few things to consider.
Added by William Vorhies on January 24, 2017 at 2:00pm — No Comments
Summary: There are five basic styles of recommenders differentiated mostly by their core algorithms. You need to understand what’s going on inside the box in order to know if you’re truly optimizing this critical tool.
Summary: In this multi-part series we walk through the full landscape of Recommenders. In this article we cover business considerations as well as issues for Recommenders as a group. In the next articles we’ll discuss the details of the five major types of recommenders, improving their performance, and finally the coming impact of deep learning on Recommenders.
Summary: As deep learning expands those capabilities are finding their way into the not-for-profit community in the service of conserving the earth’s wildlife and forests.
The for-profit world may be driving AI but it’s a solution to many problems in the not-for-profit…Continue
Added by William Vorhies on January 3, 2017 at 10:11am — No Comments
Summary: A great story about an AI-powered massive on-line open learning platform focused on STEM education. The platform and its content is to be available across many languages to serve students anywhere in preparing for a better life in STEM careers.
Added by William Vorhies on December 27, 2016 at 10:00am — No Comments
Summary: The largest companies utilizing the most data science resources are moving rapidly toward more integrated advanced analytic platforms. The features they are demanding are evolving to promote speed, simplicity, quality, and manageability. This has some interesting implications for open source R and Python widely taught in schools but significantly less necessary with these more sophisticated platforms.
Summary: The data science press is so dominated by articles on AI and Deep Learning that it has led some folks to wonder whether Deep Learning has made traditional machine learning irrelevant. Here we explore both sides of that argument.