Big Data and Analytics Hub spoke with IBM Distinguished Engineer John Thomas (@johnjaithomas) about some of the importance of tuning information architecture to make algorithms meet enterprise needs, as well as how machine learning can most effectively be applied in hybrid scenarios in 2018.
Big Data and Analytics Hub spoke with IBM Distinguished Engineer John Thomas (@johnjaithomas) about some of the importance of focusing on information architecture to make algorithms meet enterprise needs, as well as how machine learning can most effectively be applied in hybrid scenarios in 2018.
If the first things that come to mind when you think of AI assistants are the likes of Amazon Alexa or Google Home, it’s time to learn about embodied cognition, AI that can physically interact with its environment. A year ago, IBM researchers did just that and brought Watson services into the
It can be difficult to keep up with all the best podcast episodes during the year. That's why we've compiled the Top 10 podcasts of the year from the IBM Big Data & Analytics Hub Insights Podcast feed right here.
As happens so often, IBM is quietly laying the groundwork for the future. A recent step toward that future is TJBot, an unassuming, do-it-yourself cardboard robot that opens a window into what AI researchers are calling “embodied cognition.
Readers of the IBM Big Data & Analytics Hub were hungry for knowledge this year. They voraciously read blog posts about incorporating machine learning, choosing the best possible data model, determining how to make the most of data science skills, working with open source frameworks and more.
Information analytics has never been a “one size fits all” proposition. That applies to the hardware and software technologies organizations employ, the information being parsed and the goals of specific projects.
Machine learning concerns in Silicon Valley tend to be different from those elsewhere in the U.S. — and outside of the U.S. So, here are five tips for those hearing about machine learning efforts in Silicon Valley, but who work elsewhere. These suggestions consider where machine learning and data