Many companies are expected to pursue data management, advanced analytics and cognitive computing to stay competitive and drive revenue. Except for a handful of leaders such as LinkedIn, Netflix, Nordstrom, Target and Verizon, most companies are still struggling to close the gap between data
As the expression goes, "There’s no AI without IA." In other words, enthusiasm for AI has led many to jump in head first. But without a strong technology foundation, companies could be setting themselves up for obstacles.
The search function is a very powerful tool, assuming you have concrete keywords or concepts to find in your data. And that does not even take into account the size of the information you might be searching.
Join us 27 February at 1 PM ET for "Machine Learning Everywhere: Build Your Ladder to AI." Visit the event landing page to learn more about the event and register for a calendar reminder: ibm.com/mleverywhere
Today, unstructured information represents more than 90% of the information within organizations. This IDC case study estimates that the digital universe will grow 40 percent per year over the next decade and, by 2020 it will reach an astounding 44ZB or 44 trillion gigabytes.
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.
Organizations everywhere, from massive governments to the smallest start-ups, are in a race for the best-possible data expertise and tools. To help your team understand the data science journey, IBM created the Data Science for All webcast.
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