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
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.
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
One of the hallmarks of the cognitive era of business is that companies can can be positioned to unlock insights from unprecedented volumes of data. Advancements in cognitive computing and artificial intelligence (AI) might hold the most significant opportunity where companies can win with data-
If you read a lot of development blogs nowadays, you’ll probably notice a common theme: developers don’t want to deal with databases. They want to focus on designing, building, testing, and deploying applications that deliver value to the business as quickly as possible.
Big data isn’t just getting bigger. It’s getting more valuable. As companies work to unlock more value from their data, one of the biggest challenges to address is disconnected data silos. Big companies don’t have one data lake, they have data lakes, ponds and pools.
Context-aware stream computing helps you become more responsive to emerging opportunities. By using innovative technologies to understand the context of data and analyze data in real time, you can put data to work.
With the Geospatial Analytics service in IBM Bluemix, you can monitor moving devices from the Internet of Things. The service tracks device locations in real time with respect to one or more geographic regions. Geospatial Analytics can be used as a building block in applications that support
Fundamentally, machine learning is a productivity tool for data scientists. As the heart of systems that can learn from data, machine learning allows data scientists to train a model on an example data set and then leverage algorithms that automatically generalize and learn both from that example
January marked the release of the long awaited Hidden Figures movie featuring an all-star cast and highlighting the contributions of both women and IBM's technology to history. Hidden Figures tells the true story of three African-American female mathematicians, Katherine Johnson, Mary Jackson, and
IBM’s community of big data developers continues to grow. As our Big Data Developer meetup program moves into its fifth year, this worldwide community of customers, partners and IBM developers is on the verge of enlisting its 100,000th member—when we published this blog, we counted 99,100.
IoT is the next goldmine of data. Today, it’s still largely untapped information that is primarily used for operational monitoring. By combining that data with traditional “corporate” data, you can improve customer service through faster problem recognition and response, react more quickly to a
Elderly care is on tap to be a critical need in the coming decades. See how Caregivers.com is using cloud computing and mobile technologies to provide greater choice for families and higher wages for in-home caregivers.