Data scientists have a lot of tools at their disposal, but not all of them are equally accessible. Aiming to put IBM's Watson AI within closer reach, analytics firm Columbus Collaboratory on Thursday released a new open-source R extension called CognizeR.
R is an open-source language that's widely used by data scientists for statistical and analytics applications. Previously, data scientists would have had to exit R to tap Watson's capabilities, coding the calls to Watson's application programming interfaces (APIs) in another language, such as Java or Python.
Now, CognizeR lets them tap into Watson's so-called "cognitive" artificial-intelligence services without leaving their native development environment.
"Data scientists can now seamlessly tap into our cognitive services to unlock data that lives in unstructured forms like chats, emails, social media, images, and documents," wrote Rob High, vice president and CTO for Watson, in a blog post.
Watson services for language translation, personality insights, tone analysis, speech-to-text conversion, and visual recognition are among the first to become available through CognizeR.
The new tool is now available for download from Columbus Collaboratory’s GitHub repository.
"What's really interesting is that people will now be able to take their preexisting R models and embed them in Watson," said David Schubmehl, a research director with IDC. "This will give data scientists a leg up, since they don't have to restart what they've been working on."
In particular, the new extension will open Watson up to data scientists who have been working with predictive analytics models -- "a category of folks that previously haven't been able to work with it as easily as other tools on their plate," Schubmehl said.
"IBM is putting a huge emphasis on its analytics portfolio, and applying Watson is one of their anchor offerings," added Nik Rouda, a senior analyst with Enterprise Strategy Group, via email. "The goal is to use 'cognitive' computing to combine machine learning with assessments of confidence and human wisdom for feedback."
Given that R is one of the most popular languages for data science today, including machine-learning applications, "this fills a prior gap in which many data scientists couldn’t directly use their preferred analytics tools with Watson," Rouda said.