Making the case for AI, or any nascent technology for that matter, can be a struggle for companies today. While large enterprises know they need to be fast, agile and innovation-obsessed to survive disruption, their age-old policies, antiquated systems, disconnected data and entrenched corporate
At IBM, we led the humans to the moon and coined the term machine learning 50 years ago. Now we are helping organizations scale the ladder to AI to reap rewards in growth, productivity and efficiency with IBM Watson. This journey to AI mirrors the history of travel. In this blog, I’ll describe how
This unified end-to-end platform, Cloud Pak for Data, delivers these data and AI capabilities as container-based microservices that help to power new and existing enterprise applications to run on cloud or on-premises. The platform makes it easy to implement data-driven processes and operations and
IBM is announcing the latest update to the IBM Cloud Pak for Data platform, Version 2.5. We are extremely excited for this release, as it brings to a head three key areas we’ve been building towards over the last year and a half: Red Hat integration, new key built-in capabilities and last but not
Today’s data science and analytics teams are often composed of individuals with a variety of skill sets, educational backgrounds, levels of exposure to open source tools and professional needs. Here’s a typical breakdown:
Business professionals need straightforward ways to first discover and then
Today, “doing more with less” is a key principle driving business strategy across many resource-intensive industries. Organisations are looking to get more out of artificial intelligence (AI) and machine learning (ML) than just great insights. They need access to recommendations that help simplify
Seizing the AI opportunity to tap new sources of energy inspired one ExxonMobil leader to take a collaborative approach to its big data problem. Now she’s been recognized by IBM as a top woman AI leader.
AutoAI, a powerful automated AI development capability in IBM Watson Studio, won the Best Innovation in Intelligent Automation Award, chosen by a panel of 13 independent judges yesterday for the AIconics AI Summit in San Francisco.
68 percent of surveyed businesses recently responded that they use machine learning (ML) or plan to do so in the next three years. AI technologies rapidly are becoming how businesses distinguish themselves from competitors. But choosing the best way to implement AI isn’t always a straightforward
The best data catalogs can automate the process to collect, classify and profile data to ensure the highest standards of quality. Here are three popular use cases detailing why companies are moving towards IBM’s Watson Knowledge Catalog.
Artificial intelligence and machine learning (ML) have become very popular recently due to their ability to both optimize processes and provide the deep insights that push enterprises and industries forward. In fact, 68 percent of respondents in a recent 451 Research Report, Accelerating AI with
Note: This blog post was authored by Aaron Baughman with Stephen Hammer, Eythan Holladay, Eduardo Morales and Gary Reiss.
Tennis play at the US Open consists of 254 matches in the men’s and women’s singles events totaling tens of thousands of points. During the tournament’s two weeks, many matches
The fusing of analytics with leading technologies can unlock significant business value and bring new transformation opportunities for enterprise companies. In order to be successful, analytics-based initiatives such as AI and the Internet of Things (IoT) need massive amounts of big data—and also
Choosing the right data management solutions as the foundation for AI is crucial. Enabling AI optimization and usability is paramount, as is easy scalability to accommodate the increasing amount of data used by AI applications. This is true no matter where you store your data: on-premises, in the
In my last blog post, I covered how you can deliver an AI pilot in just eight weeks and at the same time design your program in a way to scale the AI across your enterprise. Culture, architecture and technology is fundamental to move from AI pilot to AI @ Scale. I also discussed how IBM is helping