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
Recently, I sat down with Kyle Weeks, Program Director for Ecosystems in Data Science and AI. I wanted to review some exciting new opportunities made possible by several recent developments in IBM Data Science:
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
Will AI take over the world? Or, more to the point, will it take over the humankind? It seems to have invaded the public consciousness, sparking concerns that AI will take away jobs. This fear is driven in part by companies using AI to deliver cost savings across their businesses, including areas
In business, aspiring to world-class is not enough when your competitors are already there. About half of the companies listed on the S&P 500 will be replaced over the next 10 years. Compared to the past, what’s unique about the disruption happening today is the rapid pace of change. During
In this week's episode of Making Data Simple, we are joined by guest John DeNero, who is a professor at UC Berkeley. John specializes in teaching artificial intelligence, and he won a distinguished teaching award in 2018. Host Al Martin and John discuss methods of teaching AI, the state of the
On June 12th, IBM debuted AutoAI, a new set of capabilities for Watson Studio designed to automate critical yet time-consuming tasks associated with designing, optimizing and governing AI in the enterprise. As a result, data scientists can be liberated to commit more time to designing, testing and