The recent launch of Google's Bard brought another tech giant into the generative artificial intelligence space, alongside Microsoft's Bing chat and OpenAI's ChatGPT. But how many business leaders are currently using AI tech in day-to-day operations or plan to? Based on new research, a lot. Half of the companies ResumeBuilder surveyed in February said they are using ChatGPT; 30% said they plan to do so. The data included 1,000 responses from the ResumeBuilder's network of business leaders.

At the start of the pandemic, many consumers began doing something they'd never done before: ordering groceries online. For people worried about getting sick, buying food online seemed safer than a trip inside a supermarket. Many grocers made it easy for shoppers to pick up their items curbside at appointed times. The phenomenon became so widespread, in fact, that grocery chains' call centers quickly became inundated with customers checking in on orders. "The call center volume at every grocer spiked," Karl Haller, partner at IBM's Consumer Industry Center of Competence, recalled in an interview.

Grown men wearing tights like to yell terrible things at Fred DeJesus. DeJesus is an umpire in the outer constellations of professional baseball, where he's been spat on and, once, challenged to a postgame fight in a parking lot. He was born in Bushwick, Brooklyn, to Puerto Rican parents, stands five feet three, and is shaped, in his chest protector, like a fire hydrant; he once ejected a player for saying that he suffered from "little-man syndrome." Two years ago, DeJesus became the first umpire in a regular-season game anywhere to use something called the Automated Ball-Strike System. Most players refer to it as the "robo-umpire."

As big data continues to grow, extracting value from it calls for new tools. Increasingly, businesses that rely on data to drive decisions are applying AI to surface actionable insight quickly and accurately. Finding innovative solutions to the problems raised in data analytics, particularly with respect to adapting machine learning to credit scores, is what they've been working on for the past six years at Experian's DataLabs. The EVP and Global Head of the labs, Eric Haller, spoke to All Analytic about the new direction for predictive modeling. There's a difference between how modeling was done in the past and the possibilities of current approaches.

At Experian DataLabs in San Diego, a team of scientists is thwarting bad guys with math. A top-five U.S. credit card issuer recently dumped about 6 billion transaction records on Experian DataLabs to see if its machine-learning mathematical formulas could do a better job of rooting out credit card fraud than the bank's existing system. Experian scientists used neuro-embedding/natural language processing techniques to understand the "syntax" of the credit card data, computer scientist Honghao Shan said. "We thought we had figured it out and went back to them," said Eric Haller, head of Experian DataLabs. "They said, 'How did you do that?' … It turns out we reduced their false positives by half."

At Experian DataLabs in Carmel Valley, a team of scientists is thwarting bad guys with math. A top-five U.S. credit card issuer recently dumped about 6 billion transaction records on Experian DataLabs to see if its fancy machine learning mathematical formulas could do a better job of rooting out credit card fraud than the bank's existing system. Experian scientists used neuro-embedding/natural language processing techniques to understand the "syntax" of the credit card data, said Honghao Shan, a Ph.D. computer scientist. "We thought we had figured it out and went back to them," said Eric Haller, head of Experian DataLabs. "They said, how did you do that? You identified fraud that we can't identify ourselves. And it turns out we reduced their false positives by half."