Cookie Policy

We are undoubtedly living through a generational change. When I first heard the term GenAI, I actually thought it was some sort of slang for this being the time of the next generation of AI. It is. But that’s not what GenAI is meant to mean. Generative AI will change how we work across industries all over the world.

There was much fulminating and frothing about the shenanigans at OpenAI, the Microsoft-backed developer of ChatGPT. CEO Sam Altman was fired, then he might not be fired, then he went to work for Microsoft, then he wasn’t fired and now he seems to back where he started, with the people who fired him fired instead.

The promise of AI is plain and simple: greater product development efficiency and faster time to market. Products are becoming more complex, yet engineering teams don’t have more time. Using historical or current engineering test data to train self-learning models, today’s visionary engineering leaders are reducing testing time and simulations for products with even the most intractable physics, increasing competitiveness and speeding time to market.

According to the view of almost 100 CIOs, 39% of line managers are not fully satisfied with the applications that IT has provided. CIOs say that the thing that creates the most problems for line managers is application failure, which to them means both actual outages and situations where performance doesn’t meet business goals. Just under three-quarters of these same CIOs say that satisfying line managers is the responsibility of their network and IT management systems and staff, and only ten percent of CIOs say they’re satisfied the responsibility is being met. Why? Because, CIOs say, they’re managing the wrong thing.

Harry Belafonte might have said ‘me say day, me day day-o’ back in the day, but The Linux Foundation say DAOS. The newly launched DAOS Foundation is an initiative aimed at advancing the governance and development of the Distributed Asynchronous Object Storage (DAOS) project.