by Louis Columbus @LouisColumbus VentureBeat presents: AI Unleashed - An exclusive executive event for enterprise data leaders. Hear from top industry leaders on Nov 15. Reserve your free pass DevOps teams rely more on AI-coding assistants to boost team productivity by automating coding tasks with only the most conscientious scanning final code for security flaws, Forrester warns in their 2024 cybersecurity, risk, and privacy predictions. The research and advisory firm predicts inconsistent compliance and governance practices combined with many Devops teams experimenting with multiple AI-coding assistants simultaneously to increase productivity will lead to flawed A.I. code responsible for at least three publically-admitted breaches in 2024. Forrester also warns that A.I. code flaws will pose API security risks.

AI-coding assistants are redefining Shadow I.T.

49% of business and technology professionals with knowledge of AI-coding assistants say their organizations are piloting, implementing, or have already implemented them in their organizations. Gartner predicts that by 2028, 75% of enterprise software engineers will use A.I. coding assistants, up from less than 10% in early 2023. Devops leaders tell VentureBeat it’s common to find multiple AI-coding assistants being used across teams as the pressure to produce a high volume of code every day is growing. Tighter timelines for more complex coding combined with the proliferation of over 40 AI-coding assistants available is leading to a new form of shadow I.T. where Devops teams switch from one A.I. assistant to another to see which delivers the highest performance for a given task. Enterprises are struggling to keep up with the demand from their Devops teams for new AI-coding tools approved for use corporate-wide.

CISOs face a challenging balancing act in 2024

A robust boycott campaign targeting Western companies that support Israel is gaining momentum in the Arab world. Sparked by social media, the …

The air in Silicon Valley was charged as OpenAI’s Dev Day announcements—boasting custom GPTs, GPT-4 Turbo, and an Assistants API—sent ripples of …

Understanding How LeMa Works: The journey to enhanced mathematical reasoning within artificial intelligence starts with foundational models like LLaMA-2, which are designed to simulate flawed reasoning paths in math word problems. Following this, a more advanced model such as GPT-4 steps in to critique these paths, identifying and explaining errors before suggesting corrections. These iterative refinements are then fed back into the initial models, enriching their learning and reasoning capabilities.