 | | | When Will Generative AI Reach its Full Potential? |
Waiting for the Future
A popular story this week was CIO's list of the 10 most overhyped technologies in IT. It may or may not shock you to know that generative AI was number one on the rankings, and that prompted many readers to ask when Gen AI will reach its full (and extensive) potential.
Smart Answers, speaking with the wisdom of our crowds of experienced editors, suggests that adoption of generative AI is indeed in its infancy, but that it will reach fruition in the next few years. Organizations are expected to spend $150 billion (about $460 per person in the US) on generative AI tech initiatives by 2027. Smart Answers predicts a total economic impact of $11 trillion (about $34,000 per person in the US) in the same period.
With numbers like that, it is to be hoped that the potential of gen AI is truly unlocked in such a short time frame.
Find out When do IT leaders think generative AI will reach its full potential?
Transparencyâs
If you thought you were having a bad week, spare a thought for the executives who lead CrowdStrike. CSO this week carried the story of CrowdStrike's CEO George Kurtz having to apologize to the companyâs customers and partners for crashing their Windows systems. The company described the error that caused the disaster, saying it wasn't the result of a cyber-attack, rather a 'defect' in the company's Falcon security system.
It's a tough spot to be in, but that response prompted large numbers of CSO readers to ask about the need for transparency in security companies. Clearly an important subject: Smart Answers points out that not only is transparency important in building trust between customer and cyber security provider, but it may also have implications around liability.
Find out What is the importance of transparency in cybersecurity companies?
Spotting a Fake
CSO this week reported that the increasing volume and sophistication of fake network traffic has raised the cybersecurity stakes, jeopardizing sales and marketing efforts and complicating defense and protection.
It certainly grabbed the attention of our CISO audience, as many of you asked Smart Answers how to go about combatting fake network traffic. Smart Answers recommends machine learnings as at least part of the solution: saying that cybersecurity professionals can use machine learning to detect fake traffic by monitoring traffic patterns and identifying differences that could indicate malware or unauthorized access. Find out more below!
Find out How can cybersecurity professionals use machine learning to detect fake traffic?
About Smart Answers
Smart Answers is an AI-based chatbot tool designed to help you discover content, answer questions, and go deep on the topics that matter to you. Each week we send you the three most popular questions asked by our readers, and the answers Smart Answers provides.
Developed in partnership with Miso.ai, Smart Answers draws only on editorial content from our network of trusted media brandsâCIO, Computerworld, CSO, InfoWorld, and Network Worldâand was trained on questions that a savvy enterprise IT audience would ask. The result is a fast, efficient way for you to get more value from our content.
|
|
| |
|
|
