Tech-28

Set-out on your journey to achieve at a degree thought not previously possible.

Generative AI has passed the Peak of Inflated Expectations on the Gartner Hype Cycle for Artificial Intelligence — but that’s hardly the only development of note. See the key take-aways: https://gtnr.it/3UNYT8X 💡 Follow Gartner for IT for more actionable, objective insight #GartnerIT #GenAI #HypeCycle #AI

The 2025 CIO and Tech Executive Agenda is officially out and the focus is clear... It's time to build strong digital foundations and foster agility. Are you ready to lead and build digital for the future? Discover the key actions to drive impactful change: https://gtnr.it/4eD1KZb #GartnerIT #CIOAgenda #CIO #ExecutiveTrends

To determine the potential of AI technologies across the Business Capability Model (BCM), Chief Enterprise Architects will need to develop an understanding of the kinds of problems AI can solve. Build a model that defines and classifies the types of AI technologies: https://gtnr.it/4fEQ75a #GartnerIT #EnterpriseArchitecture #ArtificialIntelligence #AI

75% of CIOs have increased their AI budgets for 2024. But is your organization truly ready to implement AI and prove its value? Join our complimentary on-demand AI webinar where experts will help CIOs and IT leaders determine the value from AI and GenAI, and assess if your IT infrastructure is ready to maximize that value: https://gtnr.it/3TXrIiD 💡 Follow Gartner for IT for more actionable, objective insights. #GartnerIT #CIO #AI #ITLeaders

Amid the hype over GenAI, it’s important to ask if GenAI — or another kind of AI — is really what you need. Here’s when NOT to use generative AI: https://gtnr.it/484uJnd 💡 Follow Gartner for IT for actionable, objective insight. #GartnerIT #GenAI #ArtificialIntelligence #AI

As GenAI continues to extend its reach, so much about this emerging technology remains uncertain, including the security concerns it raises. Identify the top security risks for GenAI in our complimentary webinar: https://gtnr.it/3BvU6lP 💡 Follow Gartner for IT for more actionable, objective insights. #GartnerIT #GenAI #Security #Webinar

A 2024 Gartner poll confirms interest in GenAI remains strong. Learn the GenAI landscape to fuel pilots and bigger successes later: https://gtnr.it/4eZDRvK #GenerativeAI #GenAI #Leadership

Midsize CIOs are facing: 1️⃣ The promise of AI increasing productivity 2️⃣ An anticipated increase in IT budgets 3️⃣ The post-pandemic talent crisis Learn how to navigate these challenges at Gartner IT Symposium/Xpo: https://gtnr.it/4eFB8I8 Gartner for IT | #GartnerSYM #MSE #CIO #IT

“A team of engineers at AI inference technology company BitEnergy AI reports a method to reduce the energy needs of AI applications by 95%. The group has published a paper describing their new technique on the arXiv preprint server. As AI applications have gone mainstream, their use has risen dramatically, leading to a notable rise in energy needs and costs. LLMs such as ChatGPT require a lot of computing power, which in turn means a lot of electricity is needed to run them. As just one example, ChatGPT now requires roughly 564 MWh daily, or enough to power 18,000 American homes. As the science continues to advance and such apps become more popular, critics have suggested that AI applications might be using around 100 TWh annually in just a few years, on par with Bitcoin mining operations. In this new effort, the team at BitEnergy AI claims that they have found a way to dramatically reduce the amount of computing required to run AI apps that does not result in reduced performance. The new technique is basic—instead of using complex floating-point multiplication (FPM), the method uses integer addition. Apps use FPM to handle extremely large or small numbers, allowing applications to carry out calculations using them with extreme precision. It is also the most energy-intensive part of AI number crunching. The researchers call their new method Linear-Complexity Multiplication—it works by approximating FPMs using integer addition. They claim that testing, thus far, has shown that the new approach reduces electricity demand by 95%. The one drawback it has is that it requires different hardware than that currently in use. But the research team also notes that the new type of hardware has already been designed, built and tested. How such hardware would be licensed, however, is still unclear—currently, GPU maker Nvidia dominates the AI hardware market. How they respond to this new technology could have a major impact on the pace at which it is adopted—if the company's claims are verified.” Link to paper is here: https://lnkd.in/g_sEJMcz https://lnkd.in/gqAFeZYM