Large language models (LLMs) often appear to be in a fight to claim the title of largest and most powerful, but many organizations eyeing their use are beginning to realize big isn’t always better.
The adoption of generative artificial intelligence (genAI) tools is on a steep incline. Organizations plan to invest 10% to 15% more on AI initiatives over the next year and a half compared to calendar year 2022, according to an IDC survey of more than 2,000 IT and line-of-business decision makers.
And genAI is already having a significant impact on businesses and organizations across industries. Early adopters claim a 35% increase in innovation and a 33% rise in sustainability because of AI investments over the past three years, IDC found.
Customer and employee retention has also improved by 32%. “AI will be just as crucial as the cloud in providing customers with a genuine competitive advantage over the next five to 10 years,” said Ritu Jyoti, a group vice president for AI & Automation Research at IDC. “Organizations that can be visionary will have a huge competitive edge.”
IDC
While general purpose LLMs with hundreds of billions or even a trillion parameters might sound powerful, they’re also devouring compute cycles faster than the chips they require can be manufactured or upscaled; that can strain server capacity and lead to an unrealistically long time to train models for a particular business use.
“Sooner or later, scaling of GPU chips will fail to keep up with increases in model size,” said Avivah Litan, a vice president distinguished analyst with Gartner Research. “So, continuing to make models bigger and bigger is not a viable option.”
Dan Diasio, Ernst & Young’s Global Artificial Intelligence Consulting Leader, agreed, adding that there’s currently a backlog of GPU orders. A chip shortage not only creates problems for tech firms making LLMs, but also for user companies seeking to tweak models or build their own proprietary LLMs.
“As a result, the costs of fine-tuning and building a specialized corporate LLM are quite high, thus driving the trend towards knowledge enhancement packs and building libraries of prompts that contain specialized knowledge,” Diasio said.
Additionally, smaller domain specific models trained on more data will eventually challenge the dominance of today’s leading LLMs, such as OpenAI’s GPT 4, Meta AI’s LLaMA 2, or Google’s PaLM 2.
Smaller models would also be easier to train for specific use cases.
LLMs of all sizes are trained through a process known as prompt engineering — feeding queries and the correct responses into the models so the algorithm can respond more accurately. Today, there are even marketplaces for lists of prompts, such as the 100 best prompts for ChatGPT.
But the more data ingested into LLMs, the the greater the possibility of bad and inaccurate outputs. GenAI tools are basically next-word predictors, meaning flawed information fed…
2023-09-16 09:00:03
Source from www.computerworld.com rnrn