Cnvrg.io, an Intel company and provider of artificial intelligence (AI) platforms and large language models (LLM), has released the results of its ML Insider 2023 survey. Despite the interest, most companies have yet to develop generative AI (GenAI) technology.
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cnvrg.io's ML Insider Survey, now in its third year, provides analysis of the machine learning industry, highlighting key trends, points of interest and challenges facing industry professionals. I.A They experiment. This year's report provides insights from a global team of 430 technology professionals
„Although it is still in early development, the I.A „Generative technology is one of the most talked about technologies in 2023. The survey suggests that companies may be reluctant to adopt GenAI because of the barriers they face when implementing LLM,” said Markus Flierl, corporate vice president and general manager of Intel Cloud. Services. “With greater access to cost-effective infrastructure and services like those provided by cnvrg.io and the Intel Developer Cloud, we expect greater adoption in the coming year as existing LLMs become easier to fine-tune, customize and deploy. Without the need for AI talent.” To manage the problem.
GenAI Adaptation Trends
The survey reveals that adoption of large language models (models for training-generating AI applications and solutions) within organizations remains low.
Three-quarters of respondents report that their organizations have not yet implemented models GenAI, 10% of respondents report that their companies have rolled out such solutions for manufacturing in the past year. It also shows that US-based respondents (40%) are significantly more likely to implement GenAI models than those outside the country (22%).
Although adoption hasn't taken off, companies that have implemented GenAI models in the past year are reaping the benefits. Half of respondents say they have benefited from improved customer experiences (58%), improved efficiency (53%), improved product capabilities (52%) and cost savings (47%).
Adoption Challenges
The survey indicates that most companies are approaching GenAI Build your own LLM solutions and customize them according to your use cases. However, nearly half of respondents (46%) see infrastructure as the biggest barrier to developing LLM products.
The survey highlights other challenges that slow adoption of LLM technology in organizations such as lack of awareness, cost and compliance. Of those surveyed, 84% agree that they need to improve their skills due to the growing interest in LLM adoption, while only 19% say they have a solid understanding of the mechanisms behind how LLMs develop responses.
This reveals the knowledge gap as a potential barrier to adoption GenAI This is reflected in companies citing complexity and a lack of AI talent as the biggest barriers to AI adoption and adoption. Additionally, respondents cited compliance and privacy (28%), reliability (23%), high cost of implementation (19%), and lack of technical skills (17%) as the biggest concerns about implementing LLM in their businesses. When considering the biggest challenge in bringing LLMs to production, nearly half of respondents point to infrastructure.
Creative AI It is influencing the industry. Compared to 2022, popular AI use cases in 2023 saw a 26% increase in the use of chatbots/virtual agents and a 12% increase in translation/text generation. This could be due to the rise of LLM technology in 2023 and the advancement of GenAI technology. Companies that have successfully implemented GenAI In the past year they have reaped benefits from the use of LLMs such as better customer experience (27%), greater efficiency (25%), improved product capabilities (25%) and cost savings (22%).
