Python, the Programming Language of Choice for AI Development
A Snowflake report reveals data on the rise of artificial intelligence and conversational applications.
The rise of artificial intelligence (AI), especially generative AI, is being felt by users and businesses alike. In less than a year, chatbots have gone from representing 18% of LLM (large language model) applications to 46%.
This is revealed in Snowflake’s Data Trends 2024 study, which also highlights that during 2023, an average of 90 AI applications were developed per day.
Regarding the place of development, this report explains that the trend is towards programming LLM applications directly on the platform where the data is managed. And when it comes to developing AI projects, Python stands out as the programming language of choice. The strengths of this language are ease of use, an active community and a broad ecosystem of libraries and frameworks.
65% of the developers surveyed are working on LLM projects for work purposes. It is also known that in the last year companies have increased their use of unstructured data processing by 123%.
From 82% of LLM applications requiring typing in 2023 to 54% in 2023, the landscape is shifting towards chatbots that interact via iterative text.
“Conversational applications are booming,” says Jennifer Belissent, Principal Data Strategist at Snowflake, “because it’s the natural way humans interact. And now it’s even easier to interact by conversing with an app.
“We expect this trend to continue as it becomes easier to build and deploy conversational LLM applications, especially knowing that the underlying data remains well governed and protected,” says Belissent, “With that peace of mind, these new highly versatile, interactive chatbots will meet both business needs and user expectations.
“The important thing,” she says, “is to understand that the era of generative AI does not require a fundamental change in data strategy. However, it does require an accelerated execution of that strategy. It requires breaking down data silos even faster and opening up access to data sources, wherever they are in the enterprise or in the broader data ecosystem.
