Exploring the Crucial Role of Open Source in the Future of AI and LLMs -

The role of open source in shaping the future of AI and Large Language Models (LLMs) raises intriguing questions, contingent on how one interprets open source in the context of the AI era.

Leveraging Open Source as a Solution to AI Ownership Concerns

Recent findings from the State of Open: The UK in 2023, Phase Two, shed light on the perception of open source as a pivotal aspect in addressing apprehensions about AI ownership. Among the 224 surveyed UK residents, 40% view open source as integral to resolving AI ownership issues. In contrast, a mere 15% hold a differing perspective. This underscores the ongoing discourse surrounding the ownership of vast datasets generated by LLMs.

Examining Commercial LLM Reluctance and Production Adoption

A study by Predibase titled "Beyond the Buzz: A Look at Large Language Models in Production" underscores a prevailing reluctance to fully embrace commercial LLMs in production environments. Based on a survey of 150 participants conducted from May to July 2023, a mere 13% reported having at least one LLM in production within their enterprise. In contrast, 44% revealed that their organizations have primarily employed LLMs for experimental purposes.

Of the extensive 85% planning to deploy or currently employing LLMs, only 27% anticipate the utilization of commercial versions in production. Nearly half (49%) of respondents without plans for commercial LLMs cite concerns about sharing proprietary data with vendors. Comparatively, 17% attribute their decision to the perceived high scalability costs associated with commercial LLMs.

Slight Deceleration in Traditional AI Open Source Projects

While discussions abound, the expansion of new traditional AI projects has exhibited a slight deceleration. The OECD AI Policy Observatory reports a 6% growth in AI-associated GitHub projects, totaling 348,934, from 2020 to 2022. This growth rate pales in comparison to the remarkable 203% surge observed from 2016 to 2018, resulting in 194,268 projects. Remarkably, contributions to these projects reached a peak in 2020 but subsequently dropped by 7% by 2022.

A Shift in AI-Related Concepts

The surging popularity of LLMs and the applications they enable has potentially altered the landscape of AI projects. It's plausible that the evolving nature of AI-related concepts has led to an undercounting of projects. This shift prompts considerations about whether current AI-related terminology accurately captures the scope of innovative endeavors.

Contributions and Usage of Open Source AI

Open source initiatives remain pivotal for AI and machine learning developers. According to the AI & Machine Learning Survey Report by Evans Data in Q2 2023, 89% of AI/ML developers have contributed to an AI project. A comparable statistic emerged from SlashData's Q3 2022 report, where 73% of developers contributed to a vendor-owned open source community. However, these statistics may overstate actual contributions, as evidenced by JetBrains' State of Developer Ecosystem 2022 findings, suggesting that only 54% of machine learning developers have actively contributed.

Navigating Vendor-Dominated Open Source Communities

Understanding the dominance of vendor-controlled open source communities involves assessing contributions from multiple angles. The "Elephant Factor" offers a metric to gauge vendor control, while repository location and project governance also play roles. Notably, PyTorch and TensorFlow communities receive significant contributions from AI/ML developers, with 35% and 34% respectively. These figures are indicative of the active involvement of developers in vendor-run open source communities.

Framework Adoption and Preferences

When evaluating framework usage, PyTorch emerges as a preferred choice over TensorFlow. Stack Overflow's 2023 Developer Survey reveals that 54% of data science and ML specialists use PyTorch, compared to 48% for TensorFlow. Moreover, Scikit-Learn enjoys widespread usage at 71%. However, adoption rates among all professional developers range between 9% and 10% for these frameworks.

Harnessing the Power of GPUs and Edge Computing

The preference for dedicated GPUs in individual development work is evident among 57% of AI/ML developers, while 42% favor shared GPUs across multiple tasks. This preference underscores the need for efficient resource allocation while also acknowledging potential cost implications. With Nvidia's CUDA playing a vital role, Nvidia stands poised to capitalize on the growing demand for computational power driven by LLM proliferation.

Charting AI Adoption Challenges and Success

Predibase's study indicates that relinquishing access to proprietary data poses a challenge for 33% of respondents considering LLM adoption. Customization and fine-tuning also emerge as significant inhibitors, cited by 30%. Delving into fine-tuning complexities, only 22% of respondents have achieved success in this domain. A notable hindrance to fine-tuning is the perceived lack of expertise to navigate the intricate process, with 45% attributing their reluctance to inadequate data for the task.

As the landscape of AI continues to evolve, the role of open source remains pivotal. The nuanced interplay between open source initiatives, commercial solutions, and developer involvement shapes the trajectory of AI adoption, ultimately defining the landscape of AI innovation.

That's it for today, See you in the next article.