{"id":114124,"date":"2023-10-15T09:51:30","date_gmt":"2023-10-15T09:51:30","guid":{"rendered":"https:\/\/www.techopedia.com"},"modified":"2023-10-15T09:51:30","modified_gmt":"2023-10-15T09:51:30","slug":"generative-ai-crossroads-open-source-vs-proprietary-models","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models","title":{"rendered":"Generative AI Crossroads: Open Source vs. Proprietary Models"},"content":{"rendered":"
In the realm of technology, the timeless clash between open source<\/a> and proprietary<\/a> models is witnessing a new battlefield with generative AI<\/a>.<\/p>\n As businesses are actively exploring generative AI solutions, with a significant 19% of companies already in the pilot or production stages<\/a>, it has become pivotal to choose between open-source and proprietary models.<\/p>\n In this article, we delve into these models, exploring their merits and demerits.<\/p>\n The debate between open-source and proprietary development models is not new; it has been a cornerstone of the software industry for decades. It originated in the early 1980s when Richard Stallman initiated the GNU General Public License (GPL)<\/a> movement to counter the rising dominance of proprietary software.<\/p>\n This movement gained momentum with the release of the Linux kernel<\/a> in 1991, offering an alternative to the proprietary Unix<\/a> operating system.<\/p>\n Today, this competition has evolved and expanded, spanning various software categories such as web browsers<\/a>, productivity applications, databases<\/a>, web servers<\/a>, cloud computing services<\/a>, mobile operating systems<\/a>, and development tools<\/a>.<\/p>\n The choice between open-source and proprietary software depends on individual needs, goals, and preferences.<\/p>\n Proprietary software often provides specialized features, dedicated support, and seamless integration with other products from the same vendor. In contrast, open-source models offer accessibility, customization, transparency, and the power of crowdsourced development.<\/p>\n Many argue that open source excels in the marketplace due to these benefits.<\/p>\n Open-source vs proprietary software now has a new battlefront: generative AI.<\/p>\n While it might seem like a conventional battle, a fundamental difference sets it apart. Unlike the open-source movement, where resources like investment, brainpower, and effort can be crowdsourced<\/a>, generative AI demands substantial data and energy.<\/p>\n Both resources are becoming increasingly expensive and, for the most part, out of reach for open-source contributors.<\/p>\n As a result, creating an open-source generative AI model is not entirely cost-free. It may involve expenses for data labeling and infrastructure costs for training the AI models.<\/p>\n However, it’s important to note that this investment is significantly more cost-effective in the long run when compared to proprietary generative AI, which typically involves licensing fees.<\/p>\n Transparency plays a vital role in the context of open-source generative AI models, especially given the black-box nature of these AI systems, particularly when they are used in critical applications.<\/p>\n Furthermore, optimizing an open-source generative AI efficiently can reduce latency and enhance performance. Moreover, having the source code in-house gives organizations complete control over their data, ensuring that sensitive information remains within their network and mitigating the risk of data breaches or unauthorized access.<\/p>\n Additionally, pre-trained open-source generative AI models can be fine-tuned to align with an organization’s specific requirements, and the AI can also be trained on specific datasets<\/a>. In contrast, making these changes or specifications on a proprietary generative AI often entails working with a vendor, incurring both time and financial expenses.<\/p>\n In contrast to open-source, proprietary generative AI offers a level of reliability that stems from dedicated development and maintenance by a specialized team of experts. These models are not the result of haphazard community contributions but are meticulously crafted and fine-tuned by a select group of individuals with a profound understanding of AI intricacies.<\/p>\n Moreover, organizations that opt for proprietary generative AI benefit from tailored support and specialized knowledge. This is complemented by the presence of service level agreements<\/a> (SLAs) and technical assistance, offering a reassuring layer of security, especially for mission-critical operations.<\/p>\n The ease of integration into existing infrastructure and rigorous quality control measures render proprietary AI solutions ideal for businesses of any scale. In essence, proprietary generative AI presents a dependable and fully supported solution to businesses.<\/p>\n In the world of open-source generative AI, Meta’s LLaMa2<\/a> is a standout language model known for its adaptability and versatility.<\/p>\n This model, which boasts an impressive range of parameters from 7 to 70 billion, can be readily accessed through platforms like Watsonx.ai<\/a> and Hugging Face<\/a>. BigScience’s Bloom<\/a>, on the other hand, is a multilingual model that was developed transparently by a vast AI research community, emphasizing the importance of openness and collaboration in the field.<\/p>\n The Technology Innovation Institute’s Falcon LLM<\/a> is a notable contender that offers remarkable problem-solving capabilities while consuming fewer resources.<\/p>\n Additionally, fine-tuned models like Vicuna<\/a> and Alpaca, which are based on the LLaMa architecture, have managed to deliver performance levels that are on par with GPT-4<\/a>.<\/p>\n Open-source generative AI models have found widespread application across diverse sectors. IBM and NASA’s collaboration resulted in the development of an open-source Language Model (LLM)<\/a> trained on geospatial data, contributing to climate change-related initiatives.<\/p>\n Healthcare organizations have been leveraging open-source generative AI for applications spanning diagnostics, treatment optimization, patient data management, and public health initiatives. The financial sector has also embraced its own dedicated open-source LLM, FinGPT<\/a>, for various financial applications.<\/p>\n In the world of proprietary generative AI, industry giants like OpenAI and Google are setting the pace. OpenAI’s GPT-4, with approximately 1.8 trillion parameters, demonstrates exceptional problem-solving abilities and content generation. Google’s Bart<\/a>, with 137 billion parameters, interprets and responds to human queries swiftly and accurately.<\/p>\n These proprietary generative AI tools find applications across diverse organizations. Duolingo<\/a> introduced Duolino Max, incorporating GPT-4’s natural language processing. Khan Academy<\/a>‘s Khanmigo<\/a> is a GPT-4-powered AI chat tool, and Microsoft’s Bing Chat service<\/a> leverages GPT-4 to enhance search and conduct natural-language conversations.<\/p>\nThe Ongoing Debate: Open Source vs. Proprietary Models<\/span><\/h2>\n
The New Frontier: Generative AI<\/span><\/h2>\n
Generative AI Landscape: Open-source vs. Proprietary AI Models<\/span><\/h2>\n
The Dilemma: Open Source vs. Proprietary Generative AI<\/span><\/h2>\n