{"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 Ongoing Debate: Open Source vs. Proprietary Models<\/span><\/h2>\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

The New Frontier: Generative AI<\/span><\/h2>\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

Generative AI Landscape: Open-source vs. Proprietary AI Models<\/span><\/h2>\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>\n

The Dilemma: Open Source vs. Proprietary Generative AI<\/span><\/h2>\n

The debate surrounding open-sourcing generative AI models has increased, especially considering a recent incident where researchers asked a proprietary generative AI system called MegaSyn <\/a>to create toxic molecules, resulting in some resembling known nerve agents.<\/p>\n

This raises a burning issue: opponents of open-sourcing generative AI believe that it should be locked up to prevent misuse, while proponents of open-source argue that proprietary models concentrate too much power in the hands of a select few.<\/p>\n

The Bottom Line<\/span><\/h2>\n

Generative AI is transforming industries, but choosing between open-source and proprietary models is crucial. Open-source champions customization and transparency, while proprietary offers reliability and support.<\/p>\n

Open-source generative AI demands resources but provides control and cost-efficiency. Proprietary models boast specialized expertise and security.<\/p>\n","protected":false},"excerpt":{"rendered":"

In the realm of technology, the timeless clash between open source and proprietary models is witnessing a new battlefield with generative AI. As businesses are actively exploring generative AI solutions, with a significant 19% of companies already in the pilot or production stages, it has become pivotal to choose between open-source and proprietary models. In […]<\/p>\n","protected":false},"author":7914,"featured_media":114299,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_lmt_disableupdate":"","_lmt_disable":"","om_disable_all_campaigns":false,"footnotes":""},"categories":[573,609],"tags":[1285,1284],"category_partsoff":[],"class_list":["post-114124","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-open-source","tag-open-source-generative-ai","tag-proprietary-generative-ai"],"acf":[],"yoast_head":"\nGenerative AI Crossroads: Open Source vs. Proprietary Models - Techopedia<\/title>\n<meta name=\"description\" content=\"A deep dive into the evolving landscape of generative AI through the lens of open-source and proprietary models, and their pros and cons.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Generative AI Crossroads: Open Source vs. Proprietary Models\" \/>\n<meta property=\"og:description\" content=\"A deep dive into the evolving landscape of generative AI through the lens of open-source and proprietary models, and their pros and cons.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models\" \/>\n<meta property=\"og:site_name\" content=\"Techopedia\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/techopedia\/\" \/>\n<meta property=\"article:author\" content=\"https:\/\/scholar.google.com.pk\/citations?user=CuQ9S_kAAAAJ&hl=en\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-15T09:51:30+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/10\/machine_learning_04-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Dr. Tehseen Zia\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@techopedia\" \/>\n<meta name=\"twitter:site\" content=\"@techopedia\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dr. Tehseen Zia\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models\"},\"author\":{\"name\":\"Dr. Tehseen Zia\",\"@id\":\"https:\/\/www.techopedia.com\/#\/schema\/person\/fb77f994a3e084b6598f08565f584002\"},\"headline\":\"Generative AI Crossroads: Open Source vs. Proprietary Models\",\"datePublished\":\"2023-10-15T09:51:30+00:00\",\"dateModified\":\"2023-10-15T09:51:30+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models\"},\"wordCount\":1037,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.techopedia.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/10\/machine_learning_04-1.jpg\",\"keywords\":[\"Open-source Generative AI\",\"Proprietary Generative AI\"],\"articleSection\":\"\",\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models\",\"url\":\"https:\/\/www.techopedia.com\/generative-ai-crossroads-open-source-vs-proprietary-models\",\"name\":\"Generative AI Crossroads: Open Source vs. Proprietary Models - 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