In the ever-evolving landscape of artificial intelligence (AI), a critical distinction exists between open source and closed source AI. Whilst there is plenty of white noise regarding the pros and cons on AI and digital tools, understanding the open and closed source concepts is pivotal for developers, businesses, and enthusiasts aiming to leverage AI and digital strategies effectively.
Open-Source AI: Embracing Collaboration and Transparency
Back in March ’23 Meta’s Llama open-sourced LLM was leaked to the public causing ‘hares to run around the tech industry track’. In response Google also suffered a communications leak, with their internal comms on their concern for a lack of open-source strategy. With modern day tech needing to be agile, flexible, and cost effective, Open source can be seen as a must have for the following reasons:
- Community Collaboration:
Open-source AI thrives on community involvement. Developers worldwide contribute to projects, bringing diverse expertise and perspectives. This collaborative approach accelerates innovation, with community members identifying bugs, cyber-risks, adding features, and enhancing algorithms.
- Ethics, Transparency and Trust:
Ethics is a crucial area for any firm engaging AI and this they should ensure that have a charter or code of practice for deploying their AI. Transparency is a hallmark of open-source AI. The availability of source code allows users to scrutinise the software for security vulnerabilities, ethical concerns, and bias. This openness fosters trust among users and stakeholders. The CISI have an excellent Ethics and AI CPD certificate available, to provide an in-depth understanding for these issues
- Cost-Effectiveness:
Open-source AI solutions are typically free to use, reducing the financial barrier to entry. Businesses and individuals can deploy robust AI technologies without hefty licensing fees, democratising access to cutting-edge tools.
- Customisation and Flexibility:
Users of open-source AI can modify the source code to suit their specific needs. This flexibility is invaluable for creating tailored solutions that align precisely with unique business requirements or research goals.
- Fostering Innovation:
The open-source model encourages experimentation and innovation. Researchers and developers can build on existing frameworks (yes legacy frameworks), fostering a culture of continuous improvement and rapid technological advancement.
Closed Source AI: Proprietary Control and Exclusive Features
Open AI is predominately closed source (so not very Open pardon the pun). This approach has been the norm across tech providers given the commercial, tailored support opportunities and perceived control required for competitive advantage for the following reasons:
- Proprietary Advantage:
Closed source AI is typically developed by private companies, which retain exclusive control over the source code. This control allows companies to protect their intellectual property and monetise their innovations through licensing and subscription models.
- Integrated Support and Services:
Closed source AI solutions often come with comprehensive support and professional services. Users benefit from dedicated customer support, regular updates, and seamless integration with other proprietary tools.
- Focused Development:
Companies behind closed source AI can direct development efforts towards specific features and functionalities that align with their business strategy. This focused approach can result in highly specialised and optimised AI solutions.
- Data Privacy and Security:
For businesses concerned about data privacy, closed source AI can offer more control over data handling practices. Companies can ensure that sensitive information is managed according to stringent internal policies and regulatory requirements.
- Consistency and Reliability:
Closed source AI solutions often undergo rigorous testing and quality assurance processes, providing users with reliable and stable software. The controlled development environment helps maintain consistency and performance standards.
Conclusion: Choosing the Right Approach
The choice between open source and closed source AI depends on various factors, including availability, budget, specific needs, and desired level of control. Open-source AI champions collaboration, transparency, and accessibility, making it ideal for those who value community-driven innovation and customisation. Conversely, closed source AI offers proprietary advantages, dedicated support, and enhanced control, appealing to organisations seeking reliable and specialised solutions.
Both open source and closed source AI play pivotal roles in advancing the field of artificial intelligence, each contributing uniquely to the ecosystem. One point we would make is Open source is an inevitability and (in our opinion) there will be a need for LLMs to offer both. This also goes hand in hand for data-lakes as our recent article, which argued for tech firms to finally offer data-lakes (either with open/closed source data) to ensure firms have the data they need to build compliant and profitable business services.
By understanding their respective merits and distinctions, stakeholders can make informed decisions to harness the full potential of AI technology plus take an informed and evidence-based approach to evaluating the quality of the tech they are engaged with.
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