The idea ofartificial intelligence(IA) isn't new, mas avanços recentes em tecnologias relacionadas a transformaram em uma ferramenta utilizada por todos nós diariamente.The growing importance and proliferation of AI is, at the same time, emocionante y potencialmente alarmante, pois as bases de muitas plataformas e recursos de IA são essencialmente caixas-pretas controladas por um pequeno número de corporações poderosas
Large organisations, como a Red Hat, they believe thateveryone should have the ability to contribute to AI. Innovation in AI should not be limited to companies that can afford large amounts of processing capacity and the data scientists needed to train theselarge language models(LLMs)
Instead, decades of open source experience in software development and community collaboration enable everyone to contribute and benefit from AI, at the same time as helping to shape a future that meets our needs. There is no doubt that an open source approach is the only way to reach the full potential of AI, making it safer, accessible and democratized
What is open source
Although the term "open source" originally refers to a software development methodology, it has expanded to encompass a more general form of open work, decentralised and deeply collaborative. The open source movement now goes far beyond the world of software, andthe open source waywas embraced by collaborative efforts worldwide, including sectors such as science, education, government, manufacturing, health and more
Open source culture has someprinciples and core valuesque a tornam efectiva e significativa, for example
- Collaborative participation
- Shared responsibility
- Open exchanges
- Meritocracy and inclusion
- Community-oriented development
- Open collaboration
- Auto-organisation
- Respect and reciprocity
When open source principles form the basis of collaborative efforts, history shows that incredible things are possible. Some important examples range from the development and proliferation of theLinuxas the most powerful and ubiquitous operating system in the world until the emergence and growth of theKubernetesand the containers, beyond the development and expansion of the Internet itself
Six advantages of open source in the AI era
There are countless benefits to developing technologies through open source, but six advantages stand out from the rest.
1. Increase in innovation speed
When technology is developed collaboratively and openly, innovation and discovery can happen much more quickly, unlike closed organisations and proprietary solutions.
When work is openly shared and others have the ability to create based on it, as equipes economizam uma quantidade enorme de tempo e esforço porque não precisam começar do zero. Nuevas ideas pueden ampliar los proyectos que vinieron antes. This not only saves time and money, but also strengthens the results as more people work together to solve problems, shareinsightsand review each other's work
A broader and more collaborative community is simply able to achieve more: promoting people and connecting expertise to solve complex problems and innovate more quickly and effectively than small isolated groups.
2. Democratise access
Open source also democratizes access to new AI technologies. When you search, codes and tools are shared openly, this helps to eliminate some of the barriers that normally limit access to cutting-edge innovations
TheInstructLabis a great example of this premise. The initiative is an independent open source AI project that simplifies the process of contributing skills and knowledge to LLMs. The aim of the effort is to enable anyone to help shape thegenerative AI(IA generativa), including those that do not have the skills and training in data science normally required. This allows more individuals and organizations to contribute to the training and refinement of LLMs reliably
3. Enhanced security and privacy
How open source projects reduce entry barriers, a larger and more diverse group of collaborators is able to help identify and address potential safety challenges present in AI models as they are being developed
Most of the data and methods used to train and fine-tune AI models are closed and maintained by proprietary logics. Rarely do outsiders manage to gain any insight into how these algorithms work and whether they contain any potentially dangerous data or inherent biases
If a model and the data used to train it are open, however, any interested person may examine them, reducing security risks and minimising platform biases.In addition, the contributors to open philosophy can create tools and processes to track and audit the future development of models and applications, allowing to monitor the development of different solutions.
This openness and transparency as wellinspira confiança, once users have the opportunity to directly examine how their data is being used and processed, so that they can verify if their privacy and data sovereignty are being respected. Moreover, companies can also protect their private information, confidential or proprietary using open source projects like InstructLab to create their own fine-tuned models, on which they maintain strict control
4. Provides flexibility and freedom of choice
Although monolithic LLMs, proprietors and black box are what most people see and think about generative AI, we are beginning to see a growing push towards smaller AI models, independent and developed for a specific purpose
Thesesmall language models(SLMs) are usually trained on much smaller datasets to give them their basic functionality, and then they are even more tailored for specific use cases with domain-specific data and knowledge
These SLMs are significantly more efficient than their larger cousins, e demonstraram ter um desempenho tão bom (se não melhor) quando usados para o propósito pretendido. They are faster and more efficient to train and deploy, and can be customised and adapted as needed
And it is largely for this reason that the InstructLab project was created. With him, you can take a smaller open source AI model and expand it with the data and additional training you desire
For example, you can use InstructLab to create a highly tailored and developed customer service chatbot for a specific purpose, maximising best practices in the organisation. This practice allows you to provide the best of your customer service experience to everyone, everywhere, in real time.
And, more important, this allows you to avoid being locked into a supplier and provides flexibility in terms of where and how you implement your AI model and any applications built upon it
5. Enables a vibrant ecosystem
In the open community, “nobody innovates alone“, and this belief has persisted since the early months of the community's founding.
This idea will remain valid in the AI era within Red Hat, open solutions leader, que fornecerá várias ferramentas e estruturas de código aberto na forma doRed Hat AI,solução com a qual parceiros irão gerar mais valor a clientes finais.
A single supplier cannot provide everything an organization needs, or even monitor the current speed of technological evolution. The principles and practices of open source accelerate innovation and enable a vibrant ecosystem by fostering partnerships and collaboration opportunities between projects and industries
6. Reduce costs
In early 2025, it is estimatedthat the average base salary of a data scientist in the United States exceeds US$ 125.000, with more experienced data scientists potentially earning significantly more
Obviously, there is a huge and growing demand for data scientists with AI, but few companies have high hopes of attracting and retaining the specialized talent they need
And truly large LLMs are exorbitantly expensive to build, to train, maintain and implement, demanding entire warehouses filled with highly optimized (and very expensive) IT equipment and a huge amount of storage
Open models, menores y diseñados para propósitos específicos y aplicaciones de IA son significativamente más eficientes para construir, train and implement. They not only demand a fraction of the computing power of LLMs, projects like InstructLab enable people without specialized skills and experience to actively and effectively contribute to the training and fine-tuning of AI models
Clearly, the cost savings and flexibility that open source brings to AI development are beneficial for small and medium-sized enterprises aiming to gain a competitive advantage through AI applications
In summary
For the construction of a democratic and open AI, it is crucial to use the open source principles that enabled cloud computing, the internet, Linux and many other open technologies, poderous and deeply innovative
This is the path that Red Hat is following to enable AI and other related tools. Everyone should benefit from the development of artificial intelligence, thus, everyone should be able to help determine and shape their path, and contribute to your development. A inovação colaborativa e o open source não são essenciais como incontornáveis para o futuro da disciplina
