Though we have been largely intrigued by the AI-models that have made life simpler like ChatGPT, their training model datasets have been targeting a specific task. One has to understand that often necessary amounts of datasets might not always be available and in such a situation, the AI-models should be designed in such a way that it could easily replace the task-specific models, instead get trained from a broad data-set model and perform the desired task.
If AI could do that, adoption would turn faster because older AI-training models demand time and money which is not feasible when you are running multiple task-specific businesses.
How could we deal with the problem then?
Enter: The Foundation Model
Through the foundation model, AI could provide a frugal alternative to get adoption across a wide spectrum of industries without having to source labeled datasets originating from every specific segment for training purposes.
What is the Open-Source Foundation Model?
An open-source foundation model is a curation that helps in training the AI-model with general data which can be adaptive based on the requirement. Due to such a model, the AI systems can perform transitional computations which leads to homogenization of the data to derive desired outcomes.
For example, a neural network which has been built on top of a foundational model might examine millions of datasets of images to identify the query, which is cat. When they identify the patterns of the pixels in a cat’s image, it can easily learn from such experiences. Hence the system can self-learn and develop the skills over time from a broad range of datasets instead of relying on specific task oriented labeled datasets, which might cost time and money.
How Open-Source Foundation Model Shall Help AI in Increasing its Abilities?
Outputs are Closer to Expectations
When you are choosing an open-source AI foundation model, it would give much more balanced and realistic outputs. For example, a startup was building an object prototype system. When they generated the image through DALL-E, which was a closed foundation model, it looked more artistic; however, when they switched to Mid-Journey, which was also a closed source foundation model, the image looked animated.
Now, when Stable Diffusion, an open-source foundation model came into play, the image appeared much more realistic and natural. In this case, it was the purpose that defined the efficacy of the AI. For example, if someone was looking for an image that connected with a flower breed, in such a case, Stable Diffusion would make more sense since the outputs are closer to what they expect. Whereas, if someone wants to query for an NFT, in such a situation, it makes more sense to go for Mid-journey or Dall-E.
Presence of open-source foundation models have helped enterprises quickly adopt AI since they are not bothered about building the infrastructure from the scratch and they can quickly get the desired outcomes at almost a fraction of cost through a large treasure trove of data, which is open source and easily available. However, just Imagine a situation when these open-source foundation models go missing. Would it be still feasible to deploy AI in your business model as conveniently as it is possible at this juncture?
Optimum Training Environment
95% of the global leaders have weighed in favor of open-source foundational models for building the right infrastructure.
Because when you are going for custom models, they are very hard to construct. Despite a lot of generic APIs which are easily available, they do not give specific use-cases level effect to get to the desired results. However, when businesses go for open-source foundation models, AI’s capability increases by leaps and bounds because downloading the open-pretrained model and adjusting the same as per the required use-cases is much easier.
For example, ClearML is adopting a wider range of OSS that helps in improving the user-interface, back-end and different other components. Furthermore, they are also providing open-source CI/CD workflows following the same model. All these open-source software have enabled faster AI development since the community can exchange information on a real-time basis and such changes are incorporated as and when required. Thus, helping in powering a digital innovation in making AI mainstream for the future.
How AIs Can Overcome Challenges Through Open-Source Foundation Model?
Imagine an organization has been using AI-recruitment software which has the learning data-sets available from a specific geographical location which practices racial biases. What would be the outcome of such AI in place? The results will be biased, unethical and non-rational. That’s what an old existing AI-data model can do to AI’s capability. They would lead to the following problems:
All of these could lead to significant biases in the AI-training models, distorting the outcomes. However, when open-source foundation model comes into the picture, the following things can be achieved through the introduction of a decentralized open-source AI model, the existing AI can increase its capability by:
- Introducing Economic Solutions: Where AI would combine with DLT or Decentralized Ledger Technology to develop a platform that shall be using smart-contract solutions. These smart contracts would aid in improving automation and building up the next gen decentralized open-source AI solutions that can help in creating the right amount of trust, which shall drive adoption.
- Technological Solution: Since the coming age shall belong to adopting AI and we are about to witness magnanimous utilization of data. Keeping such a situation in mind, a decentralized AI platform using an open-source foundation model should be more preferred to safeguard critical IT infrastructure and data from the threat of cyber-attacks.
Moving forward in time, one shall have to give due importance to the fact that AI’s mainstream adoption means data shall be subject to use and misuse at the same time. Therefore, preparing solutions around such datasets that can not only help in training different AI models but also making them appear safe and sound would propel better adoption in the near future.
Key Tools to Consider for Building the Next Gen AI Models
What would open-source foundational training models do to help build sustainable AI models? It could help in averting multiple training biases. For example, imagine Amazon’s AI training models training specific datasets. At no point, they be including those data that speaks or conveys a negative narrative about the brand. However, when we replace the same with open-source training models like TensorFlow, Model Zoo, or Hugging Face, it shall drive away biases and open doors for the AI models to get trained from a wider range of data.
Because all the experiences that brands, consumers and necessary stakeholders experience, would be fed into the training models which shall be training the AI software to perform their task. Hence helping the AI tools to increase their capabilities further.
IBM has applied for patenting of its 9,130 AI models which shall be used by more than 44% of the global companies in some way or the other in their operational process. With such a huge intake of companies relying on AI to improve their business process, it would be harrowing to see AI software’s getting trained by a single source which can be used as a brilliant tool for setting agendas and spreading false propaganda in a subtle way.
The remarkable progress in AI capabilities, propelled by open-source foundation models, has left an indelible mark on a wide array of industries and applications. These models serve as a robust launchpad for developers and researchers, empowering them to devise highly specialized and efficient AI solutions. As AI continues its upward trajectory, the open-source community remains instrumental in nurturing collaboration, innovation, and knowledge dissemination, ultimately pushing the frontiers of AI’s potential.
VE3 is adept at helping organizations harness the power of AI by capitalizing on open-source foundation models. Our team of seasoned professionals excels in designing and deploying bespoke AI models, tailored to meet your organization’s distinct requirements, ensuring optimal performance and seamless integration with existing infrastructure. Collaborating with VE3 enables your organization to stay at the forefront of AI advancements, streamline operations, and drive data-driven decision-making. With our vast experience and unwavering commitment to client success, VE3 emerges as the ideal partner to navigate the intricacies of AI adoption and unlock the full potential of this game-changing technology.