Large language models (LLMs) like GPT-4 and other forms of AI have the potential to unlock new business opportunities – but they come with formidable challenges. In short, AI-based tools must be tailored to the context of the organization in which they are used for them to deliver real value.
As customization helps handle the specific requirements unique to each corporate environment, an off-the-shelf tool is insufficient to meet most organizations’ needs. AI models should be fine-tuned with data referring to the problem that the company wants to solve with them, but to do this, high-quality, extensive, and relevant data is required. If that data lies outside the boundaries of the organization, data collaboration platforms can be used to fine-tune these AI models.
Data collaboration platforms act as intermediaries between the organization owning the AI in need of fine-tuning and the organization owning the data that can enable it. Examples include Snowflake, Sherpa.ai, Tune Insight, TripleBlind, DSpark, Data Republic, Ocean Protocol, Gaia-X, Dawex, Enigma, and Transformers. The organizations using them range from large banks to telecommunication companies, insurance companies, hospitals, and many more.
These types of platforms can provide a privacy-preserving training space on high-quality, abundant data, ensuring compliance with privacy laws and unleashing the full potential of fine-tuned models. They can also be used to bring together partners, clients, and even competitors, given the right set of agreements.
To understand how your company might benefit from a data collaboration platform, ask yourselves these three questions.
Models based on publicly available data from the internet, for example, may not account for the nuances of specific communities or users. Leveraging data collaborations to ensure data diversity can significantly improve the performance of an AI model.
Given the possibilities offered by data collaborations, business leaders can benefit if embracing collaboration – not only with partners or with clients, but even with competitors. By pooling resources and knowledge, companies can collectively enhance AI models, leading to innovations and efficiencies that might not be achievable independently.
Since data is only a reflection of reality – and reality can rapidly change – fine-tuning AI models with the latest data is crucial. If that data exists outside of the boundaries of the organization, data collaborations can help. For example, with Gaia-X’s platform, autonomous cars can report changes or obstacles in the road. With that, business leaders in the industry can find data that reflect the latest state of the problem that the AI needs to solve, be it the drivability of roads, clients’ preferences, customer lifetime values, or any other variable of that kind.
Business leaders considering these questions carefully will be in a better position to develop useful AI tools with the potential to achieve business excellence in a responsible and successful way.
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