The Large Language Models (LLM) that power Generative Artificial Intelligence (Gen AI) have caught the imagination of business leaders. Everyone is talking about the power of Gen AI. However, it’s when we implement these models that their biases, hallucinations and challenges become evident. Suddenly, the attention shifts from the model to the very foundation it relies on – the data. To quote Forrester, “As the demand for AI expands, so does the need for relevant data to develop the models that power AI.”
At a recent LinkedIn Live session that featured my peers from WNS and guest speaker Mike Gualtieri, VP & Principal Analyst at Forrester Research, we discussed the need for underpinning Gen AI models with a robust data strategy. Companies aiming for success in Gen AI must not only focus on refining their models but also on crafting an encompassing data strategy. This includes breaking down internal data silos, emphasizing data governance and recognizing the pivotal role that every team member plays as either a data producer or consumer.
In today's world, access to an LLM isn't a game-changer. What provides a competitive edge is a company's proprietary data. It’s this unique, industry and client specific information that allows businesses to fine-tune their AI models, making them tailor-fit to address particular needs and challenges.
High-quality proprietary and public datasets enable acute insights, foster better decision-making and can catalyze innovation. This in-depth understanding of data empowers organizations to grasp their customer needs more effectively, enabling refined product and service offerings. The key to ensuring data integrity? Embedding it at the heart of the AI lifecycle facilitated by strong source systems and software integration.
It's essential to recognize the potential pitfalls of data, especially when training Gen AI. Large datasets, while incredibly valuable, can inadvertently introduce biases and data hallucinations. Data biases manifest when a dataset harbors an uneven representation leading to skewed outcomes. Then there are data hallucinations, where the model produces false results that seem correct or finds patterns that aren't really there because it's reading too much into the finer details.
To circumvent these challenges and optimize Gen AI's capabilities, the chosen data must be assessed on quality, structured appropriately, and preferably current. Organizations must deploy models within curated data environments to ensure reliability when refining their use cases. A rigorous selection process not only hones the model but also ensures its outcomes are aligned with the high standards required to deliver business goals. Of course, this also needs to be supported by comprehensive testing to validate accuracy against the expected outcomes.
The future of Gen AI is exciting, but its success is, and will always be, inextricably tied to the quality of data it relies upon and is trained on. As we move forward in this increasingly data-driven era, it's critical for companies to not only develop advanced AI models but also invest significantly in ensuring the integrity and relevance of their data.
To delve deeper into Gen AI’s increasing impact, watch our insightful discussion now.
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Generative Artificial Intelligence
27 November 2023