Not a day goes by without artificial intelligence being mentioned everywhere. Not a moment goes by these days without AI weaving itself into any conversation or news item. We are living in the AI age; we are living it. Our expectations from AI are limitless. Businesses are scrambling to bring AI into everything they do. They are promising that AI will bring about substantive benefits to every customer and everything their customer does.
The Challenge of Making AI Work
Despite the hope and optimism, practitioners of AI are very aware that making AI work for us is non-trivial. This is due to a plethora of factors. These factors can be broadly categorized into two areas: Model and Data.
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AI Models: Evolution at Lightning Speed
There has been a profound increase in the number of AI models that are available today. The amount of work that has gone into creating and fine-tuning models is immeasurable. Thanks to the continued advancements and availability of GPUs (Graphics Processor Units), models continue to evolve at what looks like the speed of light. Many of these models are also widely available to anyone – thanks to the practice of open-sourcing model code by researchers and most companies. All someone needs is a computer and Internet access, and they can run most of these models, albeit not at scale.
The Shift from Model-Centric to Data-Centric AI
To make an AI model work, one needs data—good and relevant data. To keep these models working effectively, one needs the right kind of data on a continuous or periodic basis. However, data is a difficult commodity. Though we feel mired in data these days, it is often like trying to find drinking water while in the ocean. We are at an inflection point in the industry today, witnessing a massive transformation from a model-centric world to a data-centric world.
The Data Dilemma: Scarcity Amidst Abundance
Unfortunately, relevant data is often not available to the team or person working with these models. Data bottlenecks and sparsity stand squarely in the way. Sensitivity, confidentiality, compliance, and regulatory aspects often make data inaccessible. For instance, cross-border data transfer could be very expensive and also have to comply with data sovereignty restrictions, which make these transfers nearly impossible.
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Challenges of Sparse and Noisy Data
Even if data is accessible, it is often sparse – for example, missing data, noisy data, and data of interest that is not sufficiently expressed (e.g., anomalies, outages, fraud). At times, finding valuable data feels like searching for the proverbial needle in a haystack for data scientists.
Synthetic Data: A Game-Changer for AI
Synthetic data can help resolve these bottlenecks and unlock the true potential of AI models. In addition to model building and training, synthetic data can be used for a plethora of applications, such as testing, incident response, sales enablement, and data sharing with collaborators. Generative AI-based synthetic data platforms can bridge the gap between available operational data and the desired outcomes targeted by domain data scientists.
Incorporating Domain-Specific Constraints
Besides, to train AI models effectively, data scientists need to incorporate domain-specific requirements and constraints. For instance, the take rate of a feature in a car is typically known and constrained within a region or country, influencing how AI models interpret and predict outcomes.
The Future: A Smooth Transition to a Data-Centric World
Thanks to Generative AI-based synthetic data capabilities, we can now make a smooth transition to a data-centric world. By overcoming data limitations, AI can become more accessible, efficient, and impactful across industries.
