Better data, not just larger data
By Francis Beland, Executive Director, OASIS Open
The fundamental concept that “better data, not just larger data,” is essential for the practical application of AI is essential, as the quality of the data utilized for training models is essential to yielding reliable and accurate AI systems. To elaborate further on this point, here are the key points to consider:
Relevance: It is not the case that more data necessarily results in more relevant data. For an AI to learn efficiently, it must be provided with data immediately pertinent to its intended purpose. In this context, better data suggest data that accurately correlates to the challenges the AI will resolve.
Quality: The AI models are only as effective as the data used to train them. Poor-quality data can lead to erroneous models and bad outcomes. Thus, better data stands for clean and well-structured data that can offer a lucid and precise depiction of what the AI must learn.
Bias: Larger datasets may have prejudices which, if not identified and eliminated, can be taken on board and propagated by AI models. Better data is free of negative biases, or at least those biases have been acknowledged and mitigated.
Overfitting: Instructing an AI with a large dataset can result in overfitting, whereby the model gets overly familiar with the training data and performs poorly when presented with new data. Better data encompasses a reasonable measure of variety to assist the AI in generalizing correctly.
Efficiency: Processing substantial quantities of data needs ample computational resources and time, which may only sometimes be required or rewarding. Better data could indicate selecting a more reduced yet insightful subset of the data, accomplishing similar or even better outcomes quicker and at a more cost-effective cost.
Ethics and privacy: Obtaining and using more extensive data can regularly lead to ethical and privacy issues, primarily when dealing with individual data. Better data emphasizes the careful and ethical use of pertinent data, adhering to privacy rules and legal regulations.
Although more big data may enhance the performance of AI systems to a certain degree, the quality, applicability, and ethical use of the data are usually more critical for reaching successful AI deployments.