Unlocking Data in the Age of AI
Companies are awash in a sea of data and looking for a glass of freshwater insight to drink.
While big data has transformed some industries, unlocking the true insights behind many forms of data remains impractical for many companies outside the Tech Giants.
True, machine learning algorithms and cloud data solutions have made new approaches possible, but in many cases such solutions are only workable if they can be deployed rapidly, are cost-effective, and can be maintained in an enterprise environment.
The problem of unlabeled data
For most tasks, the results of machine learning algorithms are better with labeled data, and even unsupervised algorithms need some process to evaluate the quality of the outcomes. But the cost to annotate data can range from $0.08 per example to many times that AWS Sagemaker. GPT-3.5-turbo, by contrast, costs a mere $0.002 per 1,000 tokens, and so brings costs down by an order of magnitude.
Unlike humans on MTurk, CrowdFlower, or other services, GPT and other LLMs aren’t actively trying to find loopholes in your setup to get paid without delivering the content you want.
While LLMs have other biases, they are consistent over time and scale and so these shortcomings can be engineered around.
Get in touch if you’re interested in a custom-designed solution to classify texts, annotate or summarize texts, whether for financial or legal insights, customer preferences, opinions, and behaviors, or generating structured knowledge graphs.
Beyond labeling
Oh, wait, but how good are the quality of the labels generated? GPT can also answer that question. Or train a custom model to evaluate the output that doesn’t share the same biases as the model used for labeling.