I’ve spent the last eight years working with AI, learning the ins and outs of building and applying AI solutions in business. After making countless mistakes, I created my own method for building and applying the technology.
That was fine and dandy until the fall of 2022, when ChatGPT was released and gave a sudden rise in the usefulness and adoption of generative AI. For my consulting business TodAI, that meant a lot of new projects involving generative AI and a lot of learning. After several projects, I’ve identified places where generative models are clearly distinct from other AI when applying them in a business. Some are small, and others are very significant.
How do these new generative AI models change the game for applied AI?
Terminology
It’s easier to discuss the changes if we distinguish between generative AI and predictive AI.
Generative AI refers to large pre-trained models that output texts, images or sounds from user-provided prompts. The output is (potentially) unique and mimics human-generated content. It’s based on the prompt and the data used to train a large pre-trained model. Text-generating models such as OpenAI’s GPT or Googles Bard are also known as large language models (LLMs).
Predictive AI comprises models that output one or more labels (prediction or classification) or numbers (regression or time series). It includes:
● Image building blocks: image classification, object detection and image segmentation
● Tabular building blocks: prediction, regression and forecast
● Text building blocks: text classification, named entity recognition and Intent analysis
An alternative and more accurate name for predictive AI in an academic sense is discriminative AI. However, I use “predictive” as it might resonate better with most people.
Generative AI can also predict
Generative AI (such as GPT) also can be used to solve predictive problems. ChatGPT can learn to classify texts through a few examples (few-shot learning) or no examples at all (zero-shot learning). The functionality might be the same, but there’s a technical difference. Generative AI doesn’t need you to train an algorithm that produces a model that can then classify. Instead, the generative model gets the examples as a part of the prompt.
The upside of using the generative models for predictive tasks is that implementation can be done immediately. However, there are downsides, such as:
● no way to calculate the expected performance through (for example) accuracy measures
● the generative model might provide an output that isn’t a part of the list of provided labels
● each prompt for output might affect future output
● generative models tend to “forget” the initial examples as they have a limit for how many prompts they can remember
Knowing how well an AI solution works is more complicated and takes more time
A good rule of thumb is that if you can’t achieve an OK accuracy of your model within 24 hours of work, you either have the wrong data or the wrong scope.
For example, a model predicting housing prices that predicts with 50% accuracy after 24 hours of modelling work will never see more than 60% or 65% accuracy, no matter what clever algorithm or fine-tuning you apply. If 60% isn’t good enough for your business case, you need to acquire more, other or better data, or change your business scope.
Following the 24-hour rule means that AI solutions that will never work are spotted early and scraped or redefined. The 24-hour rule has saved me from countless embarrassing failures, and it works as accuracy is an excellent indicator (not equal to, though) for the business value you can expect.
But that rule is no longer helpful in generative AI as there are no accuracy measurements during development. For example, if your business case is generating sales emails for a group of sales reps, you can’t measure the “accuracy” of the output. The business outcomes you’re trying to achieve might be faster communication with clients (through writing speed) or more sales (through better emails). These outcomes are hard to measure during development. Writing speed in particular is challenging to measure, as the output has to be checked and edited by a sales rep, and testing that speed requires involving the sales rep.
Generative AI requires closer domain expert collaboration
The result of this challenge is that domain experts must be involved closely in the development process to help adjust the output and measure the effect on the business outcome you’re trying to achieve. The days when you could rely on data scientist training and fine-tuning alone until a satisfying solution is ready are over.
Picking use cases should be based on ease of testing
Delloite’s helpful guide about generative AI suggests that use cases for generative AI should be based on the effort it takes to validate output and the effort it would take a human to generate the same content.
