AI development is the process of creating and deploying artificial intelligence (AI) models. It involves meticulous planning and execution through several critical phases that are key to achieving success.
The AI landscape is undergoing significant transformations as the industry shifts from reaction machines to generative models that can create new solutions based on existing data sets. These changes are driven by advances in computational power, algorithms, and learning methods. The rapid growth of the industry has led to concerns about the impact of AI on jobs, the environment, and society.
Reactive machines are limited to reacting to different kinds of stimuli based on preprogrammed rules, and they lack memory. IBM’s Deep Blue that beat chess champion Gary Kasparov in 1997 was an example of this type of machine.
Generative models use complex algorithms to analyze patterns in large amounts of data and make decisions based on those observations. These algorithms can often pick up on societal biases and inequalities that may lead to discriminatory outcomes.
These generative models require vast amounts of high-quality data to train and perform well. Getting the right data is often difficult, expensive, and time-consuming. Additionally, the enormous data centres required to run these machines use a significant amount of water for air-cooling purposes, which has raised concern over their impact on energy consumption and global water supply issues.
To mitigate these challenges, AI developers must continually refine their generative models for better performance and accuracy. Fine-tuning can be as simple as incorporating user feedback into a chatbot or virtual assistant, and it can involve adding or modifying logic and AI components to improve performance. For instance, if a customer query is not understood by the system, an AI developer might add a more specific trigger to prompt the model for additional context.