Bridging AI and GMP Standards in the Future of Pharma

CONCEPTS AND DEFINITION OF ARTIFICIAL INTELLIGENCE

Artificial Intelligence (AI) generally refers to machines that partially replicate human intelligence in an artificial manner. A fitting quote from Elaine Rich in 1983 defines AI as “the study of how to make computers do things at which, at the moment, people are better.” AI encompasses a variety of technologies, including machine learning (ML) algorithms that learn independently from collected data. These range from simpler methods like linear regression or random forests to more complex ones such as neural networks. The latter connect neurons in a network modeled after the structure of the human brain, linking one neuron (nerve cell) to a network of other neurons. Large neural networks are referred to as deep learning when they excel in processing images, text, or speech, although they require a significant amount of data and computational power. Therefore, deep learning is the closest approximation to the brain, although even here, much is simplified or adapted since our brain cannot be replicated one-to-one. Neurotransmitters in the human brain, such as dopamine, are not simulated, and the cells in the brain are organized in a linear manner rather than the more “chaotic” network found in the brain.
Thus, the term Artificial Intelligence leads through several intermediate terms like machine learning to the subcategory of deep learning.

HISTORICAL OVERVIEW OF AI