LEVERAGING AI FOR ENTERPRISE SUCCESS

Just in case you haven’t checked your email for the last 18 months, Artificial Intelligence (AI) and Machine Learning (ML) have rapidly emerged as transformative tools for modern organizations. As a senior ML consultant, I’ve observed firsthand the impact of these technologies across various sectors. Let’s take a look at a comprehensive overview of AI’s role in modern enterprises, focusing on practical applications, potential benefits, and key considerations for implementation.

UNDERSTANDING AI AND ML IN THE ENTERPRISE

The original intent of these tools many decades ago was to duplicate human intelligence using mechanical processes.  At its core, AI/ML refers to the development of computer systems capable of performing tasks that typically require human intelligence.  There are ongoing debates if these systems truly exhibit true reasoning or intelligence (or even if humans are uniquely intelligent!), but the surprising utility of the current AI/ML wave has huge implications for business organizations and the wider enterprise.

Just to be clear, ML is a broad concept encompassing various techniques for task-specific learning.  Deep learning, on the other hand, is a more sophisticated and specific type of machine learning that involves artificial neural networks with multiple layers to learn data patterns.  In the general trade press, it’s important to note that the term “machine learning” is more commonly used, even though the more accurate term is “deep learning.”

PREDICTIVE AI AND GENERATIVE AI

Within Deep Learning, there are two primary and important subsets: Predictive AI and Generative AI.

Predictive AI focuses on making predictions or forecasts based on structured, historical data. It aims to identify patterns in the data and use them to predict future outcomes.  Predictive AI models are trained on labeled datasets, where the algorithm learns the relationships between input characteristics and the corresponding target variable.  For example, the impact of rising temperatures on predicting the weather, or the color of a worn truck tire to anticipate a dangerous failure.  Predictive AI is particularly useful when there is a need to anticipate specific outcomes based on available structured data.  Weather prediction, image classification, recommendation systems, autonomous vehicles, anticipating hardware/software failures, detecting email anomalies, etc, are examples of predictive AI.  For many use cases, Predictive AI is an excellent technique to use and has been successfully deployed in production for at least the past 10 to 15 years.  There are several excellent production-quality Java toolkits for deploying Predictive AI, such as JSR 381 Visual Recognition, Amazon’s DLJ, and Deep Netts.

Another very important subset of Deep Learning is Generative AI or “GenAI”.  This is the type of AI that the world is currently excited about over the past 18 months. GenAI focuses on creating new data samples that resemble the input data it was trained on. Instead of predicting from existing data, GenAI generates novel, synthetic data based on learned patterns.  Generative AI is currently extremely popular for its ability to create human-like text, images, and sound.  Generative AI typically deals with a type of natural language processing (NLP) that uses an innovative architecture called “Transformers,” which was developed (and patented) at Google in 2017.  These systems use Large Language Models (LLMs) that are trained on huge amounts of data on the Internet to extract patterns.  While you will find popular LLMs available from large companies such as OpenAI, Google, and Microsoft, there currently are many thousands of open-source models that you can run directly on your laptop or in the cloud.  The growing popularity of these open-source LLMs is a significant trend to monitor, especially with distributed AI intelligence at the edge potentially being a big business opportunity.

Both forms of Deep Learning are powering new types of computing systems for enterprises.  Predictive AI is centered around making predictions based on existing patterns, while generative AI is focused on creating new, realistic data. While Generative AI certainly has captured the attention of the world’s enterprises (and countries!), both approaches have their own set of applications and are valuable in different contexts within the field of artificial intelligence.  There are also use cases that combine the strengths of each type of deep learning into a powerful combination.

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