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Shearman & Sterling LLP 5 | Leveraging the Broad Potential of Artificial Intelligence While Mitigating the Risks While artificial intelligence (AI) in one form or another has been around for years, the potential business and growth opportunities created by AI have exploded with the advent of commercially available Generative AI. Companies should understand the intricacies of the evolving technology to avail themselves of the significant and potentially transformative benefits that AI can confer while simultaneously acknowledging and managing the associated risks. And they should do so now, because even if they have not formally incorporated AI into their suite of technologies, employees are likely already availing themselves of the many public and easily accessible AI platforms. This article provides an overview of the AI technology that is currently available and makes recommendations on how companies can leverage existing technology, position themselves to take advantage of what comes next, and appropriately mitigate the associated risks. WHAT IS AI? AI encompasses a broad spectrum of technologies and applications, all of which share a common theme: the use of technology that are capable of imitating intelligent human behavior. AI applications include: Machine Learning Machine learning is a foundational subfield of artificial intelligence that allows machines to learn and improve their performance on a specific task over time. The technology leverages the development of algorithms and models designed to make predictions, identify patterns, or optimize decisions based on data. These algorithms and models have the capacity to perform complex tasks in ways that closely resemble human problem-solving. Neural Networks Neural networks consist of interconnected nodes organized into layers and are modeled on the structure and functioning of the human brain. Each node processes multiple inputs and produces an output, which is sent to other nodes. As data moves through the nodes, each node performs a different calculation. For example, if the neural network is trained to determine whether a picture contains a person, each of the nodes within the network would analyze the data and produce an output signifying the presence or absence of a person in the picture. Deep Learning Deep learning networks are neural networks with several layers. The multi-layer structure in these networks allows for processing extensive amounts of data and capturing intricate patterns and hierarchies in data. So in the example above regarding recognizing a picture containing a person, some layers might detect the presence of certain facial features, while other layers would gauge if the arrangement of these features suggests the presence of a face. Natural Language Processing Natural Language Processing (NLP) enables machines to understand texts and spoken words similar to how humans understand these texts and words. Using NLP, algorithms and models can process written or spoken words and interpret the speaker or writer's intent and sentiment. NLP is used to perform specific tasks and produces analysis or results in the form of human-like speech or text. Leveraging the Broad Potential of Artificial Intelligence While Mitigating the Risks Emily Westridge Black, K. Mallory Brennan, and Abdul Althebaity Insights