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Voice of Experience

Voice of Experience: January 2024

AI Column: A Primer on Artificial Intelligence

Jeffrey M Allen and Ashley Hallene


  • Artificial Intelligence has gained significant prominence in recent years and has the potential to reshape industries, improve our daily lives, and raise important challenges and ethical questions.
  • The recent development of ChatGPT has rapidly accelerated the growth of the sophistication of AI and opened the door to many new applications.
  • The evolution of AI in the face of ChatGPT has put AI in our homes, cars, offices, and health and medical care.
AI Column: A Primer on Artificial Intelligence
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VOE’s Editorial Board has recognized the growing importance of Artificial Intelligence (AI) to our professional and our personal lives.  They have asked us to help members of the Division better understand AI, what it does, and what problems it creates in the form of a new AI column.  As the first in the series, we thought that a primer on AI would serve the readers best.  We made that decision after consideration by the Editorial Board and recognizing the many questions we regularly receive about AI.

Artificial Intelligence

Artificial Intelligence has gained significant prominence in recent years. It has the potential to reshape industries, improve our daily lives, and raise important ethical questions. This primer aims to summarize AI, its history, key concepts, current applications, challenges, and prospects.  We will touch upon a number of topics in this primer that we will explore in greater depth in future columns.

Artificial Intelligence refers to the capability of a machine or computer program to perform tasks that typically require human intelligence. Such tasks include problem-solving, learning, understanding natural language, recognizing patterns, and decision making based on data.

We have had and used AI in our lives increasingly for many years.  For example, the software we use to search statutory or caselaw databases for specific information relevant to our needs represents an application of AI in our professional lives.  The use of and our reliance on AI has grown incrementally over the years.  You have probably seen that in the evolution of that same search process for case law and statutes.  The software has grown more and more sophisticated as AI evolved.  Examples of AI in our personal lives include virtual assistants, such as Siri, Alexa, and Google Assistant, all of which employ AI to understand and respond to voice commands. 

The recent development of ChatGPT has rapidly accelerated the growth of the sophistication of AI and opened the door to many new applications.  For example, in healthcare, AI aids in diagnosing diseases, predicting patient outcomes, and drug discovery. In finance, AI does risk assessment, fraud detection, and can monitor high-frequency trading. In transportation, self-driving cars and AI-based traffic management systems may improve safety and efficiency.


ChatGPT is an artificial intelligence chatbot that uses natural language processing to create humanlike conversational dialogue. A form of “generative AI,” ChatGPT creates humanlike images, text or videos in response to prompts or instructions we provide.  The GPT stands for "Generative Pre-trained Transformer," which refers to how ChatGPT processes requests and formulates responses. ChatGPT trains with reinforcement learning through human feedback and reward models that rank the responses. This process allows augmentation of ChatGPT with machine learning to help improve future responses.  The language model responds to questions and can compose written content of a variety of types for personal and professional uses, including articles, social media posts, essays, code and emails.

Two recent advances have played a critical part in generative AI going mainstream: transformers and the breakthrough language models they enabled. “Transformers” are a type of machine learning that make it possible for researchers to train increasingly large models without labelling all the data in advance. New models train on billions of pages of text, resulting in answers with more depth.  Transformers unlocked a new notion called attention that enabled tracking connections between words across pages, chapters, and books rather than in individual sentences.  Transformers can also use their ability to track connections enabling scientific analysis of such things as DNA.

Rapid advances in large language models (“LLMs”) having billions or even trillions of parameters opened the door for generative AI models to write engaging text, paint photorealistic images, and even create somewhat entertaining sitcoms on the fly.  The rapid evolution of the capabilities of ChatGPT has expanded the use of AI, changing the nature of the beast and its role in our society.  The evolution of AI in the face of ChatGPT has put AI in our homes, our cars, our health and medical care as well as our offices.  Many industries have started considering the trade-off between human workers and a ChatGPT powered AI. Some have gone farther and replaced human workers with AI.

AI’s Future:  AI continues to advance; and nothing suggests that its growth will slow down in the near future.

Collaboration: Expect to see increased collaboration between humans and AI, with AI augmenting human capabilities in many fields, including law, medicine, research, and creativity.

Ethics:  We need to ensure ethical and responsible development of AI. This includes transparent algorithms, avoiding bias, and ensuring human oversight.

Governance:  Governments and organizations have started developing guidelines and regulations for AI to ensure safety, fairness, and accountability in its deployment.  Those processes remain in their infancy and do not yet offer any significant protection.

Challenges and Ethical Considerations

Fairness:  AI can inherit biases intrinsic to data on which they train.  Those biases can influence the outcomes selected by the AI model.  Ensuring fairness in AI processes poses a significant challenge.

Privacy and Security Issues: AI systems collect and analyze vast amounts of personal data.  This should and does raise concerns about privacy and data security. We need to ensure the safeguarding of such data, which may include personal information about individuals.

Automation/Job Loss:  Automation of tasks through AI will likely lead to job displacement in some industries. We need to ensure that the workforce receives preparation.  As a society, we need to consider what we will do about the displaced workers, their potential futures and economic security.

Existential Risks:  AI’s increasing power and flexibility creates concerns about the potential for misuse or unintended consequences that could pose existential risks to our society and our population. Industry and governments have raised these concerns and the need for regulation to mitigate these risks.  Doing so will require responsible AI development and implementing effective monitoring and governance.

Glossary of AI-Related Terms:  We did not design this glossary as all-inclusive of every term you might encounter in connection with AI.  It includes the terms we think you will most likely encounter in articles and discussions about AI.

Artificial Intelligence:  Artificial Intelligence refers to the development of computer systems capable of performing typically requiring human intelligence.

Narrow (weak) AI: Also known as Weak AI, Narrow AI is designed for specific tasks, such as virtual personal assistants (Siri, Alexa), recommendation systems, and chatbots.

General (strong) AI: AI with human-level intelligence capable of performing any intellectual task a human can. True General AI remains a long-term goal.

Machine Learning (ML): A subfield of AI that focuses on developing algorithms and models enabling computers to improve performance on a task through experience, without explicit programming.

Supervised Learning: ML where the model trains on a labeled dataset, learning to make predictions or decisions based on input data.

Unsupervised Learning:  ML where the model learns patterns and structures in data without labeled examples, often used for clustering and dimensionality reduction.

Reinforcement Learning: ML where agents learn to decide by interacting with an environment and receiving rewards or penalties.

Deep Learning:  A subfield of machine learning using artificial neural networks, specifically deep neural networks, to model and solve complex problems, often achieving state-of-the-art performance in tasks like image and speech recognition.

Neural Networks:  Computer models inspired by the structure and function of the human brain, consisting of interconnected nodes (neurons).

Natural Language Processing:  A field of AI focusing on enabling computers to understand, interpret, and generate human language.

Sentiment Analysis:  Analyzing text to determine the sentiment or emotional tone; often used in social media monitoring and customer feedback analysis.

Big Data: Large and complex datasets not easily managed or analyzed with traditional data processing tools. AI often leverages big data for training and decision-making.

Data Mining:  Seeking and discovering patterns, trends, and insights in large datasets.

Neural networks: Layers of interconnected nodes. Different types of layers perform specific functions in deep learning models.

Algorithm: A step-by-step set of instructions or rules for solving a specific problem or performing a specific task. AI uses algorithms to train models and make predictions.

Metrics: Tools used to evaluate the performance of supervised learning models in tasks like classification and regression.

Autonomous: We describe a machine as autonomous if it can perform its task or tasks without human intervention.

Chatbot: A  program designed to communicate with people through text or voice commands in a way that mimics human-to-human conversation.