As legal professionals, we are stepping deeper into the digital age, so terms such as “Artificial Intelligence,” “Machine Learning,” and “Automation” not only dominate tech discussions but also have found their way into our professional lives. The prevalence of these terms, coupled with the emergence of Generative AI, begs the question: Do we know what they are and how they can — and will — impact our lives and work?
Understanding the nuances and distinctions between these concepts is crucial, not just for tech enthusiasts but for anyone interacting with modern technologies. This becomes even more significant when we look into a specialized legal field such as eDiscovery technology, where these technologies are beginning to play a more significant role. In the eDiscovery process, AI and related technologies can significantly reduce manual effort and time required, if used appropriately.
So how do we define AI? And how do we demystify some of the jargon filling our newsfeeds? In this post, we’ll look at some of the most important AI terms in the field today and explain their importance in the practice of eDiscovery.
AI Terms You Should Know
Artificial Intelligence (AI)
At its core, AI mirrors or simulates human intelligence within machines. Encompassing a vast area within computer science, AI equips machines with capabilities once thought to be the exclusive domain of humans: from recognizing patterns and images to understanding human speech.
The prowess of AI shines in eDiscovery, where there’s a need to comb through enormous datasets swiftly. Here, AI has the potential to optimize search processes in tandem with human intelligence. It can also adapt and evolve, recognizing intricate patterns to locate and retrieve the most pertinent information. This has the potential to save legal professionals countless hours sifting through data.
Machine Learning (ML)
Often thought of as synonymous with AI, Machine Learning is, in fact, a subset of AI. Where AI is the broad goal of autonomous machine intelligence, ML is the specific method being used to bring that vision to life. ML systems don’t just follow explicit instructions; they use algorithms to parse data, learn from it, and make informed decisions.
Within eDiscovery, ML goes beyond rudimentary searches. It can be employed for sophisticated tasks like predictive coding, and analyzing past data to anticipate and categorize forthcoming documents, thereby enhancing efficiency and accuracy.
Treading into the realm of ML, we find specialized approaches such as Unsupervised Learning. Here, the learning model is unique because it doesn’t rely on labeled or categorized data for training. Instead, it autonomously discerns patterns, structures, or anomalies within the data. Its strength lies in its ability to unearth previously unnoticed relationships or patterns.
In eDiscovery, this becomes especially vital. Legal professionals can leverage unsupervised learning to identify hidden relationships or patterns in data sets, offering insights that could have easily been overlooked using traditional methods.
Venturing a layer deeper into ML, we encounter Deep Learning. Imagine a structure modeled around the human brain with numerous layers working in tandem. Utilizing artificial neural networks that mimic our brain’s neurons, deep learning processes information in intricate ways.
In the realm of eDiscovery, this is invaluable. As datasets grow more complex, the depth at which deep learning operates could ensure that even the minutest, most nuanced pieces of information are not missed, enhancing the precision and reliability of document categorization.
Generative AI stands as one of the most exciting frontiers in the AI domain. While traditional AI models respond to inputs with outputs, generative AI takes a leap forward. It can conceive and produce entirely new content, from crafting coherent text pieces to generating realistic images or even simulating human voices. The underlying magic here often involves models like Generative Adversarial Networks (GANs). These are essentially AI systems “competing” with each other, one generating content and the other evaluating it, leading to refined outputs.
Translating this to eDiscovery, the implications could be profound. Instead of just searching and categorizing existing data, imagine an AI that could extrapolate and visualize potential data structures or patterns based on current and past datasets. The evolution from mere data analysis to predictive generation marks a transformative shift in how we harness AI.
Robotic Process Automation (RPA)
In an age where efficiency is paramount, automation stands as a beacon. While it’s frequently conflated with AI, RPA primarily focuses on the systematic execution of repetitive tasks, sans human involvement. Unlike AI, which is characterized by its ability to learn and evolve, automation operates within a predefined set of rules.
Shifting to eDiscovery, RPA manifests its prowess by seamlessly managing tasks. Whether it’s the ingestion of vast data sets, eliminating redundant data, or the preliminary sorting of information, automation ensures that these processes are streamlined, consistent, and efficient.
Upon first hearing, “machine intelligence” might seem just another term for AI. However, a deeper understanding reveals a nuanced difference. Machine intelligence encapsulates not just the algorithmic learning of machines but also their capability to emulate human-like decision-making processes. It is a blend of learning (often from ML) and reasoning, mimicking cognitive human abilities.
Obviously, there’s plenty of time for this to change, but eDiscovery in its current state actually does not employ machine intelligence. While eDiscovery does leverage algorithms to parse data, it does not emulate human-like decision-making.
By harnessing statistical algorithms combined with machine learning techniques, predictive analytics forecasts likely future outcomes rooted in historical data analysis.
For fields such as eDiscovery, this can be invaluable. Being able to anticipate data trends based on collected data can shape litigation strategies or even predict the potential relevance of data segments. This proactive approach can save time, and resources, and streamline the legal discovery process.
In an age when these terms are utilized every day, it becomes pivotal to question our usage of these terms. For instance, AI or RPA? The distinction here is clear-cut: If software is designed solely for efficiency in repetition, it’s RPA. But, if it’s designed to evolve by gleaning insights from data, we’re talking about AI. Or is it Deep Learning or Machine Learning? Deep learning is but a specialized branch within the grand tree of machine learning. It delves deeper, leveraging intricate neural networks, making it the expert scholar within the broader domain of ML. Overall, it is important to understand these distinctions, as these tools have become increasingly pervasive in our society.
Ultimately, the meteoric advancements in AI, its subsets, and automation are radically reshaping industries, with eDiscovery being a notable example. The potential is limitless, and the implications profound. But, to navigate this landscape, it is imperative we grasp these AI terms with clarity and precision.
To learn more about Casepoint’s approach to GenAI, read our official statement here.