Digital White Papers

I L T A W H I T E P A P E R | C O R P O R A T E L E G A L D E P A R T M E N T S 17 C L O S I N G T H E S K I L L S G A P : A I , A N A L Y T I C S A N D T H E L E G A L P R O F E S S I O N can learn when you walk by and what temperature settings you like, essentially storing the patterns of your weekday and weekend routines and controlling things accordingly. These limited-memory AI tools refer to past experience and present data to make decisions, continuously and automatically adjusting as more data is introduced. Neural networks are the third category of AI. These are machines that have some features of a human mind and the ability to interact with and execute tasks set by humans. Neural networks commonly take the form of robots or web bots. Neural networks fall within a commonly referenced form of AI called machine learning. The fourth and final category – and the ultimate goal of AI – is self- awareness. A self-aware machine would have very high-level intelligence and a consciousness of itself and its environment, being able to accomplish all the tasks encompassed by the prior three categories. While self-aware machines are the AI that we commonly see in fiction, we're far from encountering this level of artificial intelligence in our day-to-day lives. What we see more commonly in everyday use, including in the legal industry, is machine learning. A Look Inside Machine Learning Machine learning tools have been revolutionizing the way the legal industry does business for the past decade. These types of AI tools are focused on discovering patterns in data and generating insights from those patterns. This pattern recognition can advance into deep learning on the data, where the tools are mimicking human neural networks and making detailed sense of patterns by excluding noise and sources of confusion from the data. Data can often be noisy, unreliable and unpredictable, but advanced machine learning tools can scan the data and determine what is real and what likely is not. Machine learning neural networks function like black boxes. There's an input layer where humans introduce data to help the AI parse the parameters of the problem to be resolved. Then you want the technolo to reference all the data you have on a particular problem or circumstance in order to develop new inferences about it. The output layer reports back the results and conclusions of those inferences. When it comes to training and utilizing AI, the more data you have, the better. Neural networks traditionally use historical data. If you are lacking in data or context for your data, it can be augmented by external data sources until you achieve the results you're seeking. AI in the Legal Profession While any discussion of AI can start to feel overly technical, in reality AI is everywhere, and you do not need to be a data scientist to take advantage of it. AI- powered tools have already become an integral part of our day-to-day lives, and the legal practice is certainly no exception. Today's legal industry is brimming with special- use tools and point solutions designed to make the practice of law more efficient and effective. Better yet, they are designed to be used by lawyers to discover trends and answer critical questions without having to write or even understand complex data models. The coding and programming remain the work of the data scientists, while everyone else reaps the benefits. While any discussion of AI can start to feel overly technical, in reality AI is everywhere, and you do not need to be a data scientist to take advantage of it.

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