Today, Artificial Intelligence (AI) is no longer solely the
domain of science fiction writers and Hollywood film studios.
Unbeknownst to most consumers, AI has quietly made its way into
many aspects of daily life, from depositing cheques through your
phone to curated and targeted advertisements on social media
While many of these developments have the potential to increase
our productivity and quality of life—or have already done
so—our laws and the courts are playing catch-up. This article
will provide a primer on AI, and explore its application in
business and in life and the potential legal issues that will arise
as AI continues on its exponential growth trajectory.
What is AI?
Before exploring the legal implications of AI, it is helpful to
clarify what AI is. Different sources provide different
definitions, but in its most simple formulation, AI can be thought
of as the ability for computers to accomplish tasks normally
associated with humans acting intelligently. Most AI used today
does not actually replicate or mimic human intelligence but rather
uses a more sophisticated form of traditional programming. In
traditional computing, the programmer instructs the computer what
to do in every possible scenario. The programmer supplies the
intelligence, and the computer simply executes the task. In AI, the
computer is taught to make decisions on its own, by analyzing large
data sets and drawing its own inferences and conclusions.
There are principally two types of AI—generalized AI and
applied AI. Generalized AI refers to a machine or a system that can
handle any task thrown at it. Applied (or narrow) AI refers to a
machine or system that can perform a specific task in a manner that
mimics a component (but not all) of human intelligence. While
Generalized AI remains elusive, numerous advances in applied AI
have emerged in the past few years. One of these advances is the
concept of machine learning (ML). Nvidia, a company at the
forefront of ML development, describes ML as "the practice of
using algorithms to parse data, learn from it, and then make a
determination or prediction about something in the world. So rather
than hand-coding software routines with a specific set of
instructions to accomplish a particular task, the machine is
"trained" using large amounts of data and algorithms that
give it the ability to learn how to perform the task."1
In ML, as the system continues to complete its task, it learns
from user input and becomes more and more intelligent. For example,
when you accidentally type the wrong thing into a Google search
bar, it often asks if you meant to search for something else. If
you click on Google's suggestion, it assumes that its
predictive algorithm was correct, thereby validating the system. It
uses this user feedback to improve suggestions going forward.
In an effort to help regulated entities and interested parties evaluate whether the use of distributed ledger technology (DLT) would enable them to meet their regulatory obligations and to fast-track...
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