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Companies across industries are exploring and implementingartificial intelligence(AI) projects, from big data to robotics, to automate business processes, improve customer experience, and innovate product development. According toMcKinsey, embracing AI promises considerable benefits for businesses and economies through its contributions to productivity and growth. But with that promise comes challenges.

Computers and machines dont come into this world with inherent knowledge or an understanding of how things work. Like humans, they need to be taught that a red light means stop and green means go. So, how do these machines actually gain the intelligence they need to carry out tasks like driving a car or diagnosing a disease?

There are multiple ways to achieve AI, and existential to them all is data. Withoutquality data, artificial intelligence is a pipedream. There are two ways data can be manipulatedeither through rules or machine learningto achieve AI, and some best practices to help you choose between the two methods.

Long before AI and machine learning (ML) became mainstream terms outside of the high-tech field, developers were encoding human knowledge into computer systems asrules that get stored in a knowledge base. These rules define all aspects of a task, typically in the form of If statements (if A, then do B, else if X, then do Y).

While the number of rules that have to be written depends on the number of actions you want a system to handle (for example, 20 actions means manually writing and coding at least 20 rules), rules-based systems are generally lower effort, more cost-effective and less risky since these rules wont change or update on their own. However, rules can limit AI capabilities with rigid intelligence that can only do what theyve been written to do.

While a rules-based system could be considered as having fixed intelligence, in contrast, amachine learning systemis adaptive and attempts to simulate human intelligence. There is still a layer of underlying rules, but instead of a human writing a fixed set, the machine has the ability to learn new rules on its own, and discard ones that arent working anymore.

In practice, there are several ways a machine can learn, butsupervised trainingwhen the machine is given data to train onis generally the first step in a machine learning program. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.

The anticipated benefits to AI are high, so the decisions a company makes early in its execution can be critical to success. Foundational is aligning your technology choices to the underlying business goals that AI was set forth to achieve.What problems are you trying to solve, or challenges are you trying to meet?

The decision to implement a rules-based or machine learning system will have a long-term impact on how a companys AI program evolves and scales. Here are some best practices to consider when evaluating which approach is right for your organization:

When choosing a rules-based approach makes sense:

The promises of AI are real, but for many organizations, the challenge is where to begin. If you fall into this category, start by determining whether a rules-based or ML method will work best for your organization.

This article was originally published byElana Krasner on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.

Published June 13, 2020 13:00 UTC

Read more:
The key differences between rule-based AI and machine learning - The Next Web