Companies across industries are exploring and implementing AI (AI) projects, from big data to robotics, to automate business processes, improve customer experience, and innovate development. consistent with McKinsey, “embracing AI promises considerable benefits for businesses and economies through its contributions to productivity and growth.” But thereupon promise comes challenges.
Computers and machines don’t inherit this world with inherent knowledge or an understanding of how things work. Like humans, they have to be taught that a red light means stop and green means go. So, how do these machines actually gain the intelligence they have to hold out tasks like driving a car or diagnosing a disease?
Data or bust
There are multiple ways to realize AI, and existential to all of them is data. Without quality data, AI may be a pipedream. There are two ways data are often manipulated—either through rules or machine learning—to achieve AI, and a few best practices to assist you to select between the 2 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 as rules that get stored during a knowledge domain. These rules define all aspects of a task, typically within the sort of “If” statements (“if A, then do B, else if X, then do Y”).
While the amount of rules that need to be written depends on the number of actions you would like a system to handle (for example, 20 actions means manually writing and coding a minimum of 20 rules), rules-based systems are generally lower effort, less expensive and fewer risky since these rules won’t change or update on their own. However, rules can limit AI capabilities with rigid intelligence which will only do what they’ve been written to try to to.
Machine learning systems
While a rules-based system might be considered as having “fixed” intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. there’s still a layer of underlying rules, but rather than a person’s writing a hard and fast set, the machine has the power to find out new rules on its own, and discard ones that aren’t working anymore.
In practice, there are several ways a machine can learn, but supervised training—when the machine is given data to coach on—is generally the primary step during a machine learning program. Eventually, the machine is going to be ready to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.
Where to start out with an organization’s AI strategy:
The anticipated benefits to AI are high, therefore the decisions a corporation makes early in its execution are often critical to success. Foundational is aligning your technology choices to the underlying business goals that AI was set forth to realize. What problems are you trying to unravel, 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 company’s AI program evolves and scales. Here are some best practices to think about when evaluating which approach is true for your organization:
When choosing a rules-based approach makes sense:
- Fixed outcomes: When there’s a little or a fixed number of outcomes. for instance, there are only two states that an “Add to Cart” button is often in, either pressed or not. While it’s possible to use machine learning to detect whether a user pressed the button, it wouldn’t add up to use that sort of method.
- Risk of error: The penalty of error is just too high to risk false positives and thus only rules—which are going to be one hundred pc accurate—should be implemented.
- Not planning for ML: If those maintaining the system don’t have machine learning knowledge and therefore the business doesn’t have plans to source for it moving forward.
When to use machine learning:
- Simple rules don’t apply: When there are no easily definable thanks to solving a task using simple rules
- Speed of change: When situations, scenarios, and data are changing faster than the power to repeatedly write new rules.
- Natural language processing: Tasks that decision for an understanding of language, or tongue processing. Since there are an infinite number of the way to mention something, it’s unrealistic, if not downright impossible, to write down rules for the normal language. The innate, adaptive intelligence of machine learning is optimized for scale.
The promises of AI are real, except for many organizations, the challenge is where to start. If you fall under this category, start by determining whether a rules-based or ML method will work best for your organization.