Machine Learning, Demystified: Practical Use Cases for Today’s AI
Modern machine learning holds a lot of promise, but for many companies, it can be difficult to imagine how they might leverage AI to their advantage. AI has not yet reached the level of general intelligence, where it would be useful for any task. Instead, machine learning’s usefulness is currently limited to very specific types of tasks and genres of problems. (Although the possibilities are growing rapidly.)
It’s certain that AI-driven insights and applications are the future of computing. However, getting started with AI can be a major challenge for many companies. In our consulting practice, we help companies decide if machine learning is right for their use case and how best to leverage it. In this article, we’ll look at the common types of tasks that are uniquely suited to machine learning’s strengths.
Is There a Difference Between AI & Machine Learning?
Before we get into practical use cases, let’s clear up common misconceptions about what machine learning is.
Artificial intelligence is a broad term for when a computer learns to do a task better than a human. From speaking languages to understanding facial expressions, there are a whole host of tasks we humans do every day that are incredibly difficult for computers to accomplish. Any time a computer learns to do something a human can do, that’s artificial intelligence — no matter how the computer learned it.
Machine learning actually refers to a specific field of artificial intelligence. It’s a method for teaching computers new skills. At its core, machine learning involves fine-tuning statistical models while iterating through very large datasets. With each new data point, the model improves, and the computer can make more accurate predictions for subsequent data points.
So, machine learning is a subset of AI. It’s not the only way to teach a computer to do a task. However, in the age of big data, it’s a major way companies can leverage that data to drive insights.
Machine learning is a hot buzzword at the moment. Many companies are rushing to implement some form of AI as customers, shareholders, executives, and investors expect “intelligent” solutions and features.
However, before companies attempt to implement machine learning, it’s critical that they understand what problems machine learning is good at solving. Indeed, poor planning often leads to lackluster results. Recent research has found that nearly 85% of AI projects fail to deliver on their original promises. Therefore, thinking critically about your business challenge and understanding the strengths and weaknesses of machine learning is essential to success.
When you have a large dataset where a record needs to be correctly sorted based on multiple related, but not necessarily causal factors, machine learning can be very powerful.
For instance, oil and gas companies are using regression models to predict the likelihood of success for various potential drilling sites, given testing data about the surrounding area and geology. They use all the data at their disposal to classify and triage drill sites into various categories.
Additionally, retailers use similar models to categorize the types of shoppers that visit their stores, both online and brick and mortar. Cross-referencing credit card data with order totals, types of items purchased, and order frequency, companies can classify shoppers based on their needs and preferences and market to them accordingly.
2. Computer Vision
Any time a computer needs to interpret images, computer vision plays a major role. Luckily, many of these models are already very well-developed and open-source. They’re ready to be trained and deployed quite quickly for many computer vision applications.
Machine learning is literally driving the advent of autonomous vehicles. It’s also behind the facial recognition technology in most smartphones.
One of our clients is using computer vision to identify electronics components in order for their customers to order replacement parts. Working on that project, our consultants are training machine learning models on millions of images in order to accurately identify the various components.
3. Speech & Text Language Processing
Computers can’t speak English, yet. However, using machine learning, they can translate speech to text. They can also predict the sentiment and intention of your speech by learning words, phrases, and their intended meanings.
For companies, this means intelligent chatbots can offer context-aware help and recommendations as part of customer support. Additionally, many companies now offer Google Assistant and Amazon Alexa integrations in order for customers to use their products from smart speakers in their homes.
Furthermore, any time a company wants to analyze large amounts of text-based data, machine learning comes into play. Parsing through digitized books, historical records, social media postings, and more can lead to powerful insights about aggregated sentiment, noteworthy events, and more.
Machine Learning Is Not a Silver Bullet
While it’s powerful, machine learning cannot solve every problem. Furthermore, developing custom machine learning applications is both expensive and risky. For most companies, deploying a pre-trained model or running simple statistical analysis are low-hanging fruit before diving deeper into machine learning.
When correctly deployed by an experienced team, machine learning can produce transformative insights. However, to avoid yet another failed AI initiative, companies need to be thoughtful about how and why they’re using machine learning.
Founded in 1991, Intertech delivers software development consulting and IT training to Fortune 500, Government and Leading Technology institutions. Learn more about us. Whether you are a developer interested in working for a company that invests in its employees or a company looking to partner with a team of technology leaders who provide solutions, mentor staff and add true business value, we’d like to meet you.