Do You Understand the Difference Between AI and ML?

Posted by Trevor Warren, Data Architect, - Feb 4, 2021

Too often, the terms artificial intelligence (AI) and machine learning (ML) are used interchangeably. It’s not entirely wrong, as they are related, but they are not the same thing. There’s a difference between AI and ML. Do you know what that is?

When developing a data strategy, chances are you’ll want to address the big data that drives artificial intelligence and machine learning. To do this, it’s important to understand the definition of both and how they work together to make smart decisions about your vital institutional data. Artificial intelligence is a larger concept that makes machine learning possible. Let’s take a deeper look into how that happens to better understand the difference between AI and ML.

What is Artificial Intelligence?

Artificial intelligence is the overarching discipline focused on “making machines smart.” This concept drives technology that helps machines simulate human thinking capabilities and behaviors. It’s the “intelligence” that machines can exhibit.

Artificial intelligence covers everything from old-school AI to more futuristic technologies. No matter the level of technological complexity, a machine running AI needs to complete algorithm-based tasks to deliver “intelligent” behavior.

Facial recognition and artificial intelligenceRight now, machines perform what is known as narrow AI. Also called, “weak AI,” this is the only artificial intelligence performed to date. It’s goal-oriented and intelligently performs the singular tasks it is programmed to do, whether it’s driving a car or delivering accurate facial recognition. It doesn’t mimic human intelligence but, instead, simulates human behavior.

The machines are taught tasks using natural language processing and simulate the desired behaviors by performing learned tasks. Think about how you interact with Amazon Alexa or Google Assistant. These services use narrow AI to respond to your commands and queries.

Other narrow AI applications include, but aren’t limited to, content recommendations based on listening or viewing habits, disease mapping and predictions in healthcare settings, malware and virus monitoring tools and filters and more.

Artificial General Intelligence, or “deep AI,” is where the data scientists and researchers aspire to be within the next decade. Right now it’s still a concept in which general intelligence machines not only mimic human behaviors but have the ability to actually learn and apply this intelligence to in-depth problem solving, acting and thinking like a human being.

Researchers are still experimenting with ways to create a conscious machine programmed with powerful cognitive capabilities. These abilities would reach beyond the singular tasks of narrow AI into a realm of building knowledge gleaned from experience and using it to solve problems.

What is Machine Learning?

If artificial intelligence is the overall concept, machine learning serves as the vehicle that carries the AI concept forward into action. It’s the practice of using provided data and training algorithms to make the singular decisions we discussed in the section above. Simply put, machine learning is the application of artificial intelligence.

Machine learning relies on data. As a great article in the MIT Technology Review notes, if data “can be digitally stored, it can be fed into a machine-learning algorithm.” This data can be numbers, words, images, statistics...you name it.

business documents on office table with smart phone and digital tablet and stylus and two colleagues discussing data in the backgroundWhatever your organization collects and stores in a data lake and data warehouse can be used toward machine learning, even if it’s a massive amount of data. ML takes this data and examines it for patterns. It then uses these patterns to create predictive models to determine future outcomes. The more data that you can feed to an algorithm over time, the better and more accurate the predictive results will be.

There’s also another type of learning to be aware of as you explore these concepts. Deep learning. Also called deep neural learning, this machine-learning subset uses neural networks similar to that of the human brain to mimic data processing for such actions as speech recognition, object recognition, language translation and more. It can do this using both structured and unstructured data. We’ll dive further into deep learning in a future blog.

How Can I Get Started Using AI and ML?

There are many reasons why adopting AI and ML as part of your data strategy makes sense. It allows you to make the most of all that data you have stored in those data lakes and data warehouses. It’s a complicated process, however, and you want to be sure you are using the right data for the right automated task.

A July 2020 article in Tech Target’s Search Enterprise AI notes that the “wrong use case is the downfall of many machine-learning applications.” Using the wrong data also is another stumbling block that organizations encounter when trying to implement AI and ML into their data strategy.

Data experts... can help you assess your processes and your available data. They also will help you realize which of your use cases are right for AI and ML so you can incorporate these technologies at scale and gain the greatest efficiencies from using them. There is room for AI/ML in marketing, sales, R&D, IT operations, HR and more.

That’s where data experts come in. Professionals in the data sector can help you assess your processes and your available data. They also will help you realize which of your use cases are right for AI and ML so you can incorporate these technologies at scale and gain the greatest efficiencies from using them. There is room for AI/ML in marketing, sales, R&D, IT operations, HR and more.

In various enterprise settings such use cases can include, to name only a few:

  • image of a chat bot in spaceReal-time chatbot agents
  • Recommendation engines
  • Customer analysis
  • Market research
  • Pricing determination
  • Virtual assistants
  • Fraud detection
  • Automated software texting
  • Maintenance tracking and analytics

In any appropriate use case, using the most suitable data, machine learning drives artificial intelligence to allow organizations to enjoy better, more targeted internal and external insights, improved productivity, better forecasting and more targeted customer service. It’s something worth starting to explore if you haven’t already started this journey in your data strategy.

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Meet the Author

Trevor Warren, Data Architect

Trevor Warren, Data Architect

Trevor has nearly a decade of experience in solving problems for complex computer systems and improving processes. Trevor earned a Master of Science in Data Science. He is also a Google Cloud Certified Professional - Cloud Architect and Data Engineer.

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