Updated on October 09, 2023
- 6 min
What is the difference between Artificial Intelligence (AI) and Machine Learning (ML)?
Closely associated with each other in any conversation surrounding AI, many people wonder, what is the difference between AI and machine learning (ML)? Both terms are used interchangeably, but it should always be noted that machine learning is simply a subset of artificial intelligence. AI as a whole is made up of a variety of components, machine learning being one of them. With a focus on emulating human thoughts and actions, artificial intelligence is primarily geared toward performing real-world tasks. While this isn’t possible without the help of machine learning, it focuses on pattern recognition, decision-making, and other related abilities. For this article, you'll get an answer to the question: What is the difference between AI and ML? We’ll explore the topic enough to provide a solid overview so you aren’t left with any questions by the end.
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When you take a look at artificial intelligence from a bird’s eye view, you’ll see that it’s hard at work on strategy and technique. However, the underlying capabilities of AI are found in machine learning and other components like deep learning.
ML is just one branch of AI, but it plays a crucial role in capabilities like developing algorithms, executing complex tasks without direct instructions, and much more. The systems that makeup what ML is capable of rely on inference and patterns instead of direct programming language.
Overall, there are quite a few distinct functional differences between AI and ML, but in the same light, you'll find numerous similarities as well. To better understand the topic, we can dive into a few specific differences and similarities between the technologies.
What Do They Have in Common?
You can summarize the similarities between the two in just a few focal points. People let the similarities and differences confuse them, but there’s quite a bit that AI and ML share. Keep in mind that the similarities stretch past simple things like programming or data analysis. The list below highlights some interesting shared traits between AI and ML.
- The ability to achieve human-like problem-solving
- Numerous applications across a long list of industries
- They share a history and capability in computer science
Although there are many experts on the topic, the everyday individual doesn’t need to be a tech guru to understand how AI and ML work. Of course, the best way to get there is to understand the main differences as well. You’ll find that when it comes to the differences, they’re a little bit more nuanced compared to the traits they share.
A Breakdown of the Differences
As mentioned earlier, one of the immediate differences between the technologies is that machine learning is a subset of AI. Once you look past that, the unique characteristics of ML really start to shine through. While artificial intelligence's primary objective is to complete complex tasks, ML is more concerned with analyzing large datasets.
Through identifying patterns and using statistical models, ML can deliver results that can offer endless probabilities. Another core differentiator are the methods that AI and ML employ in their processes.
AI methods include:
- Neural networks
- Deep learning
- Rule-based systems
- Machine learning
- Genetic algorithms
ML methods include:
- Broken down into supervised and unsupervised learning
- Solve problems using data values labeled input and output
- Discovers patterns in unlabeled data
- Takes a more exploratory approach with its capabilities
It’s pretty evident what makes both technologies unique. At the same time, it also shows why it’s crucial that they work together. Both need each other, and each has an essential role to play in the future developments of artificial intelligence tech. After understanding their methods, you want to take a look at how implementation works.
For implementation to be a possibility, the first step requires selecting and preparing a training dataset. This process also includes selecting a pre-existing ML model or strategy. The last step focuses on refining the dataset to ensure accuracy.
In regard to artificial intelligence, implementation is known to be a bit more difficult. This is due to the complexities behind using prebuilt AI solutions for integration through APIs. You also want to consider the infrastructure requirements that come with each implementation. It isn’t uncommon for requirements to be unique and vast based on the computational analysis methodology and task at hand.
For those wondering what machine learning and AI are, looking at their differences can offer a clear-cut understanding of the topic. Putting your hands on the tech is one thing, but diving into the technicalities helps to remove some of the mystery behind the tech.
As people continue to navigate the ins and outs of the question of what is AI and machine learning, the tech further integrates into everyday life. Both technologies are becoming more prominent in the business world as more people realize the efficiencies they have to offer.
In the corporate world, employees spend a lot of their time on tasks that can be entirely managed by AI. One clear example would be managing emails, which tend to take up two to three hours out of the work day. With AImReply, you can automate email responses on a whim without compromising on context, personality, or quality.
You’ll also get AI-driven insights that are able to analyze your emails and quickly generate relevant, targeted responses. Overall, AI and ML should be seen as helpful tools, and you can see them in all their glory in tools like AImReply.
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