Machine Learning vs Artificial Intelligence: Key Differences

It’s very common to hear the terms “machine learning” and “artificial intelligence” thrown into the wrong context. This is an easy mistake to make, as they are two separate but similar concepts that are closely related. That said, it’s important to note that machine learning, or ML, is a subset of artificial intelligence, or AI.

To better understand these two concepts, let’s first define each of them:

  • Artificial Intelligence (AI): AI is software or a process designed to mimic human thought and process information. AI includes a wide range of technologies and fields such as computer vision, natural language processing (NLP), autonomous vehicles, robotics and finally machine learning. AI enables devices to learn and identify information to solve problems and extract insights.
  • Machine Learning (ML): Machine learning is a subset of AI, and it’s a technique that involves teaching devices to learn information fed to a data set without human interference. Machine learning algorithms can learn data over time, improving the accuracy and efficiency of the overall machine learning model. Another way to look at it is that machine learning is the process that AI goes through when performing AI functions.

Key aspects of artificial intelligence

Many definitions of artificial intelligence have appeared over the years, which is one of the reasons why it can seem somewhat complicated or confusing. But in its simplest form, AI is a field that combines computing and robust data sets to achieve effective problem solving.

The current field of artificial intelligence includes subfields such as machine learning and deep learning, which involve AI algorithms that perform predictions or classifications based on input data.

AI is sometimes divided into different types, such as weak AI or strong AI. Weak AI, also known as narrow AI or narrow artificial intelligence (ANI), is AI that has been trained to perform specific tasks. It’s the most apparent form of AI in our daily lives, enabling apps like Apple’s Siri and self-driving vehicles.

Strong AI includes Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI is only theoretical at this point and refers to a machine with intelligence equal to that of humans. AGI would be self-aware and able to solve very complex problems, learn and plan for the future. Going even further, ASI would surpass human intelligence and capabilities.

One way to understand AI is to look at some of its various applications, including:

  • Speech Recognition: AI is the key to many speech recognition technologies. Also called computer speech recognition or speech synthesis, it relies on NLP to translate human speech into written format.
  • Computer vision: AI allows computers to extract information from digital images, videos, and other visual input. Computer vision is used for photo tagging, medical imaging, self-driving cars and more.
  • Customer service: AI is powering chatbots throughout the customer service industry, changing the relationship between businesses and their customers.
  • Fraud detection: Financial institutions use AI to spot suspicious transactions.

Key aspects of machine learning

Machine learning algorithms rely on structured data to make predictions. Structured data is data that is labeled, organized, and defined with specific functionality. Machine learning usually needs this data to be preprocessed and curated, otherwise it would be handled by deep learning algorithms, which is yet another subfield of AI.

When we look at the broader concept of machine learning, it quickly becomes clear that it is a very valuable tool for businesses of all sizes. This is largely due to the massive amount of data available to organizations. Machine learning models process data and identify patterns that improve business decision making at every level, and these models update themselves and improve their analytical accuracy every time.

Machine learning consists of a few different techniques, each working differently:

  • Supervised teaching: Labeled data “supervises” algorithms and trains them to classify data and predict outcomes.
  • Unsupervised learning: A machine learning technique that uses unlabeled data. Unsupervised learning models can analyze data and discover patterns without human intervention.
  • Reinforcement learning: This technique trains models to make a sequence of decisions, and it is based on a reward/punishment system.

Difference in AI/ML skills

Now that we’ve separated the two concepts of artificial intelligence and machine learning, you’ve probably guessed that each requires a different set of skills. For people looking to get involved in AI or ML, it is important to recognize what is required for each.

When it comes to AI, the skill set tends to be more theoretical than technical, while machine learning requires highly technical expertise. That said, there is a crossover between the two.

Let’s first look at the main skills required for artificial intelligence:

  • Data science: A multidisciplinary field focused on using data to derive insights, data science skills are crucial for AI. They can include everything from programming to math, and they help data scientists use techniques like statistical modeling and data visualization.
  • Robotics: AI provides robots with computer vision to help them navigate and sense their surroundings.
  • Ethics: Anyone involved in AI should be well aware of all the ethical implications of this technology. Ethics is one of the main concerns regarding the deployment of AI systems.
  • Knowledge of the domain: By having domain knowledge, you will better understand the industry. It will also help you develop innovative technologies to address specific challenges and risks, to better support your business.
  • Machine learning: To truly understand AI and apply it in the best possible way, you need to have a solid understanding of machine learning. While you may not need to know all the technical aspects of machine learning development, you should know the fundamentals.

When we look at machine learning, the skills tend to get much more technical. That said, it would be beneficial for anyone wanting to get involved in AI or ML to know as much as possible:

  • Programming: Every machine learning professional should be proficient in programming languages ​​such as Java, R, Python, C++, and JavaScript.
  • Math: ML professionals work extensively with algorithms and applied mathematics, which is why they must have strong analytical and problem-solving skills combined with mathematical knowledge.
  • Neural network architecture: Neural networks are fundamental for deep learning, which is a subset of machine learning. ML experts have a deep understanding of these neural networks and how they can be applied across industries.
  • BigData: An important part of machine learning is big data, where these models analyze large data sets to identify patterns and make predictions. Big data refers to the efficient extraction, management and analysis of huge amounts of data.
  • Distributed Computing: A branch of computer science, distributed computing is another major component of machine learning. It refers to distributed systems whose components are located on different networked computers, which coordinate their actions by exchanging communications.

These are just a few of the AI ​​and ML skills that should be learned by anyone looking to get involved in the fields. That said, any business leader would greatly benefit from learning these skills, as it would help them better understand their AI projects. And one of the biggest keys to the success of any AI project is a skilled leadership team that understands what’s going on.

If you want to learn more about how you can learn some of these AI or ML skills, check out our list of the best data science and machine learning certificates.

Sherry J. Basler