Artificial intelligence (AI) and machine learning (ML) are sometimes used interchangeably, but they are two separate things. Artificial intelligence is the ability of a robot or computer to complete tasks that are normally done by a human. Machine learning is a subset of artificial intelligence that enables a computer system or program to learn, adapt, and improve from experience without human intervention or explicit programming.
Artificial intelligence includes machine learning, but so much more. For example, AI also includes things such as smart assistants, self-driving cars, and automated financial investing. There are several aspects of both artificial intelligence and machine learning to know to be able to clearly understand the similarities and differences.
Artificial Intelligence vs Machine Learning: Side-by-Side Comparison
The following table shows a side-by-side comparison of artificial intelligence and machine learning.
|Artificial Intelligence (AI)||Machine Learning (ML)|
|What it is:||Artificial intelligence is the ability of different types of computer systems to mimic human activities or functions.|
Machine learning is the ability of a machine to learn automatically from previous data.
|Primary use:||AI works to create intelligent systems that can complete many complex tasks.||Machine learning creates machines that complete only specific tasks that they are trained for.|
|Different types:||There are six types of AI: Machine Learning, Neural Networks, Robotics, Expert Systems, Fuzzy Logic, and Natural Language Processing.||Machine learning is categorized into three areas: Supervised, Unsupervised, and Reinforcement Learning|
|Problem-solving ability:||AI is designed to operate independently and solve many problems.||Machine learning is designed to solve one specific problem as quickly and efficiently as possible.|
|Initial release:||While there isn’t an official release date, John McCarthy and Marvin Minsky presented their ideas on AI at the Dartmouth Summer Research Project on Artificial Intelligence in 1956.||In 1959, Arthur Samuel was the first to use the term Machine Learning.|
|Data style:||AI can work with structured and unstructured data.||Machine learning can only work with structured, and sometimes semi-structured data.|
|Influential developers:||Influential developers include John McCarthy, Marvin Minsky, Alan Turing, Nathaniel Rochester, and Claude E. Shannon.||Influential developers include Frank Rosenblatt and Arthur Samuel.|
|General objective:||To complete tasks and improve the likelihood of success.||To enhance speed and accuracy.|
|Technologies influenced:||Examples of artificial intelligence include self-driving cars, smart assistants, disease mapping, conversational bots, and social media monitoring.||Examples of machine learning include analyzing sales data, fraud detection, product recommendations, and video surveillance.|
Artificial Intelligence vs Machine Learning: What’s the Difference?
When training computers and designing systems, it’s important to note AI and ML will often overlap. There are, however, basic differences between Artificial Intelligence and Machine learning that can help us distinguish similarities and differences.
Artificial intelligence can create independent thinking that can solve a wide variety of issues and problems while machine learning seeks to solve a single problem as accurately as possible. AI is creative and can utilize different methods of thinking while machine learning is repetitive and will go over the same problem several times to look for patterns.
Artificial intelligence can imitate human behavior and perform many of the tasks that humans do. Human tasks that require reasoning, thinking, and learning can now be performed by computers and robots through artificial intelligence. Machine learning is an actual machine learning on its own through the experience without being explicitly programmed.
Artificial Intelligence vs Machine Learning: Five Must-Know Facts
There are a few basic facts about both machine learning and artificial intelligence.
- Machine Learning is primarily about learning from data and algorithms. Machine learning, therefore, is only as good as the data that’s used.
- AI includes reasoning and self-correction while machine learning can include reasoning and self-correction when given new data.
- Machine learning is a subset of AI. Other important subsets include big data, natural language processing, robotics, and neural networks.
- AI has two words, artificial and intelligence. These two words mean “a human-designed power for thinking.”
- AI is now training computers and changing how they work in several distinct ways including how they’re programmed, what they’re used for, and even how they’re made.
Complete History of Artificial Intelligence and Machine Learning
Computers need to be able to store commands, and not just execute them, for the computer to perform tasks that involve artificial intelligence. Before 1949, computers were told what to do, but they couldn’t remember the commands they followed. Training computers to “think” for themselves was just around the corner, however.
Alan Turing was the first to explain artificial intelligence as a concept. The British mathematician’s ideas were presented in a 1950 publication. The paper, Computing Machinery and Intelligence, asks the question, “Can machines think?” Alan Turing was influential in developing theoretical computer science.
Approximately five years later, Herbert Simon, Cliff Shaw, and Allen Newell presented the Logic Theorist. This was a program that could mimic the problem-solving skills of humans. Many considered this to be the very first AI program. It was presented at a conference aptly titled the Dartmouth Summer Research Project on Artificial Intelligence. This conference was led by Marvin Minsky and John McCarthy in 1956.
Machine Learning is sometimes thought to start as far back as 1949 when Donald Hebb presented his theories on communication between brain neurons in a book that was called, The Organization of Behavior.
In the 1950s, Arthur Samuel from IBM created a computer program that initiated alpha-beta pruning. This was in a computer program that was used for playing checkers. The program included a scoring function that was to measure the chances of either side winning. The minimax algorithm was developed from this program.
By 1957, Frank Rosenblatt combined Arthur Samuel’s efforts with those of Donald Hebb’s and created what was called the “perceptron.” This was to be a machine and not a program. The software was designed in a custom-built machine for the IBM 704. It was called the Mark 1 perceptron. Machine learning was closely related to AI until the 1970s. It then began to branch off in new ways.
How do You Use Artificial Intelligence and Machine Learning?
The following are specific examples of how artificial intelligence is used:
- Personal Assistants: Specific examples of personal assistants powered by AI include Siri by Apple, Google Home by Google, and Alexa by Amazon. Personal assistants programmed with AI can help users by answering questions, sending messages, booking hotels, and keeping a personal calendar organized.
- Self-Driving Cars: Self-driving cars involve limited memory and artificial intelligence. The AI will make immediate decisions based on data that has recently occurred. These cars use sensors to identify everything from traffic signals to curvy roads, and civilians who may be crossing the street.
- Industrial Robots: Most robots are not programmed with artificial intelligence. Also, robotics in and of itself isn’t an example of artificial intelligence. Robotics is the field that deals specifically with the physical aspect of robots. However, adding an artificial intelligence algorithm to a robot can enable the machine to complete complex tasks. With a path-finding AI algorithm, an industrial robot can navigate through a warehouse autonomously. The robot may even be able to monitor its performance.
- Computed Tomography: Artificial Intelligence is currently mimicking human behavior in the area of computed tomography (CT). This diagnostic procedure is often just called a CT scan. In some cases, it is going beyond human capabilities. For example, computed tomography (CT) is the ability of computers to search for cancers. These machines now can predict lung cancer with an astounding success rate of 94 percent.
- Robotic Vacuums: Robotic vacuums are examples of artificial intelligence. These vacuums can quickly scan a room and figure out the most efficient routes for cleaning around obstacles. The vacuums can perform their tasks with very little human interaction because of the extensive computer simulation that goes into the development of these machines.
Examples of Machine Learning include the following:
- Video Surveillance: Computers are now trained to monitor several video cameras in a home, a store, or a large factory. The computers can track unusual behavior for hours and days at a time and aren’t prone to human distraction.
- Social Media: Through machine learning, social media platforms learn what sites an individual visits and the friends people have. After learning a person’s patterns, new friends are suggested based on these patterns.
- Search Engine Results: Machine learning can refine search results by monitoring and tracking what a person searches for and how many pages they open after searching and the results displayed.
Another way to understand the differences and similarities between AI and machine learning is to study the different careers individuals may pursue in each specific field.
Careers in Artificial Intelligence
Artificial intelligence is a general term for a variety of smart technologies. Skills needed to specialize in AI are sometimes not as technical, but more theoretical. Those working in the field of machine learning, however, need to have a great amount of technical expertise. They must understand how to build models through simulation. Simulation in AI may involve creating a computer program that represents the actual activity or task artificial intelligence will complete.
People looking for a career in artificial intelligence would need specific skills in algorithms and how to analyze them. An understanding of data science, data mining, program design, and robotics would be important. They would also need to understand machine learning since this is a subset of AI. Finally, it’s necessary to study the ethical concerns in regards to developing safe and responsible new technologies.
Careers in Machine Learning
Those looking for a career in the more specialized field of machine learning should have a solid foundation in physics, applied mathematics, and neural network architectures. It’s also likely they’ll need to know programming, probability, statistics, and algorithms.
Specific degrees that a person might receive before embarking on a career in machine learning might include an undergrad degree in mathematics or computer science. Specific jobs they may hold include machine learning engineer or business developer.
Artificial Intelligence vs Machine Learning: Which is Better? Which Should You Use?
Neither AI nor Machine learning is better. They are simply different. The goal of an artificial intelligence system is to solve problems and perform tasks that are normally accomplished by humans. This means that the system must operate with independent and autonomous intelligence. When given different data sets and facts, AI will analyze and interpret the data and then generate different conclusions.
When engineering machine learning, the goal isn’t necessarily to solve many problems. (Potentially solving many problems would be necessary for a virtual assistant or a surgical robot.) Instead, machine learning is about solving a specific problem in the most effective way possible.
Artificial intelligence requires not only intelligence and understanding of facts, but the ability of a computer to have discernment as well. AI, whether in computer or robot form, can perform many tasks that in previous years could only be accomplished by humans.
When determining which is better or which one you should use, the answer depends on what you need assistance with and what goals you’re hoping to accomplish. The following are areas and specific tasks that artificial intelligence and machine learning are used in. This may help determine which is the best choice in certain cases.
When Should You Use Artificial Intelligence?
If you’re creating a system for any of the following, you’ll need to implement AI for it to be effective.
- Speech and Handwriting Recognition
- Gaming that Includes Activities Such as Chess or Poker
- Extensive Medical Processes Such as Robotic Surgeries
- Personalized Tutoring Systems in Education
- AI used in Simulation Modeling
When Should You Use Machine Learning?
Since machine learning focuses on patterns and accuracy, the following are examples of when you’ll need to specifically use machine learning.
- Using Sensors and Wearable Devices
- Filtering E-mail Spam
- Apps that Store Traffic Facts to Help People find the Best Routes