Artificial Intelligence (AI) vs. Machine Learning (ML): Key Comparisons

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Within the past decade, the terms artificial intelligence (AI) and machine learning (ML) have become buzzwords often used interchangeably. While artificial intelligence and machine learning are closely related and share similar characteristics, they are not the same thing. Instead, ML is a major subset of AI.

Artificial intelligence and machine learning technologies are all around us, from the digital voice assistants in our living rooms to the recommendations you see on Netflix.

Although artificial intelligence and machine learning have penetrated many human fields, there is still a lot of confusion and ambiguity regarding their similarities, differences, and primary applications.

Here’s a more in-depth look at AI versus machine learning, the different types, and how the two revolutionary technologies compare to each other.

What is Artificial Intelligence (AI)?

Artificial intelligence is defined as a computer technology that simulates(s) a human’s ability to solve problems and make connections based on insight, understanding, and intuition.

The field of artificial intelligence emerged in the 1950s. However, earlier mention of artificial beings with intelligence can be identified in various disciplines such as ancient philosophy, Greek mythology and fairy tales.

One of the notable projects of the 20th century, the Turing Test, is often referenced when referring to the history of artificial intelligence. Alan Turing, also referred to as the “father of artificial intelligence,” created the test and is best known for creating a cipher-breaking computer that helped the Allies in World War II understand secret messages sent by the German army.

The Turing test is used to determine if a machine is capable of thinking like a human. A computer can only pass a Turing test if it responds to questions with answers that are indistinguishable from human responses.

Three main capabilities of an AI-powered computer system include intent, intelligence, and adaptability. AI systems use mathematics and logic to complete tasks, often involving large amounts of data, which otherwise would not be practical or feasible.

Popular Artificial Intelligence Applications

Modern artificial intelligence is used by many technology companies and their clients. Some of the most popular AI applications today include:

  • Advanced Web Search Engines (Google)
  • Self-driving cars (Tesla)
  • Custom Recommendations (Netflix and YouTube)
  • Personal Assistants (Amazon Alexa and Siri)

One example of AI that stole the spotlight was in 2011, when IBM’s Watson, an AI-powered supercomputer, participated in the popular TV game show. Risk! Watson shook the tech industry to its core after beating two former champions, Ken Jennings and Brad Rutter.

Outside of using game displays, many industries have adopted AI applications to improve their operations, from manufacturers deploying robots to insurance companies improving their risk assessment.

Read also: How artificial intelligence is changing the way we learn languages

types of artificial intelligence

AI is often divided into two categories: narrow AI and general AI.

  • Narrow AI: Many modern applications of AI are narrow AI, created to complete specific, specific tasks. For example, a chatbot on a business website is an example of narrow AI. Another example is a machine translation service, such as Google Translate. Self-driving cars are another app for this.
  • Artificial General Intelligence: AI differs from narrow AI in that it also includes machine learning (ML) systems for different purposes. Can learn more quickly than humans, complete intellectual tasks, and perform better.

Regardless of whether AI is classified as narrow or general, modern AI is still somewhat limited. He can’t communicate quite like humans, but he can simulate feelings. However, AI cannot own or “feel” emotions the way a person does.

What is Machine Learning (ML)?

Machine learning (ML) is a subset of artificial intelligence, where a group of algorithms build models based on sample data, also called training data.

The main purpose of an ML model is to make accurate predictions or decisions based on historical data. ML solutions use vast amounts of semi-structured and structured data to make predictions and predictions with a high level of accuracy.

In 1959, Arthur Samuel, a pioneer in artificial intelligence and computer games, defined machine learning as a field of study that enables computers to learn continuously without being explicitly programmed.

An ML model that is constantly exposed to new data learns, adapts, and evolves on its own. Many companies invest in ML solutions because it helps them make decisions, predict future trends, learn more about their customers, and gain other valuable insights.

ML . types

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. A data scientist or other machine learning practitioner will use a specific version based on what they want to expect. Here’s what each type of ML entails:

  • ML Supervised: In this type of ML, data scientists will feed the ML model called training data. They will also define specific variables that they want the algorithm to evaluate to determine correlations. In supervised learning, the inputs and outputs of information are determined.
  • Unsupervised ML: In unsupervised ML, the algorithms train on the unlabeled data, and ML will scan it to identify any meaningful connections. Unlabeled data and ML output are predefined.
  • Learning reinforcement: Reinforcement learning involves data scientists training machine learning to complete a multi-step process with a predetermined set of rules to follow. Practitioners program ML algorithms to complete a task and will provide it with positive or negative feedback on its performance.

Popular ML Apps

Major companies like Netflix, Amazon, Facebook, Google and Uber own ML an essential part of their business operations. ML can be applied in many ways, including via:

  • Email Filter
  • speech recognition
  • Computer vision (CV)
  • Spam/fraud detection
  • Preventive maintenance
  • Malware Threat Detection
  • Business Process Automation (BPA)

Another way to use ML is to operate digital navigation systems. For example, Apple and Google Maps applications on a smartphone use ML to scan traffic, organize user-reported incidents such as accidents or construction, and find the optimal route for the driver to travel. Machine learning has become so ubiquitous that it plays a role in defining a user’s social media feeds.

AI vs. ML: 3 Key Similarities

Artificial intelligence and machine learning share similar characteristics and are closely related. ML is a subset of Artificial Intelligence, which means it is an advanced technology to achieve this. ML is sometimes described as the latest version of artificial intelligence.

1. Continuously evolving

Both artificial intelligence and machine learning are on their way to becoming some of the most disruptive and transformative technologies to date. Some experts say advances in artificial intelligence and machine learning will have a greater impact on human life than fire or electricity.

The artificial intelligence market size is expected to reach about $1,394.3 billion by 2029, according to a report by Fortune Business Insights. As more companies and consumers find value in AI-powered solutions and products, the market will grow, and more investments will be made in AI. The same goes for ML – research indicates that the market will reach $209.91 billion by 2029.

2. Subtraction MCountless benefits

Among the other important advantages that AI and machine learning have in common is the wide range of advantages they offer businesses and individuals. AI and machine learning solutions help companies achieve operational excellence, improve employee productivity, overcome labor shortages, and get things done that have never been done before.

There are some other benefits that are expected to come from AI and machine learning, including:

  • Enhanced Natural Language Processing (NLP), another area of ​​artificial intelligence
  • Develop Metaverse
  • Enhanced cyber security
  • excessive automation
  • Low-code or no-code technologies
  • The emergence of creativity in machines

Artificial intelligence and machine learning are already impacting businesses of all sizes and types, and broader societal expectations are high. Investing and adopting artificial intelligence and machine learning is expected to boost the economy, lead to fierce competition, create a more technically savvy workforce and inspire innovation in future generations.

3. Take advantage of big data

Without data, artificial intelligence and machine learning would not be what they are today. AI systems rely on large data sets, as well as iterative processing algorithms, to function properly.

ML models only work when equipped with different types of semi-structured and structured data. Harnessing the power of big data lies at the core of both ML and AI more broadly.

As AI and machine learning thrive on data, ensuring its quality is a top priority for many companies. For example, if an ML model receives poor quality information, the output will reflect this.

Consider this scenario: Law enforcement agencies nationwide are using predictive policing ML solutions. However, there have been reports of police forces using biased training data for money laundering purposes, which some say inevitably perpetuates inequality in the criminal justice system.

This is just one example, but it shows how data quality affects the performance of AI and machine learning.

Read also: What is unstructured data in artificial intelligence?

AI vs. ML: 3 Main Differences

Even with the similarities mentioned above, AI and machine learning have differences that suggest they should not be used interchangeably. One way to keep it straight is to remember that all types of ML are AI, but not all types of AI are ML.

1 range

Artificial intelligence is an umbrella term describing a machine that includes a certain level of human intelligence. It is considered a broad concept and is sometimes loosely defined, while ML is a more specific concept with a limited scope.

AI practitioners are developing intelligent systems that can perform as many complex tasks as a human. On the other hand, ML researchers will spend some time teaching machines to accomplish a specific task and deliver accurate outputs.

Because of this fundamental difference, it is fair to say that professionals using artificial intelligence or machine learning may use different elements of data and computer science for their projects.

2. Success vs. Accuracy

Another difference between AI and machine learning solutions is that AI aims to increase the chances of success, while machine learning seeks to enhance accuracy and identify patterns. Success is not as important in ML as it is in AI applications.

It is also understood that AI aims to find the optimal solution for its users. ML is often used to find a solution, optimal or not. This is a slight difference, but it illustrates the idea that ML and AI are not the same.

In ML, there is a concept called the “paradox of accuracy”, where ML models may achieve a high accuracy value, but can give practitioners a false hypothesis because the data set can be very unbalanced.

3. Unique results

Artificial intelligence is a much broader concept than machine learning and can be applied in ways that help the user achieve the desired result. Artificial intelligence also uses logic, mathematics, and inference methods to accomplish its tasks, while machine learning can only learn, adapt, or correct itself when presented with new data. In a sense, ML has more limiting capabilities than AI.

ML models can only reach a predetermined result, but AI is more focused on creating an intelligent system to achieve more than one result.

It can be confusing, and the differences between AI and machine learning are subtle. Assume that ML is trained to forecast future sales. He will only be able to make predictions based on the data used to teach him.

However, a company can invest in artificial intelligence to accomplish various tasks. For example, Google uses AI for several reasons, such as optimizing its search engine, integrating AI into its products and creating equal access to AI for the general public.

Identify the differences between artificial intelligence and machine learning

Much of the progress we’ve seen in recent years in relation to artificial intelligence and machine learning is expected to continue. ML helped drive innovation in artificial intelligence.

Artificial intelligence and machine learning are very complex topics that some people find difficult to understand.

Despite their perplexing nature, artificial intelligence and machine learning are quickly becoming invaluable tools for businesses and consumers, and the latest advances in artificial intelligence and machine learning may change the way we live.

read the following:Is AI important to the organization?

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