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Artificial intelligence (AI) vs. machine learning (ML): Key comparisons

INDONESIAKININEWS.COM - Within the last decade, the terms artificial intelligence (AI) and machine learning (ML) have become buzzwords that ...



INDONESIAKININEWS.COM
- Within the last decade, the terms artificial intelligence (AI) and machine learning (ML) have become buzzwords that are often used interchangeably.

While AI and ML are inextricably linked and share similar characteristics, they are not the same thing. Rather, ML is a major subset of AI.

AI and ML technologies are all around us, from the digital voice assistants in our living rooms to the recommendations you see on Netflix. 

Despite AI and ML penetrating several human domains, there’s still much confusion and ambiguity regarding their similarities, differences and primary applications.

Here’s a more in-depth look into artificial intelligence vs. machine learning, the different types, and how the two revolutionary technologies compare to one another.

What is artificial intelligence (AI)? 

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

The field of AI rose to prominence in the 1950s. However, mentions of artificial beings with intelligence can be identified earlier throughout various disciplines like ancient philosophy, Greek mythology and fiction stories.

One notable project in the 20th century, the Turing Test, is often referred to when referencing AI’ history. Alan Turing, also referred to as “the father of AI,” created the test and is best known for creating a code-breaking computer that helped the Allies in World War II understand secret messages being sent by the German military. 

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

Three key capabilities of a computer system powered by AI include intentionality, intelligence and adaptability. AI systems use mathematics and logic to accomplish tasks, often encompassing large amounts of data, that otherwise wouldn’t be practical or possible. 

Common AI applications

Modern AI is used by many technology companies and their customers. Some of the most common AI applications today include:
  • Advanced web search engines (Google)
  • Self-driving cars (Tesla)
  • Personalized recommendations (Netflix, YouTube)
Personal assistants (Amazon Alexa, Siri)
One example of AI that stole the spotlight was in 2011, when IBM’s Watson, an AI-powered supercomputer, participated on the popular TV game show Jeopardy! Watson shook the tech industry to its core after beating two former champions, Ken Jennings and Brad Rutter.

Outside of game show use, many industries have adopted AI applications to improve their operations, from manufacturers deploying robotics to insurance companies improving their assessment of risk.

Types of AI

AI is often divided into two categories: narrow AI and general AI. 
  • Narrow AI: Many modern AI applications are considered narrow AI, built to complete defined, specific tasks. For example, a chatbot on a business’s website is an example of narrow AI. Another example is an automatic translation service, such as Google Translate. Self-driving cars are another application of this. 
  • General AI: General AI differs from narrow AI because it also incorporates machine learning (ML) systems for various purposes. It can learn more quickly than humans and complete intellectual and performance tasks better. 
Regardless of if an AI is categorized as narrow or general, modern AI is still somewhat limited. It cannot communicate exactly like humans, but it can mimic emotions. However, AI cannot truly have or “feel” emotions like a person can.

What is machine learning (ML)?

Machine learning (ML) is considered a subset of AI, whereby a set of algorithms builds 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 forecasts and predictions with a high level of accuracy.

In 1959, Arthur Samuel, a pioneer in AI and computer gaming, defined ML as a field of study that enables computers to continuously learn without being explicitly programmed.

An ML model exposed to new data continuously learns, adapts and develops on its own. Many businesses are investing in ML solutions because they assist them with decision-making, forecasting future trends, learning more about their customers and gaining other valuable insights.

Types of ML

There are three main types of ML: supervised, unsupervised and reinforcement learning. A data scientist or other ML practitioner will use a specific version based on what they want to predict. Here’s what each type of ML entails:
  • Supervised ML: In this type of ML, data scientists will feed an ML model labeled training data. They will also define specific variables they want the algorithm to assess to identify correlations. In supervised learning, the input and output of information are specified.
  • Unsupervised ML: In unsupervised ML, algorithms train on unlabeled data, and the ML will scan through them to identify any meaningful connections. The unlabeled data and ML outputs are predetermined.
  • Reinforcement learning: Reinforcement learning involves data scientists training ML to complete a multistep process with a predefined 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. 
Common ML applications

Major companies like Netflix, Amazon, Facebook, Google and Uber have ML a central part of their business operations. ML can be applied in many ways, including via:
  • Email filtering
  • Speech recognition
  • Computer vision (CV)
  • Spam/fraud detection
  • Predictive maintenance
  • Malware threat detection
Business process automation (BPA)
Another way ML is used is to power digital navigation systems. For example, Apple and Google Maps apps on a smartphone use ML to inspect traffic, organize user-reported incidents like accidents or construction, and find the driver an optimal route for traveling. ML is becoming so ubiquitous that it even plays a role in determining a user’s social media feeds. 


Source: venturebeatt


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