Machine Learning and Artificial Intelligence: Differences and Applications

By 9 September 2022Blog, csx-news-1

Machine learning and artificial intelligence are terms that are used interchangeably, however, both AI (Artificial Intelligence) and Machine Learning differ in various aspects. To understand Ml, we have to first understand that AI is simply the ability of machines to mimic the human cognitive process. Using a pool of data known as Big Data, machines use algorithms to learn and execute actions. While ML is part of AI, it is not equivalent to AI. Machine learning involves the execution of actions based on previous patterns and historical data- thus allowing better predictions and accurate patterns. Machine learning and AI are e two technologies that are the most popular ones utilised to build intelligent systems today. Let’s dive into the definitions of both terms to understand how AI differs from Machine learning. 

What is Artificial Intelligence?

According to Gartner’s definition, to analyze events, assist and automate judgments, and perform actions, artificial intelligence (AI) employs sophisticated analysis and logic-based approaches, including machine learning. AI aims to create intelligent computer systems that can similarly tackle challenging issues as humans. The artificial intelligence system utilizes sophisticated algorithms that can perform with their intelligence rather than having to be pre-programmed. It employs machine learning techniques like deep learning neural networks and the reinforcement learning algorithm. AI is employed in a variety of applications, including Siri, and Google’s AlphaGo.

Types of AI 

 Two main categories of AI can be distinguished based on their capabilities:

  • Weak AI
  • Strong AI

Weak AI employs a system designed to carry out one specific task for example video games such as chess or personal assistants such as Alexa or Siri. The personal assistant is designed in a way to answer specific questions. 

Whereas strong AI is a system that perform tasks that can imitate human cognition. These tasks tend to be more complex and require more elaborate systems. They are programmed to handle complex situations without human intervention.

What is Machine Learning? 

AI is a bigger concept to generate smart machines that can imitate human thinking capability and behaviour patterns, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly. Machine learning-based products like Netflix’s recommendation engine and self-driving cars have been made possible in recent years thanks to technological advancements in storage and processing power. The majority of the time, machine learning algorithms are developed using accelerated solution development frameworks like TensorFlow and PyTorch.

Uses of AI 

Examples of artificially intelligent machines include chess-playing computers and self-driving automobiles. Each of these machines must consider the effects of every decision they make because every action affects the outcome. The goal of chess is to win the game. For self-driving cars to function in a way that avoids collisions, the computer system must calculate all external data and consider it.

Surprisingly, AI is commonly used in the healthcare industry for dosing drugs and doling out treatments to patients. They are used for aiding during surgical procedures. 

AI is also widely used in the financial industry as it can detect suspicious activities in banking and finance e.g. unreliable debit card usage. AI can assist in identifying frauds without any human intervention.

activity in banking and finance such as unusual debit card usage and large account deposits—all of which help a bank’s fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making the supply, demand, and pricing of securities easier to estimate.

Applications of Machine Learning

Here are a few everyday cases of machine learning that you might run into:

Recommendations: By using historical data on consumer behaviour, AI algorithms can help identify data trends that can be applied to create more successful cross-selling tactics. Online businesses employ this strategy to present clients with pertinent product recommendations throughout the checkout process.

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability that converts spoken language into written language using natural language processing (NLP). Speech recognition is a feature that many mobile devices have built into their systems to enable voice search, such as Siri or texting accessibility improvements.

Automated stock trading: Created to optimize stock portfolios, high-frequency trading platforms powered by AI execute hundreds or even millions of trades per day without the need for a human trader.

Key Takeaways

AI and machine learning are potent tools that can help organizations get there more quickly as they try to scale and extend their operations. Additionally, AI and machine learning are becoming standard practices for businesses wanting to maintain a competitive edge in their sectors. 

  •  There are numerous ways to define AI, but one description is that “it is the study of how to train computers so that computers can do things that at present humans can do better” (AI is the study of teaching computers to perform tasks that humans currently perform better than computers). 
  • Machine learning models can create an artificially established point of view by searching for patterns and making inferences based on data. We can now simply ask the proper questions and let the computer perform the calculations rather than writing code that instructs the machine exactly how to think. The accuracy and performance of your machine learning algorithm will increase once it has a thorough understanding of all the data that is currently available.

Your business will boost operational efficiencies, decrease errors, and raise customer happiness if the proper tools are in place. Further advancements in deep learning technology will further increase the potency of these tools in the future.