AI vs. machine learning: Comparisons, Applications, and Advantages

AI vs. Machine Learning: Comparisons, Applications, and Advantages

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While they are sometimes used interchangeably, machine learning (ML) and artificial intelligence (AI) are two separate but related concepts.
In its most basic form, artificial intelligence (AI) is computer software that imitates human thought processes to carry out complicated tasks like analysis, reasoning, and learning. Whereas machine learning, a kind of AI, use algorithms developed on data to create models capable of carrying out such challenging tasks. The majority of AI nowadays is carried out using machine learning, therefore the phrases are frequently used interchangeably. Nevertheless, AI really refers to the overall idea of developing human-like cognition using software and systems, whereas ML merely refers to one technique for doing so.

What is artificial intelligence?

Artificial intelligence (AI) is computer software that imitates cognitive functions of humans in order to carry out complicated activities that were previously only able to be completed by people, such as language translation, data analysis, and decision-making.
In other words, AI is computer system code that is specifically designed to carry out tasks that demand human reasoning. AI-powered devices and systems may learn from their interactions to enhance their performance and efficiency, in contrast to automated ones that only obey a set of instructions and do them without modification.

The word “AI” serves as a catch-all for a number of related but separate subfields. Within the wider topic of artificial intelligence, some of the most typical fields you may run into include:

  • Machine learning (ML): a subset of AI in which algorithms are trained on data sets to become machine learning models capable of performing specific tasks.
  • Deep learning: A subset of ML, in which artificial neural networks (AANs) that mimic the human brain are used to perform more complex reasoning tasks without human intervention.
  • Natural Language Processing (NLP): A subset of computer science, AI, linguistics, and ML focused on creating software capable of interpreting human communication.
  • Robotics: A subset of AI, computer science, and electrical engineering focused on creating robots capable of learning and performing complex tasks in real world environments.

What is machine learning?

Machine learning (ML) is a branch of artificial intelligence that focuses on developing machine learning models that are capable of carrying out complicated tasks like classifying photos, predicting sales, or evaluating large amounts of data.
Currently, the majority of people engage with AI mostly through machine learning. You’ve probably come across machine learning in the following situations:

  • Receiving video recommendations on an online video streaming platform.
  • Troubleshooting a problem online with a chatbot, which directs you to appropriate resources based on your responses.
  • Using virtual assistants who respond to your requests to schedule meetings in your calendar, play a specific song, or call someone.

Real-world examples

Most are, you already use an AI-powered product or service on a daily basis without even being aware of it. AI and machine learning are gradually being woven into the fabric of our daily lives, from banking applications that check for dodgy activities to automatic spam filters that keep your mailbox virus-free and video streaming platforms that recommend shows to you. These are a few examples of how machine learning and artificial intelligence are utilised on a daily basis:

Health care

Patient records, diagnostic tests, and health-enabled gadgets like smartwatches all contribute to the richness of big data generated by the healthcare industry. As a result, improving outcomes in the healthcare sector is one of the most common uses of artificial intelligence and machine learning in society.
Common uses of AI in healthcare include programmes that can create individualised treatment plans, machine learning models that can scan x-rays for malignant growths, and systems that effectively distribute hospital resources.

Business

Business has had a huge influence from AI, which has been applied to automate tasks and generate meaningful insights by analysing large data sets. As a result, an increasing number of businesses are attempting to integrate AI into their operations. For instance, 91.5 percent of the organisations polled in 2020 research by NewVantage Partners confirmed continued investment in AI, which they perceived as seriously affecting the sector.

Supply chains

Global supply chains maintain the flow of products. Nevertheless, as supply chains become more intricate and integrated on a global scale, the likelihood of snags, stops, and breakdowns increases as well. Supply chain managers and analysts are increasingly relying on AI-enhanced digital supply chains that can follow shipments, anticipate delays, and solve issues as they arise to assure prompt delivery.

Artificial Intelligence Machine learning
Artificial intelligence is a technology which enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.
The goal of AI is to make a smart computer system like humans to solve complex problems. The goal of ML is to allow machines to learn from data so that they can give accurate output.
In AI, we make intelligent systems to perform any task like a human. In ML, we teach machines with data to perform a particular task and give an accurate result.
Machine learning and deep learning are the two main subsets of AI. Deep learning is a main subset of machine learning.
AI has a very wide range of scope. Machine learning has a limited scope.
AI is working to create an intelligent system which can perform various complex tasks. Machine learning is working to create machines that can perform only those specific tasks for which they are trained.
AI system is concerned about maximizing the chances of success. Machine learning is mainly concerned about accuracy and patterns.
The main applications of AI are Siri, customer support using catboats, Expert System, Online game playing, intelligent humanoid robot, etc. The main applications of machine learning are Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc.
On the basis of capabilities, AI can be divided into three types, which are, Weak AI, General AI, and Strong AI. Machine learning can also be divided into mainly three types that are Supervised learning, Unsupervised learning, and Reinforcement learning.
It includes learning, reasoning, and self-correction. It includes learning and self-correction when introduced with new data.
AI completely deals with Structured, semi-structured, and unstructured data. Machine learning deals with Structured and semi-structured data.