||1956 The terminology “Artificial Intelligence” was originally used by John McCarthy, who also hosted the first AI conference.
||The terminology “Machine Learning” was first used in 1952 by IBM computer scientist Arthur Samuel, a pioneer in artificial intelligence and computer games.
||AI stands for Artificial intelligence, where intelligence is defined as the
ability to acquire and apply knowledge.
|ML stands for Machine Learning which is defined as the
acquisition of knowledge or skill
||AI is the broader family consisting of ML and DL as its components.
||Machine Learning is the subset of Artificial Intelligence.
||The aim is to increase the chance of success and not accuracy.
||The aim is to increase accuracy, but it does not care about; the success
||AI is aiming to develop an intelligent system capable of
performing a variety of complex jobs. decision-making
|Machine learning is attempting to construct machines that
can only accomplish the jobs for which they have been trained.
||It works as a computer program that does smart work.
||Here, the tasks systems machine takes data and learns from data.
||The goal is to simulate natural intelligence to solve complex problems.
||The goal is to learn from data on certain tasks to maximize the
performance on that task.
||AI has a very broad variety of applications.
||The scope of machine learning is constrained.
||AI is decision-making.
||ML allows systems to learn new things from data.
||It is developing a system that mimics humans to solve problems.
||It involves creating self-learning algorithms.
||AI will go for finding the optimal solution.
||ML will go for a solution whether it is optimal or not.
||AI leads to intelligence or wisdom.
||ML leads to knowledge.
||AI is a broader family consisting of ML and DL as its components.
||ML is a subset of AI.
Three broad categories of AI are :
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI)
Three broad categories of ML are :
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
||AI can work with structured, semi-structured, and unstructured data.
||ML can work with only structured and semi-structured data.
AI’s key uses include-
- Siri, customer service via chatbots
- Expert Systems
- Machine Translation like Google Translate
- Intelligent humanoid robots such as Sophia,
and so on.
The most common uses of machine learning-
- Facebook’s automatic friend suggestions
- Google’s search algorithms
- Banking fraud analysis
- Stock price forecast
- Online recommender systems, and so on.
||AI refers to the broad field of creating machines that can simulate human intelligence and perform tasks such as understanding natural language, recognizing images and sounds, making decisions, and solving complex problems.
||ML is a subset of AI that involves training algorithms on data to make predictions, decisions, and recommendations.
||AI is a broad concept that includes various methods for creating intelligent machines, including rule-based systems, expert systems, and machine learning algorithms. AI systems can be programmed to follow specific rules, make logical inferences, or learn from data using ML.
||focuses on teaching machines how to learn from data without being explicitly programmed, using algorithms such as neural networks, decision trees, and clustering.
||AI systems can be built using both structured and unstructured data, including text, images, video, and audio. AI algorithms can work with data in a variety of formats, and they can analyze and process data to extract meaningful insights.
||In contrast, ML algorithms require large amounts of structured data to learn and improve their performance. The quality and quantity of the data used to train ML algorithms are critical factors in determining the accuracy and effectiveness of the system.
||AI is a broader concept that encompasses many different applications, including robotics, natural language processing, speech recognition, and autonomous vehicles. AI systems can be used to solve complex problems in various fields, such as healthcare, finance, and transportation.
|| ML, on the other hand, is primarily used for pattern recognition, predictive modeling, and decision making in fields such as marketing, fraud detection, and credit scoring.
||AI systems can be designed to work autonomously or with minimal human intervention, depending on the complexity of the task. AI systems can make decisions and take actions based on the data and rules provided to them.
||In contrast, ML algorithms require human involvement to set up, train, and optimize the system. ML algorithms require the expertise of data scientists, engineers, and other professionals to design and implement the system.