AI vs Machine Learning: An In-Depth Analysis
In AI vs machine learning, most people use these terms interchangeably, which is not right. Learn AI vs machine learning vs deep learning vs data science to get the best out of their connection.
Machine learning and artificial intelligence are very close and connected terms. Therefore, when dealing with AI vs machine learning, you are simply trying to understand their relationship. These terms have created a lot of technological buzz in the recent few years. They are important to organizations in uncovering data and streamlining processes to improve business decision-making ability.
Artificial intelligence and machine learning are growing in almost every industry by making processes easy to execute and helping the organization work smart. They are becoming a must-have technology for businesses to remain at the top of the competition.
According to researchers, the number of AI projects is expected to triple over the next 2 years. A few decades ago, AI was a science fiction topic, but now it is becoming commonplace in organizations and science fields. Even though AI and ML are related and people use them to mean the same thing, they are different in other ways. To better understand the importance these technologies bring to organizations, you need to first learn the difference between AI and machine learning.
What are Artificial Intelligence and Machine Learning?
Machine learning and AI are part of science systems that correlate with each other. These are the leading technologies in the trends and are used to develop intelligent systems. Below is an overview of the differences between AI, machine learning, deep learning and data science.
What is Artificial Intelligence?
Artificial intelligence is a term used to describe the computer science field that can make a computer device or system act and mimic human intelligence. This term comprises two words: artificial and intelligence, which means the power to think like a human being.
Therefore, AI can be defined as a technology that can be used to create a human-like system (an intelligent system that has the ability to simulate human intelligence). AI systems don’t require a programmer to pre-program them. Instead, they use algorithms that are compatible to work with their intelligence.
Artificial intelligence usually relies on some machine learning algorithms like deep learning neural networks and reinforcement learning algorithms.
AI can be categorized into 3 types considering the capabilities: weak, general, and strong AI. The current available AI is weak and general.
What is Machine Learning?
Machine learning deals with obtaining techniques and knowledge from given info. Therefore, machine learning is an artificial intelligence subfield that makes it easy for machines to learn from past data sets or experiences without installing a particular program.
Thanks to ML, a computer system will make certain decisions using previous data or make predictions without being programmed. It makes good use of structured and semi-structured information so that the learning model can give accurate predictions or generate correct results from the info given.
ML will only work on self-learning algorithms using historical information. Because of that, it performs well under specific domains. For example, if a machine learning model is for detecting cat pictures, it will only show cat images or results with cat images. However, if a new model is installed to detect cows, it will be unresponsive. ML is used in many areas of life:
Email spam filter
Google search algorithms
online recommended systems
Auto-tagging suggestion on Facebook
Machine learning can be classified into Supervised, Reinforcement, and Unsupervised learning.
What is Deep Learning?
As simple as these terms may look, they carry strong misconceptions in almost every company. We already know what AI and ML are; a program that can reason, adapt, act and sense and a program that improves performance algorithms every time it is exposed to new instruction.
On the other hand, deep learning is a part of ML that uses a comprehensive data source to make multi-layered neural networks learn. Unlike ML, deep learning is based on neural networks (artificial) and is a young AI subset.
Because deep learning algorithms need the information to learn and offer solutions, they can also be categorized as a subset of ML. Machine and deep learning are mostly used as synonyms, but the systems have different capabilities. Artificial neural networks, a multi-layered algorithms structure, have great and unique capabilities to make deep learning superior to ML in solving tasks.
Currently, all intelligence advances are working because of deep learning. Without deep learning, things like mathematics in calculators, personal assistants or chatbots, and google translate for Netflix to suggest movies, won’t exist.
AI vs Machine Learning: The Major Differences
Generally, we can say AI is a broad concept of developing intelligent machines or devices to simulate human behaviors and thinking capabilities. ML is a subset of the application of artificial intelligence that allows machines to learn how to operate in different ways without being explicitly programmed.
Other differences in AI vs machine learning include:
The main goal of AI is to develop smarter computer systems to solve complex problems. At the same time, ML aims at allowing machine systems to learn from specific data to give accurate output.
Deep learning and machine learning are the main subsets of artificial intelligence, but deep learning is the only subfield of ML.
Intelligent systems in AI perform a given task like a human, while machines in ML are taught to perform based on the given instruction to get accurate results.
AI has a wide scope range while ML has limited scope.
AI exists to create intelligent systems to carry out different complex tasks, but ML creates machines to carry out only the task trained to perform.
When you compare AI vs machine learning, AI is all about maximizing the possibility of success while ML deals with patterns and accuracy.
In terms of artificial intelligence and machine learning examples. The main AI applications are intelligent humanoid robots, SIRI, online game playing, chatbots, customer support systems, and expert systems. The main application of ML includes Google search algorithms, auto-tagging suggestions on Facebook, and an online recommender system.
AI deals with unstructured, semi-structured, and structured data, while ML deals only with semi-structured and structured data.
Artificial intelligence includes reasoning, learning, and self-correction, while machine learning involves self-correction and learning when new instruction is added.
AI vs Machine Learning: Capabilities
Many companies across every industry are now discovering benefits and opportunities from AI and machine learning. Below are just several capabilities that are needed in helping companies transform their products and processes.
Recommendation engines help organizations recommend products that customers might be interested in buying through information analysis.
Because of this capability, companies can now predict behavior and trends patterns by analyzing the cause-effect relationship in information.
Video and image processing
Companies, especially in the security sector, can recognize actions, faces, and objects in videos and images. Also, this capability implements functionalities like visual search.
Sentiment analysis is used in computer systems to categorize and identify negative, neutral and positive attitudes expressed in text form.
Neutral or natural language processing and understanding
This capability helps organizations recognize and understand the meaning of spoken or written language.
Speech recognition allows a computer system to point out words in spoken language.
Is Deep Learning Superior to Machine Learning?
Doesn’t require feature extraction
Yes, deep learning doesn’t need feature extraction. Deep learning uses artificial neural networks that don’t need feature extraction. The layer in a deep learning system can learn raw data’s implicit representations on their own.
Therefore, deep learning needs little/no manual effort to optimize processes in feature extraction. This means that feature extraction occurs within the neural network with minimal to no human input.
The use of big data
Deep learning uses a massive amount of information to top machine learning. The big data technology era will offer a wide range of opportunities for new and unique innovations in DL. Deep learning systems or models increase their output accuracy as training instructions increase, while traditional learning models stop enhancing after reaching a saturation level.
Industrial Applications of AI and ML
Many companies are now realizing the importance of AI and ML and have started developing applications to make good use of the relationship between the two fields.
Sales and marketing
AI and machine learning are used for campaign optimization, personalized offers, sentiment analysis, and sales forecasting.
Most manufacturing industries look up to AI and ML to improve the efficiency of operations and predict maintenance.
Many companies use chatbots and cognitive search to gauge customers, provide virtual assistance, and answer questions.
Finance and banking
In financial cases, AI is essential for predicting risks, offering proactive financial advice, and detecting fraud.
It helps organizations protect their own and customers’ interests by detecting unusual activities.
The introduction of AI and ML in this sector has improved the output of applications like predictive analytics and image processing for genomics research and cancer detection.
Retailers use AI and ML to build recommendation engines, enhancing customer experience and inventories optimization with visual search.
Organizations use AI and ML to improve route efficiency and predict traffic in different locations.
Importance of AI vs Machine Learning
The AI vs machine learning interaction offers a great advantage for many companies in nearly every industry. Artificial intelligence and machine learning grow as new possibilities constantly emerge. Below is a list of a few importance most organizations have realized with AI and ML.
Excellent and faster decision-making
Companies in different industries use AI to minimize human error and ML to enhance information integrity. Combining the two can result in a better decision-making model based on better, big data.
Plenty of data input sources
Thanks to machine learning and artificial intelligence, companies can have a wide scope to discover valuable structured and unstructured data sources.
Improve operational efficiency
Companies that use IA and ML can be more efficient by automating operations and processes. Because of that, more time and resources will be saved, and a reduction in the cost of operation hence improving other priorities.