Machine learning and artificial intelligence (AI), are often interchangeable, but machine learning is one subset of the larger category of AI.
In context, artificial intelligence is the ability of computers to mimic human thought and perform tasks within real-world environments. Machine learning refers specifically to technologies and algorithms that allow systems to recognize patterns, make decisions and improve their abilities through data and experience.
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Software developers and computer programmers enable computers to solve problems and analyze data. They create artificial intelligence systems by using tools like:
Machine learning
Deep learning
neural networks
computer vision
Natural language processing
Below are the key differences between machine learning and artificial intelligence, as well as their current applications in large and small organizations.
What is Artificial Intelligence?
Artificial Intelligence refers to the development of computers and robots capable of acting in ways that mimic human behavior and even surpass them. AI-enabled programs are able to analyze and contextualize data in order to give information or trigger actions automatically without human intervention.
Artificial intelligence is a key component of many technologies today, such as smart devices and voice assistants like Siri on Apple devices. Computer vision and natural language processing are two of the most popular techniques used by companies to interpret and automate human language and images. This allows them to make decisions faster and allow customers to converse with chatbots.
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What is Machine Learning?
Machine learning is one pathway to artificial intelligence. This subcategory of AI employs algorithms to automatically recognize patterns and learn insights from data. Then, they apply that learning to make better decisions.
Programmers can test and improve the perception, cognition and action of computer systems by studying and testing machine learning.
Deep learning is a more advanced form of machine learning. Deep learning models make use of large neural networks, which function as a human brain to analyze data. They can learn complex patterns and make predictions without human input.
Machine Learning and AI: How companies use them
Organizations must be able transform data into actionable insights to be successful in almost any industry. Machine learning and Artificial Intelligence give organizations the ability to automate a wide range of manual processes that involve data and decision making.
Leaders can use AI and machine learning to understand and respond to data-driven insights faster by integrating AI and machinelearning into their strategic plans and systems.
Artificial Intelligence in the Manufacturing Industry
In the manufacturing industry, efficiency is crucial to success. Manufacturing leaders can automate their business processes with artificial intelligence by using data analytics and machine-learning to create applications like the following:
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Machine learning, analytics and the internet of things (IoT) can help identify equipment problems before they occur.
An AI application is used to monitor a machine in a factory and determine when maintenance should be performed. This prevents it from failing mid-shift.
Machine learning is used to optimize HVAC energy savings and comfort levels by studying HVAC energy consumption patterns.
AI and Machine Learning in Banking
Banking industry data security and privacy are particularly important. Financial service leaders can use AI and machine learning to increase efficiency while protecting customer data.
Machine learning is used to detect and prevent cyber-attacks and fraud.
Integration of biometrics with computer vision to quickly authenticate users and process documents
To automate basic customer service functions, use smart technologies like chatbots or voice assistants
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AI Applications in Health Care
Health care relies increasingly on informatics to deliver accurate and efficient services. AI tools can improve patient outcomes, save time and help providers avoid burnout.
Machine learning allows for the analysis of electronic health records to give clinical decision support and automated insights.
Integration of an AI system to predict the outcome of hospital visits in order to reduce readmissions and increase the length of time that patients stay in hospitals
Recording provider-patient interactions during exams and telehealth appointments using natural language understanding.