Artificial intelligence (AI) and machine learning (ML) are two popular and frequently used terms today for the development of intelligent systems. While these are two related technologies, people often use one synonymously with the other, but they are not the same thing.
- Do you know what machine learning is? Understand everything about this technology
- Glossary Artificial Intelligence: Understand the main terms used in the field
- Neuroscience, Artificial Intelligence, and Data That’s Not in Big Data
In practice, all machine learning is artificial intelligence, but not all artificial intelligence is machine learning. AI is a field of computer science capable of creating a computer system that can mimic human intelligence, and artificial intelligence systems use algorithms to work their own reasoning.
Machine learning, on the other hand, is a subfield of artificial intelligence that allows robots to learn from previous data or experiences without having to be programmed for a particular task. ML gives the computer the ability to learn by itself, based on training models, to assess its performance and make predictions.
Industrial robots are good examples of AI as they have the ability to monitor their own accuracy and performance. While doing so, they can sense or detect when maintenance is needed to avoid downtime, working in new and unfamiliar environments, without needing to be programmed in advance.
Personal assistants like Google Home, Siri, Alexa and Cortana also show how artificial intelligence can help in the interaction between humans and machines. They allow users to find information, book hotels, add events to calendars, answer questions, schedule meetings or send messages in an almost intuitive way.
In the automotive industry, artificial intelligence is used to assist the driver with information about vehicle problems, fuel consumption, blind spot monitoring and other factors. In projects of completely autonomous cars, sensors, processors and convolutional networks help in decision making, detecting pedestrians and other vehicles to ensure safe driving.
learning from experiences
Most e-commerce sites have machine learning tools that provide recommendations for different products based on users’ search history. For example, if you search for cookbooks at an online store and purchase one of them, when you return to the site, the home page will show a list of books related to the topic previously searched.
The machine learning engine also makes recommendations based on what you liked, added to your cart and other related features to try to learn from human behavior. This training generates immediate responses, based on previous actions that were “memorized” in the database of the computer program learning system.
These days, most email services use machine learning tools to automatically learn and identify spam and phishing attempts. These smart filters go beyond pre-existing rules, generating new checkers based on user behavior.
“ML systems are based on three points: a task to be performed, a set of data about this task and, finally, a statistical measure that tells how well that system performs the proposed work. So, for an algorithm to be classified as ML, it is expected that, by carrying out the tasks on the experiences, it will have its quality metrics optimized”, explains AI specialist Douglas Amorim de Oliveira.
brains of the future
Artificial intelligence is important for the development of new technologies because it forms the basis of computer learning. Through AI, machines have been able to harness vast amounts of data and use what they’ve learned to make more accurate future decisions.
Algorithm-based systems are becoming responsible for new, ever-widening technological approaches. Medical breakthroughs in cancer research and even cutting-edge climate change studies blend machine learning with other artificial intelligences to build more efficient models and equipment.
Both artificial intelligence and machine learning have gone from being an invention of fiction to becoming reality. Robots that could think like humans were an excellent plot for a gripping story. Now, they are part of the daily lives of thousands of people on their smartphones, tablets and virtual assistants.
“The trend is for these two technologies to become more and more popular and permeate our everyday lives. Both ML and AI are used today when we travel helping to optimize routes or help in various areas such as medicine, agribusiness, marketing and financial markets. We are living through a great revolution that is just beginning”, highlights Oliveira.