Artificial intelligence (AI) and machine learning (ML machine learning) are two popular and frequently used terms nowadays for the development of intelligent systems. Although these are two related technologies, people often use one synonymously with the other, but they are not the same thing.
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Neuroscience, artificial intelligence and data that are not in Big Data
Na 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 on their own reasoning.
Machine learning, on the other hand, is a subfield of artificial intelligence that allows robots to learn from data or previous 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.
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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 the need for pre-programming.
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.
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 programs learning system.
Nowadays, most of the services of email uses 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 large amounts of data and use what they’ve learned to make more accurate future decisions.
Algorithm-based systems are becoming responsible by new and increasingly comprehensive technological approaches. Medical breakthroughs in cancer research and even cutting-edge studies on climate change mix machine learning with other artificial intelligences to build more efficient models and equipment.
Both artificial intelligence how much machine learning went 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 that these two technologies become increasingly 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.
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