With the advancement of AI – from advances in the field of unmanned vehicles, to the ownership of games like Poker and Go, to the automation of customer interaction services – it is ready to revolutionize businesses. But the terms AI, machine learning, and deep learning are often used randomly and often cause confusion, while each type of technology has major differences. Here is a guide on the differences between these three tools to help you master the information.

  1. AI

AI is the widest way to think about advanced computer intelligence, this technology is described as follows: “Every aspect of learning or any other feature of intelligence can actually be modeled. Describe accurately that it is possible to make a machine to simulate it.”. AI can simulate anything from a chess program to a speech recognition system like Amazon’s Alexa. This technology can be categorized into three categories: narrow artificial intelligence, artificial intelligence (AGI), and super intelligent artificial intelligence.

Narrow artificial intelligence is the AI who is skilled in a specific task, such as Google’s AlplaGo, which defeated world champion Lee Sedol in Go. This makes a difference to artificial general intelligence (AGI), where AI is simulated at close proximity to humans, and can perform a variety of tasks.

Intelligent artificial intelligence takes things one step further, this is the “intelligence which is wiser than the best human brain in all realities, including science creation, common wisdom and social skills “. In other words, it is when the machinery was out of human control.

  1. Machine Learning

Machine Learning is a sub-field of AI. The core principle of Machine Learning is the data acquisition and self-learning. Machine learning is a data analysis method that will automate the analytical modeling. Using iterative algorithms to learn from data, machine learning allows computers to find valuable hidden information that is not explicitly programmed to locate. The repetitive aspect of machine learning is important. Because when these models are exposed to new data they can adapt independently. They learn from previous calculations to make decisions as well as repeatable and reliable results

It is currently the most promising AI tool for business. Machine Learning systems can quickly apply knowledge and training from large datasets to perform facial recognition, voice recognition, object recognition, translation and other tasks excellently. Unlike manually coding a software program with specific instructions for completing a task, Machine Learning allows a self-learning system to recognize forms and make predictions correctly. Alpha Go is a perfect example of Machine Learning, as it receives and learns a great deal of data from the play and calculation of experts to beat world champion Lee Sedol. At present, large corporations such as IBM, Google, Amazon, Microsoft … provide Machine Learning platforms for enterprises to integrate into and integrate into business strategies.

  1. Deep learning

Deep learning is a specialized field of Machine Leaning. It uses a number of Machine Learning techniques to solve practical problems by exploiting artificial neural networks (based on hardware and software devices that are interconnected in some way) and simulates the making of human decisions. Deep Learning is relatively expensive, and requires large datasets for self-training, because there are a large number of parameters that need to be learned by the algorithm, which can initially generate a lot of false-positive data. For example, a deep learning algorithm can be instructed to “learn” about how a cat looks. It will have a huge set of images so that it understands very small details that distinguish a cat from a leopard, a black leopard, or a fox. Continuing with the Alpha Go example, Google explained that the system was using deep learning in a way that combined the Monte Carlo search with artificial neural networks, which was trained through supervised learning by the experts and by enhancing learning from the game itself.

Deep Learning has extensive applications in every area of your life, such as text based search, fraud detection, spam detection, handwriting recognition, image search, voice recognition, street view detection and translation, which are all tasks that can be done through deep learning, replacing many systems based on manual principles. However, deep learning is also very biased if the data set does not have the necessary parameters.

Tong Minh Duc – FPT Telecom

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