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Artificial Intelligence (AI) vs. Machine Learning vs. Deep Learning

Artificial Intelligence (AI) vs. Machine Learning vs. Deep Learning

Artificial intelligence, Machine Learning, Deep Learning … Technology is advancing by leaps and bounds and it is normal to feel lost if you don’t know it. If until today you thought it was about similar concepts, we are sorry to tell you that you are wrong. At Yeeply, our mission is to shed light on

Artificial intelligence, Machine Learning, Deep Learning … Technology is advancing by leaps and bounds and it is normal to feel lost if you don’t know it.

If until today you thought it was about similar concepts, we are sorry to tell you that you are wrong. At Yeeply, our mission is to shed light on these three technologies, so you can understand what they are and how they differ.

Find out what they are, how they relate, and what apps they have.

What is Artificial Intelligence?

Artificial intelligence (AI) refers to the ability of a machine to imitate the cognitive functions that were previously only associated with humans.

Perceiving, reasoning, learning, or problem -solving are some of the things AI can do. Although this concept still reminds us of the realm of science fiction, this technology is already integrated into our daily lives.

Think about the integration of AI in smart homes, for example. Its presence in home automation allows us to control our devices, secure our homes, and even limit our expenses. The ultimate goal of home automation is to limit the need for human participation.

Smart Homes and Automation

Smart Homes and Automation

Artificial Intelligence Background

The desire to create machines that behave like humans have been present in human history since ancient times.

However, it was not until World War II that modern artificial intelligence can be said to have appeared with Alan Turing. The math expert managed to decipher how Enigma works, with the creation of the Bomb machine.

In 1950, Turing published the article ” Computational Machinery and Intelligence “, in which he laid the foundations of artificial intelligence and proposed an experiment known today as the ” Turing test “, which determined whether a machine could think.

To do this, a judge stands in a room and discusses it with the machine. If the person cannot distinguish whether they are talking to a human or a machine, they are considered intelligent.

Examples of Artificial Intelligence

Robotic Engineers

Robotic Engineers

One of the best-known applications of artificial intelligence is the robot, already very present in the sectors such as industry. That is why the European Union has already made progress in its generalization in other areas, proposing laws on robotics to help solve any problems that may arise in the future.

The EU is overturning Isaac Asimov’s robotics laws and suggesting that robots be equipped with an emergency switch to avoid danger to humans.

In addition, it envisages the creation of a legal status of an electronic person, which will also have rights and obligations, including the payment of social security taxes to subsidize unemployment benefits, among other proposals.

Although it is still early to see how the legality of artificial intelligence will evolve, the truth is that it is a technology that is at hand and which, in a few years, will revolutionize our daily life: service to customers, autonomous vehicles, assistance robots, etc.

What is Machine Learning?

As artificial intelligence attempts to mimic human reasoning, machine learning goes further. It is this branch of artificial intelligence that allows machines to learn on their own, without depending on commands.

In reality, the “machine” is an algorithm that analyzes a volume of data, which would be unmanageable for a human being, in order to identify patterns. In other words, machine learning implies that the machine is trained to automate tasks that are impossible for a human being, and through this learning, it can make predictions.

This video explains how Machine Learning works :

However, human intervention is required for machine learning to validate the decision made by the program. The algorithm is gradually refined thanks to these corrections.

Examples of Machine Learning

Machine learning has existed for some time, even if you’re perhaps not conscious. The facial recognition photos you post on social networks or storage services in the cloud is based on this technology.

Have you logged into streaming content platforms like Netflix or Spotify? If so, you know that each platform recommends based on the content you’ve seen or the music you’ve listened to.

Another app you might not have noticed is predictive text or auto-reply in services like Gmail or LinkedIn messages. As you can see, this technology is already fully installed in our daily life, have you noticed?

What is Deep Learning?

Deep learning could be defined as a type of machine learning, but more complex.

Deep learning is a set of algorithms that mimic the neural networks of the human brain. In this technology, the machine learns by itself but in stages, or in layers. The depth of the model will depend on the number of layers in the model.

When we talk about deep learning neural networks, these can be virtual or physical. The virtual ones would be those created artificially in a computer, while to create physical neural networks, one usually uses silicon.

Examples of Deep Learning

Google Home Voice Assistant

Google Home Voice Assistant

As with other technologies, deep learning is already very present in our daily life. Of intelligent translators, voice assistants like Siri, Google Home, Cortana, or searching for similar images as Google Photos are some of the uses we have already assimilated.

But deep learning also has other very promising applications in fields such as medicine or scientific research in general. An example could be the analysis of medical images, such as X-rays or MRIs, improving the accuracy of the diagnosis. However, the demands are endless.

Did you Understand the Differences?

Although they have some common characteristics, you can see that Artificial Intelligence, Machine Learning, and Deep Learning are different technologies but with great potential.

If you are a business, have you thought about how to integrate them into your work processes or customer service? They can definitely help you make better decisions or make a competitive difference.

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