Deep Learning and Machine Learning are two of the most significant concepts in the world of Artificial Intelligence. Fundamental to the success of this technology, these pillars have essential differences that we will address below.
Artificial intelligence is one of the most popular topics of the digital era, but it gained a never-before-seen projection with the arrival of ChatGPT in October 2022.
As a result, more and more people began to show interest in Artificial Intelligence, and new technologies began to emerge to compete with the OpenAI chatbot.
But after all, how do these technologies work? It is impossible to answer this question without first addressing deep Learning and machine learning, fundamental pillars to enable AI as we currently know it.
That’s why we prepared this article with everything about these concepts, their differences, and their applications in the world of artificial intelligence.
What Is Machine Learning?
Machine Learning (or machine learning) is the use of algorithms to organize data, identify patterns, and enable computers to perform tasks without prior programming.
In other words, Machine Learning allows artificial intelligence to learn from the information provided to them over time without the need for human intervention.
Therefore, these algorithms (which are the rules or step-by-step steps necessary to solve a problem) train machines to perform tasks autonomously.
Whenever they are exposed to new information, machines use previously learned calculations to generate intelligent responses.
Therefore, Machine Learning allows artificial intelligence to continue learning and shaping itself – all based on the initial programming of each tool.
The basic premise for machine learning to work is the use of Big Data to create new algorithms and enhance the understanding of the new data that continues to feed your system.
The Types Of Machine Learning
There are two main types of Machine Learning: supervised and autonomous. In the case of supervisees, the entire data flow and machine training needs to be done based on human interventions, which insert new information and generate feedback.
Autonomous algorithms work without human supervision: everything is done and evaluated by the system itself. Here, the processing of activities usually relies on the help of Deep Learning, which we will talk about below.
What Is Deep Learning?
Deep Learning (or deep Learning) is a part of Machine Learning that uses advanced technology algorithms to replicate the neural network of the human brain.
The origin of Deep Learning in the world of artificial intelligence arises from the difficulty that these technologies demonstrated in performing simple human actions, but which were difficult to translate into algorithms.
Faced with this challenge, Deep Learning was created by defining a hierarchy of concepts. Each concept is defined based on its relationship with more straightforward concepts, and so on.
Thus, the principle of artificial neural networks was created, which supports connections and data propagation directions in several processing layers, all to emulate the functioning of neurons.
Soon, it was possible to scale machine learning and give birth to deep Learning as we know it today, applied in the leading artificial intelligence tools. In fact, both concepts are present and are true pillars of the world of artificial intelligence.
Without the ability to learn on its own, based on the described logic of the algorithms, no AI resource is capable of sustaining itself in the market.
Deep Learning makes it possible to feed AI tools with countless data generated through text, images, or voice. From there, they process the imputed language and perform very complex tasks without any human help.
The Difference Between Deep Learning And Machine Learning
As we briefly explained, Deep Learning is the part of Machine Learning that allows tools to develop the ability to perform complex tasks autonomously.
Machine learning has its origins in the 1980s, being one of the first ways to put the recently emerged artificial intelligence technology into practice.
It is, by definition, a science that makes computers perform actions that they were not programmed to do simply through the ability to learn on their own from the data received.
Deep Learning is a subcategory of Machine Learning, a more sophisticated algorithm that was developed from neural networks.
Going beyond the first machine learning algorithms, Deep Learning emerged in 2010, being able to use big data to function as if it had a mind of its own.
Examples Of Using Deep Learning And Machine Learning
There are a number of technologies that use deep learning and machine learning algorithms to improve the customer experience and, in some cases, even exist.
This is the case, for example, with virtual assistants such as:
- Google Assistant.
This technology would not be viable without the existence of Deep Learning, which enables tools to recognize human voice input, process this data, and offer appropriate responses to requests.
Another valuable resource that has been enabled by machine learning and enhanced by deep Learning is online translators, such as Google Translate.
It made it possible to improve automatic translations to offer more accurate results, interpreting texts and even images as a whole, not just term by term.
Finally, popular chatbots – whether WhatsApp or tools like ChatGPT and Google Bard – only exist because Deep Learning makes it possible to capture text, voice, and image input and respond accordingly.
Artificial Intelligence Is Already A Reality!
Artificial intelligence is here to stay. With ChatGPT, Bard, LlaMa, and other resources on the horizon, there is a real fight for prominence in this emerging market.
This is why understanding deep Learning and machine learning, fundamental pillars of AI, is an essential task for professionals from the most diverse areas.
As you have learned, deep Learning is an evolution of machine learning, allowing technologies to process data and produce “human” responses to user requests.