Artificial Intelligence, Machine Learning, Deep Learning… are terms that we have become accustomed to hearing in recent years. We often take them as synonyms, or for each other, but in reality they present some differences. But talking about Deep Learning today is really talking about a type of advanced artificial intelligence, which uses complex algorithms to process Big Data, and produces contextualized results, simulating the way the human brain processes and shares information.
If we want to be even more specific, we could say that deep learning is artificial intelligence based on several layers of neural networks, with algorithmic training that “teaches” these networks to mimic human brain activity. To achieve this, it uses a data set that is massive enough to address one or more use cases. Thanks to this architecture, this type of AI can handle high-level computing tasks, such as natural language processing (NLP), fraud detection, autonomous vehicle driving or image recognition. And yes, as you can imagine, it is also the foundation on which the new Generative AI models are built.
How is it different from machine learning? Actually, theoretically not too much. Deep learning is actually a specialized type of machine learning: it offers more power and can handle large amounts of different types of data, whereas a typical machine learning model operates on more general tasks and on a much smaller scale. Deep learning is mainly used for more complex projects, such as the design of an automated chatbot or the generation of synthetic data, for example.
Different ways of learning
Deep learning models are designed to be able to learn in different ways. Although there are subsets and nuances in each of the types of learning that we are going to see below, these “teaching methods” are the most common.
- supervised learning: Although almost any machine learning model moves in this terrain, deep learning models do not lose this capacity when incorporating new skills. This type of learning typically involves data labeling and training on how the results match certain inputs.
- Unsupervised learning: unlabeled and unstructured training data is used and requires the deep learning model to find patterns and possible answers in the training data on its own. This type of training does not require human intervention and is unique to deep learning models and other models based on more complex AI algorithms.
- Semi-supervised learning: deep learning models receive both unlabeled and labeled data in their training set, forcing them to simultaneously give the expected results and infer the results based on unstructured or unlabeled input.
- Self-supervised learning: Self-supervised learning is when the deep learning model itself creates its own labels and structures to better interpret its training data set and possible results.
- Transfer learning: a foundational AI model can be optimized to learn how to handle new tasks, without having received specific training on how to do it.
- Reinforcement Learning: This type of learning occurs when a model updates its behavior based on the feedback obtained from previous results. This method makes it easier for models to improve their decision making, especially in areas such as autonomous driving.
Main advantages of deep learning
Deep learning has a number of notable advantages due to its ability to imitate human behavior and produce amazing results. One of the areas where it shines is in creating the foundation for Generative Artificial Intelligence. This approach allows users and businesses to generate original content on an impressive scale, especially when it comes to natural language.
Another crucial advantage is that deep learning can understand and work with unstructured information effectively. In the realm of artificial intelligence, unstructured data sets such as images and audio have historically been challenging for many models to interpret. However, neural networks in deep learning have the ability to understand and process this type of data without requiring extensive labeling or preparation. This capability greatly simplifies the incorporation of unstructured data into the model training process, which speeds the development of AI solutions.
A prominent feature of deep learning models is their ability to Recognize patterns and complex relationships in data. In this sense, the architecture of neural networks in these models allows them to reflect even the most complex forms of human thought, such as decision making. This ability manifests itself in your ability to understand the connections (and their relevance) between different data patterns, as well as the relationships present in your training data sets.
On the other hand, by modeling the functioning of the human brain, these models are highly adaptable and able to tackle multiple tasks simultaneously. Using strategies such as transfer learning, a deep learning model can leverage knowledge gained from one task to apply it to another. This allows models to be continuously trained and retrained, enabling them to effectively take on a wide variety of cases.
and its drawbacks
Despite its great advantages, the use of these models is not free from presenting some drawbacks that companies have to assume, such as their high energy consumption or costs that can be significant.
Deep learning models require more power than traditional machine learning models, which in some cases can make training these models incredibly expensive and require more hardware and computing resources than a company can afford. Not to mention its environmental impact and energy consumption. Some studies, in fact, suggest that generative AI models already account for a higher carbon dioxide emission than many airlines.
On the other hand, in addition to the cost of training these models, it is estimated that the use of some of their key components, such as GPU units, has had (and continues to have) a direct impact on the shortage of components that has affected both the technology industry as well as other sectors, in recent years.
More worrisome than the above, however, is that although theoretically AI specialists and data scientists are the ones who should really understand how neural networks work, in many learning models (especially unsupervised), they do not quite understand why. complete how the results are produced; or how are the processes that deep learning models follow to reach many of their conclusions. All this can lead to a lack of transparency, on a model that we must not forget, it depends fundamentally on the data with which it has been trained.
And this is where many experts see the main problem. In an ideal world, data sets they should be fair, large, varied, and without errors. But this, as we know, is often not the case. Any error or bias, however small, becomes more serious as the model is optimized and scaled.
In short, although deep learning is a powerful tool for AI development, it requires a huge amount of dedicated resources and raises some concerns that need to be addressed. Despite this, no one denies that at this time, the pros outweigh the cons and that what we have seen so far is only the beginning of the revolution that is yet to come.
