I have always been fascinated by new technology. I play around with home automation and all the artificial intelligence tools there are without knowing how they are controlled and how they work.
So I had to learn something about the technology behind them. This is a brief explanation of deep learning and what I learned.
To give myself a picture of what artificial intelligence, machine learning, and deep learning are, I think they are a house.
The roof is artificial intelligence and covers the other two.
Machine learning is the walls, a "ruff" part of the house, underneath the roof. Deep learning is the furnishings of the house, all rooms, furniture, decoration, and so on.
Did you get the picture?
So deep learning is a branch of machine learning and artificial intelligence that draws its inspiration from how the brain is supposed to work, specifically from what is known about the neural networks that make up the brain. It includes using a huge data set to train artificial neural networks so they can learn and decide for themselves.
Natural language processing, picture and speech recognition, and even gaming have all benefited from deep learning. Self-driving cars, facial recognition, and language translation are other uses for it.
Deep learning frameworks and tools are also going to keep getting developed and improved. These tools promote the creation and training of deep learning models for academics and developers, and future developments in these tools are likely to facilitate the creation and deployment of deep learning applications.
The capacity of deep learning to learn from and make decisions based on massive volumes of data is one of its primary features. Traditional machine learning algorithms call for manual feature extraction, in which the relevant data features are found and added to the model. This strategy can be time-consuming and a source of error because it requires a deep understanding of both the data and the problem at hand.
Deep learning algorithms, on the other hand, can automatically learn characteristics from raw data, which enables them to learn complicated correlations and produce precise prognoses. When working with high-dimensional data, such as photos or text, where manually extracting characteristics may be challenging or impossible, this is extremely helpful.
Learning hierarchical data representations is another benefit of deep learning. Each feature is handled as an independent variable when using traditional machine learning, where the data is frequently supplied to the model as a flat structure. This can be a weakness when working with complicated data because it might not be able to capture the correlations between the various aspects.
The hierarchical representations of the data work in lower layers learning basic features, and higher layers learn more intricate features depending on the basic features. Due to the hierarchical structure, the model may learn more complicated and abstract correlations in the data.
Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders are a few examples of deep learning models. As they can learn from the input and extract features that are well-suited to the structure of words, images, and videos, CNNs are frequently utilized for image and video analysis. RNNs, on the other hand, can process tasks involving the processing of natural language because they can handle sequential input and recognize the relationships between the words in a sentence. For dimensionality reduction and data denoising, autoencoders are a form of unsupervised learning model that can learn to compress and rebuild data.
Deep learning model training can be computationally demanding because it necessitates numerous forward and backward network runs to update the model's weights and biases. Backpropagation is the term for this procedure, which takes a lot of data and processing power.
The model's lack of interoperability is one of the deep learning difficulties. It may be challenging to grab how the model generates its predictions because it immediately learns about complex relationships with the data. This can be a weakness in some situations where understanding the logic behind a model's predictions is important, such as in medical diagnosis or financial decision-making.
Despite these difficulties, deep learning has demonstrated considerable promise in a range of applications and has the potential to completely transform several sectors. It is a quickly developing area, and we will probably see even more amazing outcomes from deep learning in the future, as more data becomes accessible and processing power rises.
What are the limits of deep learning?
There are several limitations to deep learning and its potential applications. There is always a danger if we fully rely on the technique, and lose the knowledge and ability to control the output. Humans still have to have the knowledge and ability to read and understand the results of a deep learning system.
There is a problem, as a deep learning system can handle an extremely large amount of data we humans never can, so following the algorithm is almost impossible; if there is a small bug in the data, we could miss it. And that could result in, for example, a wrong diagnosis or an economic disaster.
This video Artificial Intelligence and consciousness is showing us, philosophically, some of the dangers and problems we could meet if we have an uncritical belief in artificial intelligence and deep learning, and let the fascination of the technique overwhelm us with no reflection.
In some way, the algorithms controlling artificial intelligence are a reflection of us and what data we put into the algorithm.
For example, Microsoft had to shut down the AI-controlled Twitter account they had because of the racist tendencies it started to have.
The AI Twitter account was constructed to build the responses on comments on tweets, and as we know, trolls and assholes have a higher tendency to comment than others.
We have to reconsider and reevaluate all of the knowledge and science we have.
Understanding science isn't static, it is always progressing knowledge.
As we once believed, the sun, the moon, and the planets were thought to rotate around the Earth. Research by astronomers like Nicolaus Copernicus and Galileo Galilei revealed that the sun, not the Earth, is the center of the solar system.
People in the future, if the Earth still exists, perhaps we will think we must have been a bit stupid in 2022 as knowledge progresses and we learn new things.
So, to continue, the input of data is essential for artificial intelligence.
Wrong data, wrong expression.
Right data, right expression.
But who can sort out right from wrong or can artificial intelligence do it?
This problem is discussed by Joanna Bryson in this video, the importance of regulation and transparency of companies working with AI.
The quantity of data and computer power needed to build deep learning models is, as I mentioned before, one restriction. Although deep learning algorithms may infer complicated associations from data, doing so necessitates a lot of data. In situations when data is hard to come by or scarce, this can be challenging. The training procedure can also be computationally demanding, needing strong hardware, and frequently taking a long time to complete.
The inability to analyze deep learning models is another drawback. It can be challenging to comprehend the model's predictions. This can be a weakness in some situations, like when making financial or medical diagnoses, where understanding the logic behind the model's predictions may be crucial. The models also have the risk of being overused, especially in applications like autonomous driving or medical diagnosis. Although deep learning models can provide remarkable results, they are not always error-free and can make mistakes. It is crucial to thoroughly consider the trustworthiness and restrictions of deep learning models.
Additionally, deep learning models have the potential to be used maliciously. Deep learning algorithms can produce fake media, like deepfake videos that can be used to circulate rumors or sway public opinion. Deep learning models may also be used in cyberattacks, or to get around security. Even though it may affect employment, it's vital to take this into account and deal with any possible negative effects, when the models' ability to automate specific operations, workers who are replaced by these models run the danger of losing their jobs.
The future of deep learning will depend significantly on the availability of data. Larger and more precise, deep learning models can be trained as more data becomes accessible. This might produce even more remarkable outcomes in a range of applications.
Deep learning is expected to continue to be a key force in the field of artificial intelligence, and we can predict seeing it used to solve a wide spectrum of problems and applications.
Last but not least, not every problem is best solved by deep learning algorithms. Depending on the data structure and the issue at hand, conventional machine learning techniques may sometimes exceed deep learning models. Before implementing deep learning, it is crucial to properly consider its applicability to the particular challenge at hand.
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