What Is Deep Learning?
Deep learning can be defined as a subset of machine learning algorithms that progressively uses multiple layers to extract more and more features from the raw input.
It works by using artificial neural networks (ANNs), which comprise layers of interconnected nodes. Each node performs a simple calculation on the data it receives, and the results serve as input for the next layer. The more layers, the more complex the network can be and the more complex the patterns it can learn.
How Is Deep Learning Achieved?
Like many AI-related algorithms, it involves using ANNs to serve as the computational muscle. The ANN is a network of machines (nodes) structured in a way that mimics the computational process of a human brain. These networks have dozens of layers, which allow deep learning algorithms to learn deeper and more complex relationships from the data it has been trained on.
An ANN is trained on vast amounts of data to serve a particular use case or interconnected use cases. For instance, if an ANN is to be used for image recognition, it would be fed reams of image data until it ‘learns’ to recognise human-relevant patterns. After an ANN achieves this, it is then fine-tuned and optimised for better performance.
Are Deep Learning, LLMs & Artificial Neural Networks Interconnected?
For any AI-based system that we use today – ChatGPT, for instance – there are three key components. At the base layer would be an ANN, providing all the necessary computational power required.
It is the architecture of the whole system, as it is the overarching approach that an ANN takes to learn complex relationships between data. It can also be pictured as a blueprint of sorts for the whole AI system.
Completing the system is a large language model (LLM), a specialised deep learning algorithm that is specifically designed for language processing. It leverages the power of ANNs and deep learning algorithms to understand and respond to human language.
What Is The Relationship Between Deep Learning and Machine Learning?
Deep learning and machine learning (ML) have a parent-child relationship in the sense that the former is a subset of the latter.
Machine learning algorithms are simpler and broader in scope than deep learning algorithms, which tend to be highly specialised and specific in their scope and applications.
Machine learning algorithms require structured data sets and need frequent human intervention during training. On the other hand, deep learning algorithms, which are computationally complex, can work on unstructured data and require little to no human intervention during the training phase.
What Are The Use Cases of It?
It is the architectural approach behind many of the AI-based use cases that we see today. The following are some of the most popular deep learning applications:
Image Recognition: Deep learning algorithms can be trained to identify objects in images, such as faces, cars and animals. This enables applications like facial recognition and self-driving cars.
- Natural Language Processing: It can be used to understand and generate human language. This is used in applications like chatbots, machine translation and sentiment analysis.
- Speech Recognition: It can be used to convert spoken language into text, enabling use cases in dictation software, virtual assistants and more.
- Recommendation Systems: It can be used to recommend products or services to users based on their past behaviour. Online shopping sites, search engines and streaming services use recommendation engines based on deep learning to make personalised recommendations.
What Are The Challenges With Current Deep Learning Algorithms?
Deep learning algorithms currently face several challenges related to data dependency, computational cost, generalisability, among others.
Deep learning models require vast amounts of high-quality, labelled data to effectively train. Gathering and annotating such data can be expensive, time-consuming and impractical in certain domains. This data dependency limits the applicability of deep learning to problems where abundant data is readily available.
More importantly, deep learning models often struggle to generalise their performance to unseen data or situations outside their training set. This leads to unexpected errors and unreliable outcomes when deployed in real-world scenarios. The lack of generalisability can cause significant issues in applications like autonomous vehicles, where unexpected errors can have serious consequences.
Further, the internal workings of deep learning models can be opaque, making it difficult to understand why they make certain decisions. This lack of explainability raises concerns about bias, fairness and accountability in real-world applications.
What Are The Concerns About Deep Learning?
While it offers immense potential in various fields, there are legitimate concerns about its development and usage. One of the concerns is that deep learning algorithms can acquire biases in the training phase.
It often relies on vast amounts of personal data, raising concerns about privacy breaches and unauthorised use of sensitive information. Further, adversarial attacks can exploit vulnerabilities in deep learning models to manipulate their outputs for malicious purposes, posing security risks in critical applications.
Concerningly, these algorithms have also been weaponised in the form of autonomous weapons, surveillance systems, and misinformation and disinformation.
