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Deep learning is an advanced type of artificial intelligence that uses neural networks and complex algorithms to process big data and produce detailed and contextualized outputs, simulating the ways in which human brains process and share information.
This type of artificial intelligence is the foundation for a number of emerging technologies, but despite its many advantages, it also brings forth distinct disadvantages that users need to be aware of.
A quick summary: There are both pros and cons to the practice of deep learning. As far as pros go: users can benefit from a machine learning solution that is highly scalable, automated, hands-off, and capable of producing state-of-the-art AI models, such as large language models. However, the cons are also significant: Deep learning is expensive, consumes massive amounts of power, and creates both ethical and security concerns through its lack of transparency.
Table of Contents: Deep Learning Advantages and Disadvantages
What Is Deep Learning?
Deep learning is a type of artificial intelligence that consists of neural networks with multiple layers, algorithmic training that teaches these neural networks to mimic human brain activity, and training datasets that are massive and nuanced enough to address various AI use cases. Deep learning uses large language models.
Because of its complex neural network architecture, deep learning is a mature form of artificial intelligence that can handle higher-level computation tasks, such as natural language processing, fraud detection, autonomous vehicle driving, and image recognition. Deep learning is one of the core engines running at the heart of generative AI technology.
Examples of deep learning models and their neural networks include the following:
- Convolution neural network (CNN).
- Recurrent neural network (RNN).
- Generative adversarial network (GAN).
- Autoencoder.
- Generative pre-trained transformer (GPT).
Also see: Generative AI Companies: Top 12 Leaders
Deep Learning vs. Machine Learning
Deep learning is a specialized type of machine learning. It has more power and can handle large amounts of different types of data, whereas a typical machine learning model operates on more general tasks and a smaller scale.
Deep learning is primarily used for more complex projects that require human-level reasoning, like designing an automated chatbot or generating synthetic data, for example.
Learn more: Machine Learning vs. Deep Learning
Deep Learning vs. Neural Networks
Neural networks constitute a key piece of deep learning model algorithms, creating the human-brain-like neuron pattern that supports deep model training and understanding. A single-layer neural network is what’s used in most traditional AI/ML models, but with deep learning models, multiple neural networks are present. A model is not a deep learning model unless it has at least three neural networks, but many deep learning models have dozens of neural networks.
Also see: Best Artificial Intelligence Software 2023
The Pros of Deep Learning
Capable of Different Learning Styles
Deep learning models are designed to handle various inputs and learn through different methods. Many businesses choose to use deep learning models because they can learn and act on tasks independent of hands-on human intervention and data labeling. Their varied learning capabilities also make them great AI models for scalable automation.
Although there are subsets and nuances to each of these learning types, deep learning models can learn through each of the following methods:
- Supervised learning: Although nearly any machine learning model can handle supervised learning, deep learning models don’t lose this capability when taking on other learning skills; this type of learning usually involves data labeling and training on how exact outputs match up with exact inputs.
- Unsupervised learning: Unlabeled, 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, requiring them to simultaneously give expected outputs and infer outputs based on unstructured or unlabeled inputs.
- Self-supervised learning: Sometimes considered a subset or step of unsupervised learning, self-supervised learning is when the deep learning model creates its own labels and structures in order to better interpret its training dataset and possible outputs.
- Transfer learning: A foundation model can be fine-tuned and learn how to handle entirely new tasks without necessarily receiving specific training on those tasks. While other types of models are capable of basic transfer learning, most cannot handle transfer learning at the scale and complexity that deep learning models can.
- Reinforcement learning: This type of learning happens when a model updates behaviors based on environmental feedback to previously produced outputs. Reinforcement learning in deep learning makes it possible for these models to better handle split-second decision-making in different scenarios, including in video games and autonomous driving.
Deep Learning and Generative AI Development
Generative AI models are the latest and greatest in the world of artificial intelligence, giving businesses and individuals alike the opportunity to generate original content at scale, usually from natural language inputs.
But these models can only produce logical responses to user queries because of the deep learning and neural network mechanisms that lie at their foundation, allowing them to generate reasonable and contextualized responses on a grand scale and about a variety of topics.
More on this topic: Top 9 Generative AI Applications and Tools
Deep Learning Works Well With Unstructured Big Data
Unstructured datasets — especially large unstructured datasets — are difficult for most artificial intelligence models to interpret and apply to their training. That means that, in most cases, images, audio, and other types of unstructured data either need to go through extensive labeling and data preparation to be useful, or do not get used at all in training sets.
With deep learning neural networks, unstructured data can be understood and applied to model training without any additional preparation or restructuring. As deep learning models have continued to mature, a number of these solutions have become multimodal and can now accept both structured written content and unstructured image inputs from users.
Recognizes Complex Data Patterns and Relationships
The neural network design of deep learning models is significant because it gives them the ability to mirror even the most complex forms of human thought and decision-making.
With this design, deep learning models can understand the connections between and the relevance of different data patterns and relationships in their training datasets. This human-like understanding can be used for classification, summarization, quick search and retrieval, contextualized outputs, and more without requiring the model to receive guided training from a human.
High Levels of Scalability and Configurability
Because deep learning models are meant to mimic the human brain and how it operates, these AI models are incredibly adaptable and great multitaskers. This means they can be trained to do more and different types of tasks over time, including complex computations that normal machine learning models can’t do and parallel processing tasks.
Through strategies like transfer learning and fine-tuning, a foundational deep learning model can be continually trained and retrained to take on a variety of business and personal use cases and tasks.
The Cons of Deep Learning
High Energy Consumption and Computation Requirements
Deep learning models require more computing power than traditional machine learning models, which can be incredibly costly and require more hardware and compute resources to operate. These computing power requirements not only limit accessibility but also have severe environmental consequences.
Take generative AI models, for example: Many of these deep learning models have not yet had their carbon footprint tested, but early research about this type of technology suggests that generative AI model emissions are more impactful than many roundtrip airplane fights. While not all deep learning models require the same amount of energy and resources that generative AI models do, they still need more than the average AI tool to perform their complex tasks.
Expensive and Scarce Infrastructure Components
Deep learning models are typically powered with graphics processing units (GPUs), specialized chips, and other infrastructure components that can be quite expensive, especially at the scale that more advanced deep learning models require.
Because of the quantity of hardware these models need to operate, there’s been a GPU shortage for several years, though some experts believe this shortage is coming to an end. Additionally, only a handful of companies make this kind of infrastructure. Without the right quantity and types of infrastructure components, deep learning models cannot run.
Limited Transparency and Interpretability
Data scientists and AI specialists more than likely know what’s in the training data for deep learning models. However, especially for models that learn through unsupervised learning, these experts may not fully understand the outputs that come out of these models or the processes deep learning models follow to get those results.
As a consequence, users of deep learning models have even less transparency and understanding of how these models work and deliver their responses, making it difficult for anyone to do true quality assurance.
Reliant on High-Quality Data and Training Practices
Even though deep learning models can work with data in varying formats, both unstructured and structured, these models are only as good as the data and training they receive.
Training and datasets need to be unbiased, datasets need to be large and varied, and raw data can’t contain errors. Any erroneous training data, regardless of how small the error, could be magnified and made worse as models are fine-tuned and scaled.
Security, Privacy, and Ethical Concerns
Deep learning models have introduced a number of security and ethical concerns into the AI world. They offer limited visibility into their training practices and data sources, which opens up the possibility of personal data and proprietary business data getting into training sets without permission.
Unauthorized users could get access to highly sensitive data, leading to cybersecurity issues and other ethical use concerns.
More on a similar topic: Generative AI Ethics: Concerns and Solutions
Bottom Line: The Potential of Deep Learning
Deep learning is a powerful artificial intelligence tool that requires dedicated resources and raises some significant concerns. However, the pros outweigh the cons at this point, as deep learning gives businesses the technology backbone they need to develop and run breakthrough solutions for everything from new pharmaceuticals to smart city infrastructure.
The best path forward is not to get rid of or limit deep learning’s capabilities but rather to develop policies and best practices for using this technology in a responsible way.
