Deep learning: The mechanics of magic

What is deep learning? 

Often abbreviated to DL, deep learning is a subset of machine learning and artificial intelligence: a technique of training computers to mimic the way neurons in the human brain process and learn information – it is quite literally an artificial neural network. 

With deep learning, machines are taught to process data in a way that simulates the many layers of our brain, so they can now perform tasks that were once exclusive to human talents. We are already seeing numerous deep learning applications and uses for DL meaning things like image recognition, language translation, and even autonomous driving, are all within the realm of computer capabilities. In essence, deep learning is a powerful tool for unlocking complex insights hidden within vast amounts of data. 

But what’s behind this fascinating technology? At the core of deep learning are artificial neural networks (ANNs), designed to emulate the complex network of neurons in the human brain. These networks begin with an input layer that gathers raw data – think images or text. As data flows through hidden layers, the ANN transforms it, extracting increasingly abstract features that help recognize intricate patterns. The output layer then delivers predictions, bridging the gap between data and actionable insights.

Deep learning features diverse architectures, each tailored for specific tasks. Here are a few examples:

• Convolutional neural networks excel at image processing, skillfully identifying patterns in visual data.
• Recurrent neural networks are adept at handling sequential data, using memory elements to recall past inputs – ideal for language tasks.
• Generative adversarial networks have two components – the generator and discriminator – that compete to produce incredibly realistic data, such as lifelike images from random noise.

This basic deep learning definition just hints at its complexity. To truly grasp the impact of deep learning, we need to look under the hood and see what’s driving this technology. It’s not just the data but the remarkable way these systems learn, adapt and improve with every bit of information they process. So, what’s really happening behind the scenes? Let’s break down how deep learning works and discover the mechanisms powering this paradigm shift.

How does deep learning work?

The natural human brain is made up of millions of interconnected neurons. Modelled on nature, deep learning algorithms use artificial neural networks made of multiple layers of interconnected nodes, called artificial neurons or units. Each layer processes information from the previous layer and passes it on to the next, gradually extracting higher-level features and patterns.

Deep learning models are trained on large amounts of labelled data, known as training sets, to learn how to recognize and classify patterns. This might be a simple challenge like the “Dogs vs Cats” dataset – a standard computer vision dataset in which photos must be classified as either containing a dog or cat. More sophisticated datasets might include wine appreciation or how to spot fake news amid authentic reporting.

During training, the parameters and weights of the neural network are adjusted to minimize the difference between predicted outputs and true outputs. Across multiple iterations, the model becomes increasingly finessed until it is capable of making predictions or performing tasks with a high degree of accuracy and precision.

Assorted programming languages have emerged for implementing deep learning algorithms. Many are looking to use deep learning with Python for its simplicity, readability and extensive “libraries” (pre-written bundles of code which make it easier for developers to solve common problems). Popular libraries like TensorFlow and PyTorch provide a rich set of tools and functionalities for building and training deep learning models.

Deep learning vs machine learning

Deep learning is an approach to creating rich hierarchical representations through the training of neural networks with many hidden layers. It is an evolution of machine learning and one that needs much less help from humans. While basic machine learning models do improve at performing their specified functions as they absorb new data, if they return an inaccurate prediction, an engineer must intervene and make adjustments. Deep learning, though, makes its own adjustments, course-correcting without the need for human assistance.

Deep learning applications

Deep learning is already positively impacting our lives as it is deployed across many different sectors. Its profound influence is not just reshaping technology; it is redefining the very way we experience life.

• Computer vision: From unlocking your phone with just a glance to helping doctors detect diseases early, deep learning is reshaping how machines “see” the world. In healthcare, it is revolutionizing medical image analysis, while in security and retail, it powers facial recognition and product identification.
• Natural language processing: Deep learning powers tools like Google Translate, bridging language gaps across the globe, and fuels voice assistants like Siri and Alexa, transforming your voice commands into actions. Behind the scenes, deep learning models like GPT (Generative Pre-trained Transformer) craft human-like conversations and generate text for everything from chatbots to news articles.
• Healthcare: Deep learning is a silent force behind modern healthcare, identifying tumours in medical scans and predicting patient outcomes with uncanny accuracy. The result? Faster treatments, more personalized care and a future where medicine is tailored to the individual.
• Autonomous vehicles: Self-driving cars use deep learning algorithms to help detect objects and make real-time decisions. Whether it’s identifying a stop sign or navigating a crowded street, deep learning is steering us toward a future of safer roads and driverless transport.
• Financial services: From spotting fraudulent transactions in real time to predicting stock market swings, AI is reshaping the financial world. Banks are using deep learning models to assess credit risk more accurately, helping lenders make faster, smarter decisions.
• Robotics: Robots on factory floors are becoming more autonomous, handling precise tasks like assembly and sorting with near-perfect accuracy.
• E-commerce and retail: Online platforms use deep learning algorithms to predict what you’ll buy next, offer tailored product suggestions, and even let you search with images instead of words. Retailers are also leveraging AI for inventory management, ensuring shelves are stocked and customers are never disappointed.
• Entertainment and media: What’s your next Netflix binge or Spotify playlist? Streaming services analyse your preferences to serve up exactly what you didn’t know you wanted.
• Energy and utilities: In power plants and smart grids, deep learning is driving efficiency. By analysing sensor data, it predicts equipment failures before they happen, cutting downtime and reducing costs. Deep learning models help utilities anticipate demand and optimize power flow, ensuring a more reliable and sustainable energy supply.
• Personalized marketing: Behind every banner ad or email offer, there’s an AI ensuring your message hits home. Advertisers can now craft highly targeted campaigns, analysing your preferences, browsing history and social media activity to deliver ads that resonate.

What are the limitations of deep learning?

Despite its remarkable capabilities and breathtaking potential, DL is not without its limitations and issues. It demands high memory and storage capacity and consumes data at such a rate that obtaining high-quality, diverse and well-labelled datasets can be challenging, especially in domains where data is scarce or expensive. It’s also hungry for training resources and power and can be susceptible to overfitting – performing well on training data but poorly on new, unseen data.

In common with most areas of AI, deep learning also presents ethical concerns around data security and the inherent human biases and inaccuracies which can pervade areas of its learning.

Framework for deep learning

Although the potential benefits of deep learning are enormous, good governance of the technology will be essential to help us realize them. The development of appropriate legal and ethical frameworks will be critical to build trust and mitigate risks.

International Standards will have an important role to play in ensuring the responsible adoption of deep learning within the field of AI. ISO, in collaboration with the International Electrotechnical Commission (IEC), has already published a number of standards through its dedicated group of experts on artificial intelligence, and is building an evolving framework of guidance for deep learning applications around the world.

Businesses and policymakers can already benefit from these standards as part of their assurance of ethical and safe deployment of deep learning models. Complying with International Standards builds trust between clients and contemporaries in all sectors.

• ISO/IEC TR 24029-1:2021Robustness of neural networks – Part 1: Overview
• ISO/IEC 24029-2:2023Robustness of neural networks – Part 2: Methodology for the use of formal methods
• ISO/IEC 42001:2023AI management systems

The future of deep learning

Deep learning is still in its infancy, but it’s expected to grow exponentially and is likely to transform society. The integration of deep learning with other cutting-edge technologies – for example, combined with augmented reality or virtual reality – could revolutionize the way we experience and interact with the world around us. A simple walk down the street could be augmented by instant information about nearby buildings and landmarks. Virtual worlds will become increasingly immersive and realistic.

We can expect to see deep learning applied to fields such as agriculture, energy and manufacturing, where it has the potential to optimize processes, improve efficiency and drive innovation. And, more importantly, to accelerate solutions to planet-wide problems like climate change and food security.

Guided by International Standards and driven by human creativity and ingenuity, the future landscape of deep learning is fascinating, inspiring and almost without limit.