Deep learning—a subset of AI machine learning—uses multi-layered neural networks to mimic human brain functions, enabling computers to recognise patterns and make decisions. It excels in tasks requiring human-like intelligence, learning directly from vast data sets without manual feature extraction.
For as long as digital systems have existed, the driving objective of computer scientists has been to improve their capabilities to the point of human-like intelligence. Unfortunately, while computers have time and again demonstrated their superiority in terms of memory, speed and accuracy, they have never been able to match the human brain when it comes to interpreting the outside world and using that information to inform logical, evidence-based decisions making.
Deep learning intends to change this. Built on complex networks designed to emulate the decision-making processes of the human brain, this technology is instrumental in enabling machines to recognise patterns, make predictions and perform tasks that were previously outside of the capacity of digital intelligence.
Although a relatively new technology, the history of deep learning can be traced back to the mid-20th century with the introduction of the concept of 'neural networks'. However, it wasn't until very recently that deep learning became a reality in AI, fuelled by exponential increases in computing power and data availability. Deep learning's significance lies in its unparalleled ability to process and learn from unstructured data, making it instrumental in many different areas—image and speech recognition, natural language processing, autonomous vehicles, and much more.
The advent of widely available AI technology has made 'deep learning' a prominent term in business and other circles. That said, there is sometimes confusion about what deep learning actually entails and how it relates to machine learning (ML).
Machine learning encompasses the tools, techniques and processes used in training algorithms to make predictions or decisions based on data. Traditional ML techniques are adept at handling structured data and can perform a wide range of tasks, from spam filtering in emails to recommending products based on user history. These methods often require manual intervention for feature extraction and selection, where experts determine what aspects of the data the model should focus on.
Deep learning is likewise used to teach algorithms to take informed action based on sets of data, making it a subset or type of machine learning. What distinguishes deep learning from ML is that it does not rely on manual intervention when dealing with unstructured data (such as images and text). Its neural network design allows it to automatically identify the relevant features in the data, making it uniquely suited to tackling more complex and nuanced tasks—and without depending heavily on human experts.
Both deep learning and machine learning are types of AI, attempting to replicate human-level intelligence in digital systems.
Deep learning operates through neural networks, which are complex algorithms modelled after the human brain. These networks consist of layers of interconnected nodes or neurons, each designed to perform specific computations on data as it passes through the network. The process begins with the input layer, which receives raw data and initiates the data processing sequence that will traverse through one or more hidden layers before reaching the output layer.
Training a neural network involves adjusting the weights of connections between these neurons to minimise the difference between the predicted output and the actual output, a process known as backpropagation. During training, the network uses a set of data with known outputs to learn by example. The model makes predictions based on the input data, calculates the error in its predictions, and then adjusts the weights in reverse—from the output back toward the input layer—with the purpose of decreasing the error in subsequent predictions.
As the model iterates through numerous cycles of predictions, errors and weight adjustments, it becomes increasingly refined and capable of handling real-world data. Once adequately trained, the deep learning model can then be applied to new, unseen data, making intelligent decisions based on the patterns it has learnt during training.
Deep learning makes it possible for digital systems to do something they have never been capable of before: interpreting the world in a human-like way. Large, complex data sets no longer need to have their relevant features identified or explained for the system to incorporate them into its algorithm. Instead, deep learning empowers machines to extract useful information from resources that were once outside the purview of AI— videos, audio, images, dialogue etc.
Taking intelligent automation further than ever before, deep learning allows computers to undertake generative tasks that once relied on human creativity, such as producing realistic images, composing music, holding intelligent conversations or coding powerful software programs. Additionally, deep learning's high recognition accuracy powers advancements in areas where the margin for error is nearly non-existent: digital assistants, fraud detection, facial recognition, autonomous vehicles and medical devices. These applications underscore the technology's potential to enhance safety and convenience.
To accomplish the monumental tasks associated with recognising and interpreting unstructured data, deep learning relies on several distinct models. These models vary significantly in terms of architecture and function, each designed to excel at different categories of tasks.
Convolutional neural networks are specialised in processing structured grid data. CNNs use convolutional layers to filter inputs for useful information, efficiently reducing the data's complexity while preserving its essential features. This ability makes CNNs highly effective for tasks like image recognition, object detection and even medical image analysis (where recognising patterns and spatial hierarchies play key roles in medical diagnosis).
Recurrent neural networks are designed to handle sequential data. RNNs can maintain information in 'memory' over time, making decisions based on both the current input and information from previous inputs. This characteristic is particularly beneficial for applications like language translation, speech recognition and time series prediction.
Transformer models are rapidly becoming the foundation of modern natural language processing (NLP). Unlike RNNs, transformers process entire sequences of data simultaneously, making them extremely efficient and effective for handling large text datasets. Their architecture allows for an improved understanding of the context within the data, powering tasks such as text summarisation, question answering and detailed text generation.
The multilayer perceptron is the most basic form of neural networks, consisting of a minimal number of layers. These are described as 'feedforward', as the data evaluation process only moves in one direction and lacks any form of feedback loops. Although less complex than some other models, MLPs are sometimes better suited to unpredictable or irregular data sets.
As previously mentioned, deep learning machines are composed of intricate architectures known as neural networks. These networks are structured in layers, each with a specific function, working together to process data, recognise patterns and produce outputs based on inputs. These layers are the most fundamental components of deep learning technologies:
The input layer is the gateway for data entering the neural network. Each neuron in the input layer represents a feature of the input data, preparing it to be processed by subsequent layers. The nature and structure of the input layer vary depending on the type and format of data being analysed.
Hidden layers reside between the input and output layers, where the bulk of computation takes place. These layers are described as 'hidden' because they do not directly interact with the external environment. Instead, they perform transformations on the inputs they receive, extracting and refining features as the data moves deeper through the network. More sophisticated deep learning systems typically feature a high number of complex hidden layers.
The output layer is the final stage of a neural network, producing the results of the computation for external use. The output layer interprets the features extracted by the hidden layers into a comprehensible outcome—the number of possible outputs correlates with the number of nodes in the output layer.
Deep learning's capacity to interpret complex data and learn from it without explicit programming can be applied in many ways, such as:
Computer vision
Computer vision enables machines to interpret and interact with the visual world, revolutionising industries from security to healthcare by automating tasks like image recognition and object detection.Natural language processing
NLP makes it possible for machines to understand, interpret and generate human language. This technology drives chatbots, real-time translation services, document summarisation, business intelligence analysis of written content, and more.Automated driving
Through deep learning, autonomous vehicles can interpret sensor data to make split-second decisions in dynamic environments, creating an extra layer of safety and control.Speech recognition
This technology changes human speech into a format that machines can understand, making user interfaces more intuitive and accessible. This is used to convert spoken dialogue into text format and to create real-time subtitling.Predictive analytics
Leveraging historical data to forecast future trends, deep learning helps optimise data-based decision-making in finance, healthcare, marketing and other sectors.Recommendation engines
Personalising user experiences on digital platforms, deep learning improves content, product recommendations and advertising, significantly impacting e-commerce and entertainment.Reinforcement learning
By learning optimal actions through trial and error, deep learning models excel in complex decision-making tasks, from strategic game playing to robotics.Robotics
Deep learning empowers robots to interact with their environments in more sophisticated and autonomous ways, enhancing their application in manufacturing, exploration and personal assistance.Signal processing
Advanced signal processing with deep learning improves the efficiency and accuracy of extracting information from various data types, advancing communications, healthcare and environmental monitoring.
Considering the use cases mentioned above, the real-world application of deep learning is almost limitless. The following are among the most noteworthy ways that deep learning is making an impact:
In government and law enforcement, deep learning algorithms analyse transactional data to detect patterns indicating fraudulent or criminal activities. Through speech recognition, computer vision and other applications, deep learning helps in sifting through large volumes of data, enhancing the speed and accuracy of investigative analysis and evidence extraction. Beyond law enforcement, governments can use deep learning to better understand citizen wants and needs, and to forecast future outcomes and trends.
The financial sector leverages deep learning for predictive analytics, which drives algorithmic trading, risk assessment for loans, fraud detection and management of credit and investment portfolios. Deep learning's ability to parse through unstructured data to identify patterns helps financial institutions make more informed decisions.
In customer service, AI-driven tools such as chatbots and virtual agents can handle a variety of tasks, from answering frequently asked questions to routing calls to the appropriate department or agent.
Image recognition applications built on deep learning assist medical imaging specialists and radiologists in analysing a greater number of images in less time. This technology enhances early detection of diseases while also optimising diagnostic accuracy.
Deep learning personalises the shopping experience by predicting customer preferences and streamlining inventory management. It assists retailers in anticipating customer needs, optimising stock levels, and finding the best quality products at the lowest prices.
Manufacturing uses deep learning to improve quality control, predictive maintenance and operational efficiency. By identifying defects, monitoring processes and predicting equipment failures, deep learning reduces downtime and maintenance costs in industrial settings.
Deep learning enhances productivity and operational planning in transportation and logistics. Predictive maintenance software anticipates potential failures, while real-time vehicle tracking optimises fleet management.
Utilities companies apply deep learning to improve call centre operations, maintenance scheduling and load forecasting. By analysing diverse data types, deep learning gains the insights it needs to inform sales and load strategy and recommend corrective actions for line and equipment maintenance.
While deep learning offers some significant advancements in a number of sectors, it also presents several challenges that can hinder its application and effectiveness. Organisations looking to leverage deep learning must navigate these obstacles to fully capitalise on its capabilities. When considering deep learning solutions, be aware of the following:
Deep learning models are data-hungry, requiring vast amounts of high-quality, labelled data to train effectively. This demand can be a significant barrier, especially in fields where data is scarce, sensitive or expensive to acquire.
Data augmentation techniques, which enhance the size and diversity of datasets by creating modified versions of existing data, may be the solution. Additionally, transfer learning can be utilised, where a model developed for one task is repurposed on a second, related task, reducing the need for large datasets in the new domain.
The training process for deep learning models can be a slow, laborious process—particularly as models and datasets grow. This can hinder the development and deployment of AI solutions.
Techniques such as distributed computing can be employed to speed up the training process by dividing the workload across multiple processors. More efficient algorithms and hardware accelerators may likewise reduce training times.
The complexity of deep learning models often requires substantial computational resources, which can be prohibitively expensive for smaller organisations or researchers.
To address this, users can employ cloud-based services that offer scalable AI processing capabilities. Users pay for the computational power they need, when they need it. Another approach is to optimise model architecture, making models lighter and faster without compromising on performance.
Deep learning models, especially those with many layers, can act as 'black boxes', where it becomes difficult to understand how they arrived at a certain decision. This lack of transparency can be problematic, especially in industries that require clear audit trails and accountability.
Developing more interpretable models or incorporating techniques like explainable AI (XAI) that help clarify the decision-making process can increase transparency.
Deep learning models sometimes become too finely tuned to the training data, memorising it rather than learning to generalise from it. This is called 'overfitting', and it results in poor performance on new, unseen data.
To prevent overfitting, techniques such as regularisation and dropout can be used to penalise complexity or randomly omit units during training, teaching the model to perform well across different datasets.
Challenges aside, deep learning offers a range of advantages that make it an indispensable tool in modern technology and business sectors. Key benefits of deploying deep learning include:
Deep learning models are renowned for their accuracy, particularly in tasks that involve extremely complex data sets. This is due to the models' ability to learn directly from large amounts of raw data, refining their algorithms and improving their accuracy continuously as they process more information.
Deep learning picks up where traditional machine learning models tend to fall short: working with un-processed, unstructured data. Deep learning models can automatically detect and learn the notable features from complex data without human guidance. This simplifies the data preparation process and enhances the machine's ability to discover insights from data that is otherwise too varied for conventional analysis methods.
Deep learning excels at identifying hidden patterns and relationships within the data that might go unnoticed by human experts. By revealing these patterns, deep learning provides organisations with new insights to drive innovation and improve strategies.
Deep learning can be applied in an unsupervised manner, meaning it can learn and make sense of the data without needing labelled datasets. This is especially valuable when dealing with large amounts of unlabelled data where manual labelling would be impractical or impossible.
Adept at working with volatile data (such as real-time data streams in areas like social media trend analysis or stock market predictions), deep learning is capable of adjusting to changes in patterns of data dynamically. This is crucial for applications where data is constantly evolving.
The cloud is uniquely suited for running deep learning algorithms, offering additional advantages for those who choose to pair these technologies. Deep learning in the cloud is unmatched in terms of scalability and speed, empowering organisations with the increased resources and processing power they need to address even the most diverse data sets.
For businesses that want to put their unstructured data to intelligent use, ServiceNow provides the answer. By embedding deep learning algorithms into IT management solutions, ServiceNow enables organisations to automate and optimise tasks such as incident detection, work routing and knowledge management. Natural language understanding and processing on the Now Platform® ensures that virtual agents can interact seamlessly with users, providing support and solutions that are both timely and contextually relevant. Additionally, ServiceNow's intelligent document processing and predictive analytics streamline complex operations and decision-making, allowing businesses to anticipate needs and align their strategies to achieve their goals.
ServiceNow's comprehensive AI capabilities extend across a range of functions including machine learning, generative AI and data analytics, all integrated within a secure cloud environment. This empowers businesses to implement advanced AI solutions without requiring deep expertise in data science, making it accessible for every user in every sector.
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