The field of Artificial Intelligence (AI) is expansive and constantly expanding at a rapid pace. Its primary objective is to enable robots to acquire knowledge and perform tasks similarly to humans, which optimises their ability to engage in different industries. AI comprises a broad spectrum of sub-fields. To accomplish complex duties, networks of computational nodes, which imitate the neurons in the human brain, are employed through a technique known as deep learning.
Examples of deep learning are pervasive in our daily existence. From unlocking our smartphones using facial recognition to language translations and automated recommendations, the application of deep learning is evident. Furthermore, deep learning methods are progressively being utilised for automated text production and summarisation.
This article will elucidate the reasons and approaches through which artificial intelligence in deep learning is transforming the industry.
It’s like performing magic on extensive data.
The proliferation of colossal data sets arising from the user activity on renowned social networking platforms including Facebook, Twitter, and Instagram has become an inevitable outcome. Such data encompasses comments, tweets, videos, and photographs, and constitutes a valuable resource for digital marketers to reach their intended audiences and derive significant insights into how their customers interact with the content. Sponsored social media content provides marketers with a potent mechanism to leverage this data effectively.
As data sets continue to expand, the accuracy of conventional machine learning (ML) models can become increasingly restricted. In recent times, deep learning models have gained increasing traction and popularity for effectively utilising large data sets. This can be attributed to the superior ability of deep learning models in handling extensive quantities of data in contrast to conventional ML models.
The aptitude to adapt and learn independently
Deep learning models hold a significant edge over conventional machine learning models in processing unstructured data. They can receive input data from a diverse range of sources including text, audio, and visual formats, which grants them an increased adaptability over traditional ML models, that are only capable of handling structured data. As such, deep learning models are particularly advantageous in the fields of natural language processing and image recognition, where the provided input data is generally unstructured.
The Convolutional Neural Network (CNN) models were devised to facilitate the rapid and efficient processing of digital images and videos. The computational expense of extracting features from these networks is significantly lower than what would be required if formulated manually by a human designer. Moreover, Deep Learning (DL) models have the ability to create features autonomously that have not been encountered before, rendering them superior to conventional methods of feature extraction.
A comprehensive approach to problem-solving
Typically, Deep Learning models employ a thorough, exhaustive, and holistic strategy to tackle any problem. A prime exemplar of this is YOLO (You Only Look Once), which is an extraordinary tool for object detection. As its name suggests, YOLO can receive an image as input, break it down into segments, and recognise objects and their locations in a single iteration. This is achievable owing to the intricate design of neural networks.
In neural networks, the learning process occurs over successive stages, referred to as “steps,” across all of the hidden layers. For instance, when the network receives an image as input, the initial layers are trained to identify elementary features such as lines and edges. Subsequent layers are then trained to detect more intricate features, such as shapes, colours, etc. To illustrate this concept, refer to the example of YOLO V5 object detection showcased in the image below.
Economical savings and concurrent processing
The usage of Graphics Processing Units (GPUs) and superior-quality hardware is indispensable for training deep learning models. Although the training process may be protracted, the resulting models can aid in curtailing costs. In the absence of deep learning models, faulty predictions or sizeable margins of error could prove disastrous for large corporations. However, the adoption of deep learning models can significantly mitigate the possibility of calamities. Furthermore, computational times can be drastically reduced by dispatching data across multiple workstations and training deep learning algorithms in parallel using distributed systems, potentially halving the time required.
Versatility and modularisation
Deep learning algorithms are highly scalable, enabling their implementation in businesses of any magnitude. Due to its impressive modularity, deep learning (DL) can be utilised across a diverse range of applications without necessitating extensive modifications. Additionally, several practical concerns can be resolved through simple fine-tuning of pre-trained deep learning models established on transformers such as BERT and GPT 2. To enhance efficiency in model management and training, Google Cloud provides a hosting environment.
Establishing a novel healthcare system
The implementation of deep learning is facilitating slow but significant success in healthcare, as proven by Qure.ai, an AI product developer that focuses on radiology. Their qXR technology can detect any indications of disease or abnormalities in chest x-rays in merely a few minutes. This serves as a verification of the potency of deep learning and its capability to revamp the healthcare industry.
The implementation of deep learning to medical imaging, particularly x-rays, has the potential to revolutionise healthcare by providing accurate diagnoses in remote and underserved regions. qXR is a robust tool that utilises multiple deep learning models for the recognition of infectious illnesses in patient x-rays. This technology can significantly curtail the time needed to deliver high-quality medical assistance to people worldwide, regardless of their access to radiologists or radiology technicians. The integration of qXR with basic smartphone applications could aid in providing widespread access to this technology and make its implementation more convenient.
The image below depicts how a 3D deep learning model can be utilised for the classification of brain MRIs in the domain of medical imaging.
Contribution to Marketing Research
Enterprises have historically employed statistical and classical models to forecast customer lifetime value, purchasing behaviour, and other akin variables. However, in recent years, it has become increasingly apparent that the application of deep learning techniques surmounts the more traditional statistical modelling solutions for such issues.
Thanks to its nonlinear layer structure, neural networks have the capability to accurately forecast shifting consumer behaviours and trends. With the incorporation of relevant variables into its predictive model, neural networks can develop a more holistic comprehension of the customer. To enhance its predictive abilities further, deep learning can also be employed for processing audio inputs.
Utilisation in academic pursuits
While deep learning is predominantly known for facilitating object identification and voice recognition, it is also playing an instrumental role in several research efforts across various disciplines. Through its capacity to process unstructured data and detect intricate connections, this technology has brought forth new opportunities for spectral data, atomistic modelling, and much more. As a result, researchers in fields like materials science, physics, biotechnology, and more can now explore a plethora of prospects.
In the domain of metallurgy and materials research, Artificial Neural Networks (ANNs) have been employed to simulate and anticipate the flow stress of austenitic stainless steel based on several experimentally-derived factors, including temperature and strain rate.
Rise in the employment of self-driving cars
Self-driving cars have been recognised as a possible game-changer for the future, with Deep Learning (DL) playing a significant role in this revolution. DL is being implemented in multiple fields of autonomous vehicle development, including localization, perception, prediction, and decision-making. Neural networks are used to identify lanes through line recognition, determining the appropriate lane for the vehicle to travel. The data required for training these models is gathered using sensors.
Deep learning can be a valuable resource for efficient feature engineering when there is a dearth of proficiency and knowledge. By utilising deep learning models, it is possible to generate potent solutions from scratch without manual feature extraction. However, despite its scalability, deep learning does need access to high-performance computing resources.