Deep learning is at the forefront of a unique technological revolution that has led to remarkable changes in different industries and has pushed the boundaries of artificial intelligence (AI). Deep learning is a subset of machine learning that uses artificial neural networks (ANNs) to enable machines to acquire knowledge from extensive datasets and perform tasks without accurate programming. This technology has become increasingly important because it can deal with complex issues, such as natural language processing (NLP), and independent decision-making.
According to a new report published by Allied Market Research, the global deep learning industry is expected to display a notable CAGR of 37.8% over the projected period.
Increased adoption of cloud-operating technology augments industry growth–
Cloud analytics refers to a combination of technologies, infrastructure, analytical tools, and techniques that enable clients to extract the necessary information from a large volume of data. In addition, cloud-based deep learning analytics allows institutions to reduce operational, infrastructure, and storage costs. Moreover, this cloud-based approach to deep learning analytics provides enhanced security and guarantees critical data, thus creating favorable opportunities for the market. Furthermore, cloud computing enables institutions to analyze large datasets and improve their analytics capabilities, thus driving future demand for deep learning. The rapid deployment of interconnected devices in education institutions and government initiatives aimed at increasing digitization contribute further to the growth of this industry. For example, in July 2020, universities such as the Australian Catholic University (ACU) executed data lakes. ACU used Microsoft Power BI and Azure data platforms to develop a unified data environment, which allowed a thorough review of student development. This integration helps students in need of further support and prevents possible dropouts.
Dynamic applications in the landscape of deep learning-
Virtual assistants:
Cloud-based applications known as Virtual Assistants can comprehend natural language voice instructions and accomplish tasks on the user’s behalf. Amazon Alexa, Siri, Cortana, and Google Assistant are notable examples of virtual assistants. These assistants depend on devices connected to the Internet to release a complete set of functionalities. Virtual assistants enhance the user experience by leveraging past interactions every time a command is given.
Chatbots:
Chatbots can solve customer issues. A chatbot is an artificial intelligence program that engages in online conversations through text or speech synthesis. It can interact and achieve tasks like humans. Chatbots are widely used in customer engagement, social media marketing, and instant messaging with clients. They provide spontaneous responses to user inputs and rely on machine learning and deep learning algorithms to generate different types of responses.
Healthcare:
Deep Learning has been implemented in the Healthcare industry, enabling the utilization of computer-aided diagnosis and computer-aided disease detection with the assistance of Deep Learning techniques. Its application extends to drug discovery, medical research, and the identification of life-threatening diseases like diabetic retinopathy and cancer, expedited through medical imaging advancements.
Robotics:
Deep learning has been extensively used in the production of robots capable of performing tasks similar to those performed by humans. Deep learning-enabled robots rely on up-to-the-minute data to detect obstacles along their path and immediately strategize their route. Its application encompasses the transport of goods in hospitals, warehouses, factories, manufacturing products, and inventory management, among others.
Boston Dynamics Robots respond to individuals if they are pushed, they can empty a dishwasher, regain their balance after falling, and perform various additional duties.
Image captioning:
Image captioning is a well-known technique for generating a textual description of an image. This process involves utilizing computer vision to comprehend the content of the image and employing a language model to convert the understanding into an appropriate sequence of words. To create a coherent sentence, a recurrent neural network like LSTM is utilized to transform the labels. Microsoft has developed a caption bot that allows users to upload an image or provide its URL to obtain a textual description. Caption AI is another application that recommends suitable captions and hashtags for pictures.
Advertising:
Deep learning is utilized in Advertising to enhance a user’s experience, helping in the enhancement of the importance of ads and boosting the advertising campaigns. By implementing Deep Learning, ad networks can decrease costs by halving the cost per acquisition, reducing it from $60 to $30. This technology enables the creation of predictive advertising driven by data, real-time bidding for ads, and the targeting of display advertising.
Natural language processing (NLP):
Deep learning is effective in Natural Language Processing (NLP), which involves enabling robots to understand and interpret human language. It should be noted, however, that human language poses significant challenges for robots to comprehend due to various factors such as alphabet, words, context, accents, handwriting, and more.
Deep learning-based NLP is tackling these challenges by training computers, using techniques like Autoencoders and Distributed Representation, to generate appropriate responses to linguistic inputs.
Therefore, deep learning has become very important in almost all sectors of the business world, leading to a simplification of daily tasks.
Remarkable advancements in the global deep learning industry:
Intel Corporation, Qualcomm Technologies, Inc., IBM Corporation, Amazon Web Services, Inc., Google LLC, and NVIDIA Corporation are some of the leading players in the global deep learning industry and are extensively investing in R&D activities to boost their product lines. In addition, market participants implement various strategies, including new product launches, mergers and acquisitions (M&A), and collaborations with other companies. For instance, in June 2023, GE HealthCare introduced Sonic Deep Learning, a DL-based technology. This innovation aims to greatly speed up magnetic resonance imaging (MRI) image acquisition and has received approval from the FDA. Sonic DL enables the realization of novel imaging approaches, including high-quality cardiac magnetic resonance imaging in a single heartbeat.
On the other hand, in May 2023, Google enhanced the open-source TensorFlow tool to accelerate machine learning progress. With a focus on the evolving TensorFlow environment, the company has implemented several updates and enhancements to open-source AI (ML) innovations. Within the TensorFlow ecosystem, the Keras API suite plays a key role by providing Python-based DL capabilities to the core TensorFlow technology. In addition, Google announced two new Keras tools, namely KerasNLP for natural language processing and KerasCV for computer vision (CV).
Additionally, in September 2022, Altair, a provider of computational science and artificial intelligence, released the acquisition of Rapid Miner, a well-known software company specializing in advanced data analytics and machine learning (ML). Altair intends to strengthen its comprehensive data analytics (DA) portfolio through this acquisition, which offers promising prospects.
Conclusion:
The global deep learning industry is witnessing considerable growth and innovation due to the adoption of cloud-operating technology in various industries. Nevertheless, cumulative expenditure in the manufacturing and healthcare sectors will provide new opportunities for the industry in the coming years.
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