Global Deep Learning Market Growth, Forecast: A Complete Research Report

A Market Research that gives a global perspective on the market size, trends, forecast, and growth on Deep Learning, the companies using it extensively, and the countries which are market leaders in it.
Source: Austin Distel from Unsplash

Machine Learning refers to the ability of a computer to “learn” in an autonomous manner without the human, translation of a text into other languages, automatic captioning of images, and automatic text generation.


The USA leads the Deep learning market and to determine the growth, we can look at the research conducted by Grand View Research and according to Exhibit 1 in the appendix,  

the market size which was 100 Mn USD in 2014 is estimated to reach 2090 Mn USD in  2025(Forecast) or 2.09 Bn UDS. That is a rise of around 2000%. This data is based on market research which was conducted in 2017. 

The market is divided into Software, Services, and Hardware and the data provided is cumulative of all three components. Currently, the software segment is moving towards software as a service implementing modern technologies like big data deep learning. 

Also, if we see the company wise data as given in Exhibit 3 in the appendix, we have the number of patents done by the leading companies in the given domain. From 2010 to October 2020, we see a 303%  increase in the number of patents done in the field of Artificial Intelligence and Machine Learning. It shows the growing trend of these technologies and how the future is going to be data-oriented.


As can be seen from Exhibit 6 in the Appendix, worldwide revenue from the deep learning chip market is estimated to grow to 20 billion USD in 2027 up from 2.6 billion USD currently. The majority f the revenue is generated in North America. That is an increase of more than 700%. For the growth pattern, we may also refer to exhibit 1 where the revenue growth is forecasted to be around 2 Billion USD in 2025. 


According to the info given in Exhibit 3, As of October 2020, IBM is leading in the ownership of active machine learning and artificial intelligence patent families across the globe. It owns 5,548 patent families. Till 2018 Microsoft led this category but now it ranks third with 5330 active patents.  Samsung is a close second with 5,500 patent families.  

We can also refer to the data given in Exhibit 7 to analyze the Machine Learning adoption rate of various organizations. The stats compare the current versus the future which is the anticipated machine learning maturity level in organizations. By the survey, 21% of the respondents are in the evaluation stage of the use cases while 17% have developed models but are still working toward production. 

Some insights can also be derived from Exhibit 4 about the adoption of these technologies functionalities where, Research and development’s adoption of data science and machine learning technologies is the fastest among enterprise departments, as over 70% of respondents in the field of research and development reported of deploying data science and machine learning which was followed by executives in the management with 40% adoption rate in 2019.

Popular ML Features and Their Importance

By the info given in Exhibit 5, Support for a good range of regression models is that e most vital feature organizations need in data science and machine learning technologies as of  2019, with about 66 percent of respondents reporting this feature to be critical or important.  Hierarchical clustering and textbook statistical functions are on top of the list. 

The response is divided into a scale of 1 to 5 with the following key: 

1 Unimportant 
2 Somewhat Important 
3 Important 
4 Very Important 
5 Critical 

Market Segmentation

By Industry/Sector


1. Retail Banking: 

Products such as Watson, IBM’s cognitive AI service, are already helping businesses across multiple sectors improve their performance by analyzing unstructured data, using machine learning (ML) to build knowledge into their stems, and creating automated tools that can engage in natural language conversations. Retail banks are participating in this revolution, and a survey f financial executives carried out by Narrative Science and the National  Business Research Institute found that 32% of respondents reported that their organization employed various AI tools like predictive analytics, voice recognition, etc in carrying out their daily operations. 

Many areas of banking have huge potential for applying AI. Especially tasks that are repetitive and hence can be easily implemented through automation.  

According to a recent Accenture survey, most providers believe AI will have a  profound effect on the delivery of banking services: 

• 79% of surveyed banks agree that AI will transform their information  gathering and customer interaction capabilities 

• 78% think that AI will help to create simpler user interfaces, leading to a  more human-like customer experience.  

• 76% believe that most banks will be using AI interfaces as their main customer channel within the next three years. 

2. AI applications are relevant for banking: 

RPA: One of the least glamorous but at the same time most important manifestations of AI is RPA. RPA refers to the use of AAI-enabled software  (often termed robots or virtual workers) to handle high-volume repeatable tasks that previously could only be carried out by humans. RPA is particularly suited to rules-based tasks, such as the entry, validation, and manipulation of data, and the creation, uploading, and exporting of data files.

In banking, this can be applied to back-office processes such as account reconciliation, report generation, mortgage approval, notification of delinquent loans, and audit support. It is less well suited to tasks or processes that require judgment or interpretation. The commercial benefits can be significant. According to  Capgemini, one robot can do the work of up to five human employees, while  Accenture reports that RPA can cut operating costs by up to 80% and reduce the time spent completing tasks by 80–90%. 

NLP: NLP refers to the ability of computer software to understand human communication in written or spoken form and respond in kind. This involves using ML algorithms to translate natural language into a form a computer can understand, and vice versa.

To work effectively these algorithms must develop a good understanding of context and linguistic structures to accurately interpret what is being said and make the correct inferences. NLP  can be used in both back-office and customer-facing roles in banking. Behind the scenes, banks have access to huge amounts of data, from multiple internal and external sources, that they can use to improve their performance and gain a competitive edge.

However, research by IBM has found that around  80% of this data is unstructured and comes from sources as disparate as Word documents, emails, text, social media posts, and audio files. Banks can use  NLP applications to, for example, track mentions of their brand on social media and distinguish between positive and negative sentiment, or to gather and understand sector-specific information from the media and other sources to make more informed decisions. Customer-facing operations can also benefit from NLP, typically through the use of chatbots and virtual assistants.

These can be used to respond to frequently asked questions and routine transactional requests, thus freeing up customer service representatives to deal with more complicated inquiries They can also be used to provide targeted assistance to consumers during important tasks,  such as applying for a product online, thus helping reduce abandonment rates.

While some banks are using dedicated chatbots that operate within their digital banking interfaces, others are integrating with popular interfaces such as Siri, Alexa, and Facebook Messenger to allow customers to communicate with them without having to launch their mobile app. 

Image analytics: Image analytics (also known as “computer vision”) refers to the process of deriving information from visual data and converting this into machine-readable form, and involves the use of techniques such as pattern recognition and digital geometry.

The most basic use of this technology concerns the extraction of data from documents. Examples from a banking context including remote deposit capture, where information from a cheque is read via a smartphone camera and used to verify the transfer of the appropriate funds, or the scanning of ID documents to verify identities during onboarding or knowing your customer processes.

More advanced applications include facial recognition (based on pattern recognition technology), which is commonly used for customer identification purposes.  

Automotive industry: The application of AI is as old as 1962, when the first industrial robot was employed at General Motors in New Jersey, America. Japan was one of the first countries in the world to recognize its importance and the potential this nascent technology had.

In  1981, Takeo Kanade created the world’s first robotic arm in which servo motors directly acted as the joints of the arm, making human arm-like movement possible. This called for an era when robotic arms started replacing manual labor in car manufacturing plants on an industrial level.

Since that era, AI and its myriad technologies have grown manifold and resulted in wide-scale use of AI in manufacturing industries. With the advent of technology, cars are becoming smarter with sensors in the front, back, and sides to capture real-time data. This data can then be used to identify anomalous patterns which AI uses to device methods that can avert dangerous situations like accidents. 

Deep Learning and Autonomous Cars: The number of self-driving cars is expected to touch the figure of 10 million by 2020 as cited by a study conducted by Garret/Galland Research.  Annual sales of a self-driving car are also expected to cross more than 30 million by the middle of this century.

Keeping the vital hardware aside i.e. cameras, sensors, ultrasonic devices, the reason for the proliferation of self-driving vehicles is AI and its associated technologies like deep learning. Deep learning technologies trains cars to act in place of humans by learning over a huge number of data sets and experience and act in real-time scenarios without human interventions.

This is very valuable as it is impractical to expect a software developer to write codes for every possible scenario a driver may face. The key to the success of deep learning is the availability of a huge repository of data on which the technology can be trained to be as effective as possible.

One of the innovative ways to tackle this problem is by communication. Cars can communicate with each other and in general train each other based on the experiences of each car without having to face it first-hand. Industry examples are Waymo, a self-driving car by Google which is fed with 2 Mn miles of driving data throughout the world.

A few key examples can be found in Exhibit 8 for company-wise description and application of Deep learning. Also, Refer to Exhibit 9 for the scale of automation in the automobile sector. 

• Health 

One of the most important changes caused by AI is that the shift from mere treatment of diseases (reactionary mindset) to a treatment that focuses on diagnosing illnesses at an early stage or before they occur (preventive mindset). 

1. New Market Creation: AI is additionally creating new markets within the healthcare industry. for instance, Norwegian company Your.MD has developed an AI-powered mobile app that matches a patient’s symptoms to publicly available data collected from various sources and offers them personalized advice regarding their ailments.

This mainly works by filtering out those that don’t need medical care and letting the doctors specialize in those that need the foremost attention. This has resulted in the creation of a replacement healthcare market called ‘pre-primary care’, during which the patient winds up doing more for themselves and acts as a driver for large-scale behavioral change. 

2. Early diagnosis: In line with a 2017 study conducted by the Mayo Clinic, a non-profit practice and research group, original diagnoses within the U.S. are revised by a second medical professional 88% of the time.

A previous study conducted by The National Academies of medication in 2015 revealed that diagnostic errors were to blame for up to 10% of all patient deaths and up to 17% of all hospital complications.

Advancements in AI have resulted in the early detection of diseases through the employment of deep learning to analyze huge amounts of information and recognize patterns, a process especially useful in diagnostics.  Some samples of AI systems designed for early diagnosis are IBM Watson, Google DeepMind,  Pathway Genomics, Lumiata, and therefore the same may be cited in Exhibit 10 of the appendix. 

3. Robotics: Just fifteen years ago, the sphere of healthcare robotics was mostly fantasy.  a corporation called Robodoc, an IBM spinoff was the primary to develop a robotic system for orthopedic surgeries. although the technology worked, the corporate couldn’t achieve commercial success and eventually stop working.

However, recent advancements in robotics and AI have resulted in an exceedingly strong increase in the research and practical use of healthcare robotics. 

4. Drug discovery: As per a study conducted by the Tufts Centre for the Study of Drug  Development (CSDD), the typical cost of developing a drug that gains market approval is  US$2.6 billion, and also the process takes quite 10 years. Even then, but 10% of potential medicines make it to promote, in keeping with Jackie Hunter, CEO of BenevolentBio, the life sciences arm of London’s BenevolentAI.

Aside from being time-consuming and expensive, this process also limits the amount of diseases scientists can concentrate on. Machine learning algorithms can play an unlawfully important role in reducing the time and value by using previously generated data to determine patterns and decipher which experiments must be done. Other algorithms also can be wont to predict the side effects of certain chemical compounds on humans, thereby speeding up the approval process. 

5. Retail: The statistic shown in Exhibit 2 shows the machine learning use cases in the expectation-maximization in the retail industry worldwide as of 2019. As per the survey, 45% of the respondents use Machine  Learning to engage customers with their organization.


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1 Response

  1. Shivam Awasthi says:

    An insightful article. It’s amazing where are we now.


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