The Latest Deep Learning Industry Statistics Explained
The deep learning market was valued at USD 3.02 Billion in 2020 and is projected to reach USD 26.64 Billion by 2028.
The statistic indicates that the deep learning market, referring to the application of artificial intelligence algorithms to create sophisticated patterns and abstractions, was worth $3.02 billion in 2020 and is forecasted to grow significantly to reach $26.64 billion by 2028. This represents a substantial compound annual growth rate over the forecast period. The market’s growth is likely driven by increasing demand for deep learning applications in various industries such as healthcare, finance, automotive, and others, as well as advancements in technology and computing power that enable more complex and efficient deep learning models. The significant projected growth suggests a strong market potential and interest in leveraging deep learning technologies for various innovative applications in the coming years.
The deep learning market is growing at a compound annual growth rate (CAGR) of 39.2% from 2021 to 2028.
The statistic stating that the deep learning market is growing at a compound annual growth rate (CAGR) of 39.2% from 2021 to 2028 indicates that the market is experiencing significant and sustained expansion over the specified time period. A CAGR of 39.2% implies that the market is expected to more than double in size every two years. This rapid rate of growth suggests a high level of demand for deep learning technology and applications, likely driven by advancements in artificial intelligence and machine learning. Such growth may present opportunities for businesses operating within the deep learning sector, as well as for investors looking to capitalize on the market’s expansion.
North America dominates the global deep learning market, followed by Europe.
The statistic ‘North America dominates the global deep learning market, followed by Europe’ suggests that North America, which includes countries such as the United States and Canada, holds the largest share of the market for deep learning technology globally. This dominance indicates that a significant portion of deep learning research, development, and application activities are concentrated in North American countries. Following North America, the continent of Europe holds the second-largest share of the global deep learning market. This positioning indicates that European countries are also significant players in the adoption and advancement of deep learning technologies, albeit to a lesser extent than North America. The statistic highlights the geographical distribution of influence and activity in the growing field of deep learning within the global market.
Hardware holds the largest share of the deep learning market.
The statistic that hardware holds the largest share of the deep learning market indicates that the expenditure on hardware components used in deep learning applications surpasses spending on other elements such as software or services within the industry. This suggests a strong demand for specialized hardware solutions like GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) that are essential for accelerating the training and inference processes in deep learning models. The prominence of hardware in the deep learning market underscores the critical role that efficient and powerful hardware plays in enabling organizations and research institutions to effectively implement and scale their deep learning initiatives.
The finance industry is predicted to have the fastest growth in the application of deep learning through 2028.
The statistic suggests that the finance industry is forecasted to experience the most rapid growth in utilizing deep learning technology up until the year 2028. Deep learning, a subset of artificial intelligence, involves the use of neural networks to model and process complex data. The prediction implies that financial institutions, such as banks and investment firms, are increasingly embracing deep learning approaches to enhance decision-making processes, improve customer service, reduce operational costs, and manage risk more effectively. This trend indicates the growing importance of advanced analytics and artificial intelligence in transforming and improving the efficiency of financial services in the coming years.
Software solutions are predicted to see a CAGR of 42% through 2027 in the deep learning market.
This statistic refers to the projected Compound Annual Growth Rate (CAGR) of software solutions within the deep learning market, estimating a growth rate of 42% from the present year through 2027. A CAGR of 42% indicates a strong upward trend in the adoption and implementation of deep learning software solutions over the specified period. This growth is expected to outpace the overall market growth rate, suggesting a significant demand for deep learning technology among businesses and industries looking to leverage artificial intelligence for data processing and decision-making purposes. The high CAGR forecast highlights the immense growth potential and market opportunity for software providers operating in the deep learning sector.
By 2025, 80% of enterprises will shift from piloting to operationalising AI, driving a 5X increase in streaming data and analytics infrastructures.
This statistic predicts that by 2025, the majority (80%) of enterprises will move from merely testing or piloting artificial intelligence (AI) technologies to fully integrating and implementing them into their day-to-day operations. This transition is anticipated to result in a significant increase – fivefold – in the adoption of streaming data and analytics infrastructures. Essentially, it suggests that the business landscape is evolving rapidly towards a future where AI is not just seen as a novelty or experimental tool, but as a fundamental component driving operational efficiency and strategic decision-making across various industries.
The deep learning software market, which includes applications, platforms, and infrastructure software, will increase from $3 billion in 2017 to $67.2 billion by 2025.
The statistic indicates a significant projected growth in the deep learning software market from $3 billion in 2017 to $67.2 billion by 2025. This growth reflects the increasing adoption of deep learning technology across various industries such as healthcare, finance, automotive, and retail, driven by advancements in artificial intelligence and machine learning. The expansion of the market includes applications, platforms, and infrastructure software, highlighting the broad scope of deep learning technologies that are being developed and deployed. This growth trajectory underscores the growing importance and potential of deep learning in transforming industries and driving innovation in the coming years.
Research and development activities remain the largest sector using deep learning applications.
The statistic “Research and development activities remain the largest sector using deep learning applications” suggests that the field of research and development is the primary industry utilizing deep learning technology. This indicates that organizations involved in R&D activities, such as scientific research, pharmaceuticals, technology development, and innovation, are adopting deep learning techniques to enhance their processes and outcomes. The prevalence of deep learning applications in this sector can be attributed to the technology’s ability to analyze vast amounts of data, enable complex decision-making, and drive advancements in various fields. As a result, R&D activities benefit from the capabilities of deep learning to accelerate research, improve product development, and make informed decisions leading to innovation and progress.
Healthcare and automotive industries are also major sectors with a high adoption rate of deep learning models for various applications.
The statistic indicates that the healthcare and automotive industries have shown significant interest and investment in utilizing deep learning models for a variety of applications within their respective sectors. Deep learning, a subset of artificial intelligence, involves algorithms that attempt to mimic the way the human brain works by processing data through layers of neural networks. In healthcare, deep learning models are being used for tasks such as disease diagnosis, personalized treatment plans, and medical image analysis. Similarly, in the automotive industry, these models are being applied for autonomous driving, predictive maintenance, and quality control. The high adoption rate of deep learning in these industries suggests a recognition of the potential benefits it offers in terms of efficiency, accuracy, and innovation.
The North American region is expected to occupy more than half of the deep learning market share by 2027.
The statistic “The North American region is expected to occupy more than half of the deep learning market share by 2027” indicates that North America is projected to have a dominant presence in the deep learning market by capturing over 50% of the market share by the year 2027. This suggests that companies in North America are likely to invest significantly in deep learning technologies and solutions, positioning the region as a key player in the global deep learning industry. Factors contributing to this trend could include the presence of major technology companies, robust research and development infrastructure, and a favorable regulatory environment for technological innovation in North America. This statistic underscores the importance of North America in driving advancements and growth in the field of deep learning over the coming years.
Medical and drug research constitutes 45% of the AI workload, and genomic research constitutes 29%.
The statistic reveals the distribution of workload within the field of artificial intelligence, indicating that medical and drug research accounts for 45% of the total workload while genomic research comprises 29%. This data suggests a significant focus on applying AI in the healthcare and life sciences sectors, specifically in areas such as drug discovery, disease diagnosis, and genetic analysis. The higher percentage allocated to medical and drug research implies a greater emphasis placed on developing AI solutions for healthcare applications, potentially reflecting the importance and impact of AI in advancing medical and pharmaceutical innovations. Meanwhile, the substantial proportion assigned to genomic research underscores the significance of AI in analyzing genetic data and its potential to drive advancements in personalized medicine and precision healthcare.
Automotive companies using deep learning are anticipated to climb from around 7,000 companies in 2020 to potentially more than 12,000 companies by 2025.
The statistic suggests that the adoption of deep learning technologies within the automotive industry is expected to significantly increase over the next few years, with the number of companies utilizing this technology projected to grow from an estimated 7,000 in 2020 to potentially exceeding 12,000 by 2025. This trend indicates a growing recognition of the value and benefits that deep learning algorithms can bring to the automotive sector, such as enhancing autonomous driving systems, improving vehicle safety, optimizing manufacturing processes, and enabling innovative features and services within vehicles. The anticipated surge in the use of deep learning technology reflects a broader trend towards the digital transformation of the automotive industry, where data-driven approaches are becoming increasingly essential for staying competitive and meeting evolving consumer demands.
AI patents, including deep learning, grew with a CAGR of 35% from 2010 to 2018.
The statistic ‘AI patents, including deep learning, grew with a compound annual growth rate (CAGR) of 35% from 2010 to 2018’ indicates that the number of patents related to artificial intelligence, including deep learning technologies, increased steadily over the period of 2010 to 2018 at a rate of 35% per year on average. This significant growth rate signals a rising interest and investment in AI technologies within the innovation landscape, as evidenced by the increasing number of patents being filed in this field. The high CAGR suggests a rapid expansion of AI-related innovation during this period, showcasing the increasing importance and potential impact of AI technologies across various industries.
By 2022, 30% of AI (including deep learning) will rely on edge computing.
This statistic suggests that by the year 2022, approximately 30% of artificial intelligence systems, including those based on deep learning algorithms, will utilize edge computing technology. Edge computing refers to the practice of processing data closer to the source or device generating it, rather than relying solely on centralized cloud servers. This trend indicates a shift towards more efficient and faster processing of AI tasks by distributing computational workloads to edge devices like smartphones, IoT devices, and edge servers. By leveraging edge computing, AI systems can reduce latency issues, enhance privacy and security, and improve overall performance, making them more practical and responsive in real-world applications.
Most companies (90%) in 2020 aimed to maintain or increase their AI (including deep learning) investments in response to COVID-19.
The statistic indicates that a significant majority of companies, 90% specifically, in 2020 chose to either maintain or increase their investments in artificial intelligence (AI), including deep learning technologies, in response to the challenges posed by the COVID-19 pandemic. This suggests that companies recognized the potential of AI technologies to help them adapt to and navigate through the disruptions caused by the crisis. By prioritizing their AI investments, these companies likely aimed to leverage advanced data analytics, automation, and other AI capabilities to enhance their operational resilience, efficiency, and innovation in the face of unprecedented uncertainties brought about by the pandemic.
In 2020, deep learning was the major AI technology where 74% of organizations invested.
The statistic indicates that in the year 2020, the majority of organizations (74%) chose to invest in deep learning as their primary artificial intelligence technology. This finding suggests that deep learning, a subset of machine learning that utilizes neural networks to model complex patterns and decision-making processes, has gained significant traction and importance within the business and technological landscape. The high level of investment in deep learning emphasizes its perceived value and potential for driving innovation, efficiency, and competitive advantage in various industries. Organizations are likely leveraging deep learning for tasks such as image and speech recognition, natural language processing, and predictive analytics to enhance decision-making processes and deliver more intelligent and targeted solutions.
Only 22% of enterprises consider themselves successful at monitoring and scaling AI and deep learning
The statistic that only 22% of enterprises consider themselves successful at monitoring and scaling AI and deep learning suggests that a significant majority of businesses are facing challenges in effectively implementing and expanding their artificial intelligence technologies. This could indicate difficulties in overseeing and adjusting AI systems to ensure optimal performance, as well as struggles in expanding AI capabilities across different areas of the business. The low success rate in monitoring and scaling AI and deep learning may point towards a lack of expertise, resources, or strategic planning in deploying AI technologies effectively, which could potentially hinder these enterprises from fully leveraging the benefits of AI for competitive advantage and business growth.
By 2025, 75% of commercial enterprise applications will use AI (including deep learning).
The statistic ‘By 2025, 75% of commercial enterprise applications will use AI (including deep learning)’ signifies a rapid and widespread integration of artificial intelligence technologies into various business applications. This projection suggests that AI, including deep learning techniques, will become a pervasive tool in industries, reshaping how enterprises operate and make decisions. The increased adoption of AI is likely driven by its proven ability to enhance efficiency, decision-making processes, and overall performance in diverse business functions such as customer service, marketing, operations, and finance. Organizations are expected to leverage AI-based solutions to gain a competitive edge, improve productivity, and deliver more personalized and relevant experiences to customers.
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