Sunday, July 3, 2022

The Deep Learning Market in 2022

Deep learning is a subset of machine learning technology. It consists of three or more layers of artificial neural networks where the learning can be supervised, semi-supervised, self-supervised, or unsupervised.

Used to improve performance and automation, deep learning simulates the patterns of human thought, reducing the need for constant human supervision and intervention in complex applications and processes.

See below to learn all about the global deep learning market:

Deep learning market

The deep learning market was estimated to be worth $6.85 billion in 2020. Projected to maintain a compound annual growth rate (CAGR) of 39.2% over the forecast period from 2021 to 2030, it’s expected to reach $179.96 billion by the end of it.

Regionally, the deep learning market is segmented as the following:

  • The U.S. market had an estimated value of $1.3 billion in 2020
  • The Chinese market is projected to follow a CAGR of 37.9%, reaching $7.4 billion by 2027
  • Japan and Canada are expected to grow by 35.2% and 33.9% over the forecast period from 2020 to 2027
  • Within Europe, Germany has one of the highest CAGRs at 27.7%
  • The Asia-Pacific market, led by Australia, South Korea, and India, is forecast to reach $5.4 billion by 2027

By industry vertical, security held the largest share in the deep learning market, followed by marketing.

Other notable industry verticals for deep learning include:

  • Health care
  • Manufacturing
  • Automotive
  • Agriculture
  • Retail
  • Human resources

The financial services industry, in particular, is leading the way toward adopting AI strategies. The Economist Intelligence Unit (EIU) says that 86% of financial services firms plan on increasing AI investments by 2025.

Deep learning features

Deep learning neural networks are composed of numerous layers of interconnected nodes. Starting with an input layer, the nodes receive data that’s forwarded to the nodes in the following layer. The output layer is where the final result is produced, both are called visible layers.

The progression of computations through the layers determines how a deep learning model thinks, produces predictions, and calculates and corrects errors. Deep learning networks become smarter and more accurate with more time and data to train.

The hidden layer includes all the node layers between the visible input and output layers. They vary in complexity and structure, depending on the type of data they process and the task at hand.

Here are a few types of neural network algorithms:

Long short-term memory networks

Long short-term memory networks (LSTMs) are recurrent neural networks that can identify and remember longer strings of information and patterns.

By remembering previous inputs, LSTM algorithms are best used in series predictions, such as speech recognition and composition as well as pharmaceutical developments.

Multilayer perceptrons

Multilayer perceptrons (MLPs) are a type of feed-forward neural network where data travels in a single, linear direction from the input layer to the output layer. The input and output layers are directly connected and have the same number of nodes.

With data translating from the input node to its corresponding output node, MLP networks’ pattern recognition capabilities are best used for speech and image recognition and machine translations.

Recurrent neural networks

Recurrent neural networks (RNNs) work in repeating loops, allowing output from previous LSTMs to turn into input for the next phase of processing. 

The continuous loop processing enables the network to polish its predictions over time and eliminate errors. RNNs are most commonly used in developing chatbots, text-to-speech technologies, and natural language processing (NLP).

Self-organizing maps 

Self-organizing maps (SOMs) are an unsupervised learning method that uses data visualization in reducing the dimensions of the input data, while maintaining its topological structure.

Compressing the data without distorting it lets SOMs help people understand high-dimensional data through visualization. SOMs can be used on a variety of data sources from geography and sociology to text mining.

“Inspired by the way the human brain processes information, deep learning-capable machines can use large amounts of data to identify patterns, classify information, and make decisions by labeling and categorizing what they see,” says Fredrik Nilsson, a member of the Forbes Technology Council.

By mimicking and simulating the human brain, deep learning applications are able to generate and process massive amounts of data, while eliminating the possibility of fatigue or monotony.

“Embedding AI across your business has the power to enhance differentiation and competitiveness, increase productivity, influence retention, and even change the course of disease — and it is happening across industries and within all aspects of businesses,” says Beena Ammanath and Kay Firth-Butterfield for the Economic World Forum.

“It is influencing everything from the re-creation of business and operating models to hiring and retention strategies to creating new corporate cultures that not only embrace, but enable the use of deep learning.”

Benefits of deep learning

Deep learning applications can do more than replace the thought and pattern recognition of the human brain. With enough resources, data, and time, deep learning technology can have numerous benefits for its users, including:

  • Efficient use of unstructured data
  • Save time needed for data labeling
  • Create smart predictive models
  • Reliable automation
  • Easily scalable
  • Cost-effective in the long-term
  • Flexible architecture

“Research conducted by my company indicates that deep learning increases detection accuracy from 85% to around 95%, reducing false alarms by two-thirds — a huge help for staff,” says Nilsson with the Forbes Technology Council.

Deep learning use cases

See how several organizations in different industries are using deep learning:

Institute of Robotics and Mechatronics

The Institute of Robotics and Mechatronics and part of Germany’s national aerospace institute, Deutsche Luft und Raumfahrt (DLR). With more than 150 researchers onboard, the institute is one of the world’s largest robotics research institutions.

The institute has been working on its humanoid robot named Justin since 2009. Over the years, advances in software and hardware allowed it to use Justin in numerous trials, from medical assistance to operating in hostile environments on and off the earth.

Looking to adapt Justin for terrestrial use, the institute shifted its focus to AI and machine learning, using Google Cloud to get the most out of its software for a more autonomous result.

“We wanted to use deep learning techniques to make our robots more intelligent and autonomous. But training these models requires an extreme amount of compute power for training and running simulations. That’s where Google Cloud comes into play,” says Berthold Bäuml, head of the Autonomous Learning Robots Lab at DLR

“With Google Cloud, we can train our robots between five and 10 times faster than before and really explore things like deep reinforcement learning.”

Since moving to Google Cloud, the institute’s Justin was able to distinguish different materials better than humans with touch and perform dextrous in-hand manipulation of objects.

Clemson University

Clemson University is a public research university based in Clemson, South Carolina. 

The university’s IT administrators are responsible for managing access for over 50 departments to the university’s high performance computing (HPC) resources. This, however, resulted in delays in application access and application installation and updates, slowing down the university’s research.

Deploying NGC containers on the NVIDIA GPU cloud enables the university to use its preferred deep learning frameworks on its computing structures without needing the IT admins present 24/7.

“Containers from NVIDIA GPU Cloud have automated application deployment, making users self-sufficient, while allowing us to focus on other critical priorities,” says Ashwin Srinath, research facilitator, IT department, Clemson University.

“With the adoption of NGC, we’re able to help researchers with real problems.”

With NVIDIA, the IT team at the university was able to drastically reduce maintenance efforts and spend less time on software installs and updates.

MathWorks

MathWorks is a technology company that provides mathematical computing software for scientists and engineers. MATLAB is the company’s integrated development environment used by researchers for data processing and analytics.

Working with AWS, MathWorks used its various tools and offerings to optimize its deep learning capabilities, offering high scalability, data availability, and security.

“This new combination of deep learning and simulation data is something that we’re starting to see pop up in pretty much every field of science and engineering,” says Sam Raymond, postdoctoral researcher, Stanford University.

“It’s really kind of exploding. Even some of our customers have said that AI is going to be the undercurrent across all of their big initiatives. AI and deep learning have much better results than traditional methods.”

With AWS, MathWorks was able to increase computing speeds 100x and scale to accommodate growing datasets, saving researchers six months.

Deep learning providers

Some of the leading providers of deep learning services and applications include:

  • AWS
  • Fujitsu
  • Google Cloud
  • IBM
  • Microsoft
  • Micron Technology
  • General Vision
  • Intel
  • Qualcomm
  • NVIDIA

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