The technology industry continues to be a hotbed of innovation, driven by the emergence and adoption of game-changing technologies such as artificial intelligence (AI), advancements in deep learning algorithms, and technologies like reinforcement learning, neural networks, and neural architecture search (NAS). Generative adversarial networks (GAN) may integrate transfer learning to leverage pre-trained models or knowledge from related domains, accelerating training and improving output quality. In the last three years, over 3.6 million patents have been filed and granted in the technology industry, according to GlobalData’s report on Innovation in Artificial Intelligence: Generative adversarial network (GAN).

Data Insights

Innovation in Artificial Intelligence: Generative adversarial network (GAN)

However, not all innovations are equal; their evolution follows an S-shaped curve from early emergence to acceleration, stabilizing at maturity. Identifying a particular innovation's stage is essential for understanding its adoption level and future trajectory.

300+ innovations will shape the technology industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the technology industry using innovation intensity models based on over 2.5 million patents, over 300 innovation areas will shape the industry's future.

Within the emerging stage, technologies like finite element simulation, ML-enabled blockchain networks, and generative adversarial networks (GAN) are in the early application stages. Accelerating innovation areas include demand forecasting applications and intelligent embedded systems, while maturing areas feature wearable physiological monitors and smart lighting, now well established in the industry.

Innovation S-curve for artificial intelligence in the technology industry

AI in Technology: innovation areas

Emerging Accelerating
1 Chaos models 3D model simulation
2 Object recognition AI Drone controls AI
3 Digital pathology AI Circuit designing AI
4 Motion artefact analysis Dynamic image reconstruction
5 AI in radiology Speech synthesis AI
6 Quality control AI Intelligent contact centers
7 Defect detection models Voice recognition AI
8 Smart inspection AI in genome analysis
9 Neural net architecture Forward inferencing
10 Intelligent voice assistant Intelligent predictive maintenance
11 Image smoothing AI in EHR
12 Emotion AI
13 Quantum AI
14 GenAI for coding
15 Deep learning inference engines

Generative adversarial network (GAN) is a key innovation area in artificial intelligence
Generative adversarial networks (GANs) are machine learning algorithms consisting of two neural networks engaged in a competitive process, creating new data where training data is limited. GlobalData’s analysis highlights over 230 companies in the development and application of GANs, spanning technology vendors and start-ups.

Key players in generative adversarial network (GAN)
Stradvision, Samsung Group, Alphabet, Tencent, Huawei, Baidu, Intel, Illumina, Robert Bosch Stiftung, Ping An Insurance, SoftBank Group, Siemens, NEC, Microsoft, Capital One Financial, NVIDIA, ADOBE, Stryker, Magic Leap, Twitter, Toshiba, Koninklijke Philips, General Motors, LG, Canon, Furukawa, Nokia, Beijing Electronics, Beijing SenseTime, Sony Group, JD.com, Xiaomi, KLA, Enlitic, and Accenture are leading companies engaged with GAN technology.

Generative adversarial network (GAN) allows the generation of high-quality synthetic data for data augmentation, content creation, and simulation, fostering innovation in AI-generated content across various domains. For more insights on how AI is transforming the technology industry, refer to GlobalData’s thematic research report on Artificial Intelligence (AI) – Thematic Intelligence.