The technology industry continues to be a hotbed of innovation, with activity driven by the widespread adoption of artificial intelligence (AI) and the increasing need for accurate pattern recognition, classification, and prediction tasks across industries such as finance, healthcare, and robotics. This has resulted in the growing importance of technologies such as advanced optimisation algorithms, parallel computing, deep learning and reinforcement learning. In the last three years alone, there have been over 3.6 million patents filed and granted in the technology industry, according to GlobalData’s report on Innovation in Artificial Intelligence: Radial basis function (RBF) neural networks. Buy the report here.
However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
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 built on over 2.5 million patents, there are 300+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, finite element simulation, machine learning (ML) enabled blockchain networks and generative adversarial network (GAN), are disruptive technologies that are in the early stages of application and should be tracked closely. Demand forecasting applications, intelligent embedded systems, and deep reinforcement learning are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are wearable physiological monitors and smart lighting, which are now well established in the industry.
Innovation S-curve for artificial intelligence in the technology industry
Radial basis function (RBF) neural networks is a key innovation area in artificial intelligence
Radial basis function (RBF) neural networks are feedforward artificial neural networks that employ radial basis functions as activation functions to compute the network's output. By combining the inputs and neuron parameters using these functions, RBF networks excel in tasks such as pattern recognition, function approximation, time series prediction, and classification. They are especially effective when dealing with problems that involve a high volume of input variables.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 40+ companies, spanning technology vendors, established technology companies, and up-and-coming start-ups engaged in the development and application of radial basis function (RBF) neural networks.
Key players in radial basis function (RBF) neural networks – a disruptive innovation in the technology industry
‘Application diversity’ measures the number of different applications identified for each relevant patent and broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to radial basis function (RBF) neural networks
Source: GlobalData Patent Analytics
Baidu is a leading patent filer in the field of radial basis function (RBF) neural networks. One of the company’s patents describes a method and device for vehicle contour detection using point cloud data. The method involves acquiring point cloud data for training, creating label data to indicate vehicle contour points in the training data, training a convolutional neural network model using the labelled data, and using the trained model to detect vehicle contours in new point cloud data. This approach ensures precise vehicle contour detection.
By geographic reach, Illumina leads the pack, followed by Medical IP and Hyperfine Research. In terms of application diversity, Medical IP holds the top position, followed by Smart Eye and Magic Leap.
RBF neural networks can handle complex problems with many input variables that makes them a powerful tool for solving real-world challenges and advancing AI capabilities. The innovation in RBF neural networks enables enhanced data analysis, improved decision-making, and the development of more accurate and efficient AI systems.
To further understand how artificial intelligence is disrupting the technology industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) – Thematic Intelligence.