TOP 10 DEEP LEARNING TRENDS AND PREDICTIONS TO WATCH FOR IN 2022
DEEP LEARNINGLATEST NEWSTOP LISTby Sayantani SanyalDecember 24, 2021
Deep learning is one of the most attractive branches of machine learning that is dominating the tech industry
AI and machine learning are considered as the foundations of tech transformation in the modern industry. Integrating machine learning algorithms into business operations has made enterprises more intelligent and efficient. As the next paradigm change in computing is underway, the evolution of deep learning has also caught the attention of industry specialists and tech giants. Currently, deep learning technology is an integral part of global industries. Artificial neural networks are at the core of the deep learning revolution. Deep learning trends predicted by experts reveal that this advancement of ML and its adjoining technologies has minimized the overall error rates and has also improved the performance of the networks for a particular task. In this article, we enlist the top deep learning predictions and trends that tech enthusiasts can watch out for in 2022.
- Independent deep learning: Even though DL has excelled in several fields, the technology’s reliance on huge amounts of data and computing power has always been one of its limitations. But the year 2022 might witness the integration of unsupervised learning in DL, where instead of training a system with labelled data, it is trained to self-label the data itself using raw forms of information.
- Integrating hybrid models: The year 2022 might witness the convergence of symbolic AI and deep learning. Symbolic AI initially dominated the tech domain back in the 70s or 80s, where the machine learned to interpret its environment by creating internal symbolic representations of the problem and analysing the human decisions for the same. These hybrid models will be aimed to use the advantages of symbolic AI and integrate it with deep learning for enhanced solutions.
- Using deep learning in neuroscience: Several research operations in neurology have revealed that the human brain is comprised of neural nerves. These artificial neural networks in the computer are synonymous with the ones humans possess in their brains. With the help of this phenomenon, scientists and researchers have been able to find out thousands of remedies and theories in neurology. Deep learning has provided the much-needed boost that neuroscience required a long time ago.
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- Full-stack deep learning: We are moving towards a future where the demand for full-stack deep learning will continue to grow. This will result in the creation of various frameworks and libraries that will enable tech users and engineers to automate shipments tasks and various other activities. It will also help engineers to quickly adapt to new business needs and processes.
- General adversarial networks (GAN): GANs provide an approach to generative modelling using deep learning algorithms, and convolutional neural networks. It produces samples that can be used to check discriminative network and unwarranted content to balance processes and increase accuracy.
- System 2 deep learning: Experts believe that system 2 DL will enable the generalization of the distribution of data. Currently, the systems need to train and test datasets with a similar distribution. System 2 DL will make it possible by using real-world data, which are also non-uniform.
- Dive in use of convolutional neural networks: CNN models are extensively used in computer vision activities like identifying objects, faces, and images. But besides CNNs, human visual systems can also recognize them under different backgrounds, angles, and views. While trying to identify images in real-world object datasets, CNNs experience a 40%-50% drop in performance.
- Increased use of edge intelligence: Edge intelligence transforms the methods of acquiring data and processing it. It converts the procedures from data storage devices in the cloud to the edge. The advent of EI has made the data storage devices somewhat independent by moving decision-making closer to the data source.
- Multi-modal learning in DL: AI has gotten better in multi-modalities within a single ML model, such as text, vision, speech, and other technologies. Developers are now trying to integrate these modalities in machine learning and deep learning to improve networking and efficiency in tasks.
- Higher level of NLP: Currently, ML-based NLP is at its nascent stage. But currently, there is no such algorithm that will enable NLP systems to identify the meanings of different words in different situations and act accordingly. Implementing DL will boost the efficacy of these NLP systems and help machines understand customer queries swiftly.