Machine learning and artificial intelligence (AI) are the backbones of modern technological transformation. Companies have benefited from this by becoming smarter and more efficient, incorporating machine learning algorithms into their traditional operations.
Deep learning is widely used in a variety of industries around the world, with the main component being artificial neural networks. In this article, we’ll have a look at a few deep learning trends for 2022, including:
- Self-supervised learning
- Neuroscience-based deep learning
- System 2 deep learning
- Deep learning + symbolic AI
- Containerized workloads
- AI and model training
Deep learning’s dependency on large amounts of computer power and data has been its limitation. Unsupervised learning is an exciting new technique that is trained to self-label data by using raw forms of data, instead of training a system with labeled data.
The system learns to label this raw data, with input components being able to anticipate other parts of the input in a self-supervised system.
Neuroscience-based deep learning
Computer-based artificial neural networks are similar to those in human brains. Deep learning is helping neuroscience with a much-needed boost, using stronger and more powerful deep learning implementations.
Neuromorphic computing, for example, is an important approach. Referring to hardware that simulates the brain structure, it aims at trying to reduce the differences between artificial and biological neurons. The former is simple, having been built assuming that biological neurons are just dumb calculators that can do math, which is far from the truth.
System 2 deep learning
System 2 deep learning may still be fairly new, but experts believe it’ll allow for a more generalized data distribution. Systems now need datasets with comparable distribution to train and test, however, System 2 deep learning can do this by using non-uniform, real-world data.
Unlike System 1, which works both automatically and quickly with no sense of voluntary control, System 2 devotes attention to those mentally demanding activities that tend to be linked with subjective experiences of choice, agency, and concentration.
Deep learning + symbolic AI
Symbolic AI is a top-down approach to AI. It aims at giving machines intelligence by using a high level of symbolic representation of issues, following the physical system hypothesis by Allan Newell and Herbert A. Simon.
Hybrid models try to combine deep learning and symbolic AI so that these systems can learn visual consents, semantic parsing of sentences, and more without being explicitly supervised.
Deep learning workloads are more and more containerized, which helps to support autonomous operations. These technologies help companies have isolation, unlimited scalability, portability, and dynamic behavior in MLOps. The management of AI infrastructures is now more automated and business-friendly.
With containerization at the core, Kubernetes will help cloud-native MLOps to integrate with other more mature technologies. This will lead companies to have AI workloads running on much more flexible cloud environments combined with Kubernetes.
AI and model training
Deep learning platforms take time to spot patterns, analyze data, and reach conclusions that can be applied to the real world. AI platforms are evolving to keep up with increasing model training demands so that instead of taking weeks of learning to start working, these platforms will have as much maturity as data analytics.
Datasets keep growing in size, and deep learning models keep becoming more resource-intensive - they need more processing power to predict, validate, and recalibrate thousands of times. Graphics Processing Units (GPUs) are progressing to handle these intense demands.