Deep Learning trends and examples to look for in 2022

Deep Learning has been one of the most successful areas or Machine Learning in past years and is probably going to remain so in the future. It has been adopted by virtually all largest companies in the world and its areas of use area being larger, with some important trends to be noted. In this article i will focus on specific examples where Deep Learning is changing different industries.

1.Advanced NLP and Software Development

Deep Learning has been used in Image recognition in the past years but now its one of the dominant forms in Improved/Advanced Natural Language Processing. From speech recognition and finishing sentences to creating poems and even writing programs, coding, this is one of the most interesting areas today. Deep Learning trained models which can create the whole app just by typing the words relevant to the app functionality are becoming reality.

2. AI aided Web Design and Development.

One of the most interesting and promising areas, again mostly enabled by Advanced NLP combined with the image recognition and image creation capabilities, but also cloud computing / storage services, there are many platforms now offering the creation of the whole website using AI. Deep Learning is of course the most important method deployed in majority of those cases.

3. Metaverse and Deep Learning

What i mentioned about the NLP and Image recognition in combination with other features will enable an efficient framework for what is considered a Metaverse. But its enabling much more than that. Using Deep Learning to create digital 3D worlds will probably be another big thing regarding the use of Deep Learning in Metaverse areas.

4. Augmented reality and Deep Learning

Development of Multimodal systems integrating Audio based Deep Learning, Speech recognition, Image recognition, use of Deep learning in creating and integrating 3D objects has enabled its integration as one of the most important segments of Augmented reality (AR). This is of course in line with the previous topic Metaverse.

5.Explainability and Pattern recognition in Deep Learning

Deep Learning is becoming more detailed in terms of what it does best, pattern recognition, so now its not just image recognition, we are talking about its whole spectrum of recognizing shapes, objects, patterns and even explaining them . Recognizing patterns on images or using Recurrent neural networks and explaining their time series features, explaining variable importance, causal inference and centrality statistics is the key for next phases of Deep learning advancement.

6. Deep Learning and Life Science

Image recognition is probably most used AI segment in Life Science industries but in the past few year especially in areas of Radiology Research, other predictive methods have been rising exponentially too. Use of Genomics and Transcriptomics data to train predictive models is one of the focuses and Bioinformatics is definitely one area where Deep Learning is being developed extensively. Another important segment is AI aided Biochemical and Pharmaceutical Engineering. Repurposing of molecules using again text and numerical data is another interesting research area in Life Science industry. How computational power and high complexity Artificial Neural Networks also enable previously unthinkable molecule simulations contributing and enabling the previously mentioned.

7. Autonomous systems

Robotics, autonomous systems in car industry, manufacturing, hospitality and many other areas are one of my predictions for exponential growth in 2022. Of course, this means integrating Deep Learning with different hardware, which is another interesting area, integrating AI systems with different hardware which will need to be addressed.

8. Art

AI is proving to be one of the essential tools in creating Generative Digital Arts and Deep Learning is one of the main areas to look for. High computation power allows for designing previously unimaginable generative arts, blends of millions of images or abstract designs. Development of NLP and Audio Deep Learning practice enables AI aided music composers much more options and improved workflows.

By Darko Medin, a Data Scientist