As technology is changing very rapidly, we are seeing there are lot of new trends are emerging in the filed of data science /AI/ML whatever you say.
Here are some latest Trends as of 2021-22
- Automated Machine Learning (AutoML)
AutoML enjoys a steadily increasing popularity. Not least driven by the numerous successes in practical analyses. In a world where more and more devices produce data and are networked with each other, the data “produced” grows disproportionately. Therefore, AutoML is of urgent necessity to gain knowledge from these rapidly increasing data on time. We assume that AutoML becomes even more critical in the coming years and that the analysis methods deliver even more precise and faster results. The field of activity of the data scientist will not disappear, but rather, his focus will shift to more specific or sophisticated analysis techniques.
In short: AutoML saves time and money (you don’t need a larger team of data science and machine learning experts). It is also the easiest and cheapest way to enter the world of artificial intelligence or machine learning.
- Explainable AI (XAI)-Responsible AI
Explainable artificial intelligence (XAI) is the attempt to make the finding of results of non-linearly programmed systems transparent to avoid so-called black-box processes. The main task of XAI is to make non-linear programmed systems transparent. It offers practical methods to explain AI models
3.Multi-Modal Learning
AI is getting better at supporting multiple modalities with in a single ML model, such as text, vision, speech and IOT sensor data. Developers are starting finding innovative ways to combine modalities to improve the tasks.
4.Democratized AI
Improvements AI tooling are lowering the level of expertise required to build the models which makes the more people with access to the raw materials of knowledge, tools, and data required to build an AI system, the more innovations that are bound to emerge. Efficiency improves and engagement increases.
And now most of the people are sharing their code/taught as a open source through which people can validate as well as understand the work.
