Top Machine Learning Developments of 2021

By NIIT Editorial

Published on 08/02/2022

5 minutes

Machine learning has been consistently having contributed to the growth of high on growth startups and organizations. Regardless of whether you’ve been associated with the tech domain or not, there is an immense likelihood that you would have heard of machine learning. As of writing, machine learning is being used by a plethora of industries such as Google, Netflix, Amazon, and Microsoft. As per research, this industry is expected to be valued at $30.6 billion by 2024. Perhaps this is one reason that the best data analytics certifications are inculcating ML in their modules. A Salesforce Research Survey found that 83% of IT leaders believe that ML along with AI better customer engagement. That said, here are the top developments of this sector for the year 2021.

Automated Machine Learning (AutoML)

The two most promising components of ML appear to be the improved utility of data labeling tools and automatic tuning of neural net architecture. Using semi and self-supervised learning methods, machine learning has been really beneficial in labeling data. With automated data labeling software, the dependency on human counterparts that were earlier employed for cost optimization will be reduced. And so will be human-induced errors as a result of the same. With the propagation of AI, such solutions would become affordable in the immediate future.

AI-led conceptual design

Artificial intelligence has been used to automate data, image processing, and speech/text recognition. Some industries with ideal applications include retail and healthcare. However, in the last year, OpenAI has developed DALLE and CLIP (Contrastive Language-Image Pre-training), which are used to combine language and images to generate new visual designs centered on a textual description.

Multimodal learning

AI engineers are developing techniques to integrate multiple modalities within ML models is using vision, IoT sensors, speech analysis data. This would cumulatively help in ML algorithms that can help with document understanding.

AI-powered Employee Experience

Time and again the negative impacts of AI on dehumanizing jobs have been the subject of many case studies. However, if experts are to be believed then AI will be more of a helper for humans. It would be used to ease the pressure off overburdened professionals, especially where the workday duties are recurring and copy-able. Such aspects are also being covered in the best data science course online there is to find. One use case is chatbots, wherein conversational AI is being deployed to help those in the BPO industry.

Unsupervised Learning

Automation is pushing the needle for human-less machine operations. Unsupervised machine learning. ML cannot operate in silos. It needs a consistent feed of data to bite at in order to churn solutions for the problems it is working on. Advances in unsupervised ML, particularly, would be helpful to take this industry forward.

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