Home » Artificial Intelligence (AI) Trends in 2021: What To Expect (and What Not To Expect)

Artificial Intelligence (AI) Trends in 2021: What To Expect (and What Not To Expect)

by Udai Madhani
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Artificial Intelligence (AI) tends to develop at a breakneck speed, exploding through nearly all sectors. So, how did the previous 12 months go, and what should we anticipate from AI in 2021?

In this post, we’ll look at five developments that emerged in 2020 and that we believe will become much more prominent in 2021.

Low Code/No Code

AutoML (automated machine learning) is not a novel idea. Traditionally, AutoML has concentrated on algorithmic selection and evaluating the right Machine Learning or Deep Learning approach for a given dataset. 

The Low-Code/No-Code trend grew last year across the board, from apps to tailored vertical AI solutions for companies. Modern Low-Code/No-Code frameworks allow building whole production-grade AI-powered systems without deep programming expertise, just as AutoML allowed building high-quality AI models without in-depth Data Science knowledge.


MLOps (Machine Learning Operations, or the process of using Machine Learning in production) has been around for a while. COVID-19, on the other hand, gave a revived awareness of the need to track and control output Machine Learning instances in 2020. Many AIs acted strangely as a result of the massive changes in organizational workflows, resource control, traffic habits, and so on. 

When incoming data does not fit what the AI was taught to predict, this is referred to as Drift in the MLOps environment. Although drift and other manufacturing ML problems were well-known to companies that had previously implemented ML in processing, the improvements brought on by COVID prompted a much wider recognition of the need for MLOps. 

Similarly, when privacy laws such as the CCPA gain traction, businesses that work with consumer data may need to improve their compliance and risk management. Finally, in 2020, the first MLOps group meeting, the Operational ML Conference, which began in 2019, witnessed a substantial rise in the amount of thoughts, interactions, and attendees.

Language Models for Advanced Pre-Training

The last few years have seen significant advancements in the Natural Language Processing space, the most notable of which might be Transformers and Attention, with BERT as a popular application (Bidirectional Encoder Representations with Transformers). 

These models are incredibly strong, and they have revolutionized language translation, comprehension, summarization, and other facets of language processing. However, training these models is incredibly costly and time-consuming. 

The good news is that pre-trained models (and often APIs that have direct access to them) will give rise to a new generation of AI services that are both powerful and simple to create. GPT-3, which has been demonstrated for use cases spanning from writing programming to writing poetry, is one of the most prominent examples of an advanced model accessible via API.

Artificial Intelligence For Kids

The age at which young people may develop AIs is declining as low-code software becomes more popular. An elementary or middle school student will now develop their own AI to do everything from text classification to picture classification. In the United States, high schools are beginning to teach AI, with middle schools following suit. 

In the Synopsys Science Fair 2020 in Silicon Valley, for example, AI was included in 31% of the winning tech designs. Perhaps more remarkable is the fact that 27% of these AIs were produced by students in grades 6–8. An eighth-grader who developed a Convolutional Neural Network to diagnose Diabetic Retinopathy from eye scans was an example champion who went on to the national Broadcom MASTERS.

Synthetic Content Generation

NLP isn’t the only field of AI where major algorithmic progress is taking place. GANs (Generative Adversarial Networks) have also shown remarkable feats in the creation of art and fake images. 

GANs, like transformers, have become difficult to train and tune because they involve huge training sets. However, technological advancements have drastically decreased the amount of data used to create a GAN. 

Nvidia, for example, has demonstrated a modern augmented approach for GAN training that uses far fewer data than previous methods. GANs could be seen in everything from diagnostic devices like synthetic cancer histology photos to far more sophisticated deep fakes as a consequence of this discovery.

What Could All This Mean?

These aren’t the only AI patterns. They are notable, though, since they point in three important and vital directions.

  • The rise of MLOps and the expanded usage of AI in the real world, as shown by the problems created by COVID-19.
  • BERT and GANs are examples of continuing creativity.
  • The democratization of AI through all markets and skillsets, as shown by low-code/no-code and its potential to introduce AI to anyone from tech developers to schoolchildren.

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