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Top Artificial Intelligence Trends and Predictions for 2021

Top Artificial Intelligence Trends and Predictions for 2021

Daniel Abbott
by Daniel Abbott — 4 weeks ago in Artificial Intelligence 3 min. read
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Despite all the disaster, 2020 has been the best year for tech and the best year for AI

We already see the green shoots of recovery at the end of 2020 and 2021 holds much promise for growth and technology

In 2020, artificial intelligence moved from the hyped state to practical usage and value, as corporations began to harness its power. Data for AI was a major theme throughout 2020, from new techniques that train AI on less data to data privacy protections gaining traction.

The Covid-19 pandemic has impacted many aspects of how we do business, but it hasn’t diminished the impact AI is having on our lives.

In fact, it’s become apparent that self-teaching algorithms and smart machines will play a big part in the ongoing fight against this outbreak as well as others we may face in the future.

Top Artificial Intelligence Trends to Watch Out this Year

Artificial Intelligence (AI) was invented several decades ago. In the past, many people associated AI with robots. But, it plays a crucial role in our lives now.

Personal gadgets, media streaming gadgets, smart cars, and home appliances use artificial intelligence. Also, businesses use it to improve customer experience and management functions. Here are five artificial intelligence trends to look out for in 2021.

Top 5 Artificial Intelligence Trends

1. Could GPT-3 lead to a new way in which AI models are developed?

GPT-3 was the major story for AI in 2020 however, the effects of GPT-3 could stretch beyond NLP. It might offer a new way to build up AI software with profound effect view

With its 175 billion parameters and a massive corpus of data on which it is trained – GPT-3 is already enabling some innovative applications. But GPT-3 could help pave the way for a new way of developing AI models

In future, we will discuss the wider implications of few-shot learning models where we focus only on the forward pass complemented by massive models (like GPT-3)

2. Training on Edge devices and distributed training –

Both training on border devices and dispersed training might have a deep effect on next-generation AI programs like individuals in a healthcare or utilizing 5G.

We will discuss this tendency in The consequences of Huang’s legislation to its artificial intelligence. The purchase of ARM by Nvidia will fuel this trend.
Also read: Should You think about AI-proofing about Your Career?

3. Cloud Native development becomes the norm impacting AI

As every business attempts to develop into a data firm, a cloud-native structure driven by MLOps and Kubernetes becomes the standard because these architectures can scale cost efficiently.

Hence, AI versions are constructed and deployed in an MLOps and Cloud-Native atmosphere. We will discuss the importance of Kubernetes in An introduction to cloud-native software.

4. ML and DL could be a commodity and it will impact the pay of data scientists at the entry-level as we move to decision science

In 2021, everybody will set up ML or DL in certain form. Cloud technology will make easy ML deployments simpler.

Therefore, we could change from information science to alternative science. The output of information science is a version using performance metrics (by way of instance precision ). With conclusion science, we can take this farther.

We can propose actions and also perform these activities or play simulations with ‘what-if’ standards. That means calculations such as reinforcement learning might be part of 2021 and beyond.

Another place with climbing activity is the area of choice science (optimization( simulation), which is quite complementary to information science.

By way of instance, in a manufacturing system for a food delivery firm, a machine learning model could forecast need in a specific area, then an optimization algorithm could devote delivery employees to this area in a manner that optimizes for earnings maximization across the full system.

Conclusion science takes a probabilistic outcome (“90 percent likelihood of greater need here”) and turns it into a 100% executable software-driven activity.

5. Engineering applications will need a new approach to data science

finally, see more technology businesses research science. The recent AI/ ML/DL marketplace is significantly skewed towards fiscal services.

As more businesses adopt AI, another approach might be needed that I researched in Why would a few conventional engineers not anticipate information science
Also read: How to improve Business Leadership with Artificial Intelligence?

Conclusion

We can anticipate AI study to yield additional discoveries within the forthcoming 18 months that will boost our capacity to spot and respond to the threat of viral outbreaks.

In order for this to occur, however, it is also going to need continuing worldwide collaboration between authorities and private sector.

This plays out will most probably be influenced by international politics and legislators, in addition to the path of technological improvement.

Because of this, issues like accessibility to medical datasets and obstacles to the global exchange of information are also hot topics within the upcoming year.


Daniel Abbott

Daniel Abbott is editor in chief & research analyst at The Next Tech. He is deeply interested in the moral ramifications of new technologies and believes in leveraging the data scientist, research and content enhancement to help build a better world for everyone.

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