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5 Emerging AI And Machine Learning Trends To Watch In 2021

The world is changing and so are we. Machine learning is the future of technology. Will emerging AI make your life easier by helping you make better decisions?

There are many ways machine learning trends can impact your life. You may want to use it for tasks like finding new friends on social media. Or getting personalized recommendations from Netflix or Amazon Prime Video. Or, you may want to explore how machine learning could help solve some of the world's most challenging problems?

Where is this all going? What AI and machine learning trends do you think are important in 2021?

Machine Learning Trends in Data-Driven Decision Making

Machine learning is a subset of artificial intelligence that provides computers with the ability to learn with no programming. It's based on algorithms and statistical models that can process large amounts of data, recognize patterns in data and provide insights through reasoning as human beings would do when using their own brains.

Hyperautomation brings tasks into the machine realm and removes the need for human intervention or decision-making. This frees up time and resources creating efficiencies that were previously unattainable.

Enterprises are turning to AI and machine learning for data-driven decision making. This opens new ways to approach decisions. This is a fundamental change to decision-making. 

To date benefits of AI and machine learning are enjoyed in fraud detection, credit risk management, email spam filtering, And other areas where it is cost-effective compared to human employees.

The major differences comparing deep learning vs machine learning is how much human intervention is needed to train the AI?

Classical machine learning is more dependent on human intervention to learn. Humans determine the hierarchy of features in order for them to understand the differences between data inputs. This usually requires more structured inputted data to be able to learn.

Deep machine learning is more autonomous. It doesn't need human intervention to analyze similarities within data. Deep learning requires less structured input in order for it to learn from raw text or images.

This research and development is in the early stages but developing faster than any other time in history. AI focuses users on the user experience. It reprograms users to think of new ways of turning raw data into usable information. 

Augmented Reality

Augmented reality is a form of interactive media that allows users to interact with digital information. This happens through the use of sensory inputs such as sound, video and graphics. Adding computer-generated enhancements on top of what’s actually there in real life creates an artificial environment where training and research are safely done.

Using AI to enhance this can speed up learning in topics that are risky to practise and attain skills in real life.

The technology is being used to help train surgeons on difficult procedures. It gives them the ability to practice in a simulated environment before going into surgery.

In 2021, AI will be used to create augmented reality environments in which human skills can be practised. Training and research will take place within a safe environment where the consequences of errors are limited or controlled.

The technology relies on expert human intervention who provide real-time feedback. And alerts when there may be risks before they become catastrophic.

Increased Use of AI for Cybersecurity Applications

AI is being used as a tool to defend against cybersecurity attacks. Companies are adopting AI as a cybersecurity shield. They see that AI is also a weapon of choice for actors committing cybersecurity attacks.

Collected threats can be used against any organisation, no matter how large or small. The important thing is to use AI and machine learning for:


  • - Identifying the threat more quickly than other systems
  • - Training your system’s response so that it will learn faster from future attacks of this type
  • - Updating defences as an ongoing program



Deep learning neural systems are the best defence against intrusion. We can repel attacks in real-time, allowing for the protection of private personal data held by companies.

Cybersecurity has effectively become a game between two or more deep learning systems. Where the system that learns faster and can respond quicker will overcome the attempts of other systems challenging it. But the reality is when two AI systems attack each other, the game may never stop. 

This is a huge implication for business and government.

Automation of Manual Tasks

Deep learning trends 2021 is expanding the reach of the Internet of Things. In a world that is connected, bringing AI into play in a distributed computing environment can create outcomes on a scale we have only ever dreamt of.

Driverless Vehicles

Driverless vehicles are a problem whose solutions lie in deep learning driven by neural networks. Running real-time neural networks linked to driverless vehicles enables vehicles to be driven remotely with no human intervention.

Real-time visual mapping of the local environment can enable neural networks to run traffic movements as if they were a swarm of bees flying in close formation.

Manufacturing and Business

Robots have been around for decades. But they’ve never been more advanced than they are today thanks to advancements in artificial intelligence (AI). The future is here. Robots powered by AI can do tasks such as manufacturing goods or delivering packages without human input.

We use machine learning, recent trends and AI to help people make better decisions. Things like planning their day so that it’s more productive. Or making sure the right products get delivered to customers who’ve ordered them online.

Health and Medical

Further advances can be realized in health services and surgery, where machine learning can help to reduce the time required for training and improve patient outcomes.

Machine learning algorithms will continue to become more complex and advanced as they learn which will help to solve many of the problems in healthcare like early detection of diseases and more accurate predictions.


The issue that remains is how far should we allow the automation of real-world systems. Is there a level of human intervention that should remain, or do we move down the path of total deep learning running under neural networks independent of human decisions?

Personalized Customer Service

Conversational AI is an area of great interest to marketers and enterprises.

An AI-powered device could be anything from a speaker to an alarm clock or even your vacuum cleaner.

The exponential growth of data sets and machine learning algorithms will expand the possibilities for these technologies in many industries including retail, automotive, healthcare etc.

The Age of Automation Will Lead to AI-powered Chatbots

Chatbots have become commonplace in recent years. Many businesses use them as customer service agents or marketing tools. Enterprises use chatbots to provide customers with personalized information about products or services. Any time of day via text message or voice call.

Chatbot technology will evolve into artificial intelligence-powered chatbots. This technology will interpret natural language queries without requiring human intervention.

These virtual assistants have come a long way since Siri first debuted on iPhones back in 2011. A decade later, there’s still plenty of room for optimization and evolution of virtual assistants.

Currently, we can still identify when we are dealing with a chatbot on automated systems, but how soon will we be unable to tell the difference between a real person talking to us and a machine? Or has this day already arrived? AS AI improves it becomes less obvious and we are no longer aware of it in our lives.

The Future of AI: It Is Only Getting Started

AI is no longer a futuristic concept. This technology has become an essential part of our lives, and there are more AI-powered devices coming to the market each year. While we still have much to learn about how this technology impacts society at large, it's safe to say that the world as we know it will never be the same.

While governments are questioning the integrity and ethics of AI, its adoption and integration into the global landscape will only continue to grow.

Mainstreaming creates many questions not yet answered or even contemplated.

  • - How do these advances impact jobs?
  • - What kind of responsibility does an artificially intelligent system have if it causes harm to people or property?
  • - Can we, or should we control AI?



Longer-term predictions about Machine Learning may be difficult to make because it is being driven by rapidly advancing hardware and software advances that will change what AI can do for us.

Machine learning will lead to the creation of more complex artificial intelligence.

The current state-of-the-art in machine learning algorithms, and deep neural networks, is already producing interesting results and paving a way for other applications that involve reasoning or understanding natural language.

What we do know is as AI becomes more established and increases its reach, AI cloud-based solutions are available to even the smallest of businesses. These implementations of AI need the right infrastructure to scale with user needs.

An Explosion of Demand for AI Systems on the Internet

The rise of AI "bots" is central to current machine learning trends. In 2021 there has been an explosion in users for these services. Customers expect real-time processing. There's an established need for scalable high-speed systems running on multithreaded GPU systems. This demand makes it impractical for AI providers and start-ups to maintain their own infrastructure.

Cloud-based infrastructure can be expensive. Find out how we help companies reduce high cloud costs and increase profitability. All while relying on trusted large networks that are scalable and come with quality standards to make sure your data is safe! Get in touch with one FluidStack specialist to help you find the right solution to your project.