The past few months of global uncertainty have done little to slow the progress being made in the continuously expanding field of AI. With new machine learning startups booming, and increased levels of uptake and implementation across different industries, the AI market has gone from strength to strength.

As 2020 draws to a close, it’s time to turn our sights to the year ahead - and take a look at the topics that we’ll all be hearing more about.

The merging of the Internet of Things with AI (AIoT) will strengthen and expand

The combination of AI and IoT is an inevitable team-up that has the potential to upend many different fields, in industries such as manufacturing, automation, healthcare, energy, transport, defence, space, data mining, elder care - and much more. AIoT is opening a wide set of new opportunities, both in terms of novel services and applications as well as increased efficiency and scalability of existing processes. For example: data collected by interconnected sensors will be analyzed at breakneck speed by sophisticated AI models, leading to deeper insights and reduced reaction times.

Quantum computing developments will continue to gather steam

Quantum computing is set to revolutionize businesses by tackling problems previously thought inaccessible, and also predicting meaningful solutions. Breakthroughs owed to quantum computing will be expected in fields including healthcare, finance, chemistry, and biology, but those are not the only areas that stand to benefit: business processes including security and data analysis will be vastly changed. Quantum developments have been on an upward path for the past few years, and 2021 is looking promising for a breakthrough.

AI to leave the data center for the edge

Moving away from the established system of machine learning models being trained in data centers, learning will start to happen at the edge. This transition will address several existing issues, including reducing energy use by large data centers, improving security of private data, enabling failsafe solutions, reducing information storage and communication costs, and creating new applications via lower latency capabilities.

Reinforcement learning will become ubiquitous

Reinforced learning (RL) is quickly becoming one of the most in-demand capabilities for forward-thinking businesses. It's a particular subset of machine learning wherein the system is ‘self-teaching’, and learns via trial and error. In this process, the software is fed several different conditions that indicate its specific task performance. From ever-more sophisticated customer recommendations and chatbots all the way to optimizing autonomous driving systems, the industry applications for RL range far and wide.

The role of AI and ML in hyperautomation will continue to grow

AI and machine learning are key components – and major drivers – of hyperautomation (along with other technologies like RPA tools). In order to be successful, hyperautomation initiatives cannot rely on static packaged software - automated business processes must be able to adapt to changing circumstances and respond to unexpected situations.

That’s where AI, ML, and deep learning models come in, using “learning” algorithms alongside data generated by the automated system to allow the system to self improve over time and respond to changing business processes and requirements.

And there you have it - the hottest AI topics to watch next year.

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