What Advancements Can We Expect For AI In 2021?

IT companies and technology experts have long predicted the boom of Artificial Intelligence (AI) in the new decade. However, what was thought to be a steady and measured progression became a full-throttle dash to the finish line, thanks to the COVID-19 pandemic.

As the global health crisis continues to change the business landscape, companies are adopting every possible way to cope with the changes to their normal operations. Luckily, AI is at the frontline of it all.

What Is Artificial Intelligence?

AI is a branch under computer science that deals with the creation of systems to carry out tasks that normally require human intelligence or skills. The term may also refer to any machine that’s capable of independent thought and action. You may not be aware of it all the time, but AI is everywhere and you interact with it on the daily. Virtual assistants and self-driving cars are AI. Even the spam filter in your email client is AI.

Apart from these common examples, more complex forms of AI are also gearing up for a new wave of digital transformation. In fact, AI-driven enterprises are already transitioning into more advanced methods of managing their resources. AI and machine learning platforms, such as www.cnvrg.io, allow these enterprises to manage AI jobs and unify all data through a single dashboard. In no time, end-to-end machine learning operations will be easier than ever.

Looking at the trends in recent years, especially the huge turning point in tech last year, AI adoption shows no signs of slowing down. 2021 has already started, and so have new AI technologies. Below are some of the upgrades and breakthroughs you can expect in the months to come:

  1. High Time For Hyper-Automation

Many companies were forced to downsize their workforce and operate with nothing more than a skeleton staff. It goes without saying that this disruption has been taking a toll on business operations. As a result, these companies have resorted to robotic process automation (RPA) to take care of non-core, repetitive, and time-consuming tasks in the workflow. This way, employees — the small number of them left — can focus on more difficult tasks.

One of the limitations of RPA is that it can only process labeled and structured data. Consider this scenario: When you call a company’s support line, you usually get assisted by an automated voice machine that asks questions answerable by only yes or no, or by pressing numbers on your dial pad. This year, RPA will focus on processing unstructured and unlabeled data, such that it can ask more sophisticated questions. Sooner or later, RPA will be able to ask “Why?” and “How?”, evaluate cause and effect, as well as interpret uncommon scenarios.

  1. Enhancing Language Intelligence

In addition to RPA, automated speech recognition (ASR) is also expected to reach new heights. This is especially true for voice-driven industries, such as customer care centers. Many of these centers deployed their employees to work remotely. The lack of face-to-face support and performance monitoring caused a significant decrease in the overall quality of service. As a solution, a lot of attention is now focused on building systems that can better understand the human language.

ASR isn’t limited to reading oral and written text, though. It also checks grammar, classifies topics, and translates as necessary. On top of sharpening these abilities, AI enterprises are also working on ASR systems’ ability to detect emotions and analyze intent through context clues. Through these advancements, it will much easier for customer care centers to perform quality checks and ensure that every remote worker complies with company policies.

  1. Tightening Cybersecurity

Speaking of remote workers, they stand at risk of cybersecurity threats since a lot of them use their personal devices for work. Unsecured devices are the weakness of any IT system, thus increasing the need for more advanced detection and prevention techniques. In response to this, several companies have started to work on artificial intelligence for IT operations (AIOPs).





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