Artificial Intelligence (AI) and Machine Learning (ML) (apart from the Coronavirus) have been the hot buzzwords in the year 2020. From cutting-edge medical diagnostic systems to consumer electronics to personal assistants, these technologies are paving their way into almost everything. 

In fact, AI and ML are even changing the scenario of VC funding. Yes, Venture Capitals are becoming more interested to invest in AI startups offering the most beneficial solutions. Not only this, VCs themselves are leveraging the power of AI/ML to make investment decisions.

Such is the influence of AI and ML.

As we move forward in 2021, it’s time to take a look at the bigger picture. So, here we are with the 7 emerging AI and ML trends that startups or any other businesses must look out for in 2021.

Top 7 Artificial Intelligence & Machine Learning Trends

Big Data Analytics

With the advent of IoT and 5G devices, the exponential growth of data will continue. The more types of devices, the more will be the types of data. For instance, iPhone 12 has incorporated lidar technology, i.e., light detection and ranging, in it. Different drones and robotic equipment also leverage lidar for wider purposes. To make the most of this data from such technologies, the demand for cloud platforms with native AI solution development capabilities is increasing.

According to a survey by Deloitte, 74% of AI adopters agree that AI will be incorporated in all enterprise applications within 3 years. This will enable apps to provide new insights and deeper knowledge, empowering businesses to generate new avenues and services.   

Hyperautomation

Gartner, a reputed market research firm, came up with the IT-mega trend – hyperautomation. As per this trend, everything within the organization that can be automated should be automated. For example, legacy business processes. The pandemic bolstered this trend.

Artificial Intelligence and Machine Learning are the driving forces of hyperautomation. Wondering why? Well, automated business processes must be able to adapt to the changing needs and requirements of a growing business, which is challenging to achieve with static software. That is where the learning algorithms of AI and ML come into the role. It allows the systems to gradually learn and improve over time. 

Cloud Innovation

Companies are making a move to cloud adoption due to AI/ML-based applications and services. Similar to tons of virtual services, Artificial Intelligence is cost-effective when utilized in the cloud. Early adopters have benefited and will continue to benefit if they have their bulk data on the cloud as opposed to enterprises with on-premise infrastructure.

AI and ML embedded into cloud applications will not only provide access to data quickly but also offer value in new ways. Since the COVID-19 pandemic, more and more industries are moving to the cloud. AI in Healthcare and Finance are the most prominent examples of adopting cloud technologies with AI capabilities.

Cybersecurity

AI and ML are increasingly finding their way into cybersecurity applications. In a competition to overcome issues like threats from malware, ransomware, DDS attacks, and more, cybersecurity system developers are embedding AI and ML technology. It helps them identify threat, and especially, its variant.

Moreover, AI-enabled cybersecurity tools can gather data from a company’s different internal and external sources. Based on the information collected, it can recognize the threatening patterns, activities, and data breaches. In addition to corporate security systems, AI/ML will play a crucial role in designing tools for smart homes. 

The Rise of AIoT

The intersection of Artificial Intelligence and the Internet of Things is coined as “Artificial Intelligence of Things (AIoT)” by many. This is predicted to redefine industrial automation.

AI and ML require a bulk of data to operate successfully, whereas IoT devices produce a lot of data. So, if AI/ML and IoT are intertwined, it can bring in some outstanding results. For example, setting up an IoT network in a manufacturing plant can offer operational and performance data. When this data is run through an AI system, it can predict the areas of improvement and the maintenance cycle. Thus, it boosts efficiency.

Ethics

There has been a long time debate around the ethical challenges of AI. But most of the companies have stated the effective use of AI and ML. Most importantly, businesses are leveraging AI solutions to increase their employee’s potential. However, looking at the rapidly changing trends, customers and employees expect companies to leverage AI more responsibly. In the near future., enterprises will be more willing to do business with those who are committed to data ethics

Hybrid Workforce

Post the COVID-19 pandemic, companies will consider utilizing the RPA bandwagon effectively. That is, businesses will leverage AI and RPA to address high-volume and repetitive tasks. Besides, it is likely to see many companies adopting a hybrid work environment in the next few years. The time is not afar when the human will interact with automated bots and digital assistants in their offices.