The Top 5 AI and Machine Learning Trends to Watch Out For in 2021
The Top AI and Machine Learning Trends for 2021 include advancements in forecasting, healthcare, reinforcement learning, conversational AI and predictive maintenance.
From tech startups to global giants, organizations constantly look to incorporate trending technologies for their business expansion. Artificial Intelligence (AI) and Machine Learning (ML) are two such advanced technologies that hold the potential to present diverse cutting-edge solutions for businesses.
At present, the AI-ML industry is growing at a rapid rate and provides ample development scope for organizations to bring the necessary transformation. As per Gartner, around 37 percent of all organizations surveyed are leveraging some form of AI in their business and it is predicted that about 80 percent of modern technologies will be based on AI and ML by 2022.
Over the last few years, there have been several breakthroughs in artificial intelligence and machine learning. However, only a few businesses have so far been able to apply those to achieve the necessary business goals. (Read also: Artificial Intelligence: Debunking the Top 10 AI Myths.)
In this article, we have summarized the latest trends in AI and ML including conversational AI, the convergence of IoT and AI, reinforcement learning, and more. We hope it will help businesses in surging ahead with their AI and ML Development.
1. Business Forecasting and Analysis
The time series analysis has been popular for the past couple of years and is still a hot trend for the current year. With this technique, analysts collect and monitor a set of data over a period of time which then is analyzed and used for making smart business decisions. The ML networks can give forecasts with accuracy as high as around 95 percent if trained using diverse data sets.
In 2021 and beyond, we can expect companies to incorporate recurrent neural networks for high-fidelity forecasting. For instance, deep learning solutions can be incorporated to find hidden patterns and accurate forecasts. A real-world example of this is insurance firms detecting possible frauds that could otherwise prove costly to them.
2. AI and the Healthcare Industry
The healthcare industry has greatly benefited from the advancement in AI technology over the years. AI development companies have come up with numerous solutions to enhance the capabilities of healthcare firms. (Read also: How AI in Healthcare is Cutting Risks and Saving Money.)
The recent outbreak of COVID was greatly mitigated by the incorporation of AI and Big Data that were used to identify COVID patients and potential hot spots. Thermal cameras and smartphone apps were used for monitoring the temperature of individuals and pool data for healthcare authorities.
The use of AI can help healthcare professionals and industry at large in many ways. Using data analysis and prediction capabilities of AI and ML tools, authorities are gaining insights into the medical records of individuals for taking necessary preventive measures. A familiar use case is smart AI watches that monitor the vitals of a patient keeping a track on their health remotely.
3. Reinforcement Learning
Reinforced Learning (RL) can be utilized to a great extent by organizations in the upcoming years. It is a special application of deep learning that uses its own experiences to improve the effectiveness of captured data. (Read also: Utilizing Visual Artificial Intelligence for Ecommerce Monetization.)
In reinforcement learning, the AI software is set up with numerous conditions that define what type of action will be performed by the software. Based on various actions and results, the software self-learns actions to perform to meet the desired end goal.
An ideal example of reinforcement learning is a chatbot that addresses simple user queries like order booking, greeting, or consultation calls. Machine Learning Development Companies can use RL to make the chatbot more resourceful by adding sequential conditions to it – such as identifying sales leads and transferring calls to the relevant service agent. Some of the other applications of RL include robotics for industrial automation, business strategy planning, aircraft control and robot motion control.
4. Conversational AI
Conversational AI is the technology on which automated messaging and speech-based applications work. It can be used to communicate like a human by acknowledging speech and text, understanding the intent of a customer, deciphering different languages, and giving responses similar to the way humans do. Examples of conversational AI devices are chatbots, and smart assistants like Amazon Echo and Google Home.
However, there are a lot of improvement areas that need to be addressed by developers. Speech recognition and automated text recognition are two such challenges that require great command over natural language processing. These limitations can be overcome in various ways and one way would be to do classification/segmentation of diverse words (e.g. enabling casual words to make an order at a food restaurant application).
In recent times, companies are using conversational AI chatbots to carry out airline transactions, schedule meetings and cross-sell products thus enabling better customer experience. (Read also: Have You Heard on an Enterprise Chatbot Platform? You Will.)
5. Predictive Maintenance Using AIoT
Internet of Things (IoT)apps used for managing interconnected devices have found their use in several places including organizations, homes and enterprises. The capabilities of IoT devices can be increased when used with AI. By leveraging AI and IoT (know as artificial intelligence of things, or AIoT) technologies in software and customer relationship management (CRM), businesses can get real-time information and monitor the performance of various interconnected devices. These smart solutions can be used for predictive maintenance in industrial machines and can be used to address problems both remotely and on-site.
AIoT solutions are used by field agents to resolve on-site issues in no time. With an AIoT powered mobile application, technicians can access detailed information on the complaint and can use the right tools to solve the problem.
An example of this would be the AI applications that allow field agents to find faulty machines with the help of image recognition features embedded in Field Service applications. With predictive maintenance and defect detection, businesses can get proactive solutions to their prevalent problems in no time.
In a nutshell
The scope of AI/Machine Learning Development is diverse and varies on the basis of business needs. By suitably using these trends, firms can get real-time insights, carry out predictive maintenance, leverage accurate forecasts and a lot more. To get the maximum from AI/ML incorporation, organizations must look into the latest trends and research to develop and implement the next best solution for their business.