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AI and Machine Learning Trends in 2022

January 10, 2022

11:24 am

page-inner-01AI and Machine Learning Trends in 2022

Artificial intelligence (AI) and machine learning (ML) will continue to hold a place at the forefront of the technology sector in 2022. Rapid changes in the field hold great promise for even more significant breakthroughs.

Various factors such as growth of data-based AI, advancements in deep learning, and push for robotic autonomy to stay competitive in a global market are expected to drive the adoption of the AI solutions and services.

The global AI market size will grow by USD $58.3 billion in 2021 to USD $309.6 billion by 2026, at a compound annual growth rate (CAGR) of 39.7% during the forecast period, according to MarketsandMarkets.

Here are the trends to watch.

Explainable AI

As indicated by its name, explainable AI (XAI) refers to a form of AI where humans understand the process and the outcome. In other words, XAI enables people to explain what has occurred, what is happening right now, what the next steps will be, and what information the actions are based on.

Understanding the strengths and weaknesses of an AI model helps to maximize performance, as well as improves the ability to make improvements. XAI makes it possible to confirm or challenge existing knowledge, as well as to generate new assumptions. The global XAI market size is estimated to be USD $3.50 billion in 2020 and is predicted to reach USD $21.03 billion by 2030 with a CAGR of 18.95% from 2021-2030, according to ResearchandMarkets.com.

Digital Process Automation

Digital process automation focuses on improving and digitizing the processes in an organization, which can include people, devices, software, and information. Basic back-end integration, collaboration tools, and cloud-based architecture are the primary supporting technologies that businesses use to manage processes and automation efforts.

Increasingly, robust and real-time analytics are being deployed and used with digital process automation. Approximately 74% of organizations say they are actively looking for new ways to automate processes, according to ThinkAutomation. With the digital process automation market booming, it’s an exciting time for this type of AI technology as it expands across multiple industries.

Robotic Process Automation

Robotic process automation (RPA), also known as software robotics, uses automation technologies to integrate and perform repetitive tasks between enterprise and productivity applications. By deploying scripts that emulate human processes, RPA tools complete autonomous execution of various activities and transactions across unrelated software systems. For example, RPA tools may mimic office tasks, such as extracting data, filling in forms, moving files, etc…

In order for RPA tools in the marketplace to remain competitive, they will need to expand their offerings to include intelligent automation. This type of automation incorporates ML, natural language processing, and computer vision. The global RPA market was estimated at USD $2.09 billion in 2020 and is expected to reach USD $15.5 billion by 2030, growing at a CAGR of 27.7% due to the growing trend of cloud-based solutions and the increasing adoption of robot-based solutions across various end-user industries, according to Precedence Research.

Business Forecasting

Business forecasting uses AI to make informed predictions about certain business metrics, such as sales growth, budgeting, or customer engagement. By using business forecasting, companies can make financial and operational decisions based on past data to develop business strategies that will work with future economic conditions.

Today, big data and AI have transformed business forecasting methods. Forecasting tools offer operational and financial models to drive actionable insight as new opportunities arise and market trends shift. Improved accuracy in forecasting can have a significant impact on an organization’s effectiveness and profitability, making them extremely valuable when properly implemented.

AI in Cybersecurity

As cyberattacks grow in number and complexity, AI is helping security operations analysts stay ahead of threats with rapid insights and faster response times. AI, ML, and threat intelligence can recognize patterns in data to enable security systems to learn from past experience. Additionally, major drivers for the market’s growth are the growing adoption of the Internet of Things, as well as the increasing number of connected devices, and the vulnerability of Wi-Fi networks to security threats.

AI systems require language, speech, vision, and sensor data, along with ML algorithms, to realize the applications for cybersecurity. The AI cybersecurity market is projected to reach USD $38.2 billion by 2026 from USD $8.8 billion in 2019, at a CAGR of 23.3%, according to ResearchandMarkets.com. North America is an early adopter of innovative AI technologies as banks, government agencies, and financial institutes in this region face ever-increasing challenges related to cyber threats.

AI in Information Security

Related to AI in cybersecurity, AI in information security is designed to protect data from unauthorized access. AI tools help to ensure systems and data are safeguarded against the growing number of threats by continuing to monitor the changing identity and access management landscape, learning from security breaches in the news, getting more information about new solutions as they become available, and implementing the most relevant options for an organization.

Outdated security protocols can’t offer the dynamic tools necessary to protect against numerous modern threats. AI and ML are better at detecting unusual behavior anywhere on a network and can trigger immediate responses to detect a threat before it turns into a full-blown breach. As a result, organizations no longer have to rely on periodic software updates to identify new threats. Instead, AI and ML work together to determine if something is out of the ordinary and launch a defense as quickly as possible.

Conversational AI

Conversational AI helps in the usage of speech-based assistance like chatbots in order to automate communication and to create personalized customer experiences. These AI tools use large volumes of data, ML, and natural language processing to help imitate human interactions, recognize speech and text inputs, and translate their meanings across various languages.

In addition, conversational AI enables users to communicate with devices, applications, and websites every day using voice, touch, text, or gesture information. It is one of the most important components in open interactions and personal assistance activities. The global conversational AI market size is expected to reach USD $15.6 billion by 2027, rising at a market growth of 16.8% CAGR during the forecast period, according to KBV Research.

Ethical AI

Ethical AI is a set of guidelines that advise on the design and outcomes of AI. The cognitive biases of human beings can be seen in our behaviors and subsequently, data. Since data is the foundation for all ML algorithms, it’s important to structure experiments and algorithms with this in mind as AI can potentially amplify and scale these human biases at an unprecedented rate.

Companies are experiencing unforeseen consequences in some of their AI applications, particularly due to poor upfront research design and biased datasets. As instances of these outcomes have come to light, new guidelines have emerged to address concerns around the ethics of AI. Leading companies in the field of AI have also taken a vested interest in shaping these guidelines.

Quantum AI

Quantum computing involves use of quantum mechanics to exponentially increase a computer’s processing power and accelerate the process in various sectors. Quantum computing can be used for the rapid training of ML models and to create optimized algorithms. An optimized and stable AI provided by quantum computing can complete years of analysis in a short time.

Quantum AI is one of the most likely solutions for next-generation AI. The global quantum computing market size is expected to be valued at USD $487.4 million in 2021 and is expected to reach USD $3728.4 million by 2030 at a CAGR of 25.40% over the forecast period from 2021 to 2030, according to Quince Market Insights.

Augmented Intelligence

Augmented intelligence (also known as intelligence amplification, or IA) uses ML and predictive analytics of data sets to enhance human intelligence by improving human decision-making and, by extension, actions taken in response to improved decisions. In addition, augmented intelligence is developed for helping organizations to make more accurate, data-driven decisions in business.

Unlike the traditional view of AI as an autonomous system that operates without human involvement, augmented intelligence uses ML and deep learning to provide humans with actionable data. Furthermore, augmented intelligence is the strongest way to use and prioritize a large collection of security data by providing predictive analytics to organizations. The global augmented intelligence market size was valued at $11.73 billion in 2020 and is projected to reach $121.57 billion by 2030, growing at a CAGR of 26.4% from 2021 to 2030.

Innovations with AI and ML are crucial for continuous improvement, an important factor for digital transformation and business intelligence tools. Have questions about how AI and ML tools can help your organization? Contact the experts at InApp. We’re here to help.