Artificial Intelligence trends 2021
While the COVID-19 pandemic had an impact on many areas of business, it had little bearing on the impact of Artificial Intelligence (AI) on our daily life.
In fact, it will become clear that self-teaching algorithms and intelligent robots will play a vital role in the ongoing struggle against this epidemic, as well as future pandemics.
Without a doubt, AI will continue to be a major trend in terms of technology and innovations that will greatly impact how we live, work, and play in the near future.
According to a research, AI is currently integrated into 77 percent of the products we use.
From a variety of smart gadgets to Netflix recommendations to products like Amazon's Alexa and Google Home, AI is the driving force behind many modern technology conveniences that are now part of daily life.
The global AI industry was valued at $39.9 billion in 2019, with a compound annual growth rate (CAGR) of up to $53.8 billion reached in 2021, and predicted $309.6 till 2026.
Technology has produced tremendous advancements in various industries such as healthcare, retail, automotive, manufacturing, and finance thanks to ongoing research.
Artificial Intelligence will remain a major technical innovation in 2021 and for the near future. The rapid growth and acceptance of current and new technologies in the next decade will be remarkable.
So here's an overview of what we might predict in the coming years.
Chatbots, virtual personal assistants, and virtual customer assistants are examples of conversational AI that are becoming commonplace in businesses.
Organizations hope to expand their company effectively with AI on the rise. Conversational AI is reported to continue improve client interactions in 2021, making it an amazing year.
In 2021 and beyond, what can we expect from conversational AI? These figures will reveal:
By 2025, AI will improve 95% of customer interactions. (Source : Servion)
By 2023, the number of people using voice assistants will have tripled, from 2.5 billion in 2018. (Source : Juniper Research)
By the end of 2021, almost 80% of firms will be using chatbot systems in some form. (Source : Outgrow)
Conversational AI is a key component in creating a more personalized service as companies are looking for ways to improve their businesses.
Robotic Process Automation (RPA)
More CIOs are turning to Robotic Process Automation (RPA), an emerging technological technique, to optimize corporate operations and cut costs.
Organizations can use RPA to automate rule-based, time-consuming business procedures, enabling their employees to focus on higher-value tasks like serving clients.
RPA focuses on automating business operations that are guided by business logic and well-organized inputs.
RPA solutions can be as simple as sending an automated email answer or as complex as deploying thousands of bots, each programmed in an ERP system to do rule-based activities.
RPA is being used in a number of industries, including manufacturing, call centers, human resources, and finance.
The role of AI in Healthcare
COVID patients and key hot spots have been identified using BD extensively. AI is already helping the healthcare industry in a significant way and with high accuracy.
Thermal cameras and mobile applications have also been developed by researchers to monitor individual temperatures and collect data for healthcare institutions.
AI can help healthcare institutions in a variety of ways by data analysis and forecasting various outcomes.
AI and machine learning technologies provide insights into human health and also prescribe illness prevention measures.
In 2021, industry giants in the quantum space, including IBM, Azure, Microsoft, and Google, will attain a milestone.
"Quantum computing is currently being used in a variety of industries, including banking, finance sector, insurance, automotive, aerospace, and energy." The scope and range of industries adopting quantum will plan to increase in the near future.” - Bob Sutor, IBM Quantum Strategy and Ecosystem Vice President
The quantum craze already has begun. Across the globe, both private investors and governments have started to invest huge chunks of money into quantum research and development.
While quantum computing may appear to be alluring, most businesses are willing to adopt the technology to solve a number of computational challenges in areas such as cloud security, finance, logistics, supply chain, and drug discovery.
In 2021, ethical AI will be at the top of the list of emerging technology trends.
But, when it comes to machines and robots, how do we define AI ethics and morals? Since the 1970s, computer scientists and AI engineers have been working to solve this obstacle.
AI ethics can help in determining what is right and wrong. It is the set of ‘rules' or ‘decision route' that is used to decide AI behavior in this context.
Failure to operationalize AI ethics could put the organization at risk.
Missing out on ethical AI might lead to higher regulatory, legal, and reputational issues, as well as inefficient product development, inability to use data for AI model training, and wasteful expenses.
What can you do to help AI become more ethical? Use the three fundamental ideas below to guide you through the ethical concerns of AI:
Companies must be able to differentiate between right and wrong.
This is one of the main reasons why businesses have begun to implement ethical standards, policies, and frameworks in order to mitigate the biggest risks associated with AI.
The combo of AI and IoT (AIoT)
An organization can achieve greater heights by combining AI and IoT to provide intelligent decision-making, customer delight, precise prediction, cost savings identification, and better operational efficiency.
When AI and IoT are combined, they create a one-of-a-kind solution that has the ability to digitally transform business.
- According to a Business Insider analysis, companies are expected to invest up to USD 15 trillion in IoT by 2025.
- The potential economic impact of IoT, according to McKinsey Global Institute, will range between USD 4 trillion and USD 11 trillion by 2025.
Alexa, Siri, Google Maps, and Netflix are just few more examples of AIoT in operation.
Natural Language Processing (NLP)
NLP is nowadays one of the most widely used AI technologies. The growing popularity of NLP can be attributed to its widespread use by Amazon Alexa and Google Home.
NLP has removed the need for writing or communicating with a screen because humans can now speak with machines that understand their language.
The usage of natural language processing (NLP) in sentiment analysis, machine translation, process description, auto-video caption generation, and chatbots is expected to grow in 2021.
AI Scope in Cybersecurity
AI is being used by businesses to help them identify risks. According to a 2019 survey by Capgemini, around 69 percent of organisations predict AI will become all-pervasive in response to attacks and threats.
Artificial intelligence (AI) and machine learning (ML) have become crucial tools for businesses to address unwanted risky decision.
Technology will become more accessible in the future years, putting digital data at a very high risk of being compromised and vulnerable to hacking and phishing attacks.
AI and new technology will support the security department in combating malicious attacks in all sectors. With enhanced cybersecurity measures, AI will help prevent cybercrime in the future.
The AI-enabled framework will detect fake digital activity or transactions that match illegal behaviors.
Organizations must become alert and not let their protection down in the face of cybercriminals' unavoidable threats.
As threats continue to overwhelm businesses, the following is a list of ways organisations have started to use AI:
Threat hunting: Using behaviour analysis, AI may easily improve threat hunting. By analysing the organization's data from endpoints, for example, profiles of every existing application can be created.
Vulnerability management: AI techniques enhance the vulnerability management capabilities that organisations must deal with everyday basis. Other AI-powered solutions, such as event and user behaviour analytics, assist businesses in anomaly detection on servers or analysing user behaviour before an attack happens.
Network security: When it comes to network security, the company should focused on three areas:
- Understanding how the network environment works : businesses should begin establishing workload and application naming conventions. This is one of the main reasons why most companies waste time deciding which set of tasks belongs to which application.
- Creating security policies : security policies allow it determining whether or not a connection is authorized.
- By extensively examining the pattern of network traffic, AI improves network security. Companies such as Google, IBM/Watson, Juniper Networks, and Balbix have adopted AI best practices in cybersecurity.
AI-Powered Business Forecasting, and Analysis
With real-time alerts, AI technologies help to redefine business workflow. To better understand market needs, researchers combined hyper-automation with cognitive automation.
Content-Intelligence technologies, along with AI-friendly practices, will help digital workers build exceptional skills.
Natural language, judgement, context building, reasoning, and data-related understanding can all be automated with these skills.
Rise of a Hybrid workforce
Companies will leap on the RPA bandwagon after the COVID-19 epidemic, which means cognitive AI and RPA will be widely used to handle high-volume, routine tasks.
The workplace will transition to a hybrid workforce environment as examples of increased usage appear. Human workers will collaborate with bots and other digital assistants.
More collaborative AI experiences will result from the development of a hybrid workforce.
Facial recognition has proved to be one of the most reliable biometric authentication methods in this decade.
Investors and academics from all over the world are attracted to the accuracy and readability of the data.
This is a source of concern, and we expect a significant growth and improvement in facial recognition technology this year.
Edge computing connects gadgets to servers and data storage, allowing people to view and save data on their devices.
It is defined as real-time data processing that outperforms cloud computing services. Another type of edge computing is that which is carried out on nodes.
Edge computing a small server that's placed near a local telecoms provider. Nodes assist in the creation of a connectivity between the local service provider and the cloud.
It is less expensive, saves time, and delivers quick service to customers.