In the 21st century, we have seen significant technological advancements. During the early 2000s, there has been a rapid decline in several highly commercial and trending technologies, and several new ones have taken their place. The year 2022 has also seen several completely new technologies being introduced, especially in the fields of Machine Learning technologies and Artificial Intelligence technologies. It is likely that these new technologies will only grow in the future and may even reach the hands of ordinary people by 2022.
If you are interested in staying up-to-date with the latest trends in AI techniques and technology in 2022-23, let’s take a closer look. In order to be able to get an advantage in the market in 2022, you should check out and learn how to master the following top trending technologies in 2022.
With the advent of innovative technologies in the field of Artificial Intelligence, we have seen heuristics changing the world. Artificial intelligence has taken over every industry and profoundly influences every aspect of society in every sector. In the last few decades, technology has progressed exponentially due to the advent of the internet.
Until thirty years ago, Artificial Intelligence was regarded as a stand-alone technology, but now these technologies are widespread in every aspect of life, including education, business, and politics. This process of recreating human intelligence in Support Vector Machines is known as Artificial Intelligence, and it is referred to as AI.
The field of Artificial Intelligence encompasses a wide range of new and emerging technologies. In order to achieve operational excellence, data mining, etc., organizations of all sizes are implementing Artificial Intelligence in a rat race for decision support. It is time for us to look at the ten latest Artificial Intelligence technologies available on the market.
Top 10 Artificial Intelligence Technologies to Know in 2022
Listed below are the top 10 AI techniques and technologies to know in 2022:
Natural Language Generation (NLG)
Natural Language Generation (NLG) is the process of creating natural language text based on Machine Learning algorithms. NLG can be used to generate content for various applications, including chatbots and automated documents like reports and articles.
Natural language generation can be particularly helpful in cases where there’s no time to write or edit content manually or when you need to create dynamic or personalized messages that feel human-like.
Examples: AI-Generated Narrative Reporting and Content Creation at Scale
Robotic Process Automation (RPA)
RPA is an AI technology that automates business processes, helping organizations to reduce the time it takes to complete these tasks. It works by using robotic programs that can be easily trained and deployed on a wide range of applications and platforms.
RPA can be used to automate activities such as data extraction, processing, reporting, and customer service requests. These are typically repetitive tasks carried out by humans that are prone to errors or delays in completion. RPA technology helps businesses achieve faster response times for their customers while also reducing costs associated with these manual processes.
Examples are Customer complaints processing, Data cleansing, Compliance reporting, etc.
Speech recognition is a subset of Natural Language Processing and is one of the most important AI techniques and applications. It allows computers to understand human speech, whether it’s spoken in English, Japanese, or any other language.
In today’s world, speech recognition is everywhere. Speech recognition software converts spoken words into text so that computers can interpret what you’re saying. It’s usually done by comparing the sound of your speech patterns with an acoustic model of the sound you produced when speaking words in training mode, resulting in a text string representing what was said by you during training mode.
Examples are Siri, Alexa, Cortana, etc.
The science of bio-metrics involves recognizing, measuring, and analyzing the physical characteristics of the human body. The use of touch, images, words, and body language, creates organic human-machine interactions.
Biometric technology is used to identify people based on their physical and behavioral characteristics. It can be used to authenticate, identify, and verify individuals. Biometric systems are often implemented in security applications such as access control or law enforcement agencies.
Examples are Fingerprint Scanning, Facial Recognition, Heart-Rate Sensors, etc.
Mасhine Learning Platforms
To make computers smarter, it aims to develop novel learning methods. Many factors are driving the popularity of Machine Learning technologies platforms, including algorithms, APIs (application programming interfaces), development tools, training applications, and massive data sets. Classification and forecasting are common uses for them. A number of companies sell ML platforms, such as Amazon, Google, Fractal Analytics, Microsoft, and D Text.
Examples are Microsoft Azure, IBM Watson, etc.
Autonomous vehicles are self-driving cars that use a variety of sensors and cameras to detect obstacles and other vehicles, as well as mapping software to plan the route. Autonomous vehicles can be fully autonomous (driving without input from the driver), semi-autonomous (some driving functions are automated while others require human intervention), or a combination of both.
Examples are Tesla Autopilot, Mercedes-Benz Distronic Plus, General Motors Super Cruise, etc.
A cyber defense system detects, repairs, and mitigates attacks against systems and data infrastructure. It is possible to create learning technologies by combining neural networks that can process a series of investments with Machine Learning technologies to detect cybersecurity risks and uncover suspicious activity.
Examples are Anti-Virus software, Firewalls, etc.
Through peer-to-peer networks, data can be shared between computers and systems without having to go through servers. Networks based on peer-to-peer communication can solve even the most complex problems. Cryptocurrencies use this technology, and using individual workstations to connect instead of installing servers is a cost-effective implementation method.
Examples are Torrent, Skype, etc.
As the name suggests, image recognition pertains to the process of finding and recognizing a specific component in a video or an image. Additionally, it can be used for searching for license plates, identifying diseases, examining individuals’ personalities, and greatly assisting in the search for pictures.
Examples are Face lock in smartphones, Google lens, etc.
AI-enabled Chatbots are a great example of how AI is being used to make our everyday lives easier. Chatbots are able to hold a conversation with humans and have the ability to understand natural language. They can be used in any application where you would typically interact with another person, such as customer service, sales, or even personal banking.
Examples are Slush, HDFC Bank – EVA, etc.
Artificial intelligence is a fascinating field that continues to grow and evolve. It has the potential to revolutionize everything we do, from our daily lives to our jobs. We have covered the top 10 AI techniques and technologies that are expected to transform our world in 2022 and beyond. Each of these technologies offers something different, whether it be improved security or smarter chatbots that can help you with your work. The future looks bright!
If you’re willing to dive deep into AI and lead the AI revolution in your organization, then it is recommended that you pursue the UNext & IIM Indore’s Executive PG Diploma in Management & Artificial Intelligence.