What are the AI trends in business?

AI will be used to simplify and improve operations in 2022 and the years that follow. Businesses should aim to gain from the commercial application of AI by strengthening their IT infrastructure and data management. However, not every deployed AI model may be beneficial to businesses and suited for performance monitoring. We’ll concentrate on AI trends that are likely to become widespread 2022.

AI for Security & Surveillance

Face recognition, speech recognition, and video analysis have already used AI approaches. These strategies are the most effective combination for surveillance and biometric authentication. As a result, we may expect AI to be heavily used in video surveillance by 2022.

Artificial intelligence is advantageous for the flexible configuration of security systems. Engineers previously spent a significant amount of time configuring the system since it was activated when a certain number of pixels on a screen changed. As a result, there were far too many false alarms. Falling leaves or a running animal produced these alerts. The security system identifies things using AI, which adds to a more flexible setup.

Artificial intelligence in video surveillance can detect suspicious activity by focusing on anomalous behaviour patterns rather than faces. This skill allows for the creation of more secure public and private settings by spotting potential threats. These AI-powered video solutions could also be valuable in transportation, retail, and manufacturing.

Voice recognition is another area that offers great opportunities for AI applications. Voice recognition technologies can determine an individual’s identity. By identity, we mean a person’s age, gender, and emotional condition. The ideas underlying voice recognition for surveillance may be similar to those used by Alexa or Google Assistant. A built-in anti-spoofing model recognises synthesised and recorded voice, which is useful for security and monitoring.

One of the most crucial technologies for security is biometric face recognition. Different malicious applications try to trick security systems by providing fake photos instead of real images. To defend against such cases, multiple anti-spoofing techniques are presently being developed and used at large scale.

AI in real-time video processing

Handling data pipelines is a barrier for real-time video stream processing. Engineers strive to assure video processing accuracy while minimising latency. And AI technologies can assist in achieving this goal.

A pre-trained neural network model, cloud infrastructure, and a software layer for applying user scenarios are required to execute an AI-based strategy in live video processing. Because processing speed is essential for real-time streaming, all of these components must be tightly integrated. We can parallelize processes or develop algorithms to speed up processing. Parallelization of processes is accomplished through file splitting or by employing a pipeline technique.

Generative AI for content creation & chatbots

Modern AI models may generate text, sounds, and images that are nearly indistinguishable from non-synthetic real data.

Natural Language Processing is at the heart of text generation (NLP). Language models have emerged as a result of rapid improvements in NLP. Google and Microsoft, for example, use the BERT paradigm to supplement their search engines.

How else does the advancement of NLP-related technology benefit businesses?

To begin with, integrating NLP and AI techniques enables the creation of chatbots. According to Business Insider, the chatbot market is predicted to reach USD 9.4 billion by 2024, therefore let’s focus on how AI-driven chatbots might assist organisations.

Instead of simply obeying basic orders, the chatbot attempts to grasp people’s intentions. Companies in several fields employ AI-powered chatbots to give human-level contact to their clients or consumers. Chatbots are commonly used in the following industries: healthcare, finance, marketing, travel, and hospitality.

AI-powered chatbots aid in the automation of administrative procedures. For example, in healthcare, they are reducing the amount of manual labour. Chatbots can help patients plan appointments, provide medication reminders, and provide answers to questions. Chatbots are being used in different areas to deliver targeted messaging, boost customer engagement and assistance, and give consumers with personalised offers.

AI-driven QA and inspection

AI inspection is the most notable branch of Computer Vision. This field has been thriving in recent years as accuracy and performance have improved. Companies began to devote more computational and financial resources in order to develop computer vision systems more quickly. The rapid advancement of AI inspection is likewise linked to the rapid advancement of object detection in video frames.

Automated inspection in manufacturing implies the analysis of products in terms of their compliance with quality standards. The methodology is also applied to equipment monitoring.

Here are few use cases of AI inspection:

  1. Detecting defects of products on the assembly line
  2. Identifying defects of mechanical and car body parts
  3. Baggage screening and aircraft maintenance
  4. Inspections of nuclear power stations

Game-changing AI breakthroughs in healthcare

The next trend in the adoption of AI in the healthcare industry has been widely discussed in recent years. In the fight against COVID-19, scientists apply AI models and computer vision algorithms in fields such as pandemic detection, vaccine research, medication discovery, thermal screening, facial identification with masks, and CT scan analysis.

AI algorithms can detect and analyse potential risks and generate accurate predictions to combat the spread of COVID-19. AI also aids in the development of vaccinations by finding critical components that make them effective.

AI-powered solutions could be used as an efficient tool in the Internet of Medical Things and for dealing with healthcare-specific confidentiality challenges. When we organise AI use cases in healthcare, we see that they all have the same goal: to ensure that the patient is diagnosed swiftly and accurately.


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