According to the 2020 McKinsey Global Survey on Artificial Intelligence (AI), more than 50% of companies have adopted AI in at least one business unit or function. AI brings them tremendous value, increasing revenue and customer loyalty. The leading AI companies invest at least 20% of their earnings before interest and taxes (EBIT) in AI. This number could increase as covid-19 is accelerating digital transformation. In this article, Tech Town will join readers in learning about new AI trends that will become popular in 2022, helping businesses make accurate predictions about the future of AI and increase success rates.
AI technology trends for 2022
In 2022 and beyond, AI will be leveraged to simplify operations and make them more efficient. Businesses should strive to take advantage of the benefits of applying Artificial Intelligence to improve IT infrastructure and data management. But not every AI model will be useful and appropriate for performance monitoring. Tech Town will focus on AI trends 2021-2022 that are likely to become mainstream.
Trend 1. AI in Security & Surveillance
AI techniques have been applied for facial recognition, speech recognition and video analysis, forming an optimal combination for biometric monitoring and authentication. So in 2022, we can foresee the strong exploitation of AI in video surveillance.
Artificial intelligence helps to set up security systems flexibly. In the past, alarms would trigger when a specific number of pixels on the screen changed, resulting in too many false alarms. These alarms are caused by falling leaves or a running animal. Thanks to AI, the security system identifies objects, contributing to a more flexible system setup.
AI in video surveillance can detect suspicious activity by focusing on unusual behaviors, not faces. This capability enables the creation of more secure spaces, both public and private, through the identification of potential threats. Such AI-based video solutions could be useful in the logistics, retail and manufacturing industries.
Another niche that offers promising prospects for AI applications is speech recognition. Technologies related to speech recognition can identify identities. From identity, it is possible to determine a person’s age, gender, and emotional state. The principles based on voice recognition for surveillance could be the same as in the case of Alexa or Google Assistant. An anti-spoofing model that integrates voice detection and recording is a great feature for security and surveillance.
One of the most important technologies for security is facial biometric recognition. Malicious apps are trying to fool the security system by providing fake photos instead of real ones. To combat such cases, many anti-spoofing techniques are currently being developed and used on a large scale.
Trend 2. AI in Real-Time Video Processing
The challenge for real-time video streams is dealing with data pipelines. AI can help ensure accuracy and reduce latency in video processing.
To implement an AI-based approach to live video processing, we need a pre-trained neural network model, cloud infrastructure, and software layers to handle our scenarios. user. Processing speed is important for real-time livestreams, so all these components must be tightly integrated. For faster processing, we can parallelize processes or improve algorithms. Parallelization is achieved through file splitting or using a pipeline approach. This pipeline architecture is the best choice as it does not reduce model accuracy and allows to use AI algorithm to process video in real time without any complexity. Additionally, for the pipeline architecture, additional effects such as face detection and blurring can be applied.
Real-time stream processing is closely linked with background removal and blur applications. Demand for these tools has increased thanks to the popularity of video conferencing trends during the covid-19 pandemic. And these trends are expected to be actively developed, according to GlobeNewswire, the global video conferencing market is expected to grow from $9.2 billion in 2021 to $22.5 billion in 2026.
Trend 3. Content Creation AI and Chatbots
Modern AI models can produce text, sound, and images with very high quality, almost indistinguishable from real non-synthetic data.
At the heart of text generation is Natural Language Processing (NLP). Rapid advances in NLP have led to the emergence of language models. For example, the BERT model is being used by Google and Microsoft to complement their search engines.
The development of NLP related technologies has spurred businesses. Specifically, the combination of NLP and AI tools allows the creation of chatbots. According to Business Insider, the chatbot market is expected to reach $9.4 billion by 2024, businesses can see clear benefits from implementing AI-driven chatbots.
Chatbots try to understand people’s intentions instead of just executing standard commands. Different sectors use chatbots to communicate with their customers or users on a human level. Chatbots are widely applied in business fields: healthcare, banking, marketing, tourism and hospitality.
AI-driven chatbots help automate administrative tasks. For example, in healthcare, chatbots help to reduce the amount of manual work, by scheduling appointments, sending reminders related to taking medication, and providing patients with answers to questions. wonder. In other areas, chatbots are used to deliver targeted messages, improve customer engagement and support, and provide users with personalized offers.
Besides chatbots, NLP lies at the heart of other advanced technology solutions. For example, NLP text generation can be used in business applications.
The recent emergence of the GPT-3 model allows AI engineers to generate an average of 4.5 billion words per day. This will allow a wide range of downstream applications of AI to accomplish more useful purposes for society. It also prompted researchers to invest in technology that detects phylogenetic patterns. Note that in 2022 we will see the arrival of GPT-4 – “general artificial intelligence AI”.
We would like to draw attention to GAN (Generative Adversarial Networks) technology, which is now capable of producing images that are indistinguishable from those created by humans. It can be images of people, animals, non-existent objects, as well as other types of media, such as sound and text. Now is the best time to deploy GANs to benefit from their capabilities, as they can model the distribution of real data and find useful representations for improving AI pipelines, data security and so on. data, find anomalies, and adapt to specific real-world cases.
Trend 4. Quality assurance and AI-based inspection
The most notable branch of Computer Vision is inspection AI. In recent years, this direction is very popular because of the increasing accuracy and performance. Businesses began to invest both computer and financial resources to develop computer vision systems at a faster rate. The intensive development of AI inspection is also connected with the rapid advancement in the field of object detection in video frames.
Automated testing in production means analyzing products according to their compliance with quality standards. This method is also applicable to device monitoring.
Here are some use cases of test AI:
● Detect product defects on the assembly line.
● Identify defects in mechanical parts and bodywork.
● Check baggage and aircraft maintenance.
● Nuclear power plant inspection.
Trend 5. AI makes a breakthrough in healthcare
The next trend regarding AI implementation in healthcare industry has been much discussed in recent years. Scientists use AI models and computer vision algorithms in the fight against covid-19, covering areas such as pandemic detection, vaccine development, drug discovery, thermal screening, recognition Face modeling by mask and CT scan analysis.
To combat the spread of COVID-19, AI models can detect and analyze potential threats and make accurate predictions. In addition, AI also helps develop vaccines by identifying the key ingredients that make them work.
AI-based solutions can be applied as an effective tool in the Internet of Medical Things and to address security issues specific to the healthcare industry. If we systematize AI use cases in healthcare, they are clearly united by one purpose: to ensure that patients are diagnosed quickly and accurately.
Trend 6. Codeless AI Platform
Codeless AI platforms have enabled small companies to adopt powerful technology that was previously only available to large enterprises. Let’s find out why such platforms are important AI trends for businesses in 2022.
Developing AI models from scratch requires time and relevant experience. Adopting a codeless AI platform will simplify the task as it lowers the barrier to entry. With the advantages are:
● Perform faster than coding from scratch, working with data and debugging, saving up to 90% time.
● Lower development costs through automation, businesses have eliminated the need for teams of big data scientists.
● Ease of use with drag and drop functionality simplifies software development and enables application creation without coding.
Code-free AI platforms are in great demand in healthcare, finance and marketing – although the solutions available are not very customizable. The most searched codeless AI platforms are Google Cloud Auto ML, Google ML Kit, Runaway AI, CreateML, MakeML, SuperAnnotate,…
The evolution and future of AI
Trends show that the future of Artificial Intelligence is promising as AI solutions are gaining popularity. Self-driving cars, robots and sensors for predictive analytics in manufacturing, virtual assistants in healthcare, NLP for media reporting, virtual education assistants, AI assistants, and possible chatbots replace people in customer service. Suffice to say, all AI-powered solutions are evolving in huge strides.
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