According to the McKinsey Global AI Survey 2020, more than 50% of companies have already adopted AI in at least one business unit or function in 2020, so we are witnessing the emergence of new AI trends. Tech companies invest at least 20 percent of their profits (EBIT) in the development and application of AI technologies. This number is likely to increase as COVID-19 digitization accelerates. The pandemic has led to a surge in online activity, and industries are embracing ARTIFICIAL intelligence in business, education, administration, social and other areas to carry on normal life.
This article aims to provide an overview of the new AI trends that will emerge in 2020 and continue to increase in 2021.
Trends in artificial intelligence adoption
The level of AI adoption varies by industry. Using data from McKinsey’s Global AI survey, we can highlight four leading industries: high-tech, telecommunications, automotive, and assembly.
More and more companies are applying AI to service operations, product design, advertising and sales. In terms of investment, drug discovery and research and development received the most investment — in 2020, assets totaled more than $13.8 billion, up 4.5 times from the previous year.
Ai will drive the highest revenue growth if applied to inventory and parts optimization, pricing and promotion, customer service analysis, sales and demand forecasting.
Ai technology trends
In 2022 and the following years, AI will be used to streamline operations and improve efficiency. Companies should try to benefit from the commercial use of AI by improving IT infrastructure and data management. In this article, we look at ai trends that could become mainstream in 2021-2022.
Trend 1: AI for security and monitoring
Artificial intelligence technology has been applied to face recognition, speech recognition and video analysis. These techniques make for the best combination of monitoring. So, by 2022, we can expect a lot of use of ARTIFICIAL intelligence in video surveillance.
Artificial intelligence facilitates the flexible setup of security systems. Previously, engineers spent a lot of time configuring the system, but there were too many false positives. Thanks to ARTIFICIAL intelligence, security systems can recognize objects, which helps with more flexible Settings.
Artificial intelligence in video surveillance can detect suspicious activity by focusing on unusual behavior patterns rather than faces. This ability can create safer public and private Spaces by identifying potential hazards. This AI-powered video solution could also help in logistics, retail and manufacturing.
With years of experience in video processing technology, TSINGSEE Video deeply integrates Al artificial intelligence technology to provide massive video access, intelligent analysis and processing capabilities. At present, EasyCVR has achieved face detection, traffic statistics, vehicle detection, license plate recognition and other AI intelligent recognition technology research and development, and is widely used in traffic, logistics, security, fire and other scenarios.
Another niche that offers promise for ai applications is speech recognition. Technology associated with speech recognition can determine identity. Identity refers to a person’s age, gender and emotional state. Speech recognition for surveillance could be based on the same principles as Alexa or Google Assistant. One feature suitable for security and surveillance is a built-in anti-fraud model that detects synthesized and recorded speech.
Biometric face recognition is one of the most critical technologies for security. Different malicious apps try to trick security systems by providing fake photos instead of real images. To prevent this, multiple anti-fraud technologies are being developed and used on a large scale.
Trend two: ARTIFICIAL intelligence in real-time video processing
The challenge of handling live video streams is working with the data pipeline. Engineers aim to ensure accuracy and minimize delays in video processing. And artificial intelligence solutions can help achieve this goal.
To implement an AI-based approach in real-time video processing, we need a pre-trained neural network model, a cloud infrastructure, and a software layer for applying user scenarios. Processing speed is critical for real-time streaming, so all of these components should be tightly integrated. For faster processing, we can parallelize the process or improve the algorithm. Process parallelization is achieved through file splitting or using a pipeline approach. This pipelined architecture is the best choice because it does not reduce the accuracy of the model and allows the AI algorithm to process the video in real time without any complexity.
Modern real-time stream processing is inseparable from the application of background removal and blurring. Demand for these tools has increased as COVID-19 has contributed to the emergence and popularity of new trends in video conferencing. These trends will develop positively as the global videoconferencing market is expected to grow from $9.2 billion in 2021 to $22.5 billion in 2026, according to GlobeNewswire.
There are several ways to develop tools for background removal and blurring in live video. The challenge is to design a model that separates the people in the frame from the background. Neural networks that can perform such tasks can be based on existing models such as BodyPix, MediaPipe or PixelLib. After selecting the model, you still face the challenge of integrating it with the appropriate framework and organizing the best execution through WebAssembly, WebGL, or WebGPU applications.
Trend 3: Generative AI for content creation and chatbots
Modern AI models can produce very high-quality text, audio, and images that are almost indistinguishable from non-synthetic accurate data.
At the heart of the text is natural language processing (NLP). The rapid development of NLP led to the emergence of language models. For example, Google and Microsoft have successfully used the BERT model to complement their search engines.
** How else can nLP-related technology developments drive the company? ** First, you can create chatbots by combining NLP and AI tools. According to Business Insider, the chatbot market is projected to reach $9.4 billion by 2024, so let’s highlight how businesses benefit from ai-powered chatbot layouts.
Chatbots try to understand people’s intentions rather than just follow standard commands. Companies working in different fields use AI-powered chatbots to provide human-level communication to their customers or users. Chatbots are widely used in healthcare, banking, advertising, tourism and hotels.
Ai-powered chatbots help automate administrative tasks. In the medical field, for example, they have reduced the workload of medical staff. Here, chatbots help schedule appointments, send reminders related to medication and provide answers to patients’ questions. In other areas, chatbots have been introduced to deliver targeted messages, increase customer engagement and support, and provide personalized services to users.
In addition to chatbots, NLP is at the heart of other cutting-edge technology solutions. One example is NLP text generation that can be used for business applications.
The recently introduced GPT-3 model allows AI engineers to generate an average of 4.5 billion words per day. This will enable a large number of downstream applications of AI to be used for socially beneficial and less valuable purposes. This has prompted researchers to invest in techniques to detect generated models. Note that in 2021-2022 we will see the arrival of GPT-4, “AI for Artificial Universal Intelligence”.
Going back to generative AI, let’s focus on gans, generative adversarial networks, which can create images that are indistinguishable from human-generated images. This could be non-existent images of people, animals, objects, and other types of media (such as audio and text). There has never been a better time to implement GAN to its full potential. They can model real-world data distributions and learn useful representations to improve AI pipelines, protect data, detect anomalies, and adapt to specific real-world cases.
Trend 4. Ai-driven QA and intelligent inspection
The most compelling branch of computer vision is artificial intelligence detection. This direction has been booming in recent years as deep learning models are applied to improve accuracy and performance. More and more companies are developing computer vision systems at a faster rate.
Automated inspection in manufacturing means an analysis of whether products conform to quality standards. This method is also applicable to device monitoring. Here are a few use cases for AI detection:
- Inspect the product defects on the assembly line
- Identify mechanical and body parts defects
- Baggage inspection and aircraft maintenance
- Nuclear power station/power inspection
EasyCVR video fusion platform is based on AI computer vision technology to realize AI intelligent detection and recognition of real-time videos, such as license plate recognition, face detection, helmet detection, mask wearing detection, dangerous behavior detection and so on. It has been widely used in security, tourism, fire protection and other projects.
Trend 5: Disruptive AI breakthroughs in healthcare
Trends related to the implementation of AI in the healthcare industry have been widely discussed in recent years. Scientists are using AI models and computer vision algorithms to combat COVID-19 in areas such as outbreak detection, vaccine development, drug discovery, heat screening, facial recognition with masks and analytical CT scans.
To counteract the spread of COVID-19, AI models can detect and analyze potential hazards and make accurate predictions. In addition, AI helps develop vaccines by identifying the key components that make them effective.
Ai-driven solutions can be used as effective tools on the Medical Internet of Things and address privacy issues specific to the medical industry. If we systematize the AI use cases in healthcare, it becomes clear that they all have the same goal — to ensure that patients are diagnosed quickly and accurately.
The evolution and future of artificial intelligence
Trends point to a promising future for AI, as AI solutions are becoming commonplace. Self-driving cars for predictive analytics in manufacturing, robots and sensors, virtual medical assistants, NLP for media reporting, virtual educational mentors, AI assistants and chatbots that can replace humans in customer service — all of these AI-driven solutions are taking a big step forward.