Artificial Intelligence Technology Trends That Are Vital To Businesses

Artificial Intelligence Technology Trends That Are Vital To Businesses

This article aims to outline the new and current trends in artificial intelligence that will appear in 2020 and will continue to grow in 2021. Companies can predict the future of artificial intelligence in 2022 based on trends and successfully reduce risks.

According to the 2020 McKinsey Global Artificial Intelligence (AI) survey, in 2020, more than 50% of companies have adopted artificial intelligence in at least one business department or functional department, so we have witnessed the emergence of new artificial intelligence trends. Organizations apply artificial intelligence tools to create more value, increase revenue and customer loyalty. Leading AI companies invest at least 20% of earnings before interest and taxes (EBIT) in AI. As COVID-19 accelerates digitization, this number may increase. The blockade has led to a surge in online activities and the massive use of artificial intelligence in business, education, administration, and social fields.

This article aims to outline the new and current trends in artificial intelligence that will appear in 2020 and will continue to grow in 2021. Companies can predict the future of artificial intelligence in 2022 based on trends and successfully reduce risks.

 

AI adoption trends

The level of AI adoption varies from industry to industry. Using the data mentioned in the McKinsey Global Artificial Intelligence Survey, we can highlight four leading industries: high-tech, telecommunications, automotive, and assembly.

 

The company applies artificial intelligence to service operations, service or product design, advertising, and sales. In terms of investment, the field of drug discovery and development has received the most funds-in 2020, the total assets will exceed 13.8 billion U.S. dollars, an increase of 4.5 times over the previous year.

 

If applied to inventory and parts optimization, pricing and promotion, customer service analysis, sales and demand forecasting, artificial intelligence will drive the highest revenue growth. Use cases for reporting cost reductions are related to optimizing talent management, contact center automation, and warehouse automation.

Artificial intelligence technology trends

In 2021 and the next few years, artificial intelligence will be used to simplify operations and improve efficiency. Companies should try to benefit from commercial applications of artificial intelligence by improving IT infrastructure and data management. But not every AI model deployed can be helpful to the company and suitable for performance monitoring. We will focus on the artificial intelligence trends that may become mainstream in 2021-2022.

Trend 1: AI for security and surveillance

Artificial intelligence technology has been applied to face recognition, voice recognition and video analysis. These technologies constitute the best combination of surveillance. Therefore, by 2021, we can foresee the massive use of artificial intelligence in video surveillance.

 

Artificial intelligence is conducive to the flexible setting of the security system. Previously, engineers spent a lot of time configuring the system because it was activated when a certain number of pixels on the screen changed. Therefore, there are too many false positives. These alarms are caused by fallen leaves or running animals. Thanks to artificial intelligence, the security system can recognize objects, which contributes to more flexible settings.

 

Artificial intelligence in video surveillance can detect suspicious activity by focusing on abnormal behavior patterns instead of faces. This ability can create safer public and private spaces by identifying potential threats. This AI-driven video solution may also help logistics, retail, and manufacturing.

Another niche that offers prospects for artificial intelligence applications is speech recognition. Technology related to speech recognition can determine identity. Identity refers to a person's age, gender, and emotional state. Voice recognition for monitoring may be based on the same principles as Alexa or Google Assistant. A feature suitable for security and surveillance is the built-in anti-spoofing model that can detect synthesized and recorded speech.

 

One of the most critical security technologies is biometric face recognition. Different malicious applications try to trick the security system by providing fake photos instead of real images. In order to prevent this situation, a variety of anti-spoofing technologies are currently being developed and used on a large scale.

 

Trend 2: Artificial intelligence in real-time video processing

The challenge in dealing with real-time video streams is processing the data pipeline. The engineer’s goal is to ensure accuracy and minimize video processing delays. And artificial intelligence solutions can help achieve this goal.

 

In order 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 application user scenarios. Processing speed is critical for real-time streaming, so all 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 pipeline methods. This pipeline architecture is the best choice because it does not reduce the accuracy of the model and allows the use of AI algorithms to process video in real time without any complexity. In addition, for the pipeline architecture, additional effects that imply face detection and blur can be applied.

Modern real-time stream processing is inseparable from the application of background removal and blurring. As COVID-19 has contributed to the emergence and popularization of new trends in video conferencing, the demand for these tools has increased. These trends will be actively developed because, according to GlobeNewswire, the global video conferencing market is expected to grow from US$9.2 billion in 2021 to US$22.5 billion in 2026.

 

There are many ways to develop tools for background removal and blurring in real-time video. The challenge is to design a model that separates the person 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, there is still the challenge of integrating it with the appropriate framework and organizing the best execution process through the application of WebAssembly, WebGL or WebGPU.

Trend 3: Generative artificial intelligence for content creation and chatbots

Modern AI models can generate very high-quality text, audio, and images, which are almost indistinguishable from non-synthetic accurate data.

 

The core of the text is natural language processing (NLP). The rapid development of NLP has led to the emergence of language models. For example, Google and Microsoft have successfully used the BERT model to supplement their search engines.

 

How can the development of NLP-related technologies promote the development of the company? First, a chatbot can be created by combining NLP and AI tools. According to Business Insider, the chatbot market is expected to reach $9.4 billion in 2024, so let us highlight the ways in which companies benefit from the implementation of AI-driven chatbots.

In addition to chatbots, NLP is the core of other cutting-edge technology solutions. One example is NLP text generation that can be used in business applications.

 

The recently launched 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 low-value purposes. This has also prompted researchers to invest in technology to detect generative models. Please note that in 2021-2022, we will witness the arrival of GPT-4-"Artificial General Intelligence AI".

Going back to generative AI, we need to focus on GAN, the generative adversarial network, which can create images that are indistinguishable from artificially generated images. This may be non-existent images of people, animals, objects, and other types of media (such as audio and text). Now is the best time to implement GAN and use its capabilities. They can model the distribution of real data and learn useful representations to improve AI pipelines, protect data, find anomalies, and adapt to specific real-world cases.

Trend 4. AI-driven QA and inspection

The most compelling branch of computer vision is artificial intelligence inspection. Due to the application of deep learning models to improve accuracy and performance, this direction has been booming in recent years. The company began to invest in computing and financial resources to develop computer vision systems at a faster rate.

 

Automated inspection in manufacturing means analyzing whether the product meets quality standards. This method is also suitable for equipment monitoring.

Here are a few use cases for AI detection:

• Detect product defects on the assembly line

• Identify defects in machinery and body parts

• Baggage inspection and aircraft maintenance

• Nuclear power plant inspection

 

Trend 5: Disruptive AI breakthroughs in healthcare

In recent years, the next trend related to the implementation of AI in the healthcare industry has been widely discussed. Scientists use AI models and computer vision algorithms to combat COVID-19, including areas such as pandemic detection, vaccine development, drug discovery, heat screening, facial recognition with masks, and analysis of CT scans.

 

To counteract the spread of COVID-19, artificial intelligence models can detect and analyze potential threats and make accurate predictions. In addition, artificial intelligence helps develop vaccines by identifying the key components that make vaccines effective.

 

AI-driven solutions can be used as effective tools on the medical IoT and deal with confidentiality issues specific to the healthcare industry. If we systematize AI use cases in healthcare, it is clear that their goal is the same-to ensure that patients are diagnosed quickly and accurately.

Trend 6: No-code AI platforms in at least three areas

The codeless AI platform enables even small companies to apply previously available powerful technologies only to large companies. Let us find out why such platforms are the key AI trends for enterprises in 2021.

 

It takes time, expense, and relevant experience to develop an AI model from scratch. The use of a code-free artificial intelligence platform simplifies the task because it lowers the barriers to entry. The advantage is:

 

• Fast implementation—Compared with writing code, processing data and debugging from scratch, it saves up to 90% of time.

• Lower development costs-through automation, companies eliminate the need for large data science teams.

• Easy to use-Drag and drop functionality simplifies software development, and applications can be created without coding.

The healthcare, financial sector, and marketing sectors all require codeless AI platforms—although the resulting solutions cannot be highly customized. Among the most popular no-code AI platforms, you can find Google Cloud Auto ML, Google ML Kit, Runaway AI, CreateML, MakeML, SuperAnnotate, etc.

 

Enterprise-sized companies and medium-sized enterprises use code-free platforms to develop software solutions for image classification, recognition of gestures and sounds, and object detection.

The evolution and future of artificial intelligence

Trends indicate that the future of artificial intelligence is promising, as artificial intelligence solutions are becoming commonplace. Self-driving cars, robots and sensors for predictive analysis in manufacturing, virtual medical assistants for media reporting, NLP for media reporting, virtual education tutors, artificial intelligence assistants, and chatbots that can replace humans in customer service— —All these artificial intelligence-driven solutions are taking a big step forward.