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Artificial Intelligence Trends That Will Make a Big Difference in Business in 2023

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Artificial Intelligence(AI), is rightfully among the technologies that are fundamentally changing the modern world.

According to McKinsey’s Global Survey, The State of AI in 2021, the adoption of artificial intelligence continues to spread. 56% of all surveyed business representatives stated that their companies use AI in at least one function, and as recently as 2020, this indicator was equal to 50%.  According to the same source, the majority of survey respondents also noted the positive impact of artificial intelligence on business efficiency and cost reduction.

Expectations from AI developments are confirmed by a willingness to invest in them. The Stanford Artificial Intelligence Index Report 2022, reveals that private investment in artificial intelligence is growing rapidly, reaching an amount of about 93.5 billion US dollars in 2021, more than double the corresponding figure in 2020. Also, two-thirds of the audience of the McKinsey survey we quoted above, claim that their companies’ investments in AI will increase over the next three years.

Such investment activity will inevitably result in the emergence and improvement of new comprehensive solutions and tools and will raise the overall technological level in this area. Businesses should be ready to catch the wave. 

This article will help you to do this by clarifying the state of affairs in artificial intelligence. We will list the current trends in AI, as well as reveal the recent ones that emerged in 2022 and will be significant in 2023. 

This article also names the Top 5 industries that will be affected by AI trends in 2023 and comments on their progress. Your company will be able to use this information to plan to create additional value, optimize costs, and strengthen customer focus. You will learn how to do this using tools developed in subfields of AI such as Neural Networks, Machine Learning, Computer Vision, Expert Systems, Natural Language Processing, and Speech Processing.

Trend 1. AI Unlocks the Metaverse

While the debate about the boundaries and regularities of the Metaverse continues, businessmen are already arranging and populating it. In fact, 2023 will be a defining period for many companies with regard to how they will create their world within the Metaverse.

The latest technology in artificial intelligence is one of the key elements of Metaverse. For example, AI allows us to overcome the limitations inherent in AR and VR, and create realistic 3D images and spaces. In addition, AI systems have a built-in potential for improvement due to self-controlled learning, which may not require human participation.

It can be said about any of the latest AI technologies that find their application in the Metaverse. For example, artificial intelligence avatars, or so-called digital humans, have emerged largely due to natural language processing (NLP) and computer vision. Virtual versions of people begin to understand not only language but also the movements and emotions of each other. In this way, users can transfer a part of their interaction with others to the avatar. At the same time, the digital copy can be given complete similarity to the original or other specific features. 

The integration of AI systems and the Internet of Things (IoT), whose devices supply the necessary data, has made possible the creation of digital twins. Such virtual versions of environments or systems allow modeling their development under various scenarios. This makes it possible to predict their condition and behavior under the influence of different external factors. Therefore, the accumulation of data on digital duplicates of real objects and their aggregates, as well as the creation of management systems for such databases, is in demand. The challenge is to improve the quality of digitization, which, in turn, entails increasing the requirements for storing such huge volumes of data and for the productivity of their processing and analysis.

It is advisable for businesses to evaluate the following:

  • 3D models of which objects and systems will be primarily useful for companies and their consumers.
  • Processes involving clients and partners that can be painlessly transferred to the virtual world, and perhaps even made better there. Nowadays, no one is surprised by virtual fitting or product testing, digital modeling of decor, and much more.
  • Priorities, preferences, and expectations of your target audience in the Metaverse, which becomes a space for communication, sales, and other forms of interaction.

It is also worth considering the evolution of Metaverse in the context of the decentralized concept of modern life, preparing to enable customers to independently store and share data, process important information on their AI devices, use decentralized applications (DApps), etc. At the same time, hardware and services should not lag behind the software.

The current trend shows that appetites for AI-based products and services are only growing. According to the Worldwide Semiannual Artificial Intelligence Tracker provided by IDC (International Data Corporation), worldwide revenues for the artificial intelligence (AI) market in 2024 may be close to 500 billion US dollars. In turn, each of these AI-powered business applications can give impetus to developing other elements of the Metaverse. 

Trend 2. AI Enhances Security and Surveillance

A new level of quality in security systems has also become possible thanks to the new artificial intelligence technologies. Video surveillance can now be combined with biometric authentication using face and voice recognition, and automated image analysis. AI-based security and monitoring systems lend themselves to more precise settings and more accurately identify objects that should be responded to when they appear. Video capture and analysis software helps secure large public and private spaces by detecting potential threats. Sensitive systems provide operators with timely information, not only about the identification of unwanted guests, but also about any behavioral anomalies or suspicious activity of visitors.

Identifying a person, including their age, gender,  and emotional state, through voice recognition has also become an important feature of AI-powered applications. A built-in anti-spoofing feature that detects synthesized and recorded voice is just what is needed to keep such tools safe. Biometric facial recognition is also essential for maintaining security.

It should be noted that potential attackers and unscrupulous users also have many technologies at their disposal. Cases of spoofing attacks where a person pretends to be another person and wants to get an illegal benefit are not rare. This can be done with the help of special malicious programs, using fake photos or other people’s images, and stolen personal data. It should be borne in mind that most Internet protocols do not have mechanisms for authenticating the request source. Therefore, the functionality of safe and reliable verification of the user’s identity should be part of the software. Thus, the development of various anti-spoofing techniques will be in high demand in 2023.

The use of biometrics to realize the concept of “what-you-are” authentication makes it possible to confirm an individual’s identity by its unique characteristics, such as fingerprints, iris, voice, or face. The Edge AI approach means the installation and operation of biometric artificial intelligence programs directly on the user’s peripheral device, without the need for a constant connection to the Internet and a cloud service. The data is processed locally and autonomously on the user’s portable, perhaps even wearable, device. A common case is the use of edge biometrics for office security. In this way, such solutions have become a promising area of AI-based development for improving security and surveillance.

Trend 3. AI in Real-Time Video Processing

For efficient processing of real-time video streams, it is critical to achieve data transmission accuracy and minimize video processing latency. AI solutions are involved in the root element – data pipeline processing.

The real-time video processing system using recent developments in artificial intelligence involves close integration of a pre-trained neural network model, user scenario implementation algorithms, and cloud infrastructure. It is thanks to the integration of these elements that real-time streaming speed is achieved. Acceleration of video processing is possible in two ways – by improving algorithms and by parallelizing processes. Parallelization of processes can be done through file splitting or by applying a pipeline approach.

The pipeline architecture allows the use of an AI algorithm for real-time video processing, avoiding additional complications and maintaining model accuracy. That is why the pipeline architecture is the optimal choice for fast and high-quality video processing. Moreover, it allows for the use of additional effects for face recognition and blurring. You can delve deeper into this topic with our article on AI in real-time video processing

It is hard to imagine processing streams in real-time without the ability to apply background removal and blurring. The rapid development of video conferencing has led to a growing interest in tools for these effects. And this trend will continue to gain momentum as the global video conferencing market is expected to grow from USD 9.2 billion in 2021 to USD 22.5 billion by 2026, according to GlobeNewswire forecasts.

Background removal and blurring in  real-time video are based on creating a model that separates the person in the frame from the background. This task relies on a neural network. For its operation, you can choose one of the existing models, such as BodyPix, MediaPipe, or PixelLib. Next, you must integrate the chosen model with the relevant framework and organize the optimal execution process using WebAssembly, WebGL, or WebGPU. 

Trend 4. AI-Powered Virtual Assistants and Chatbots

Digital assistants of this type are very common and have become the first experience of interacting with AI for many. Actually, it is the interaction with a person that is the highlight of such AI solutions. For example, an AI-based chatbot is aimed not at simply following a standard set of commands, but at understanding customer intent and habits. Such tools provide their users with communication at a level close to “human-human”. As a result, necessary information is provided in the most comfortable way for a person.

The use of chatbots is promptly spreading in industries such as healthcare, finance, marketing and sales, travel and hospitality, etc., reducing the need for staff. For instance, a medical chatbot can easily handle organizing a patient’s doctor’s appointment, providing answers to frequently asked questions, and reminding people when it`s time to take their medicine and get some exercise.

In other areas, chatbots classify and forward customer requests for processing, deliver targeted messages and provide users with personalized offers and support. Educational chatbots have become irreplaceable assistants who are always at hand to consolidate knowledge at any time convenient for the learner. So, it is quite natural that, according to Business Insider, it is likely that the chatbot market will reach USD 9.4 billion in 2024.

The popularity of AI-powered virtual assistants is due to the pace of modern life. Interaction with conversational AI assistants allows a person to get desired information without breaking away from other everyday activities. Ultimately, advances in Natural Language Processing (NLP) have greatly increased the capabilities of customized automated solutions. For example, the NLP-based Question Generation system presented in the following video prevents errors in the secure authentication process.

Trend 5. No-code AI Platforms

The key to the popularity of AI technologies is their availability. The emergence of no-code AI platforms significantly lowered the threshold for even small companies to become users of cutting-edge solutions. Working with the latest artificial intelligence has ceased to be a privilege for large corporations that could afford to order lengthy and expensive software development from scratch.

Let us briefly recall what makes the no-code solutions attractive:

  • Fast development and implementation that takes 90% less time than writing code from scratch with the appropriate data collection and processing and subsequent debugging,
  • Low development cost — thanks to automation, the need for the independent collection of training data has disappeared.
  • Ease of use – you create software without writing code yourself, in particular, simply by using the drag-and-drop functionality.

No-code AI platforms are in demand in cases where customization of the developed products is not so critical. These options are often used by companies for computer programs to identify and classify images, objects, poses, sounds, etc. Google Cloud Auto ML, Google ML Kit, Runaway AI, CreateML, MakeML, etc. can be mentioned among the most popular such environments. Our scheme will help you prepare to use No-code AI platforms for your business.

How No-code AI platforms work

Top 5 Industries That Will Be Affected by AI Trends in 2023

AI Improves Diagnostic Accuracy in Healthcare

The latest AI technologies are fundamentally changing healthcare. Huge amounts of accumulated medical information have become available for processing and analysis. And this is critically important, as some predict that healthcare data volumes will grow by 36% annually in the near future. In turn, the processing of such a large amount of information also allows for the preparation of high-quality training data, which make it is possible to increase the efficiency of the AI-based models.

According to recent trends in AI, the most promising areas of development are:

  • Individual wearable and non-wearable devices that track key health indicators and provide real-time feedback, effectively becoming a personal health advisor.
  • Telemedicine is a tool to facilitate access to medical services. Never before has a patient been so free in choosing and communicating with healthcare providers. According to some data, the number of telemedicine visits in the US in March 2020 increased more than 1.5 times compared to the same period of the previous year. In this case, it should be noted that the relevant IT solutions managed to ensure remote interaction between patients and doctors even with such surges in demand.
  • Automation and customization of research and trials in the creation of drugs and vaccines, including with the help of digital twins.
  • AI-based solutions for The Internet of Medical Things, which bring medical equipment to a new level, as well as “Software as a medical device” (SaMD) type developments, which allow better use of medical device capabilities.
  • Medical image segmentation (magnetic resonance imaging (MRI), computed tomography (CT), etc.) for more efficient analysis of anatomical data.

Learn more about how image segmentation works in MobiDev’s video below.

Let us note the significant progress in solving one of the fundamental problems of medicine – diagnostics. Increasing the accuracy of diagnoses is largely achieved thanks to machine learning, which improves the quality of conclusions made on the basis of the processed data. Early diagnosis is essential for diseases such as dementia or cancer, for which early detection significantly increases the patient’s chances of successful treatment.

For example, dementia is a major challenge for health care because of the irregularity of its manifestations and the difficulty in establishing symptoms. However, the use of artificial intelligence through the creation of speech processing models makes it possible to identify communicative and logical problems that indicate the risk of this disease. Such developments for dementia diagnosis are associated with three subfields of AI – Natural Language Processing (NLP), Machine Learning (ML), and Neutron Networks – and can be used both for early diagnosis of dementia and for monitoring the progression of the disease. Neuropsychological testing for early diagnosis can be performed even on telephone recordings, and classification models help monitor changes in the patient’s condition.

The use of the latest AI technologies has significantly contributed to the emergence of new methods of diagnosing oncological diseases with AI, which provide doctors with multi-point and complete information, unlike a traditional biopsy. This is due to the possibility of conducting histopathology based on digital scanning of parts of the human body. For this, specialists use whole slide imaging (WSI). Processing information in this format was previously very difficult and time-consuming due to the huge image resolution. 

AI-Driven QA and Inspection in Manufacturing

A notable branch of computer vision is quality control using AI. Automated production control is mostly used to monitor equipment and check product quality. Productivity and accuracy of quality assurance and inspection systems are increasing. In this area, the positive impact of improving the identification of objects on video is felt.

In particular, AI-powered inspection is used to control the suitability of components for assembling cars, as well as to detect product defects on the conveyor. Further improvement in the detection of defects in manufacturing enterprises will focus on the automation of analysis and decision-making.

Use cases of AI inspection

AI software can determine the nature of defects in parts or finished products based on data from cameras and IoT sensors. The evaluation of the degree of criticality of defects and the decision-making process regarding how to deal with the identified defect also become automated. Read more about the application of the latest AI technologies in the industry in our article.

AI for Workflow Automation and Demand Forecasting in Retail

Bringing AI technologies to retail makes forecasting consumer demand more accurate. This creates prerequisites for increasing the quality of assortment planning and reliability of delivery. Machine learning algorithms can be used to create models that precisely calculate seasonal and other fluctuations in demand. In this way, it is possible to maintain product stocks in distribution centers and retail outlets at the desired level.

ML methods make it possible to forecast demand, based on the analysis of significant volumes of data from previous periods, the identification of internal relationships and regularities in demand fluctuations, as well as the probability of their repetition in certain periods.

According to McKinsey studies, companies that are the first to manage their supply chains with artificial intelligence have improved their logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent compared to latecomers.

Research shows that AI in supply chain management can reduce management errors by 20-50 percent, reduce product shortages and lost sales by up to 65 percent, storage costs by 5-10 percent, and administrative costs by 25-40  percent. 

The positive effect of high-quality long-term planning of goods orders is transmitted from retail to transport and other industries, allowing them to work rhythmically and rationally use resources.

In recent years, retail has shown many examples of AI-driven transformation. It is even possible to eliminate the classic bottlenecks of supermarkets. The implementation of self-checkout due to the automation of cash registers using computer vision saves customers’ time and increases their satisfaction. At the same time, retail chains optimize personnel costs. The use of the latest artificial intelligence technologies makes it possible to offer retail a full range of automation solutions – from partial modular automation, such as vending machines, to a fully automated “grab-and-go” store.

AI-powered autonomous retail stores allow the customer to combine the ease of online shopping with the advantages of a  brick-and-mortar outlet. Thus, each retail chain can choose the scale of AI automation for which it and its customers are ready.

Approaches tested in retail are also successfully used in catering. Innovations focus on the main stages of the digital purchase, which are identification of the buyer’s identity and tracking of his actions, product recognition, purchase verification, and payments.

Generative AI for Content Creation in Marketing

The growth in the quality of Artificial Intelligence-generated content is a particularly good example of progress in the field that we consider in this article. Research and development in these areas are united by the term Generative AI.

Natural language processing (NLP) focuses on text generation algorithms. This subfield of artificial intelligence develops models that might be used to augment the capabilities of search engines, generate text in business applications, and chatbots

The large-scale model was Generative Pre-trained Transformer 3, which uses deep learning to create human-like texts. GPT-3, which is already the third generation of this language prediction model, generates an average of 4.5 billion words per day. Further expectations of users of this type of model are related to GPT-4, designed to surpass the parameters of the previous generation sample.

It is gradually becoming commonplace that the text, animation, audio track, or images generated by AI models are in no way inferior to those created by humans. A conversation about Generative AI would be incomplete without mentioning GAN technology, or Generative Adversarial Networks, which are already capable of independently creating media of various types. 

Both approaches to using AI models for content remain promising – independent creation in various formats and software assistance to people in the process of creating text and visuals. Relevant prompts and instant selection of options for the author are already implemented in such programs as Grammarly, Google Docs, Microsoft Word, SEMRush, Adobe Premiere Pro, etc.

Implementation of the latest technologies of artificial intelligence gives impetus to the development of many industries. Content and user experience are among the fastest- growing segments of MarTech. In this connection, there are opportunities to modernize the usual workflow in marketing.

It seems that there is every reason to expect the popularity of modular content to grow. This approach involves the creation of customized content for an individual or a certain category of consumers. In this case, previously prepared thematic blocks or modules are used. Depending on the interests of the part of the audience which marketers will address, the content set will include certain blocks. In this way, peculiar conveyors and factories for the production of personalized content appear.

AI-Powered Personalization and Fraud Detection in Fintech

The financial industry always needs innovations to ensure its traditional priorities: the speed and accuracy of transactions, the prevention of errors and abuses, the preservation of the privacy of customer data, and the observance of the confidentiality of transactions

Therefore, it is no coincidence that the FinTech sector is actively implementing artificial intelligence technologies for the security of funds and personal data, generalization of client experience, preparation, and management decision-making.

The speed of information processing inherent in AI solutions makes it possible to quickly take into account recent events, detect atypical transactions, or see the synchronicity of unscrupulous actions of seemingly unrelated people. Models created with the help of Machine Learning (ML) quickly learn to recognize suspicious transactions, attempts to steal or forge a digital identity, and other possible fraudulent activities. 

Early detection of any signs of possible fraud and preventive identification of potential weak points in payment systems is the mission of AI shield for finance. Timely detection and blocking of a dubious transaction or unauthorized access to data not only saves money but also prevents the destruction of the relationship between the client and the financial organization. 

According to PwC’s Global Economic Crime and Fraud Survey 2022, 52% of companies with annual revenue of more than USD 10 billion have been the target of fraud in the past 2 years. As is known from the same survey, the most common types of fraud in financial services are customer fraud (44%), cybercrime (38%), and “know-your-customer” failure (29%). This drives the importance of AI-driven “know your customer” processes aimed at gathering information about identity, customer suitability, and associated risks.

In addition to the reliable and timely detection of fraud, the latest artificial intelligence technologies make it possible to increase the security of corporate and personal finances by other methods. Among them is the identification of the client through the processing of his speech, facial recognition, or by biometric parameters such as fingerprints, etc. Ultimately, access to accounts and information becomes multi-factorial and more customizable, as the user chooses. 

Also, innovations in the area we are considering are quite useful for improving information exchange and interaction between clients and financial institutions.

Customization is a feature that is inherent in software products based on AI. Reminders and prompts offered by a robo-adviser, as a rule, can be both text and voice. The receipt of financial news and messages can also be targeted by the industries and markets of interest to the user. 

Personalized offers are as important to the consumer of financial services as they are in other sectors of the economy. Financial organizations get the possibility of an individual approach to working with clients, thanks to the analysis of their income, expenses, requests, previous transactions, and risks.

An electronic personal investment advisor is also an option that became possible with the spread of artificial intelligence technologies. At the same time, the basic algorithms can be customized by entering additional settings, such as the acceptable level of risks, the priority of certain industries or markets, and quantitative restrictions. In combination with the ability to process market data in real-time, such tools allow you to automate investment decisions in accordance with the priorities of the owner of the funds.

In this context, it is appropriate to recall the importance of AI explanation as a factor in increasing trust in finance. Explainable AI makes the process of making financial decisions more transparent, justified, and reliable. Automation does not stop when it is less visible to a large number of clients of banks and other financial organizations, i. e. in their back offices. It is about the automation of internal work processes of such institutions. First of all, this applies to standard and repetitive operations in which it is important to reduce the likelihood of human error or negligence – checking and closing accounts, reconciling electronic documents, processing inquiries, credit risk assessments, etc. 

It should also be noted that models based on machine learning can be designed as self-improving and additionally adjusted with the acquisition of new data. And this, in turn, allows management to focus on organizational development strategies and, together with personnel, on the formation of a positive customer experience.

The Future of AI Technology

Understanding the future and current trends in artificial intelligence, as well as the information about the industries most affected by them, obtained from this article, will allow you and your teams to be on the ball in 2023.

Given the research and investment activity mentioned above, we can expect further dynamic development in the field of artificial intelligence. A range of solutions – chatbots, assistants, NLP tools, robots, and sensors for analysis and predictions – have already become mainstream, and this vector will continue and become more pronounced.

We can foresee a wider application for standard operations of AI-based easy-to-use automated products. Thus, the probability of error due to the human factor is reduced, and the working time of the personnel of the organizations is redistributed towards more creative and complex tasks.

AI trends are in line with the general economic trend of the personalization of goods and services. It is thanks to the latest technology in artificial intelligence, primarily machine learning, that personalization, based on the processing of huge data sets and the prediction of consumer behavior, is becoming a reality. The combination of artificial intelligence with cloud technologies also has serious potential.

The way to improve the use of AI is through the reproduction of human interaction patterns, as well as the transformation of human-machine interaction. This will be primarily achieved by working with language and content through multimodal learning, large language models, and natural language processing. Algorithms for machine support for people will become more complex and functional, rising to the level of advisory and training maintenance for the user.

Machine learning, and in particular deep learning, remains the field of expected technological leap in the future. It is no coincidence that MLOps, which cover the practices of deploying and maintaining machine learning models, have already emerged as a separate area of expertise and development.

The priority of the coming years is the creation of models that require fewer data and training examples for training, which will allow faster application of AI solutions for increasingly complex tasks.

Summarizing the current AI trends described above, it can be noted that companies are at different stages of mastering these technologies. Leaders are already analyzing their own relevant implementation experience, improving and scaling their AI practices. At the same time, many are still only considering and testing the possibilities that exist in this area. In this regard, it is worth paying attention to at least two positive circumstances.

First, recent developments in artificial intelligence are evolving towards automating the processes of data collection and processing, testing, and deployment of created programs. In addition, many proven patterns have already been worked out, and successful solutions are being replicated. Due to the impact of these factors, the cost of access to IT solutions based on artificial intelligence is gradually becoming acceptable for the majority of potential users.

Secondly, customers should not independently analyze current trends in AI and study their consequences for business. It is advisable to rely on the expertise of companies that specialize in development using the latest technology in artificial intelligence. Our AI consultants and engineers at MobiDev, with the help of state-of-the-art technologies, are always ready to develop a custom solution to apply the advantages of artificial intelligence to your business. 

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