State of the AI market in 2023
According to PrecedenceResearch, the global artificial intelligence (AI) market size was valued at USD 69.25 billion in 2022 and is expected to hit around USD 1,871.2 billion by 2032, progressing with a CAGR of 39.1% from 2023 to 2032. The growth of the industry is attributed to the introduction of AI technology in the following sectors: automotive (autonomous drones), healthcare (digitization of genetic material for ever faster testing of new drugs), retail (display of advertising based on collected user preferences), banking, services, and mobile development.
According to McKinsey research, the level of investment in AI has increased alongside its rising adoption. About 37% of global brands, companies, and startups have implemented AI technology. These include multinational tech giants such as Amazon, Apple, and Microsoft.
AI helps personalize communication with the user, making it more efficient and profitable for the company.
AI is embedded in the day-to-day experience of the user. When a customer performs a targeted action or moves seamlessly through an interface, complex algorithms work on the invisible technical side, solving a spectrum of problems. NLP (Natural Language Processing) is used for speech recognition, neural networks for face identification, and AR (Augmented Reality) for visualizing elements. Different areas of artificial intelligence allow for creation of unique products that can be integrated into mobile applications.
AI for marketing your business
AI solutions improve user experience and provide personalized product interactions. AI can be integrated into CRMs, marketplaces, HRMs, SaaS solutions, sales automation software, Android and iOS mobile applications, meeting schedulers, etc. For example, developing AI-enabled dashboards in the short term can open up new business opportunities.
AI can automate B2C interactions through voice or text and cross-platform communication
AI uses the data that has been embedded in it during the learning process, as well as from all previous communications with humans, and analyzes it. This enables AI to respond more accurately to customer requests and perform automatic processing. For convenience, the user should be able to receive product information on any convenient platform. When moving from a website to a mobile app and vice versa, AI will remember the history of the request. The customer will not be introduced a second time. The AI will recognize the user by phone number, account, or other embedded parameters and continue the conversation.
Analyzing user actions in the app
AI helps companies make better decisions based on analyzing customer data.
Delivering the most relevant products and services to the user is linked to understanding their behavior patterns in the web app. Major retailers are already using AI to understand search query patterns.
For example, eBay's app is powered by ML, NLP, and CV technologies.
ML recognizes a product image after the user uploads it into the search bar. ML also helps to compare prices of competing products and offer the best price to customers for quick sales.
Personalization
Web applications with AI can personalize the user interface. The built-in AI monitors user behavior and suggests customized changes for each. Popular apps implementing personalization with ML integration are Uber and Uber Eats. Users can track estimated arrival and delivery times with real-time monitoring on a map.
Predicting market needs
AI-enabled web apps using ML and predictive analytics are capable of predicting outcomes with high accuracy based on data analysis, predicting customer choices and market dynamics. AI observes certain patterns and regularities in data and customer behavior, which can further show whether a particular product or service will be relevant. Needs forecasting can be used in all industries. For example, it can be used in banking to determine currency fluctuations.
Reducing time spent on repetitive tasks
AI can replace a specialist who performs typical tasks: sending emails, chatting, making phone calls, and other functions. The role of AI in app development is gradually increasing. The adoption of AI technology facilitates digital transformation and drives business growth.
2 ways to use AI in your product
There are two main approaches to using AI in applications: server-side processing and device-side (or edge) processing. Let's explore each approach in more detail.
Server-side processing
In server-side processing, the AI models and computations are performed on the server or cloud infrastructure. The application running on the user's device sends the necessary data to the server, which then processes the data using the AI models and sends back the response. Here's how it typically works:
- Data transmission. The application sends the input data (such as images, text, or user interactions) to the server via network communication, usually through API requests.
- Server-side computation. The server receives the data, performs the required AI computations using the trained models, and generates the response.
- Response delivery. Once the processing is complete, the server sends the response back to the user's device, which can be in the form of recommendations, predictions, or any other output based on the AI model's functionality.
Advantages of Server-side Processing:
- Lower device resource requirements. The heavy computational load is handled on the server side. It relieves the device from the burden of running complex AI models, making it suitable for low-power devices or devices with limited resources.
- Centralized model management. Updates and improvements to the AI models can be deployed on the server, ensuring all users benefit from the latest advancements without requiring updates on individual devices.
- Scalability. Server-side processing allows for easier scalability as the AI models can be deployed on powerful servers or cloud infrastructure, capable of handling many simultaneous requests.
When processing data on the server side, the functionality of your application will not be available without an Internet connection, and you will have to maintain infrastructure on your own or rented servers to run models. On the plus side, there will be no limitations in computational capabilities.
Suppose your model runs directly on the phone itself. In that case, you are limited by the computing power of that device, i.e., the phone's computational potential may not be sufficient to perform the task you need.
Device-side (Edge) processing
In device-side processing, the AI models are deployed and run directly on the user's device, eliminating the need for server communication for inference. The models and computations are executed locally on the device itself. Here's how it typically works:
- Model deployment. The AI models are packaged and deployed within the application on the user's device. This can be done using frameworks like TensorFlow Lite or Core ML, designed for efficient model execution on mobile or embedded devices.
- On-device computation. When the application receives input data, it processes the data using the deployed AI models locally on the device.
- Local response. The response or output generated by the AI models is then provided directly to the user within the application without requiring server interaction.
Advantages of Device-side Processing:
- Reduced latency. Since the inference is performed locally on the device, it eliminates the need for network communication and reduces the response time, providing faster results.
- Enhanced privacy. With device-side processing, sensitive data can remain on the user's device without being transmitted to external servers, thereby increasing privacy and security.
- Offline functionality. Device-side processing allows the application to perform AI-related tasks even without an internet connection, providing uninterrupted functionality.
Choosing between server-side processing and device-side processing depends on various factors, including the complexity of the AI models, the available device resources, the need for real-time or offline processing, privacy concerns, and the scalability requirements of the application. In some cases, a combination of both approaches, known as hybrid models, may be used, where certain parts of the AI processing are performed on the server while others are executed on the device.
4 steps in the AI development process
Today, almost all major machine learning frameworks have their tools for deploying trained models on mobile devices. These include Google's TFLite for TensorFlow and Facebook's Pytorch Mobile for Pytorch. Solutions from Microsoft and Alibaba have appeared, and CoreML from Apple. For Android, Google, with its ecosystem, remains the undisputed leader. You can train a model on one framework, such as Pytorch, and convert it to the format of another framework, such as TFLite, for deployment on a mobile device.
Besides the TFLite library for running neural networks, Alphabet Inc. is developing several frameworks. Using the Firebase console, with MLKit, even a novice developer can integrate ML into Android or iOs apps.
A more experienced developer can use the MediaPipe framework. With it, you can not only use off-the-shelf solutions but also extend the functionality of your blocks.
Now let`s discuss the AI development process.
Data collection and partitioning
It is one of the main components of machine learning. The training sample's size and its partitioning quality determine what your AI will learn.
When partitioning data, you can use tools such as Labelbox. It is possible to outsource partitioning on the resource. This is relevant when working with data where markup does not require knowledge in a narrow subject area.
Selecting a suitable model and training it
The choice of model architecture depends on whether the model will run directly on the device or the server side.
To reduce the size of the model weights file, to reduce the size of the application, and to speed up its operation, various variants of pruning and quantization of weights can be applied.
To record speech/video/image on the phone and recognize them on the server, you can use ready-made solutions from TensorFlow Serving, TorchServe, and Nvidia-Triton.
Development of algorithms for processing model predictions
For such tasks as object tracking/gesture recognition, it is necessary to implement algorithms for data post-processing in addition to the models themselves. Algorithms should be independently implemented in one programming language (preferably a cross-platform language like C++ or Kotlin), or you can take a ready-made solution from MLKit or MediaPipe.
Tracking the performance of the AI models
To understand how well your models cope with the tasks, in addition to studying the application's analytics, it is necessary to allow the user to point out the errors of triggering models (misunderstood phrases, incorrectly recognized images, etc.). And based on the collected errors, restart the learning process.
AI case studies
AI technologies will be widely used to analyze customer choices, automate, optimize resource utilization, generate automated responses to queries, and increase conversion rates. Based on AI, ML, and NLP capabilities, users will receive a personalized experience.
With the help of Big Data analysis algorithms, experts use statistics for further predictive analytics. Amazon Pinpoint is a service of multi-channel marketing communications and mobile analytics that works on Big Data algorithms. It helps identify the target audience, analyze user behavior, select key messages, and track campaign results.
AI in retail mobile app development helps analyze customer behavior and increase sales by providing relevant product recommendations.
Technology plays a vital role in the personalization process. The most prominent example of an app that provides personalized user experience with the help of AI is My Starbucks. Users can order a drink on the go. The chatbot makes recommendations based on the customer's preferences. Users can place orders by voice via Amazon Alexa or the MyBarista app and then receive them at a nearby coffee shop.
AI is also being applied to sports projects. For example, the Japanese created a fencing visualization system for the Olympic Games. With the development, it became possible to track sword movements and recreate their trajectory using augmented reality.
In tennis, OSAI is designed to organize live broadcasts and works on mixed reality technology. OSAI allows fans to be more engaged and umpires to make the right decisions based on accurate digitized data.
Such solutions require specialized equipment: cameras, lidars, and other technological devices. They are inaccessible for beginners and amateur athletes, while in the training process, it is very important to track progress and analyze mistakes.
Conclusion
Real-world case studies demonstrate the diverse applications of AI, ranging from multi-channel marketing communications to personalized retail experiences and sports analytics. While specialized equipment may be required for certain applications, the importance of tracking progress and analyzing mistakes underscores the value of AI in various domains.
Maybeworks, as a reliable IT staff augmentation provider, can play a crucial role in ensuring the successful implementation of AI-driven projects. Feel free to contact us to get the top-level developers’ engagement.
