Artificial intelligence (AI) is the technology at the forefront of every industry. Its ability to compute and make intelligent decisions is causing many people to reassess their relationship with technology.
In the app industry, where most people utilize AI, the market value of AI in the Mobile Apps Market is expected to grow to $249.8 billion by 2033 from $16.7 billion in 2023.
This shows how widespread the global use of AI is becoming and how integrated the technology has become in our daily lives. Because of its many applications, a question people want answered is how these intelligent applications powered by AI can help them make money.
In this post, we will examine how developers use AI to build intelligent applications and how they are applied to financial interests.
How AI is Used in Intelligent Applications
AI can be used in several ways to build intelligent applications. The two most important areas in which developers leverage AI are the application’s data collection and the integration of machine learning.
Data Collection
A key technology behind these AI applications is the database that is used to train and provide the information for the application. At the core of many of these intelligent applications is the vector database.
Vector databases are unique in that they can store unstructured data, such as text, documents, images, videos, and even social media content. With this data, they can also perform rapid similarity searches.
As explained by a guide to MongoDB’s vector databases, these databases provide the knowledge required to understand a query without the need to define synonyms. Even when users don’t know what they’re looking for, a vector search is able to return relevant results based on the meaning of the query.
For example, a search for “ice cream” would return “sundae,” even if the user didn’t know sundaes existed.”
Similarity searches, in addition to the existing search functionality of machine learning models, allow AI applications to get relevant and contextual results.
For example, in a financial Robo-Advisory app, the users’ preferences, risk aversion, and past behaviors would be stored in the vector database, and then, based on the inputs, a similarity search would go through the data to find the most accurate financial advice.
Because vector databases are able to store data horizontally, they can efficiently scale in line with the amount of data needed to power these applications.
Machine Learning
The integration of machine learning is the key foundation of an intelligent application. An article by Software Guide on Medium detailed how machine learning models serve as the brain of an intelligent app.
The machine learning models train the AI on the algorithms needed to optimize the app and create generative AI functions. This is done using the data stored on advanced data systems, such as the aforementioned vector databases.
This allows the app to perform tasks such as analyzing data, predicting data fluctuations, making predictions, and categorizing information.
An example of how machine learning is being used to help people save money can be seen in the DoNotPay app, which uses machine learning models to train AI agents. Trained on the app’s algorithms, the generative AI agents can simplify the process of getting through the red tape of parking fines and the student finance application process.
This is done by allowing users to interact in standard English, allowing AI agents to automictically bridge the gap between everyday language and the technical demands of complex bureaucratic systems.
The AI-powered service quickly identifies factors for both appealing parking fines and finding the right financial aid and scholarships by offering actionable suggestions within seconds. AI voice applications, where machine learning models use inputted text to perform phonetic analysis, are another example of how developers leverage AI in intelligent applications.
The application produces speech sounds that represent the words in the text. These AI voice applications are now being used on YouTube to generate income, with Luca Roblox being the most famous example of successfully monetizing AI voice content.
By creating new and interesting content using AI voice content, the channel has amassed over 85,000 subscribers, earning income through subscriptions and ad revenue.
AI is transforming how we build intelligent applications, which in turn is changing how we can earn money online. For more tips on how to Make Money do check out the rest of our articles.
