What is an AI Model? The Brains Behind the Tech

Science fiction novels often describe AI models as a human brain in a box. That is both (a) creepy and (b) incorrect. An AI model, like any model, is a representation of reality. Testing models refine the aerodynamics of new vehicles. Financial models simplify revenue predictions into a single Excel sheet. Climate models simulate weather patterns to predict storms and track global warming trends. AI models represent and simplify human intelligence. 

Input data to an algorithmic model that’s been trained to identify patterns or make decisions, and you will receive intelligent results. In this article, we'll get into the how, where, and why, but it's first essential to understand that not all AI models are equal. 

The global AI market is expected to grow to “over 1.8 trillion U.S. dollars by 2030.” Within this growing field is webAI, a local AI platform that allows users to create their own custom models to deliver real-time processing and specialized decisioning. Let's discuss what an AI model is and discover the next wave of AI solutions for
real-world problems. 

Breaking Down the Basics: What Is an AI Model?

AI models are input-to-output machines. They take input data and deliver an output decision, accomplished in these three simplified steps.

1. Modeling: The first step is creating a model. This involves designing a complex algorithm(s) to analyze data, make decisions, and discover correlations between input and output variables. The best-use algorithm depends on many factors, including the type and amount of data.
2. AI Model Training: This process typically requires feeding large volumes of training data into the model through iterative test loops. A vital component of this process is analyzing results to ensure accuracy, referred to as ‘learning.’ There are several learning methods used for AI and machine learning models, including:
   
   • Supervised learning: This approach requires using labeled training data to train the model. 
   • Unsupervised learning: This approach works without labeled data and instead will recognize patterns and group data points autonomously. 
   • Reinforcement learning: This approach involves trial and error, where a model learns by receiving rewards for correct actions and penalties for errors. 
3. Inference: The last step is running live data through the trained AI model, which will make decisions or complete a task. 

‍

We’ll give an example to illustrate this process in a real-world setting. A national retailer wants to test multiple hypothetical scenarios with varying store sizes, locations, and market conditions. 

1. They'll build a model designed to analyze these variables and generate insights that align with their business objectives.
2. They'll train the model with existing store information.
3. They'll run live data like a proposed new store of (x) size in (y) location and evaluate the output revenue estimates.

Types of AI Models and What They Do

The various types of artificial intelligence models are ideal for different tasks. Let’s briefly expand on the main types of AI models.

• Classification: These models predict discrete values, which are finite, countable numbers that cannot be broken into smaller parts. Data scientists assign data points to categories, such as yes or no (binary) or multiple options. Classification models are used for tasks like recommendation systems and medical diagnostics. Email spam filters, for example, classify incoming messages as spam or not spam based on patterns in the data.
• Regression: These models predict continuous values like price or age by analyzing the relationship between independent and dependent variables. Regression can be used for simple and highly complex forecasting. This may be seen in real estate platforms that use regression models to predict property values based on location, size, and market trends.
• Clustering: These models organize data into subgroups based on shared attributes and work very well when solutions don’t need to be exact. E-commerce platforms, for instance, use clustering to group customers with similar purchasing behaviors to create personalized marketing campaigns.
• Generative: These models use data distributions to predict the joint probability of data points. Generative large language models (LLMs) may deliver entirely new text based on a prompt. For example, ChatGPT is built on a generative model, whereas spam detection software may use a classification AI model.

‍

These models can use sensitive and complex data to deliver actionable results, though results will vary. webAI's local platform is specially designed to help companies build specialized models for efficient, private, and secure performance.

Why AI Models Are Essential for Enterprise

Enterprise leaders aren’t always experts in deep neural networks or natural language processing, but these AI adopters are looking to make innovative investments in their future. Companies adopt AI technology to:

• Improve efficiency 
• Unlock data-driven decisions 
• Reduce costs 
• Improve customer experience 
• Gain competitive advantage 
• Achieve regulatory compliance 
• Boost product innovation 
• Reduce headcount
  

‍

Notably, AI models support data-driven industry decision-making by analyzing vast amounts of data to reveal hidden patterns and correlations. The real-time analysis of this data leads to responsive decisioning, which is especially vital in fast-paced sectors like finance and healthcare. Further, the deep benefits of automating repetitive, data-intensive tasks include freeing up teams to focus on higher-value work and reducing operational inefficiencies, leading to cost savings.

AI Models in Action: Real-World Use Cases

AI models offer wide applications across industries. AI-driven chatbots lead to better customer experience in retail and shorter diagnosis times in healthcare. Deep learning models can be applied to mammography interpretation in prevention treatment settings. Generative AI improves productivity and the hiring process in state governments. AI models even played a role in the development of the COVID-19 vaccine.

Building an AI Model: A High-Level Look

Creating any AI or machine learning model requires following precise steps. For example, a logistic regression-based model will quickly and accurately solve your classification-based problems. It can't do this if it's built on dirty data or without proper testing. 

Data Collection

You must use clean, relevant data to train your AI or ML models. We recommend using as many relevant documents and data points as possible to enhance the expert model you're building.

Training and Testing

The learning and testing phases for an AI or machine learning model are vital. AI models work through an iterative process of running data through the algorithm, analyzing the outcomes, and refining the output. With webAI, all it takes to train your own model is a folder containing a single document on your device. webAI supports a variety of file formats, including Word documents, PDFs, and .txt files, giving you flexibility in your data sources. 

Local Deployment with webAI

Deploying AI models logically eliminates the need for cloud infrastructure and maintains data security. webAI’s solutions are fully customizable and integrate easily with your existing systems and products. You get an intuitive set-up process and maximum control. 

The Future of AI Models

As industries continue to find new and transformative applications for AI, we’ll see large-scale adoption and shifts within the field.  

Decentralization as the Next Big Shift 

webAI’s approach represents a shift away from centralized cloud AI. Transferring large data sets to a centralized system puts sensitive data at risk and causes a delay between the data input and decisioning. Our edge AI models run on existing infrastructure and allow for local, fast decision processing to eliminate the need for continuous data transfer to a core processing system. 

AI Models Across Industries

We envision a future where businesses and individuals can create, deploy, and own their own models with ease—locally, to safeguard their data and optimize efficiency. It’s a future not where a few tech giants monopolistically control the only viable models but one where millions and millions of small, specialized models work together to solve the world’s greatest challenges. 

AI Models with webAI – Smart, Secure, and Unmatched

webAI is dedicated to local, on-device AI and disrupting traditional integration. Your data doesn’t belong on a remote server. It belongs in your hands, on your hardware, delivering real-time results without the middleman. 

AI integration is changing the fields of healthcare, aviation, finance, and more. Learn more about our processes, discover the emerging AI trends, and get the latest news. 

‍