How to make your own AI in 2024?

From excitement to business applications, Artificial Intelligence (AI) has become a part of all the circles of conversations. Out of its myriad of applications, using AI to create AI agents or AI virtual assistants stand out for their ability to handle tasks without supervision. 

Well, given you’re here, we assume you’re looking to create your own AI solution. Whether it is for personal or professional use cases, regardless of your initial motivation, we’re here to demystify the process of building your own AI agent. And we assure you that it’s easier than you think. 

In this comprehensive guide, we'll explore the essence of AI, AI agents and step by step process to create your own AI assistant for free.

What is Artificial Intelligence?

A research paper defines artificial intelligence as the ability of a machine to perform cognitive functions that we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, decision-making, and even demonstrating creativity.

In simpler terms, AI mimics human beings to perform tasks like decision making, reasoning, learning from experiences, interacting with multiple files, problem solving, creative writing or more. An AI research paper mentions at least 20 more definitions of Artificial intelligence, but all with the same gist. 

AI has two components : data and algorithms. 

Data is the foundation for building AI agents. Data can be text, images, numbers, or sounds, and is used to train & test AI algorithms. The data quality defines how well your AI agent performs. 

Algorithms are basically a set of rules AI follows to perform a task. Different AI algorithms are used by AI systems to identify patterns, learn from data, and make predictions or decisions based on the input data.

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What is an AI model?

An AI model is a mathematical representation of a real-world process or system, trained to perform specific tasks by learning from data. Think of it as a set of algorithms that can recognize patterns, make predictions, or generate outputs based on input data. The model is created through a process called training, where it learns from a dataset and improves its accuracy over time.

How AI Models Work?

Artificial Intelligence (AI) models operate by mimicking human cognitive functions to solve complex problems. The core idea is to teach machines how to "learn" from vast amounts of data, allowing them to make decisions, recognize patterns, and improve over time. But how exactly do AI models achieve this?

At the heart of AI are algorithms designed to process data and perform tasks. There are two key concepts: training and inference. During training, an AI model learns from historical data. Once trained, it can make predictions or decisions, which is called inference.

According to Dr. Andrew Ng, one of the pioneers in AI, "AI is the new electricity. Just as electricity transformed industries 100 years ago, AI is poised to do the same today"ls are now at the core of healthcare, finance, retail, and nearly every industry.

Most AI models rely on machine learning to improve. Machine learning algorithms require a significant amount of data to spot patterns. For example, a facial recognition system needs thousands of images to learn to identify faces accurately. During this process, the model constantly adjusts its internal parameters to reduce errors.

One fascinating feature of deep learning is its ability to handle unstructured data, like text, images, or sound. According to Ian Goodfellow, the creator of Generative Adversarial Networks (GANs), “One of the most powerful features of deep learning is its ability to learn representations that are crucial for tasks without requiring manual engineering” .

Real-World Example: Chatbots

Consider an AI chatbot. The model behind it uses natural language processing (NLP) to understand customer queries and respond appropriately. When a user asks, “What’s the weather today?” the model interprets the text, retrieves weather data, and generates a relevant response. Over time, these chatbots become more accurate in understanding slang, variations in phrasing, and even regional dialects.

What are the different types of AI out there?

Given we’re working on creating our own AI, let’s try to understand the different types of AI systems out there. There are 7 types of AI systems. We can categorize them broadly based on functionality and capabilities. 

Based on Capabilities 

• Narrow AI: Specialized AI for niche or very specific tasks, like Siri for voice assistance.
• General AI: AI with human-like intelligence for all tasks. It’s theoretical and not yet achieved.
• Super AI: Futuristic AI that is better than humans in all aspects. 

Based on Functionality: 

• Reactive Machines: AI that responds to only current situations. It is rule based and doesn’t store memory or has no ability to learn from the past. Eg. Chessplayer bot. 
• Limited Memory: AI agent that learns from past data to improve it’s future decisions, such as autonomous vehicles.
• Theory of Mind: Current level of AI that can understand & mimic human emotions. It can interact with humans but is still in the developing phase.  
• Self-Awareness: Hypothetical AI with complete self-consciousness and awareness. It is still not developed yet. 

Alright, not that we understand the different types of AI systems and a general understanding of AI, let’s dive into how to create your own AI. 

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How to Build Your Own AI in 6 Steps?

With access to so many conversational AI platforms and no-code platforms out there, you can build a personal AI assistant in no time. You don't need to code or understand complex mathematics to create your custom AI assistant. Let's look at the 6 steps you need to create your own AI agent.

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Step 1: Define the problem your AI agent will solve

Before even building your own AI, you need to start with a clear understanding of the problem statement. What do you want your AI agent to do?  You can either start with your own motivation or use some of the typical AI agent use cases from business or personal perspectives. 

Different AI use cases:

• Financial Services: AI significantly contributes to fraud detection in financial transactions. By analyzing customer data, it identifies patterns or anomalies that may indicate fraud, helping prevent identity theft and other financial crimes​
• Human Resources: AI performance reviews in HR departments track employee performance more effectively, aiding in identifying high-performing individuals for rewards​
• ​Customer Service Chatbots: AI enhances the effectiveness of customer service chatbots by enabling them to learn from interactions and become more accurate in responding to customer queries​
• Knowledge Management: AI can make sense of complex institutional knowledge in organizations, breaking down information silos​
• Sales AI assistant: Create an AI assistant for sales teams which can help them create pitches, draft emails, handles objections and more.

In this blog, we’ll create a conversational AI assistant for customer self service that draws information from the FAQ section and answers customer questions accurately. 

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Step 2: Identifying knowledge sources, intent & channels 

The next step is to identify the data sources to train the AI. What are the different internal or external sources of data you’ll use to train your AI agent on? 

Your data can be documents, videos, images, PDFs, webpages or more. Agent intent or agent actions would be the actions that you’d want your agents to take. Channels could be websites, Whatsapp, Zendesk, intercom, Telegram, Discord, teams or more. 

The list of data sources, intent & channels will be integral for your decision of AI platform. In our case, our data sources are website FAQs, our intent is to understand the query & answer the question & our channel is a website. 

Step 3: Choosing Tools and Technologies

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Now it’s time to choose an AI platform to build your AI agent. It is crucial to get AI platform right as it will determine how easy it is to set up, train and use your AI agent. 

There are a variety of AI platforms out there. Make a decision based on the following: 

• Data Sources 
• Agent capabilities 
• Channels 
• Setup time 
• Training time 
• Hallucinations or Accuracy of AI agents  
• Costs 

For this particular website AI assistant, we will use Alltius AI platform.

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Alltius transforms traditional customer interactions with intelligent AI assistants. While it excels in automating customer support, it stands out by enabling companies to leverage their unique data in powerful ways. What sets Alltius apart is its ability to not only streamline customer conversations but also generate AI-driven insights and personalized sales pitches. Trained on your internal knowledge and external data, Alltius can be trusted to provide accurate customer responses and support decision-making processes with actionable intelligence.

Pricing: Free plans are available.
With Alltius, you can create your own AI assistants in minutes. It can easily connect to multiple data sources, mimic humans for insightful interactions and can be implemented on multiple platforms within minutes. You can create an AI chatbot based on your data on Alltius within 15 minutes for free using free trial account. Alltius is best for creating AI assistants for sales, customer service and customers. 

Step 4: Adding Data Sources & Channels 

Now, let’s get into creating the actual AI agent. Create a free account on Alltius and log into the interface. 

Go to coach assistants and click on + Create new. 

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A dialog box will open up. Name your new assistant and select create. 

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You’ll be redirected to your AI assistant page which showcases sources and other settings. Select + Add new source. 

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Add your data sources. In our case here, we will add web pages but you can add documents, google drive links, videos, PDFs and more. Once the coaching is complete, your data is uploaded to your AI agent. Now, it’s time to test your AI agents. 

Step 5: Testing your AI agents 

Head over to the playground from your left column and select your assistant. Try talking to your AI. See if it answers your questions correctly. We suggest asking multiple questions to ensure it’s working properly. 

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Use the following to test your AI agents: 

• Evaluation Metrics: Use metrics to assess model effectiveness.
• Cross-Validation Techniques: Test the model in varied scenarios.
• Error Analysis: Analyze and correct errors for model refinement.

Once you’re sure of the AI agents, we move over to deploying your AI agent. 

Step 6: Deploy & Monitor your AI agent

Deploying your AI agent on Alltius is a simple process. Go to Channels, select your AI assistant and select the channel of your choice. A step by step instruction will give you an idea on how to deploy it on your channel. Voila, your AI agent is done! After it’s deployed, you need to continuously monitor your AI agent to make sure it’s working properly. In Alltius, you can see your agent performance using dashboards which tell you details about engagement, quality, query insights and option to download all the conversations. 

How to avoid common mistakes while making your AI assistant?

While we’ve tried to keep the entire process simple, here are some common mistakes we’ve seen people make while they’re creating their own AI assistants.

• Overcomplicating the AI agents in the first go: In order to prevent this, focus on optimizing essential features first. Then keep adding more and more capabilities on top of it. For eg. Try to create an AI agent for just summarizing your emails. And then, add more documentation to create an AI agent that can summarize emails & research on your prospects. Such layering helps in testing your AI agents before moving on to new capabilities. 
• Ignoring Data Quality: Data is the building block for your AI assistant. Ensure that your data is clean, in readable format and properly integrated with your AI agents before deploying them. 
• Overlooking Security: In personal as well as business scenarios, security of your data is important. Ensure that the AI platform you’re using has stringent security measures.

Conclusion

Creating an AI system involves a thorough understanding of its methodologies, careful selection of tools and technologies, and a focus on ethical considerations. By following these steps and avoiding common pitfalls, you can develop AI solutions that are robust, effective, and ethical. 

Check out Alltius for creating your AI assistants for sales and customer service.

Get a free demo of Alltius platform or try it yourself!

Read more:

• How to make your own AI?
• Best AI sales assistant software in 2024
• Top AI customer support software
• Top customer service chatbots
• Top conversational AI platforms

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