How To Make An AI From Scratch?

Artificial intelligence, machine learning, deep learning, generative AI, etc. are all the terms dominating the world these days. As the impacted areas of AI are expanding massively, you may be curious: how to make an AI?

In this article, we will guide you to the fundamentals of AI and a detailed guide on how to make an AI from scratch. Let’s dive in!

What Is AI?

AI, or artificial intelligence, is a field of computer science that focuses on creating systems and machines capable of performing tasks that typically require human intelligence. These tasks include learning from data, recognizing patterns, making decisions, solving problems, and adapting to new situations.

In short, AI refers to technologies that allow machines to mimic human-like behavior.

Key concepts in AI

AI encompasses several key concepts, which work together to create the foundation for artificial intelligence, enabling machines to learn, reason, and interact with the world.

Data

Data is food for AI development. AI algorithms are built and refined upon data. Hence, the quality and quantity of data are the foundation for AI success.

Algorithms

AI algorithms are like the brains behind AI operations. They process the data and extract insights from it. Different types of AI algorithms, like supervised learning (learning from labeled data), unsupervised learning (finding patterns without labels), and reinforcement learning (learning through trial and error), help machines make sense of information.

Models

AI models are like mathematical representations of a system’s behavior. These models take input data and make predictions or decisions based on it. AI models can be as simple as linear equations or as complex as neural networks, inspired by the human brain.

Automation

AI is all about automating tasks. It can handle repetitive and labor-intensive jobs efficiently. From chatbots that answer customer queries to self-driving cars that navigate traffic, automation is a key goal of AI.

Problem-Solving

AI excels at solving complex problems. It can quickly analyze data to find solutions, whether it’s optimizing supply chains, diagnosing medical conditions, or playing chess at a grandmaster level.

Human-like Behavior

One of the ultimate goals of AI is to mimic human-like behavior. This includes understanding and generating natural language (NLP), recognizing objects in images and videos (computer vision), making decisions, and adapting to new situations like a human would.

Curious about the latest technology trends beyond AI?? Read our blog post: Top 13 technology trends shaping the future.

Types of Artificial Intelligence

AI can be categorized based on capability and functionality.

Types of AI based on capability

Artificial narrow intelligence (ANI)

Narrow AI is designed for a specific task or a narrow set of tasks. It can’t perform tasks beyond its programmed capabilities. Examples include virtual personal assistants like Siri or Alexa, chatbots, and function-specified apps such as Loopsie AI photo app.

General AI (Artificial General Intelligence — AGI)

AGI, often called strong AI, can understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. AGI doesn’t exist yet, and creating it is a long-term goal in AI development.

Artificial superintelligence (ASI)

This is a hypothetical form of AI that surpasses human intelligence in every aspect. ASI would be capable of solving complex problems, learning instantly, and achieving tasks that humans can’t even comprehend. It’s purely theoretical and a topic of debate among experts.

Types of AI based on functionality

Reactive Machines

These AI systems are designed to perform specific tasks and do not possess memory or the ability to learn from experience. They follow predefined rules. Examples include chess-playing programs.

Limited Memory

AI systems in this category can consider past experiences to some extent. They use historical data to make decisions but are not true learning systems. Self-driving cars use this type of AI to navigate.

Theory of Mind

These are hypothetical AI systems that can understand and model human emotions, beliefs, intentions, and consciousness. They don’t exist yet but are a topic of research.

Self-aware AI

Also being a hypothetical concept, self-aware AI would have human-like consciousness and self-awareness. It’s more of a philosophical idea and not a practical AI type at the moment.

Programming Languages For AI Development

Before we go into LTS Group guide on how to make an AI from scratch, let’s learn some basics about the top programming languages used in AI development.

Python

Python is the go-to language for AI due to its readability and rich ecosystem of AI tools. For instance, you can use libraries like scikit-learn for machine learning or TensorFlow for deep learning. Its simplicity makes it accessible for beginners, and it’s widely used in fields like natural language processing (NLP), computer vision, and reinforcement learning.

Julia

Julia is a rising star in the AI world. It’s designed for data science and offers a balance between speed and ease of use. An example is Flux.jl, a Julia library for building neural networks. Julia’s speed makes it ideal for high-performance computing, which is essential in tasks like simulating complex systems or conducting large-scale data analysis.

R

R, although less user-friendly than Python, has a strong presence in the statistical and data analysis community. It’s excellent for tasks like data visualization and statistical modeling. Packages like ggplot2 and dplyr are widely used in data science for creating insightful visualizations and data manipulation.

How To Make An AI Application?

Here is a common approach for how to build an AI:

• Identify your problem
• Assess AI application feasibility
• Determine the right tools and platforms
• Gather and process the data
• Create the algorithm
• Train the AI algorithm
• Deploy the final product
• Maintain and refine AI models

Now, let’s go into the details of each step.

1. Identify your problem

As always in software development, start by clearly defining the problem you want to solve with AI and what ultimate goals you want to achieve. Also, be clear about what resources you have and the constraints of your project.

Example: Let’s say you work for a retail company, and your challenge is to optimize inventory management to reduce overstocking and understocking of products.

2. Assess AI application feasibility

Now you evaluate if AI is a suitable solution to your problem. Typically, you will need a professional AI consulting partner to assist you with AI assessment. This step often involves assessing data availability, technical infrastructure, costs, resources, etc.

Then, once you decide to proceed with AI solutions, consider creating a prototype, or proof of concept (PoC) to test the ideas.

In this case, you might create a basic AI model to predict product demand based on historical sales data.

3. Gather and process the data

Collect relevant data for your AI model. This step ideally involves data scientists and data analysts to create or gather, clean, preprocess, and organize the data, ensuring it’s suitable for training.

Data for your AI models often come from three common sources as below.

First-party data

First-party data is data originating from your business, typically sales & marketing data.

Common sources of first-party data include:

• CRM software
• POS
• Website analytics (Google Analytics, Google Search Console, etc.)
• Social media analytics
• Social listening tools
• Paid marketing analytics
• Email marketing analytics
• Market research analysis
• Other self-collected data sources

Second-party data

Second-party data is data shared by other companies, often in a partnership manner, to help each other gain more insights into potential customers.

For example, a clothing retailer partnering with a fashion magazine to access readers’ fashion preferences and enhance their marketing strategies.

Third-party data

Third-party data is data collected by a third party and often requires payment. The third party may be a specialized data vendor in your business domain or a data collection company that will gather new data exclusively for your business.

Using third-party data can pose some challenges, such as higher privacy risks and uncertain accuracy. Hence, choosing a trusted AI data vendor is a prerequisite when obtaining third-party data.

4. Determine the right tools and platforms

At this phase, you choose the programming language and AI frameworks that best fit your project’s needs. Common choices include Python and AI libraries like TensorFlow or PyTorch.

5. Create the algorithm

Develop an AI algorithm that will learn from the data and make predictions or decisions. This may involve coding neural networks, decision trees, or other machine learning models.

Picking up on the example, you might create an ML model that uses historical data to predict future demand for each product.

6. Train the AI algorithm

Feed the algorithm with your prepared data. The model learns from this data through iterations and adjusts its internal parameters to improve performance.

At this stage, you train the model to recognize patterns and trends in the inventory and sales data.

7. Deploy the final product

Integrate your trained AI model into your software application. Ensure it’s user-friendly and ready for use by your target audience.

Now you integrate your trained AI model into your inventory management system. The AI system can now provide demand forecasts, helping optimize stock levels.

8. Maintain and refine AI models

AI models require ongoing software maintenance and improvements. Continuously monitor their performance, collect new data, and retrain the models to adapt to changing conditions or new insights.

Throughout the process, keep in mind that AI development often involves iterations and experimentation. Initially, you input high-quality data into the AI/ML system to improve business results Then, as you gather more data, discover better algorithms, or encounter new challenges, data again go into your AI/ML system to produce even better business outcomes.

Common AI Applications

In today’s technology-driven world, AI isn’t just a term thrown around; it’s making a real impact. For example, an AI-powered audio transcription tool produces accurate transcripts of podcast episodes in minutes.

Let’s delve into some widespread applications that are shaping the future.

Computer vision

Computer vision enables machines to interpret and understand visual information from images or videos. It’s used in facial recognition, object detection, and even self-driving cars.

Customer service

AI-powered customer service uses chatbots and virtual agents to assist customers with inquiries, troubleshooting, and support. It’s available 24/7 and can handle routine customer interactions efficiently.

Take LTS Group’s Personal Document Recognition and Information Extraction System for instance. The system utilizes identification rules and image processing technology through Google Vision to automatically recognize and extract content from personal documents. This ensures high accuracy and the ability to process a large volume of documents efficiently.

Virtual assistant

Virtual assistants like Siri, Alexa, and Google Assistant use NLP to perform tasks, answer questions, set reminders, and control smart devices.

For example, LTS Group has developed and deployed an AI-powered Travel Chatbot for our business client in the tourism industry. It provides details on popular destinations, itineraries, activities, and tailored travel recommendations. It also offers information on hotels, restaurants, shopping, and allows for online reservations and pricing comparisons.

Predictive analysis

Predictive analysis uses historical data and AI algorithms to forecast future trends or outcomes. It’s used in various fields, including finance for predicting stock prices and in healthcare for disease outbreak predictions.

Recommendation

Recommendation systems use AI to suggest products, content, or services to users based on their preferences and behavior. They help users discover new things they might like, such as movie recommendations on streaming platforms or product suggestions on e-commerce websites.

Personalization

Personalization tailors content, services, or product recommendations to individual user preferences. It enhances user experiences, like personalized newsfeeds and product offerings.

Fraud prevention

AI is employed to detect and prevent fraudulent activities, such as credit card fraud and identity theft, by analyzing transaction patterns and user behavior to identify anomalies.

Data mining

Data mining uses AI and ML models to discover patterns and insights in large datasets, or big data. AI and ML’s scalability, pattern recognition, automation, speed, and generalization capabilities make them essential for extracting insights and making data-driven decisions from big data, where manual analysis might be impractical or inefficient.

Frequently Asked Questions About Making An AI

How do you create an AI?

Creating an AI involves several key steps:

• Define your goal: Determine what your AI app will do and how it will benefit users.
• Data collection & processing: Gather and curate relevant data that the AI will learn.
• Choose tools: Select the programming languages and libraries that suit your project.
• Select algorithm: Choose the appropriate machine learning algorithm or deep learning framework based on your AI’s intended purpose.
• Model training: Train the AI model using the prepared data. This involves feeding the data into the model and adjusting its parameters.
• Testing and validation: Evaluate the model’s performance using separate data to ensure it makes accurate predictions.
• Deployment: Implement the trained model in your desired application, whether it’s a chatbot, image recognition system, or something else.
• Continuous improvement: AI is an ongoing process. Regularly update and refine your AI to improve its performance.

Does AI require coding?

Yes, AI development typically involves coding.

You’ll need programming skills to gather and preprocess data, select and implement machine learning algorithms, and integrate the AI into your application. Popular programming languages for AI development include Python, R, and frameworks like TensorFlow and PyTorch.

Can I make my own AI app?

Yes, you can create your own AI app. You can choose to create your AI app independently by setting up your own servers and ensuring that your AI algorithms function correctly. This approach allows for more control but may require technical expertise.

If you have a specific idea for an AI app but lack coding skills, consider IT outsourcing and partnering with an AI development company. Your domain expertise and their technical skills can combine to bring your concept to life. Many successful AI projects result from collaborations between domain experts and developers.

Final Notes On How To Make An AI

AI applications are growing in popularity in many business operations, from efficiency improvement to sales optimization and client satisfaction elevation.

Creating an AI application may appear complex, but it doesn’t have to be overly complicated. By breaking the whole process down into smaller pieces and achievable milestones, can harness the power of AI applications to drive your business growth forward.

Need professional consultation for your AI initiatives in mind? Contact LTS Group for a free talk with our AI experts.