Have you ever wondered how to make an AI tool or platform? This post explains the foundational elements of constructing and designing an AI system.
While there are many reasons why you might want to understand how to make an artificial intelligence (AI) system, the biggest focus by far this past year has been for businesses. Perhaps you’re developing intuitive software, revamping a personal assistant tool, or even hoping to brush up on your modern tech skills—whatever your reason is for learning how to make an AI tool or platform, the fundamentals are fairly standard.
We’ll start from the beginning: It’s entirely possible to create more complex AI products from scratch, the building blocks that you’ll need to get started are similar regardless of the type of project.
In this post, we’ll highlight how to make an AI system from the ground up. Continue reading to discover basic definitions, typical steps, and what you should understand about benefits and risks for your business.
What is an AI system?
An AI system is any technological tool or platform that simulates human thought processing by utilizing computer language or machine-based software.
A fully functioning AI system might incorporate many different hardware and software components in order to create a comprehensive system. This might include robotics, natural language processing, speech recognition, machine learning, deep learning, and more.
Regardless of the system’s intended purpose, most AI systems must be able to:
Receive, process, and store incoming data
Analyze common correlations and data patterns
Mimic some degree of human cognition and thought processing
Perform a specific set of tasks or functions
Steps to producing an AI system
Before you start learning how to make an AI tool, you must establish a workable roadmap for the entire process. It’s wise to cover each step from ideation to conception in order to develop a platform that works well and meets a viable need.
Even though the technical complexities vary, the five steps below illustrate the basics of how to ensure your AI platform is set up for success.
Step 1: Determine the original need or problem
Before you set out to build your AI platform, always ask, “What problem am I attempting to solve?”
Is there a pain point that needs addressing? Is there a common frustration for which artificial intelligence has a suitable answer? When you begin with this step, you start with the end in mind. This provides the ability to customize and troubleshoot your new AI software.
Keep in mind that an AI tool likely serves as one component of a broader system. For example, if you want to design a better AI chatbot to serve customers online, consider how this new feature contributes to your overall customer service strategy.
Step 2: Acquire the necessary data
To have a functioning AI tool, the next step is to gather and sort the essential data. All AI systems leverage some sort of data, whether that includes a stable set that doesn’t change or a dynamic set of incoming data that gets processed over time.
Either way, data is the backbone of artificial intelligence. In most instances, you’ll find that two types are most prominent when building AI.
Structured data – Structured data is the type that is clearly formatted and highly searchable. It often incorporates set or standard data fields, such as phone numbers, Social Security numbers, names, and zip codes. A basic AI platform can easily read these fields for immediate interpretation.
Unstructured data – On the other hand, unstructured data is far less linear. This type of data might include conversations, information from social media, email messages, and audio or video files. Although the AI system might need to learn from unstructured data sources, it is not easily searchable.
Data tells the story that human intuition can’t; it’s the backbone behind all AI and sound business decisions.
Step 3: Build and train the AI algorithms
Once an AI platform has the data it needs, the next step is to teach it how to put that data to work. In artificial intelligence, this happens through the construction of algorithms.
With AI algorithms, the range is vast. The most simple algorithms can predict outputs based on well-defined inputs, while the most complex algorithms enable the AI system to respond to inputs based on learning and past history.
No matter which algorithm a developer chooses, that AI algorithm must be trained to perform its purpose as designed. The training process helps the AI system arrive at an accurate, fine-tuned result every time.
Step 4: Choose a computer programming language
An artificial intelligence programming language is the specific computer language that the system uses to process results. Some of the most common programming languages for AI include:
JavaScript
Python
C++
LISP
Prolog
Programming languages help AI systems learn how to process data, perform reasoning, self-correct, and respond to stimuli. Many programming languages come with their own tools, learning libraries, and open-source resources that allow developers to choose the right solution for a given AI requirement.
Step 5: Test and market the AI system
Once an AI system has been constructed using the necessary fundamentals (data sources, algorithms, and programming language), it’s time to put it to the test. Every AI tool or platform should go through a testing process to ensure accuracy and verify performance.
Testing is also important before marketing a piece of AI software to other users, especially customers. If you’re designing AI software that you intend to sell to other users or businesses, it’s vital that the system performs as expected while delivering strong results.
If you go to market with your AI system, be prepared to discuss its specific use cases, sustainability, and future potential. These selling points are critical for distinguishing your tools from others on the market.
Benefits of knowing how to make an AI platform
No matter what motivation you have for exploring AI creation, it’s helpful to know the benefits. Both developers and users of AI can anticipate positive results and outcomes when the system is built properly.
Technology expansion – Knowing the fundamentals of AI creation opens the door to limitless potential. In the next 10 years, AI’s contribution to the global economy is estimated to be $15.7 trillion. This statistic shows that there is room for innovation and growth.
Reduction of manual tasks – Artificial intelligence can also reduce manual or tedious tasks, making workplaces more efficient and streamlined. Notably, 79% of executives believe that AI makes their organizations more effective.
High uptime – AI platforms can eliminate the times when a human representative is not available. This has a positive result on customer service and global business.
Consistent results – Once programmed, AI tools also produce consistent and predictable results. Knowing how to make an AI platform can lead to greater accuracy and less human error.
Do you need to be an engineer to make an AI platform?
No, there is no specific software engineering qualification to build or design an AI platform. Any trained individual can learn how to make an AI platform that aligns with a particular need or problem.
For example, more average tech users are learning how to create personal AI assistants, with very little coding knowledge. Bot-creating platforms make it easier than ever for beginner users to jump into the world of AI creation.
Of course, the more complex AI systems (for example, machine and deep learning) require an advanced level of knowledge. Developers for these systems are usually trained in coding techniques, programming languages, and mathematical analysis.
What are the associated risks of AI creation?
As with any technical project or initiative, there are risks and disadvantages to working with AI. Anyone who wishes to learn how to make an AI platform should keep these factors in mind before publishing or advertising a finished project.
The potential risks associated with AI creation include:
Data security and privacy
Automation that contributes to job elimination
AI bias (which may influence results and predictions based on the developer)
Public misconceptions and fears
Misuse by cybercriminals and hackers
How to make an AI system with ethical considerations
To avoid falling into the trap of risks and disadvantages, AI creators must understand how to approach new designs ethically. Because AI mimics human thought without the aspect of emotion, good boundaries and protective measures are important. Many times, this simply provides reassurance to end users who have worries or doubts about AI’s power.
When making an AI platform or tool, you can adhere to ethical standards by following the principles below.
Be transparent about the purpose or intention of your AI design. You can do this by disclosing the specific pain point that it seeks to solve.
Utilize data responsibly, and don’t collect data you don’t absolutely need or have an immediate use for.
Reduce AI bias by considering all demographics and user profiles. Avoid assumptions that could lead to poor results.
Always audit performance and make quick corrections as needed.
Key takeaways
As AI systems become more commonplace in today’s digital society, chances are good that more individuals will try their hand at developing them. The needs and use cases are certainly present, which makes it all the more possible for new AI advancements to thrive.
If you’re ready to learn how to make an AI platform that works well and meets a specific need, begin with the five simple steps outlined here.
Do you want to learn even more about how AI can improve your business or marketing strategies? Reach out to us today and get connected to the perfect AI company for your specific business needs and goals.
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