Ever thought of owning your own AI?
Wouldn’t that be cool?
If so, then you have come to the right place.
Artificial Intelligence is one of the most talked about topics in the world. Famous Tech CEOs like Elon Musk, Bill Gates, and Tim Cook are very excited about the future of AI.
Have you ever wondered what is required to build an AI system by yourself?
If you are a curious cat like us or want to develop your AI system, this article is for you!
We will discuss exactly what is required to build an AI system with the various components of an AI system.
What Is Artificial Intelligence?
Building intelligent machines that can carry out tasks that would generally require human intelligence is the goal of artificial intelligence (AI). For nearly a decade, Artificial Intelligence has existed, and the developments keep getting more exciting.
Undoubtedly, its ability to enhance customer service and marketing analytics and predict the future makes it an invaluable asset across all sectors.
We are in a new era where many believe AI could be the next big thing.
A perfect example is ChatGPT-3, which has Elon Musk as one of its co-founders which is viewed as one of the best language modeling AI worldwide.
According to the global newswire, the global AI market is valued at about 328.34 billion. It is becoming an essential part of the everyday life of humans.
The impact of AI on everyday life, and most people use it without knowing it is artificial intelligence. Siri, Google Assistant, and Apple Face ID are examples of AI that are being used by people daily.
With all the hype and excitement around AI, it would be good for any developer or business to take advantage of the opportunity and learn what is required to build their own AI system
Four Components of An AI System
It would be essential to highlight specific components of the AI system. To understand how to build an AI system, we must get familiar with its components.
Learning
Just like you are learning about AI systems through this article, computer programs also acquire knowledge in many ways. Learning is an integral part of AI, and one of the easiest ways is by trial and error.
Through the process of trial and error, the computer programme keeps the accurate and positive information while discarding the others. Due to its learning process, AI can quickly build on successful outcomes in the future.
A few of the earning components of AI include memorizing individual items such as solutions to problems, learning foreign languages, and so on.
Reasoning
Reasoning has primarily been a human thing for a very long time. Now, computer programs can derive conclusions and inferences without any human input.
Basically, reasoning can be classified into two types which include inductive and deductive reasoning. Deductive reasoning guarantees a conclusion to the solution of the problem. Whereas, in inductive reasoning, the failure of the instrument is always the cause of the problem.
Typically, computer programs always use deductive reasoning, which has brought about significant success.
Problem Solving
A third major component that makes up the development of AI systems is problem-solving. Some examples include finishing with winning moves in a chess game, identifying various images in an item, suggesting corrections in word processors, and giving navigation through apps like google maps.
There are two types of AI problem-solving types: special-purpose and general-purpose methods. A general purpose covers many issues, while a special purpose is created to address specific problems.
Computer programs are typically known for problem-solving, which is why they are essential in AI systems.
Language Understanding
Language understanding helps computers understand and execute programs efficiently. The most commonly used language in the world is English. It is what software developers use to ensure computer programs’ effective and efficient delivery.
Natural Language Processing allows computers to understand written and spoken words like you and me. Computers can learn and process the languages that humans speak. Artificial Intelligence has been developed to easily understand and process the English language.
NPL can easily detect emotions in text and brand sentiments on social media through language understanding.
What is required to build and AI system:
Six Steps To Build An AI System
Identify The Problem
We often use the word ‘Problem’ to find an immediate solution. In developing an AI system, one has to be precise and direct in identifying the problem.
For instance, the creator of google maps wanted to proffer a navigation solution to those who need help locating an area or finding their way around a city/country. If you are considering building an AI system, there must be a problem to solve.
Identifying the problem is the first and foremost step in building a good AI system.
You may want to ask yourself these questions:
‘What problem am I solving here? and what do I intend to achieve with this?’
By conducting adequate research, you can pinpoint the issue by learning about the problems your intended or existing users face. Then, you can create a problem statement that aids in the creation of a more useful product and offers users extra benefits.
Adequate research about your product or business and users will aid your decision-making and problem-solving. It would also be good to note that some problems artificial intelligence can help you solve, such as customer support, fraud, image/video recognition, and data analysis.
Prepare The Data
Now that we know the problem, we can prepare the Data for use in AI applications. Data is a core component of any AI package. There are two classifications of data, namely, structured and unstructured data.
Structured Data
Structured data is data that follows a set format to maintain consistency. It is well-defined, contains patterns, and has searchable parameters and is usually found in excel files and can be accessed by individuals and computers.
Examples include phone numbers, names, dates of birth, addresses, and so on. Each of these data bits is constructed consistently from one user to the next.
Unstructured Data
Unlike the former, unstructured data does not have any patterns or stability. Pictures, videos, infographics, emails, audio, and chat messages are examples of unstructured data. It is challenging to handle unstructured data because it cannot be arranged perfectly into rows and columns.
Cleaning, organizing, processing, and storing the data will be your next course of action once you have all the data. Data filtering will help you with whatever mistakes and inconsistencies may have been input into your data. You can then use the clean data to run an AI mod
Generate Algorithms
We have come to the most crucial part of how to build an AI system. It gets exciting from here, despite the technicalities involved.
Generating algorithms are used to specify how the computer executes any necessary action. Algorithms typically contain mathematical instructions and can learn in a variety of ways. Then, when creating an AI system, you can select one of these learning methods.
Supervised Learning
The computer or device gives itself a dataset to train on and produce results. We have learning algorithms like logistic regression, random forest generation, random trees, and support vector machines. A familiar example would be an algorithm that blocks someone on social media.
Supervised learning is also usually used in predicting future values. By following specific patterns, they can predict the weather, traffic congestion, flight times at an airport,
Unsupervised Learning
This kind of algorithm’s primary goal is to figure out the fundamental pattern in the data and collect the data that may be related to one another.
We have cluster, association, and dimensionality reduction techniques. Clustering is a type of technique that involves the computer trying to collect data in various groups. Unsupervised learning helps to find unknown patterns.
The Association technique is beneficial for recommendation engines. For example, if 20 customers purchase an iPhone and AirPods to go with it, the algorithm would typically recommend AirPods to those who select iPhones to their carts.
Generating your algorithm is super fun once you get the hang of it. With supervised learning predicting outcomes and unsupervised learning getting new insights from data, you can be sure to know what results to expect or discover something new.
Train The Algorithms
Training and retraining are essential to creating an AI system because it feels right to educate the algorithm if the desired reliability is still not attained. Any chosen algorithm must be trained in order to guarantee the accuracy of your AI model.
The gathered data can be used to train the algorithm because it must learn its task. During algorithm training, model accuracy is crucial. Establishing model accuracy is essential for determining an acceptable minimum threshold.
In order to train the algorithm, the AI must identify patterns in the data and use those patterns to predict the future.
Select A Programming Language
Any language can be used for AI programming, but the best programming languages include Java, Julia, Lisp, R, Phyton, C++, Haskell, Javascript, Scala, and Prolog. Considering your goals, choose any of the languages. Python is crowned as the easiest and best programming language to build an AI system. If you are new to programming, consider using Python.
Creating with Java or C++ is not near impossible, but not easy either, as these languages are a bit complicated. C++ is high-level and ideal for AI games. You can use R for predictive analytics.
Python and R provide programmers with robust tools that include substantial Machine Learning libraries. On the other hand, you can use Scala with Java due to the many options it offers for AI development.
Python and R have extensive machine-learning libraries and are the most simple and easiest to use. They offer a decent collection of libraries that will make you spend less time creating algorithms and more time developing the actual AI model.
Run On A Good Platform
Congratulations, you are ready to run your AI on a good platform. How exciting is that?
At this stage, we are ready to deploy the final product. Some platforms provide you with all the services you need instead of you needing help to get all in one place.
These platforms, which generally provide cloud-based advanced analytics that can be utilized with and incorporate numerous algorithms and multiple languages, are designed to simplify machine learning just for you.
Platforms like Google Cloud AI Platform, Amazon Machine Learning, Microsoft Azure, and IBM Watson Machine Learning provide ready-made tools. They are accessible platforms to use.
- Amazon Machine Learning: Facilitates the creation, training, and deployment of Machine Learning models. It provides you with the right tools to build your AI application.
- Microsoft Azure: This is a straightforward tool that can be used to create, train, and deploy models. It simplifies machine learning for business and allows you to deploy your AI system into the Machine Learning Marketplace.
- Google Cloud AI Platform: It makes it simple for developers to swiftly and affordably move their ML applications from conception to development and implementation.
- IBM Watson Machine Learning: A great platform that provides you with a variety of tools that you can use to build and deploy ML models.
How Much Does It Cost To Build An AI System?
How much does implementing AI systems cost? It is a question that most businesses consider before building an AI system.
This solely depends on what you are looking to build.
For instance, a simple AI platform for video/speech detection may cost around $36 000 and $56,000.
Here are some factors that should give you an idea of the cost of the AI system you want to build.
- Hardware: The hardware you need has to be specialized to handle the voluminous data and computations. It is different from your regular computer hardware, and it is a lot more expensive. A hardware’s computational power is necessary for running and training algorithms.
But do not fret, as many have predicted that due to technological advances yearly, the cost of building AI systems will be more affordable to businesses and people.
GPUs, FPGAs, and ASICs are some hardware you can use to build AI systems. Presently, the least cost for hardware is $12 000.
- Software: Helps to gather and process data and the costs can easily be underestimated, but they are pretty costly.
Because data sets can grow in size and become difficult to clean, process, and handle, you would need to factor this cost into your budget significantly. They usually cost between $20 000 and $40 000.
Your total cost solely depends on the improvements you want to make to the efficiency of your company. Do not compare your budget for AI systems to companies like Google, Netflix, and Amazon, which invest billions in AI. The price of artificial intelligence varies depending on the level of performance you desire.
Is AI Worth The Price?
Artificial Intelligence offers great business value that would make your cost of operation cheaper and easier. It indeed depends on what you are aiming for and your vision and goal for your business.
Besides, its benefits can help you analyze the market and make market predictions. The cost can be expensive but it does offer you a lot more value for your money.
Conclusion
We have covered what is required to build an AI system in our article.
With technology nowadays, anyone can build an AI system with all the resources available for free online but in most of the cases it would take a team of specialized people like machine engineers, software developers, and data scientists to build a complete AI system.
Since AI is gradually becoming a mainstream technology in virtually every industry, it’s only logical having an AI solutions for your business.
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Thanks for Reading!
Anuoluwapo Adesina is a content writer with over 4 years of experience. She is extremely passionate about technology and artificial intelligence. As a content writer, research is my backbone to produce quality articles and blog posts. When she’s not writing about AI, she’s busy exploring cookbooks.
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