Artificial Intelligence and Machine Learning have been hot topics lately in the technology sector as more businesses explore how AI technology can be harnessed to improve business operations and outcomes.
Several AI models are currently available, including the popular ChatGPT, which has been one of the key AI systems driving public and corporate interest in AI technology. However, using a pre-built AI system like ChatGPT might not be the most effective solution to meet your organization’s unique needs.
As a result, there is growing business interest in moving away from pre-built AI solutions, and more companies are considering building their own AI system. While spearheading an AI project for your organization might seem out of reach, building AI systems is not as difficult as you think.
This post will explore what your business will need to build an AI system. However, before we cover how to build an AI, it is vital to understand the different types of Artificial Intelligence. In addition, there are varying levels of AI capabilities that you should consider before beginning an AI operation.
What Is Artificial Intelligence?
Artificial Intelligence is a term that is often used, but it is often not fully understood. Artificial Intelligence is a branch of computer science concerned with developing programs capable of thinking and completing tasks that humans perform.
Popular science fiction representations of Artificial Intelligence, such as HAL or terminators, are far from the reality of what AI systems are capable of. Artificial Intelligence is more data science than science fiction.
There are three types of Artificial Intelligence that you should understand before beginning to build an AI system. The three types of Artificial Intelligence include the following:
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI)
Artificial Narrow Intelligence (ANI)
Artificial Narrow Intelligence is also referred to as weak AI. A weak AI system is designed to complete a specific task. For example, an AI system built for natural language processing, playing chess, language translation, facial recognition, speech recognition, etc., is considered Artificial Narrow Intelligence.
ANI systems are only programmed to complete a single task. While the completion of this task might be impressive, it is only adept at a particular task, which is a far cry from the AI models we see in science fiction books and movies.
Every instance of Artificial Intelligence you have ever interacted with or heard about, from Alexa and Siri to ChatGPT, is an example of Artificial Narrow Intelligence.
Artificial General Intelligence (AGI)
Artificial General Intelligence is also referred to as strong AI. A strong AI system can accomplish any intellectual task that a human can. Strong AI systems are merely hypothetical. No Artificial Intelligence has ever exhibited the capacity to match a human’s intelligence and problem-solving skills.
Data scientists and software engineers are working towards building this type of AI system. However, there is real debate among researchers and scientists if this type of AI software is even possible to create.
Artificial Superintelligence (ASI)
If Artificial General Intelligence was merely hypothetical, ASI systems are even more so. In theory, Artificial Superintelligence would surpass human intelligence in all possible aspects.
This type of AI is most often depicted in science fiction, but if achieving general intelligence has proven impossible to this point, achieving superintelligence is far beyond the realm of our current technical capabilities.
Creating An AI System for Your Business: Key Steps
Now that you know more about what Artificial Intelligence is and what is possible, it is helpful to understand the steps involved in building an AI solution. The critical steps to building Artificial Intelligence include the following:
- Identify a problem
- Obtain data
- Choose a programming language
- Choose a platform
- Write algorithms
- Train algorithms
Identify a Problem
The first step in developing AI software is to identify a problem or task it can solve. For example, ChatGPT helps people write content, and Dall-E helps people create unique image content. What task will your Artificial Intelligence perform?
We have already explained that general and superintelligence are mere hypotheses when it comes to AI. As a result, your AI will need to do something. Before you begin writing and designing Machine Learning algorithms, you must decide what your AI solution will do.
Once you have identified a problem to solve, the next step is to obtain training data. Getting high-quality data is simpler than making improvements to the AI model. Whether you have structured or unstructured data, after collecting training data, you will need to clean it.
Data must be processed and cleaned before it can be used to train an AI system. Data cleaning fixes or removes errors from the data to improve its quality. Using good data to train AI systems is essential, or it will not function reliably.
Choose a Programming Language
There are several programming languages you can choose from. The best programming languages for AI software are C++, Python, Java, and R. Depending on the goals of your AI system; some languages will be better suited than others.
For example, if you want to build AI for video games, C++ is your best option. However, R is your best option if you want to build deep learning models for predictive analysis. In addition, Python is flexible and the most beginner-friendly programming language for AI tasks. If you’re looking to enhance your skills in any of these languages, consider exploring free Scaler courses, which provide comprehensive learning resources and support for various programming languages.
Choose a Platform
After you have decided on a programming language, it is time to choose a development framework platform. Development frameworks simplify designing, writing, training, and debugging your AI models.
Frameworks also offer templates and guides to help your team build neural networks and other predictive models. Scikit, Pytorch, and Tensorflow are the most popular frameworks and libraries for developing Machine Learning models.
Algorithms are mathematical instructions that tell your AI system what to do and how to improve its performance. The crux of an AI solution is the algorithms that power it. Once you have chosen a programming language and platform, you can write your own algorithms.
Typically, writing Machine Learning algorithms requires a data science expert or software developer that has experience with ML models and algorithms.
Writing an algorithm is not enough; you must also train it using the data you have collected. In addition, you may have to acquire additional data to improve the accuracy of your AI model. During the training process, it will also be necessary to tweak your algorithms to improve their accuracy.
An inaccurate model is worthless to your organization. Therefore, ensure that you take algorithm training seriously.
If you have successfully built and trained your model, it is time to deploy it. Of course, you should monitor it to ensure that it is performing as expected. Additional training will likely be necessary over time to hone the accuracy and improve the performance of your AI model.
In a nutshell, that is all you have to do to build an AI system of your own. But, of course, writing and training algorithms is far more complicated than it appears on paper. You will need a data science expert or team of data scientists to ensure your model is properly written and trained.
If you want help creating an AI solution for your organization, contact an experienced AI and Machine Learning development expert like Koombea.