How is Artificial Intelligence Made and Created?

Nowadays, there’s a lot of hype around Artificial Intelligence or AI. It began at the end of 2022, when OpenAI threw open its AI-based resource, ChatGPT-3, for the masses to use for free. Several new AI models are being created and used worldwide for various purposes.

Others are being updated to meet market demands. ChatGPT-3 has evolved to ChatGPT-4.5, and we’ll see more sophisticated models in the coming months.

You may be using AI or considering it. If so, we must understand how AI or Artificial Intelligence is made and created. It’s a complex process, as you might falsely believe. Instead, it’s a complex task involving several persons, teams, and data megatons. In this article, I will describe, in simple words, how AI, as we know it today, is made and created.

Let’s start by first understanding why the need for AI was felt in the first place. That would give you a better comprehension of how AI is made and created and the purposes it serves.

The Need for Artificial Intelligence

Since ancient days, humans have tried to develop resources that would unburden or simplify their tasks. The ubiquitous wheel was the first invention of this generous undertaking. The invention of the accidental discovery of the wheel made it possible for ancient humans to overcome the limitations of distance.

They could harness the various characteristics of a wheel to travel more considerable distances using crude carts, move heavy goods, or even use a wheel for grinding and shaping stuff.

Evolution bears testimony that humans always harness technology to make life easier. The same holds for AI. The first worthy effort towards replicating humans was made in 1737 in France when Jacques de Vaucanson created an automaton, often called “The Flute Player.”

Standing over 5 feet 8 inches tall, the mechanized automaton resembled a life-sized shepherd dressed in traditional clothing. Its movements appeared remarkably realistic, mimicking human actions like breathing and finger positioning while playing the flute.

The next such advance came in 1926 when US electric appliances major Westinghouse created its “Telepresence” robot for remote manipulation in hazardous environments. In Japan, WABOT-1 was developed by Waseda University and is considered the first full-scale, electronically controlled humanoid robot.

The first efforts to use AI can be traced back to 1990 when IBM created a Deep Blue supercomputer to analyze millions of chess positions beforehand and calculate the best possible moves and positioning using complex algorithms.

Though Deep Blue wasn’t really based on AI, it marked the beginning of an era where computers could copy human thoughts and moves. Unfortunately, in 1996, Deep Blue lost a series of chess games to the then-reigning grandmaster, Gary Kasparov of the Soviet Union. Consequently, IBM spent millions of dollars to upgrade Deep Blue, which won against Gary Kasparov the following year.

This triggered a series of inventions, such as Interactive Voice Response (IVR) systems, rob-advisors from financial institutions, and chatbots. All these sham human intelligence are, to some extent, using AI and other technologies.

Later AI was created in the 1950s and 1960s during the Cold War by both the US and its allies as well as the Soviet Union and its supporters. However, its use was limited to defense and aerospace purposes. It was too expensive to create, and its use could have dangerous consequences for humanity. 

Around the same time, universities and research groups also began developing AI. Over the years, the cost of making and creating AI has come down, especially with advances in computer technology and the proliferation of the Internet.

Various AI Models

Now that you know the brief history that led to AI development, let’s see how Artificial Intelligence resources are made nowadays. There are different ways AI is created. 

Artificial intelligence (AI) utilizes various models to achieve its different functionalities. Here are some prominent models, including LLMs:

 Symbolic AI

Approach: Represents knowledge and reasoning through symbols and logic rules. They are used in tasks like expert systems and decision-making.

Example: Early chess-playing AI systems like Deep Blue relied on symbolic AI techniques.

 Statistical Learning

Approach: Uses statistical models to learn from data and make predictions. Standard techniques include linear regression, decision trees, and support vector machines.

Example: Spam filters often employ statistical learning algorithms to identify and classify spam emails.

 Machine Learning (ML)

Approach: Algorithms learn from data without being explicitly programmed. Popular ML techniques include:

Supervised Learning: Trains on labeled data (e.g., images with labels) to learn a mapping from inputs to desired outputs. (e.g., Image recognition)

Unsupervised Learning: Discovers hidden patterns in unlabeled data, such as grouping similar data points. (e.g., Customer segmentation)

Reinforcement Learning: Learned by interacting with an environment and receiving rewards or penalties for actions, enabling it to improve its performance over time, such as game-playing AI. 

Example: Recommender systems on online platforms utilize ML algorithms to suggest products or content based on user data.

 Deep Learning

Approach: A subfield of ML, inspired by the structure and function of the human brain, utilizes artificial neural networks (ANNs) to learn complex patterns from large datasets.

Example: LLMs (Large Language Models) like ChatGPT-4 and its variants, as well as Google Gemini, are a type of deep learning model trained on massive amounts of text data to understand and generate human language.

 Other Models

Evolutionary AI: Inspired by biological evolution, where algorithms “evolve” through mutations and selection to find optimal solutions.

Probabilistic AI: Uses probabilistic methods to represent uncertainty and reason under incomplete information.

If you’re wondering how AI can make videos or generate voices, here’s the answer:

Video and voice generation AI falls under two main categories, depending on the specific technology and its purpose:

1. Generative AI

This broader category encompasses AI models designed to create new content, such as images, text, audio, or video. Video and voice generation AI utilize various techniques within generative AI, including:

Deep Learning: Particularly with artificial neural networks like Generative Adversarial Networks (GANs), these models learn from existing data to generate new, realistic video or audio content.

Auto-encoders: These models learn to compress and reconstruct data, allowing them to generate variations or entirely new content based on the learned patterns.

• Multimodal AI

This category refers to AI models that can process and understand different modalities of data, such as text, audio, and video. While not solely focused on generation, multimodal AI plays a crucial role in video and voice generation by:

It analyzes existing video and audio data to understand its underlying structures and relationships.

It combines information from different modalities to generate more cohesive and realistic content.

We are facilitating tasks like lip-syncing in generated videos, where the generated voice aligns with the visual movements of a model’s mouth.

Therefore, video and voice generation AI primarily falls under generative AI because it focuses on creating new content. However, it often overlaps with multimodal AI due to its need to effectively process and understand various data modalities.

Understanding the Complex Process

Since many technologies go into making or creating AI, it isn’t easy to sum it up in a few words. There are no quick-fix recipes for creating AI modules. As I mentioned, it requires entire teams of humans and megatons of data. These have to be synchronized carefully with various other technologies. The blend gives an AI module. 

Here’s a brief description of this complex process.

The process of creating AI, regardless of the specific model, generally involves these key steps:

1. Problem Definition: The first step is clearly defining the problem or task the AI needs to address. This helps in choosing the appropriate model and setting measurable goals for success.
2. Data Collection and Preprocessing: Relevant data is gathered, encompassing the information the AI will learn from to perform its task. This data might be text, images, audio, video, or any other format relevant to the problem. Preprocessing often involves cleaning, organizing, and preparing the data into a format suitable for the chosen model.
3. Model Selection and Training: A suitable AI model is selected based on the problem and data characteristics. This could be a symbolic AI model, a statistical learning model like decision trees, a deep learning model like an LLM, or a combination of various models depending on the complexity of the task.
4.  Training is the process through which the AI learns from the data. This involves feeding the data into the chosen model and iteratively adjusting its internal parameters (e.g., weights and biases in neural networks) to improve its performance on the task. Different algorithms and techniques are employed for training depending on the chosen model.
5.  Evaluation and Refinement: After training, the AI’s performance is evaluated on a separate dataset that was not used during training. This helps ensure the model generalizes unseen data well and does not simply memorize the training data. Based on the evaluation results, the model might be refined by adjusting hyper-parameters, collecting more data, or switching to a different model if necessary.
6.  Deployment and Maintenance: Once the AI’s performance meets defined criteria, it’s deployed in the real world to perform the intended task. Integrating it into software applications, robots, or other systems could involve incorporating it. Continuous monitoring and maintenance are crucial to ensure the AI continues to perform effectively and adapt to any changes in the environment or data it encounters.

Here’s how different models might influence these steps:

Symbolic AI models require handcrafted rules and knowledge bases built for the specific task. In contrast, statistical learning and deep learning models rely more heavily on learning from data during training.

The data’s complexity and the desired accuracy level will also influence the training process and the amount of data needed.

Ethical considerations are becoming increasingly important throughout the AI development process. They require careful evaluation of potential biases, fairness, and transparency in the chosen model and its implementation.

It’s important to understand that this is a high-level overview, and the specific details of creating AI can vary significantly depending on the chosen model, the complexity of the task, and the resources available.

Ethical Considerations for Making AI

However, creating AI modules continues beyond here. Its creators must ensure their AI modules don’t violate ethical considerations. In recent months, we’ve seen various AI modules fail. 

Here is an overview of some of the most significant shortcomings of AI that creators often try to consider but can fail.

Bias and Discrimination

Reason: AI models often reflect biases in the data on which they are trained. This can lead to discriminatory outputs, perpetuating societal inequalities in areas like:

Recruitment: AI-powered recruitment tools might favor specific demographics based on biases in resumes or historical hiring data.

Loan approvals: Algorithmic bias in loan approvals can disadvantage individuals from specific backgrounds.

Facial recognition: Biases in facial recognition systems can lead to inaccurate identification and unfair treatment, particularly for people of color.

Lack of Transparency and Accountability

Reason: Many AI systems’ complex algorithms, inner workings, and intense learning models are often opaque. This lack of transparency makes it difficult to understand how they arrive at decisions, hindering:

Accountability: It’s challenging to hold AI systems accountable for biased or erroneous outputs if their reasoning needs clarification.

Debugging and Improvement: Difficulty in understanding how AI systems work can hinder efforts to identify and rectify biases or errors within them.

 Misinformation and Factual Errors

Reason: AI systems, huge language models, are adept at generating human-like text but may need a deep understanding of the information they process. This can lead to:

Spreading misinformation: AI systems can generate and disseminate false or misleading information, impacting public discourse and trust in reliable sources.

Perpetuating harmful stereotypes: If the training data contains biased information, the AI system might generate outputs reinforcing harmful stereotypes or presenting inaccurate representations of reality.

Job displacement and automation

Reason: AI-powered automation is increasingly replacing human labor in various sectors. This raises concerns about complex issues such as national security, higher crime rates, and other serious problems.

Unemployment: Job displacement due to automation can lead to economic hardships and social unrest.

Inequality: Automation might disproportionately impact specific groups of workers, exacerbating existing societal inequalities.

Privacy and security concerns

Reason: As AI systems collect and analyze vast amounts of data, concerns regarding privacy and security arise:

Data breaches: Malicious actors might exploit vulnerabilities in AI systems to access sensitive personal data.

Mass surveillance: The use of AI in surveillance systems raises concerns about potential misuse and violation of individual privacy.

Lack of diversity and inclusion in development

Reason: The field of AI development has historically needed more diversity regarding its workforce and the perspectives considered during development. This can lead to:

Overlooking ethical considerations: Homogeneous development teams might need to identify and address potential ethical issues stemming from diverse perspectives.

Perpetuating existing biases: If the development team lacks diversity, the AI system might reflect the biases of the dominant group.

Therefore, it’s best to use AI as a resource or assistant and not rely entirely on any AI module for our tasks. The reason is simple: the responsibility of using AI and conforming to various ethical considerations lies solely with the user. Anyone who neglects these ethical considerations would be prone to expensive and debilitating lawsuits and other serious legal problems.

Wrap Up

AI technology remains in its embryonic stage and is likely to stay there for several years. This brings to my mind a strapline used in one of the AI-related articles published by the Boston-based magazine The Atlantic. It said, “Use it like a toy, not a tool.” This means you should never count on AI for all your tasks because the processes that go into its creation still need to be perfect.

Those working on various models continue to try to eradicate inherent snags. It’s worth remembering that human intelligence results from complex factors, primarily genetics- which scientists have yet to be able to decode and the environment where we grow and develop.

Human intelligence learns almost every millisecond and, hence, is constantly updated. This implies that it is much easier and quicker to create or make AI for apparent reasons.