How Does Artificial Intelligence Work?

7 min read

Artificial Intelligence (AI) is a technology that mimics human intelligence in performing tasks, and it can iteratively improve itself based on the information it gathers [1]. Simply put, we make machines capable of thinking, learning, and solving problems in a way that resembles human intelligence.

Basic Principles of How AI Works

AI operates using data, algorithms, and mathematical models to discover patterns and produce predictions, classifications, decisions, or new content [2]. Instead of manually programming every possible scenario, modern AI systems can learn patterns from examples [2].

The core idea is to "train" the AI by providing it with vast amounts of information or "data," where it analyzes this huge volume of data to find hidden patterns and relationships. Based on these patterns, it builds its own internal "model," and then uses this model to make predictions or take decisions when presented with new information it has never seen before [3].

AI works by training large foundation models on massive amounts of raw and unlabeled data (text, images, videos), to form a neural network containing billions of parameters representing patterns and relationships in the data [4]. A foundation model can be used in multiple applications to provide the correct and appropriate answer by adjusting the required output format according to prompts [4].

The Difference Between Machine Learning and Deep Learning

Machine Learning and Deep Learning are two closely related fields, and it can be said that Deep Learning is an advanced sub-field of Machine Learning.

Machine Learning

It is a broader set of techniques that allow systems to learn from data and make decisions without being explicitly programmed for every task. The model is given a lot of data, and a human engineer often needs to "tell" the model which features in this data are important in a process called "feature engineering." The model uses statistical algorithms to find patterns and relationships.

Machine Learning can work well with smaller datasets and is used in applications such as recommendation systems, spam detection, and house price prediction.

Deep Learning

It is a special type of Machine Learning that uses "Deep Neural Networks," which are structures inspired by the way the human brain works. The model is given raw data directly without specifying the features, and the multi-layered deep neural network automatically learns different and complex features from the data.

Deep Learning typically requires massive amounts of data and significant computing power, and it excels in complex tasks such as image and voice recognition, natural language processing, and self-driving cars.

How Do Neural Networks Learn from Data?

Neural networks learn in a way similar to how humans learn through trial and error, but on a much larger scale and at an ultra-fast speed.

Learning Stages

1. Random Start: When training begins, the network's "weights" and "biases" are completely random, meaning that initially, it knows nothing and gives incorrect predictions.

2. Feedforward (Prediction): Data passes through the layers of the neural network. Each "neuron" processes the inputs, multiplies them by its weights, adds the bias, and then passes the result to the subsequent neurons. Ultimately, the network outputs a "prediction."

3. Error Calculation: We compare the network's prediction with the correct answer and calculate the amount of "Loss." The further the prediction is from the truth, the greater the error.

4. Backpropagation: The error signal is sent backward through the network from the final layer to the previous layers to determine how much each weight contributed to this error.

5. Weight Adjustment: Based on backpropagation, the network adjusts its weights and biases using optimization algorithms such as "Gradient Descent," with the goal of reducing the error next time.

6. Iteration: This process is repeated thousands or millions of times with different data until the weights become more accurate and the network's errors gradually decrease.

Large Language Models and How They Generate Text

Large Language Models (LLMs) are an advanced type of deep learning model, specifically designed to understand and generate human language. They are characterized as "large" in terms of the amount of data they are trained on (which can reach trillions of words) and the number of Parameters they contain (billions or trillions).

Text Generation Mechanism

The primary method for generating text is predicting the next word (or "token") repeatedly:

1. Input: It begins by giving the model a "prompt" or "context."

2. Tokenization: The model converts words into "tokens," which it treats as numbers.

3. Next Token Prediction: The model processes these tokens through its neural network and calculates the probability of every possible token to be the most logical next one.

4. Selection: The model does not always choose the most probable word. There are different techniques:

• Greedy Search: Always chooses the token with the highest probability.
• Sampling: Chooses randomly based on probabilities.
• Temperature: Controls the degree of randomness; a low temperature makes the model more cautious, and a high temperature makes it more creative.

5. Iteration: Once the next token is chosen, it is added to the sequence, and the process repeats to predict the token following it.

6. Stopping: The process continues until a punctuation mark indicating the end is reached, or a special stop token.

The model receives evaluations from humans regarding the accuracy or appropriateness of the outputs, which helps improve the model's performance and steer it toward better outputs through what is known as Reinforcement Learning from Human Feedback (RLHF) [4].

AI Limitations and Risks

Basic Limitations

• Lack of Consciousness or True Understanding: It does not understand the world in the same way humans do, nor does it possess feelings or self-awareness.
• Total Dependence on Data: Its quality reflects the quality and quantity of the training data.
• Lack of Common Sense: It may struggle to infer things that are intuitive to humans.
• Difficulty Handling New Situations: It excels in tasks similar to the training data.
• The "Black Box" Problem: The difficulty in understanding why the model made a specific decision.

Hallucination Risks

Hallucination occurs when AI generates information that seems logical but is completely wrong or unsupported by facts. This happens due to:

• Probabilistic Training: The model predicts the next word based on statistics, and the most linguistically probable word may not be factually correct.
• Pressure to Generate a Response: The model is designed to produce text, so it may prefer to "make up" an answer rather than admit ignorance.
• Inconsistent Data: Learning contradictions from the training data.

Risks of Bias

Bias occurs when models display unfair prejudices toward certain groups, and it arises primarily from:

• Bias in training data: If the data reflects societal biases or is insufficiently representative.
• Bias in algorithm design: Based on how criteria are defined.
• Bias in human interaction: Learning biases from user interactions.

Future Impact on Jobs and Education

Jobs

• Automation of routine tasks: AI systems will replace humans in repetitive and rule-based tasks.
• Creation of new jobs: The emergence of roles such as AI engineers, ethics specialists, and prompt engineers.
• Transformation and enhancement of jobs: AI will become a supportive tool that enhances productivity, where humans work "with" AI.
• Demand for new skills: Critical thinking, creativity, emotional intelligence, and collaboration.

Education

• Curriculum changes: Integrating data science, machine learning, and AI ethics.
• Personalized learning: Analyzing student performance and providing materials tailored to their individual needs.
• Focus on unique human skills: Creativity, critical thinking, and collaboration.
• Lifelong learning: The necessity of continuous learning and skill upskilling.
• Increased accessibility: Tools for translation, text-to-speech, and material adaptation.

Conclusion

Artificial Intelligence consists of software that learns from data to perform tasks that typically require human intelligence, such as speech understanding, image recognition, translation, and prediction [3]. It operates through three basic steps: collecting many examples, training a mathematical model that adjusts millions of internal numbers until its error decreases, and then using it on new data it has never seen before [3].

Despite its immense capabilities, AI remains a powerful but imperfect tool. Understanding its limitations, especially the risks of hallucination and bias, is crucial for using it responsibly. Researchers are constantly working to address these issues and improve the transparency, fairness, and reliability of AI systems.

The future will not be for AI as a replacement for humans, but for those who collaborate with it—those who possess unique human skills such as creativity, critical thinking, and emotional intelligence, combined with the ability to learn continuously and adapt to rapid changes.