By demystifying how AI -- especially generative AI -- functions, we can build public trust and highlight AI as a powerful yet controllable tool.
By Sergey Galchenko, Chief Technology Officer at IntelePeer
In recent years, artificial intelligence (AI) has become a transformative force in industries from finance to healthcare. However, public perception of AI often veers toward science fiction, painting it as a futuristic technology verging on magical. This distorted view fuels skepticism and mistrust, leading many to question AI's reliability and safety. Yet, at its core, AI operates on the same foundations as other digital technologies: coding, algorithms, and mathematics.
What makes AI different is not the mechanics but the sheer complexity of these processes -- and particularly, AI's unpredictable or "nondeterministic" nature. Unlike traditional technology, which follows predictable, programmed pathways, generative AI (GenAI) and its large language models (LLMs) are inherently more ambiguous, leading to issues like "hallucinations," or inaccuracies, that can erode public trust.
To bridge this gap in understanding, it's crucial to demystify AI. AI is not "magic," but rather an advanced application of computational methods already integrated into the technology we trust daily. It's a massive amount of data steered toward a specific purpose. For example, consider a traditional Interactive Voice Response (IVR) system, which offers a set of predictable paths for user interactions. In contrast, generative AI, such as LLMs, lacks these fixed pathways, which makes it both powerful and, at times, frustratingly unpredictable.
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Generative AI is most commonly seen in forms like language models, image generators, and hybrid systems combining text, visuals, and even audio. The central task in deploying GenAI is to guide it toward a desired outcome -- achieved through a series of carefully engineered steps that "steer" it in a specific direction.
One of the most revolutionary advancements in AI has been the development of transformer-based LLMs, which can generate responses by predicting the next "token" based on the input they receive. This token-based process is essential to understanding how these AI models function:
Tokenization: An LLM divides input text into tokens. Tokens could be words, parts of words, punctuation marks, or other meaningful segments. Tokenization breaks down sentences like "Is it going to rain?" into smaller, manageable parts: "Is," "it," "going," "to," "rain," and "?". This division allows the model to understand and process each element within the sentence.
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Token Embedding: Once tokenized, each token undergoes an "embedding" process, where it is transformed into a multidimensional vector. These vectors represent the many facets of each token -- its context, meaning, and relationship with surrounding tokens -- enabling the model to understand subtleties in language.
Positional Encoding: AI models also need to understand the order of words within a sentence to maintain the intended meaning. Positional encoding ensures that "Is it going to rain?" is not misinterpreted as "going rain to it is," preserving the sentence's structure and intent.
Sentence Embedding: Finally, sentence embedding integrates tokenization, embedding, positional encoding, and vector summarization to produce a coherent response. This cumulative process allows the model to understand and answer questions with meaningful and contextually accurate responses.
The results of the sentence embedding step are then sent to a pre-trained transformer-based neural network, which predicts a number of possible output tokens and selects the token with the highest probability. This process is performed iteratively until the LLM produces the full output.
To see this process in action, imagine a user asking, "Is it going to rain?" The AI begins by tokenizing each word and symbol to understand the components of the question. Each token is then embedded as a vector to capture its meaning, with positional encoding preserving the original order. The model uses these combined embeddings to understand and summarize the query, ultimately providing an informed answer. In short, the AI breaks down the question into individual parts, like "Is," "it," "going," "to," "rain," and "?". Then, it analyzes each part to understand its meaning, keeping the original word order so the question makes sense. By putting all of this together, the AI can understand the full question and give a helpful, accurate answer.
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For many, AI remains an enigma -- and fear often follows the unknown. However, this doesn't have to be the case. Building a bridge of understanding starts with transparency. By demystifying how AI operates, we can show the public that AI is simply a tool grounded in logic, one that we can effectively shape, understand, and control. As AI continues to evolve, fostering a public understanding of its mechanics will be crucial for earning trust and acceptance and helping people feel more comfortable with the technology reshaping our world.
Embracing AI, not as a mystical force but as a calculable system, offers a clearer, more empowering vision of our tech-driven future.
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