AI Terms Explained: A Simple Guide to LLMs, Hallucinations & More

Understanding the Language of Artificial Intelligence

The realm of artificial intelligence (AI) is complex and multifaceted. Researchers and developers within this domain commonly employ specialized terminology when discussing their work.

Consequently, technical terms are often necessary when reporting on advancements in the AI industry.

To aid comprehension, we have compiled a glossary defining key words and phrases frequently used in our articles.

Why a Glossary is Important

The consistent evolution of AI necessitates a resource for understanding its core concepts. New methodologies are continually being discovered, expanding the boundaries of what's possible.

Simultaneously, identifying and addressing potential safety concerns is a crucial aspect of AI development.

Ongoing Updates

This glossary is not a static document. It will be updated on a regular basis.

New entries will be added as researchers continue to innovate and as emerging risks are identified within the field of artificial intelligence.

Our goal is to provide a consistently relevant and informative resource for anyone seeking to understand the language of AI.

Artificial General Intelligence (AGI)

The concept of artificial general intelligence, commonly known as AGI, remains somewhat ill-defined. Generally, it denotes AI systems possessing capabilities exceeding those of the average human across a wide range of tasks, potentially even the majority.

Sam Altman, CEO of OpenAI, has recently characterized AGI as being comparable to a typical human employee, suitable for collaborative work. This perspective frames AGI in terms of practical workforce equivalence.

Defining AGI: Varied Perspectives

However, OpenAI’s official charter offers a more specific definition. It describes AGI as highly independent systems capable of surpassing human performance in the majority of economically significant jobs.

Google DeepMind approaches the definition from a slightly different angle. They conceptualize AGI as AI demonstrating human-level or greater proficiency in most cognitive functions.

It’s understandable to feel perplexed by these differing interpretations. Even leading researchers in the field of AI acknowledge the ongoing ambiguity surrounding a precise definition of AGI.

Understanding AI Agents

An AI agent is a sophisticated tool leveraging artificial intelligence to execute a range of tasks for you. This functionality extends beyond the capabilities of standard AI chatbots.

These agents can handle complex processes like expense reporting, travel arrangements, restaurant reservations, and even software development and upkeep.

The Evolving Definition of AI Agents

It's important to note that the term “AI agent” is still developing, and its meaning can vary. As previously discussed, the field is rapidly evolving.

Different individuals and organizations may interpret the term differently, given the nascent stage of this technology.

Current Infrastructure and Future Potential

The underlying infrastructure required to fully realize the potential of AI agents is still under construction. Development is ongoing to support the envisioned capabilities.

Fundamentally, an AI agent represents an autonomous system. It can integrate multiple AI systems to complete tasks requiring several steps.

This allows for a more comprehensive and efficient approach to automation.

Chain-of-Thought Reasoning in AI

The human brain frequently provides answers to straightforward questions with minimal conscious effort, such as determining which animal is taller – a giraffe or a cat. However, many problems necessitate a more deliberate approach, often requiring written calculations to arrive at the correct solution.

Consider a scenario involving a farmer with chickens and cows totaling 40 heads and 120 legs; solving this typically involves formulating and solving a simple equation to determine the number of each animal (20 chickens and 20 cows).

How it Applies to Large Language Models

Within the realm of artificial intelligence, chain-of-thought reasoning for large language models (LLMs) involves decomposing a complex problem into a series of smaller, manageable steps.

This approach aims to enhance the accuracy of the final outcome.

While it may extend the time required to generate a response, the resulting answer is demonstrably more reliable, particularly when dealing with logical or coding challenges.

Development and Optimization

Reasoning models are built upon existing large language models.

They are then specifically optimized for chain-of-thought thinking through the application of reinforcement learning techniques.

This process refines the model's ability to systematically analyze and solve problems.

(See: Large language model)

Deep Learning

Deep learning represents a specialized field within machine learning, characterized by algorithms that refine their performance autonomously. These algorithms are constructed using artificial neural networks (ANNs) featuring multiple layers.

This multi-layered architecture enables deep learning systems to discern intricate relationships within data, exceeding the capabilities of more basic machine learning approaches like linear models or decision trees.

The foundational design of deep learning algorithms is inspired by the complex, interconnected network of neurons found in the human brain.

Key Capabilities

A significant advantage of deep learning models is their ability to independently identify crucial data characteristics. This eliminates the need for human engineers to manually specify these features.

Furthermore, the inherent structure facilitates algorithms capable of learning from mistakes. Through iterative processes of adjustment and repetition, these systems continuously enhance their output accuracy.

Considerations

However, achieving optimal results with deep learning necessitates substantial datasets – often numbering in the millions of data points.

Training these systems also generally requires more time compared to simpler machine learning algorithms, which can lead to increased development expenses.

(See: Neural network)

Diffusion

At the core of numerous artificial intelligence models capable of generating art, music, and text lies the technology known as diffusion.

Drawing inspiration from principles in physics, diffusion systems progressively dismantle the organization of data – such as images or musical pieces – through the introduction of noise.

This process continues until the original structure is entirely obscured. In the realm of physics, diffusion is typically a spontaneous and irreversible phenomenon.

Consider the example of sugar dissolving in coffee; it cannot be easily reconstituted into its original cube form.

The Reverse Process

However, AI-powered diffusion systems are engineered to learn a process akin to “reverse diffusion.”

This learned process enables the reconstruction of data from noise, effectively restoring the original information.

By mastering this reverse process, these systems gain the capability to generate new content by starting from random noise and refining it into coherent outputs.

The ability to recover data from noise is what allows these models to create original and complex outputs.

Diffusion models represent a significant advancement in generative AI, offering a powerful approach to content creation.

Knowledge Distillation in Artificial Intelligence

Distillation represents a method for transferring expertise from a substantial AI model – often termed the ‘teacher’ – to a smaller, more manageable model known as the ‘student’. The process involves submitting prompts to the teacher model and documenting the resulting responses.

Frequently, these responses undergo evaluation against a pre-existing dataset to ascertain their accuracy. The collected data, encompassing both inputs and outputs, is then leveraged to train the student model.

The objective is for the student model to effectively mimic the behavioral patterns of its teacher counterpart.

Benefits of Distillation

A key advantage of distillation lies in its ability to generate a more compact and computationally efficient model, while preserving much of the performance of the original, larger model. This is achieved with a minimal distillation loss.

It is widely speculated that OpenAI utilized this technique in the development of GPT-4 Turbo, creating a faster iteration of the GPT-4 architecture.

Ethical Considerations and Common Practices

Although employed internally by nearly all AI development companies, distillation can also be used to accelerate development by replicating the capabilities of leading models.

However, replicating a competitor’s model through distillation typically contravenes the stipulated terms of service for AI APIs and conversational AI platforms.

Fine-tuning: A Detailed Explanation

Fine-tuning describes the process of continuing the training of an artificial intelligence model. This subsequent training aims to enhance the model’s performance on a particular task or within a defined domain.

Typically, this is achieved by introducing new, specialized data relevant to the desired application. This data supplements the model’s initial training.

The Role of AI Startups

Numerous AI startups are leveraging pre-trained large language models as a foundation for developing commercial products.

These companies are focused on increasing the usefulness of these models for specific industries or tasks.

They accomplish this by augmenting the original training with fine-tuning, incorporating their own unique, domain-specific knowledge.

Expanding Model Capabilities

The goal of fine-tuning is to improve a model’s ability to handle specialized tasks.

This is done by exposing the model to data that reflects the nuances and complexities of the target application.

Further Information

For a broader understanding of the underlying technology, refer to information on Large language models (LLM).

GANs: Generative Adversarial Networks Explained

Generative Adversarial Networks (GANs) represent a significant machine learning framework. They are foundational to advancements in generative AI, particularly in the creation of highly realistic data. This includes, notably, the technology behind deepfake applications.

At the core of a GAN lies a dual-network system. One neural network functions as a generator, leveraging its training data to produce outputs. These outputs are then submitted to a second network, the discriminator, for assessment.

How GANs Function

The discriminator model acts as a classifier, evaluating the authenticity of the generator’s output. This evaluation process is crucial, as it provides feedback that allows the generator to refine its performance over time.

The architecture of a GAN is inherently competitive – an “adversarial” setup. The two networks are programmed to challenge each other. The generator strives to create outputs that can successfully deceive the discriminator.

Conversely, the discriminator is tasked with accurately identifying artificially generated data. This ongoing contest drives the optimization of AI outputs, leading to increased realism without requiring extensive human involvement.

Applications and Limitations

While powerful, GANs typically excel in specialized applications. They are particularly effective at generating realistic images or videos.

However, their capabilities are often less pronounced when it comes to general-purpose AI tasks. GANs are best suited for focused applications where high fidelity in a specific data type is paramount.

Key takeaway: GANs offer a unique approach to generative AI, relying on a competitive dynamic between two neural networks to produce increasingly realistic outputs.

Hallucination in Artificial Intelligence

Within the field of artificial intelligence, the term hallucination refers to the generation of incorrect or fabricated information by AI models. This represents a significant challenge to the overall quality and reliability of AI systems.

These instances of fabricated content can result in outputs from Generative AI (GenAI) that are deceptive and potentially hazardous. Incorrect responses, particularly in sensitive areas like healthcare, could have serious repercussions.

Consequently, most GenAI platforms now include disclaimers advising users to independently verify the information provided, despite these warnings often being less visible than the AI-generated responses themselves.

A primary cause of these fabrications is believed to be deficiencies within the AI’s training data. For broadly applicable GenAI models, often referred to as foundation models, addressing this issue presents a considerable hurdle.

The sheer volume of potential queries exceeds the currently available data needed to train AI models to provide comprehensive and accurate answers. Essentially, complete knowledge remains elusive.

The prevalence of hallucinations is driving a trend toward more specialized and vertical AI models. These domain-specific AIs, requiring a more focused skillset, aim to minimize knowledge gaps and reduce the spread of misinformation.

Mitigating the Risks

Focusing on specific areas of expertise allows for more targeted training data and a reduction in the likelihood of the AI generating inaccurate information.

This approach represents a strategic shift in AI development, prioritizing accuracy and reliability over broad, generalized capabilities.

Understanding Inference in AI

Inference represents the stage where an artificial intelligence model is actively utilized. Essentially, it involves deploying a trained model to generate predictions or derive insights from new, unseen data.

It’s important to note that inference is fundamentally dependent on prior training. A model must first be exposed to a dataset and learn the underlying patterns before it can accurately generalize and make predictions.

Hardware Considerations for Inference

A diverse array of hardware platforms are capable of executing inference tasks. These range from the processors found in everyday smartphones to powerful GPUs and specialized AI accelerators.

However, performance varies significantly. The computational demands of very large models can make prediction times prohibitively slow on less powerful hardware, such as a standard laptop, compared to a cloud-based server equipped with advanced AI chips.

The efficiency of inference is directly tied to the hardware's capabilities.

Relationship to Training

Inference and training are distinct but interconnected phases in the AI lifecycle. Training is the process of teaching a model, while inference is the process of applying that learned knowledge.

Without successful training, effective inference is impossible. A well-trained model is a prerequisite for accurate and timely predictions during inference.

[See: Training]

Large Language Models (LLMs)

Large language models, frequently abbreviated as LLMs, represent the artificial intelligence engines powering widely used AI assistants.

Examples include ChatGPT, Claude, Google’s Gemini, Meta’s AI Llama, Microsoft Copilot, and Mistral’s Le Chat.

When engaging with an AI assistant, you are directly interacting with an LLM.

This interaction can occur directly, or be augmented by tools like web browsers or code interpreters.

LLMs and AI Assistants: Understanding the Difference

It’s important to note that AI assistants and LLMs aren't always interchangeable terms.

For example, GPT is the large language model developed by OpenAI, while ChatGPT is the specific AI assistant product built upon it.

How LLMs Function

LLMs are constructed as deep neural networks.

These networks comprise billions of numerical parameters, often referred to as weights.

Through these parameters, the models learn the intricate relationships between words and phrases.

This learning process results in a complex representation of language – essentially, a multidimensional map of linguistic elements.

The Training Process

The creation of these models involves encoding the patterns identified within vast datasets.

These datasets consist of billions of books, articles, and transcribed conversations.

Upon receiving a prompt, the LLM generates the most statistically likely sequence of words that aligns with the input.

The model predicts the subsequent word based on the preceding text, repeating this process iteratively to construct a response.

(See: Neural network)

Neural Networks

A neural network is a complex, multi-layered algorithmic structure. It forms the foundation for deep learning and has fueled the recent advancements in generative AI tools, particularly with the development of large language models.

The concept of modeling data processing algorithms after the human brain’s intricate network of connections originated in the 1940s. However, the true potential of this approach wasn't realized until recently.

The Role of GPUs

The emergence of powerful graphical processing units (GPUs), initially developed for the video game industry, was crucial. These chips provided the necessary computational power to train algorithms with significantly more layers.

This increased layering capability allowed neural network-based AI systems to achieve substantially improved performance. This improvement spans numerous fields, including voice recognition, autonomous navigation, and even drug discovery.

(See: Large language model [LLM])

Previously, limitations in hardware restricted the complexity of these networks. Now, with advanced GPUs, more intricate and effective models can be developed and deployed.

The ability to process vast amounts of data efficiently is a key benefit of these advanced neural networks. This efficiency is driving innovation across a wide spectrum of applications.

AI Training Processes

The development of machine learning artificial intelligences relies on a procedure called training. This fundamentally involves providing data to the model, allowing it to discern patterns and subsequently produce meaningful results.

A degree of conceptual consideration arises at this stage of the AI architecture. Prior to training, the underlying mathematical framework serving as the foundation for a learning system consists merely of interconnected layers populated with random values.

It is through the training process that the AI model truly begins to form. This involves the system’s adaptation of its outputs in response to data characteristics, aligning them with a desired objective. This could range from recognizing feline images to composing poetry upon request.

It should be understood that not all AI necessitates training. Systems based on predefined rules, like basic chatbots, do not require a training phase as they operate based on explicit instructions.

However, these rule-based AI systems generally exhibit limitations compared to self-learning systems that have undergone thorough training.

The cost of training can be substantial, largely due to the extensive data requirements, with the necessary data volumes for complex models consistently increasing.

To streamline development and control expenses, hybrid methodologies are sometimes employed. An example is data-driven fine-tuning of a rules-based AI, which reduces the need for extensive data, computational resources, energy consumption, and algorithmic intricacy compared to building a model from the ground up.

[See: Inference]

Transfer Learning

Transfer learning is a technique that leverages a pre-trained artificial intelligence model as the foundation for creating a new model.

This new model is designed for a distinct, yet generally related, task. The core benefit lies in the reapplication of knowledge acquired during prior training iterations.

Efficiency and Data Considerations

Employing transfer learning can significantly improve development efficiency by reducing the time needed to build a model from scratch.

It proves particularly valuable when the dataset available for the target task is relatively small. However, it's crucial to acknowledge that this approach isn't without its constraints.

Limitations and Further Training

Models that depend on transfer learning for broad capabilities often necessitate further training.

This additional training, utilizing data specific to their intended area of application, is essential for achieving optimal performance within their specialized domain.

Related Concepts

(See: Fine tuning)

Weights in Artificial Intelligence

Weights are fundamental components in the process of AI training. They dictate the significance assigned to various features, or input variables, within the training data. This, in turn, directly influences the resulting output of the AI model.

Understanding the Role of Weights

Essentially, weights are numerical values that identify the most important elements within a dataset for a specific training objective. Their function is realized through multiplication applied to the input values.

Initially, model training often commences with randomly assigned weights. However, as the training progresses, these weights are dynamically adjusted. This adjustment occurs as the model strives to generate outputs that align more accurately with the desired targets.

Illustrative Example: Housing Price Prediction

Consider an AI model designed to predict housing prices. If trained on historical real estate data for a particular area, it might utilize weights for features like the number of bedrooms and bathrooms.

Additional features, such as property type (detached or semi-detached) and the presence of amenities like parking or a garage, would also be assigned weights.

Weight Significance

The weights assigned to each input ultimately represent the degree to which each feature impacts property value, as determined by the dataset used for training.

Therefore, a higher weight indicates a stronger influence on the predicted outcome.