Chatbots are popular applications of GenAI and LLMs. A chatbot is a computer program that interacts with humans through natural language conversations. Some chatbots use LLMs to generate content according to user inputs.

Chatbot functionality is often embedded in customer services (e.g. banking, tax, logistics, ecommerce). Chatbots might be fine-tuned on an organisation’s private datasets to answer specific queries.

General purpose AI is a new paradigm where very large AI models become competent at a wide range of tasks, often without substantial modification or fine-tuning. General-purpose AI systems are often LLMs.

Transformer architecture makes LLMs possible. Put simply, transformers convert text. This type of architecture was proposed in 2017 by 8 authors from Google in the paper Attention is all you need.

Transfer learning is a model’s ability to apply information about one situation to another. In this way, the model builds on its internal knowledge.

In GenAI, products must adapt and work well in different situations. It’s like teaching a model skills in one area and then letting the model use those skills in other areas too. In this way, models can learn from one thing and apply it to many different tasks, making them versatile and useful.

Open-source LLMs have publicly accessible source code and underlying architecture, allowing developers, deployers, researchers and enterprises to use, modify and distribute them freely or subject to limited restrictions.

Closed-source LLMs have proprietary underlying source code and architecture. They are accessible only under specific terms defined by their developers.

Open-source LLMs and closed-source LLMs can be accessed and deployed via application programming interfaces (APIs), which are sets of rules or protocols that allow two software programs to communicate with each other to exchange functionality. AI developers, deployers or users can integrate data, services and functionalities from other applications through provided APIs, rather than developing them from scratch. The following table presents different features of open-source LLMs and closed-source LLMs.

A token is the smallest unit of data used by GenAI systems.

For text-based models like GPT, a token can be a word, part of a word, punctuation marks, spaces or other elements of the text. For image-generating AI models like DALL-E, a token is a pixel of the image. For audio-based AI models like MusicLM, a token might represent a short sound segment.

Tokens allow AI models to understand, memorise and generate meaningful responses. They play a vital role in memory capacity, determining how much information the AI model can recall.

Reinforcement learning from human feedback (RLHF) resembles the human learning process. When we learn a skill our teacher/instructor says things like ‘well done’ if we do something right or ‘let’s try it again and differently’ to improve our skill.

Diffusion models are used for AI image generation. They work by destroying training data (by adding visual noise) and then learning to recover the data by reversing the noise.

AI inference is the process of applying trained machine learning models to new, unseen data to derive meaningful predictions or decisions. When users give a GenAI system a prompt, the computational system used to produce the output is called inference. The energy required for inference is much lower than training a model but is still significant and is a large part of the cost of using a GenAI system.

LLMs are trained from different types of datasets. The material in datasets may be protected by intellectual property or information privacy laws. Organisations like Common Crawl scrape the public internet periodically and create gigantic datasets that can be used for model training.

Entities that control large amounts of text, visual, musical, code or video content may license it to AI firms for a fee. Datasets may also come with licensing stipulations that dictate what others can do with that dataset, for instance whether they can share it, modify it or use it for commercial purposes.

AI developers belong to a broader category of programmers and engineers. They write code and algorithms to enable machines to perform tasks that normally require human intelligence. They build models in-house by training on a mix of public and private data for various applications, from chatbots and virtual assistants to self-driving cars.

In emerging AI regulations, developers are responsible for creating AI software products and complying with regulations.

Data labelling and annotation describe the process of tagging or labelling text, images, audio and video data for AI training.

An AI supply chain refers to the process of creating, sourcing and integrating the components needed to develop and deploy AI systems or products.

AI libraries include pre-written codes and algorithms for common AI tasks, such as data preprocessing, model training and evaluation.

Machine learning environments are where machine learning models are built, trained and deployed. Provided by the big tech corporations like Amazon and Microsoft, these environments contain fully managed infrastructure and tools to enable reproducible, auditable and portable machine learning workflows across different environments. Deployers can run training scripts or host service deployments, often referred to as computer targets, within these environments.

Fine-tuning happens during model development and deployment. It involves modifying a trained AI model with a smaller, targeted fine-tuning dataset. Fine-tuning maintains the original capabilities of a pre-trained model while adapting it to suit more specialised use cases.

While AI developers create the AI system, AI deployers make them available for use in realword applications. In some cases, the distinction between a developer and a deployer is minimal. However, deployers may be the organisations that make AI tools available to others.

Users are the individuals or organisations that interact with AI technology through text, voice, images or other inputs. Users can also be anyone whose rights and activities are influenced by AI system outputs.

AI regulation is developing as risk-based regulation. This approach imposes obligations depending on the risk level of the system. Most regulatory proposals have 3 risk categories: low risk, medium risk and high risk.

The measurement and mitigation of AI-generated risks requires human oversight, AI auditing, transparency and explainability.

The wide use of AI systems presents challenges to privacy. The datasets used to train foundation models and other machine learning systems often include personal information without consent. There have been cases where chatbots reproduce personal information from training data in response to prompts. Privacy and data protection laws impose obligations on entities that collect and process personal information.

Hallucination refers to AI models making up facts to fit a prompt’s intent. When an LLM processes a prompt, it searches for statistically appropriate words, not necessarily the most accurate answer. An AI system does not ‘understand’ anything, it only recognises the most statistically likely answer. That means an answer might sound convincing but have no basis in fact. This creates significant risks for organisations that rely on chatbots to give advice about products or services because that advice might not be accurate.

In AI regulation, AI safety means that AI should be designed, developed and used in a way that is human-centric, trustworthy and responsible (see The Bletchley Declaration by Countries Attending the AI Safety Summit, 1–2 November 2023). The main concern of AI safety is to make sure that AI systems are developed, deployed and used in ways that align with the norms and values of the broader public or specific user groups.

AI transparency is a fundamental regulatory and ethical requirement; however, transparency has different meanings for different stakeholders (e.g. developers, deployers, users, regulators). Transparency represents how well users, regulators and other stakeholders understand how the system operates and how it was built. This is important for ensuring trust in AI outputs. Transparency is related to the openness and explainability of AI systems.

Copyright law typically applies to original works of authorship, such as literary works, artistic works and computer programs. Copyright material has been included in AI training datasets without permission or rights clearance.

Data governance refers to the system of rules for management and control of data.

Data governance requirements may involve compliance with laws, standards or ethical guidelines to ensure data within an organisation is collected, stored and used appropriately.

Within an organisation, data governance responsibilities include ensuring data quality (i.e. data is accurate) as well as data security (i.e. data is appropriately encrypted and protected).

This may mean additional care when using organisational data to fine-tune a GenAI system or when using organisational or sensitive data as part of a prompt.

Data life cycle management refers to the processes for managing data from creation to disposal, including data archiving and removal.

International AI standards serve as a global governance mechanism to help achieve AI policy goals. Standards organisations create standards through stakeholder input and cover various technical and regulatory dimensions of AI systems. Compliance with standards may be required for AI products to be market-worthy; procurers may also require compliance with specific standards. In the EU system, if developers comply with standards, then they likely comply with their obligations under the EU AI Act.

AI guardrails are predefined standards, limitations and operational protocols to prevent AI systems from making decisions or taking actions that could lead to harmful or unintended consequences.

Guardrails are often sets of algorithms or rules that filter and edit inputs (i.e. prompts) and outputs of GenAI systems to ensure that outputs comply with legal and safety requirements. These guardrails are designed to prevent outputs from breaching copyright, produce political misinformation, create biased or discriminatory information or generate hate speech. This has proved very difficult to achieve in practice but remains an important aspect of AI system design.

Regulatory sandboxes are software testing environments where businesses can conduct limited testing of innovations and test their regulatory status.

This allows businesses to receive feedback from experts, including regulators, investors, innovators and other stakeholders, regarding the potential and viability of the innovation.

Human oversight is the requirement that human actors oversee the output of AI systems to ensure that systems create accurate and accountable results. There are different forms and degrees of human oversight, depending on the context and purpose of the AI system. Human oversight is usually required in high-risk systems.

AI auditing involves humans – normally researchers, programmers and regulators – looking closely at AI systems to evaluate risk and ensure AI systems act fairly and safely, and comply with relevant laws, regulations and ethical standards.

Different AI auditing methods have different advantages and limits:

• Technology-oriented audits focus on the properties and capabilities of AI systems.
• Process-oriented audits focus on technology providers’ governance structures and quality management mechanisms.

Some auditing methods may be simple compliance checkboxes; others may be comprehensive assessments of how AI systems might affect users and other stakeholders.

Explainable AI or XAI is a set of tools, techniques and algorithms designed to produce highquality interpretable, intuitive, human-understandable explanations of AI decisions. Many emerging AI regulations require some degree of explanation for higher risk system outputs.

The goal is not to prioritise complete explainability over performance or vice versa. Organisations should disclose the limits of transparency in the system to find a balanced approach that considers the risks and benefits of each AI application, while also considering human and environmental implication.

Post-market surveillance refers to monitoring the ongoing performance and safety of an AI product or service after it has been released to the market.

Floating-point operations per second (FLOPS) in the context of computing and artificial intelligence are often used to measure the processing power or performance of hardware devices like CPUs (central processing units) and GPUs (graphic processing units).

Sometimes frontier models are defined by FLOPS, and some regulatory proposals have stricter compliance obligations for systems that surpass FLOPS thresholds.

Higher FLOPS values indicate higher computational power, more complex AI models, faster data processing, better graphic performance and higher energy consumption

Training, deploying and using AI systems contribute to the global CO2 emissions. Typically, more powerful AI models require more energy. The servers that power AI models also generate considerable heat and are often water-cooled. The amount of water needed to train an AI model is immense. A team of researchers disclosed that “training GPT-3 in Microsoft’s state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret.” And running GPT-3 inference for 10-50 queries evaporate 500 mililitres of water depending on when and where the model is hosted.

LLMs are among the biggest machine learning models, spanning up to hundreds of millions of parameters, requiring millions of GPU (graphic processing units) hours to train and emitting carbon in the process.