Artificial intelligence, or “AI” has been a key concept in computer science since the 1950s, but it began capturing widespread public and business interest around late 2022. This increased attention is primarily due to groundbreaking advancements in machine learning, which are now driving major transformations in various aspects of daily life and business operations.
It’s easy to be swept away in the excitement of the latest trends and buzzwords, especially when it comes to emerging technologies. However, staying ahead of the curve means more than just following the latest craze. To make the most of new developments it's essential we become familiar with key terms and concepts. This is crucial when grappling with the underlying technology and its implications. These buzzwords will help you navigate the tech developments with greater confidence and insight.
So, in the words of Taylor Swift, who else decodes you? Here are essential AI buzzwords you need to know.
1. Artificial Intelligence
Artificial intelligence is essentially an advanced computer system capable of emulating human behaviours. It can comprehend instructions, make decisions, translate languages, and evaluative analysis based on accumulated experience. AI works by processing extensive datasets through algorithms to automate tasks that usually demand human intelligence. We see AI in action in most aspects of our lives, without even realising it – suggesting words to type, songs to listen to, and products to buy.
2. Machine Learning
If AI is the result, machine learning is the pathway to get there. It involves repeatedly processing data through algorithms, adjusting inputs and receiving feedback to enhance training. This iterative process requires huge amounts of data to detect patterns and make predictions based on the information gathered.
A machine learning process consists of six main steps: collecting data, designing the model, training the model, reviewing the results, evaluating and refining if necessary, and deployment. Cassie Kozyrkov inspired this simple analogy comparing the process of artificial intelligence to creating food in the kitchen.
Fine dining with machine learning
3. Large Language Models
Large language models (LLM) use machine learning techniques to help them better understand and use language in the way humans communicate. They are designed to enhance their ability to comprehend, interpret, and generate language in a manner that closely mirrors human communication. Continuous training through extensive data corpuses enables them to learn the nuances of syntax, semantics, and contextual meaning. This iterative training process is designed to aid the LLMs in development of sophisticated understandings of linguistic patterns and structures allowing them to perform a variety of language-related tasks with a high degree of accuracy and proficiency. The benefits extend across multiple domains by enhancing communication, facilitating understanding and automating complex tasks, to translating languages, answering questions, summarising text, and even generating code.
4. Natural Language Processing
Natural language processing (NLP) is a branch of AI focused on helping computers understand and interact with human language. By creating advanced algorithms and models, NLP enables systems to process large volumes of text, extract useful information, recognise patterns, and generate meaningful insights. Combining techniques from linguistics, statistics, and machine learning, NLP enhances human-computer communication. It includes tasks such as text and speech analysis, machine translation, text generation, and sentiment analysis, greatly improving how we interact with technology and enable more effective communication.
NLP helps teams turn unstructured data (like customer feedback, social media posts, and support tickets) into actionable insights. It powers chatbots and virtual assistants to manage routine queries, improves call routing, and automates email sorting. Additionally, NLP enhances search engines by better understanding user intent and search patterns, leading to more accurate and relevant results.
5. Sentiment Analysis
Sentiment analysis is an NLP technique that examines and interprets the emotions, attitudes, and opinions expressed in text. Also called opinion mining, it uses machine learning algorithms to assess the emotional tone of the content as expressed by the author. Typically, it categorises responses as positive, negative, or neutral, but can also provide more detailed sentiment classifications if required.
This analysis can be applied to social media posts, product reviews, or email responses to create more targeted and effective marketing campaigns, enhance customer support, and make informed decisions about brand reputation and management.
6. Generative AI
Generative AI leverages large language models to create original content rather than just repeating or summarising existing information. By analysing and learning patterns from extensive datasets, generative AI can produce new and unique outputs that are inspired by, but different from, previous examples. This technology can generate a wide range of content, including images, music, text, videos, and code.
Generative AI has a broad range of impactful applications, but it also carries risks of misuse. For example, malicious users could exploit this technology to create convincing fake news or deceptive images that seem real but are not. To address these risks, technology companies are actively working on ways to clearly identify and distinguish AI-generated content.
7. Generative Pre-Trained Transformer (GPT)
Generative AI has a broad range of impactful applications, but it also carries risks of misuse. For example, malicious users could exploit this technology to create convincing fake news or deceptive images that seem real but are not. To address these risks, technology companies are actively working on ways to clearly identify and distinguish AI-generated content.
GPT models are trained on vast and diverse datasets, enabling them to produce coherent and contextually relevant text. They excel at a variety of tasks, including language translation, text generation, and code completion.
8. Prompts
A prompt is an instruction given to an AI system, such as a large language model, that guides it in performing a specific task or generating a response. This input can be in the form of text, images, or code. For instance, if you want the AI to generate a specific type of content or perform a particular task, the prompt should clearly articulate what you're asking for.
In the context of interacting with AI, the precision of the prompt is crucial. Users must provide detailed and clear prompts to AI to get the results you want. If you are vague or unclear in your prompt, the AI might not deliver the outcome you expect. The quality of the AI's output often depends on how well the prompt conveys the intended task or question.
Who else decodes you?
Taylor Swift
9. Token
In AI, particularly in natural language processing and machine learning, a token is a unit of data that algorithms process. Tokens are essentially pieces of text and can be made up of whole words, sub-words, characters, or even punctuation marks. They are not always neatly aligned with word boundaries and may include trailing spaces or partial words. 1 token approximates 4 English characters.
10. Context Window
Where a token is a unit of data, the ‘context window’ refers to the number of tokens the model can consider at once. A context window helps the system understand text by breaking it into smaller, more manageable pieces, or tokens. These tokens are organised in a specific way, called positional encoding, which helps the AI make sense of the content and provide relevant and meaningful answers.
11. Context Window Size
The context window size is the number of tokens both preceding and following a specific word or character, the target token, to determine the boundaries within which AI remains effective. In other words, it is the amount of text the model can receive as input when generating or understanding language. This eliminates unnecessary checks on the conversation history and is essential in determining a model's ability to make coherent and contextually relevant responses or analyses.
Important to note, context window size, which varies between models, incorporates a set of user prompts and AI responses from recent user history. However, AI cannot access a data set history that is outside the defined context window size and will instead generate incomplete, inaccurate output. Not all context windows perform equally and therefore is crucial to consider when using a large language model.
12. Transformer
Transformers are a type of deep learning model designed for advanced language processing to understand contextual relationships between words in a sentence. They excel at understanding the relationships between words in a sentence and can handle complex language tasks. Transformers enable teams to develop sophisticated language models that can automate tasks like language translation, content generation, and information extraction.
This technology leads to more accurate and nuanced language understanding, improving machine translation, sentiment analysis, and overall natural language comprehension. By leveraging transformers, teams can improve their natural language processing applications, enhance data analysis, and gain deeper insights, resulting in more impactful client engagements.
13. Hallucinations
Generative AI systems create diverse content such as stories, poems, and songs however, when accuracy and truthfulness are essential, these systems can fall short as they cannot inherently distinguish between real and fabricated information. This limitation can then lead to the generation of inaccurate or misleading responses, often referred to by developers as "hallucinations" or "fabrications."
To address these inaccuracies, developers use a technique called "grounding," where they supply the AI with additional information from reliable sources to improve its accuracy on specific topics. Despite these efforts, the system may still produce incorrect predictions, especially if it lacks current information or relies on outdated training data. Continuous updates and refinements are essential to enhance the reliability of generative AI outputs.
Partner with Siera Data and we’ll climb the AI mountain together
As AI evolves rapidly, it becomes a game changer for managing risk events with speed, accuracy, and defensibility. Understanding the basic “buzzwords” is just the start; the key is to implement tailored AI strategies that fit your specific needs.
Siera Data goes beyond fundamental knowledge by applying advanced technologies like AI, machine learning, and sentiment analysis to help clients streamline processes and extract valuable insights from their data. This approach builds confidence and enhances your ability to handle risk and regulatory compliance effectively. By leveraging these innovations, Siera Data supports clients in using AI for legal, compliance, and investigative purposes, ensuring they are well-equipped to navigate complex challenges.
For more information on how Siera Data can turbo charge your AI experience, contact us today.
