We take a deep dive into the world of Artficial Intelligence, Machine Learning and Large Language Models to bring you this definitive A-Z guide...
Inspired by a recent guest lecture from AI consultant Lucas Nottaris of visium.ch, and the growing prevalence of all things AI in our daily lives, we decided to put together this handy guide to explain some of the key terms associated with this fast-moving tech.
Lucas' lecture made us realise that, while we are familiar with a lot of the buzz-words, our understanding and distinctions between the specific terms are maybe not very in-depth.
So, we hope that this directory proves a helpful reference point to familiarise yourself with some of the key phraseology associated with these rapidly-evolving systems and tools...
A - Algorithm
An algorithm is like a recipe a computer follows to solve a problem or complete a task. It’s the backbone of many AI applications. For instance, Google Search uses complex algorithms to sift through the web and bring you the most relevant results. Similarly, Netflix leverages algorithms to analyse your viewing habits, recommending shows you’re likely to enjoy based on patterns from millions of other users. These algorithms constantly evolve, learning from new data to improve accuracy and relevance over time.
B - Bias
AI bias occurs when systems reflect or amplify prejudices present in their training data. It’s like when you have a friend who always suggests the same type of restaurant because that’s all they know. In AI, this can lead to unfair outcomes, such as facial recognition software that performs poorly with certain skin tones due to a lack of diverse training data. Addressing bias involves diversifying datasets and constantly monitoring AI systems to ensure fair and equitable performance for all users.
C - Chatbot
Chatbots are virtual assistants that engage in conversation with users, providing help and information. Think of them as 24/7 customer service reps. They’re used widely by companies like Amazon, where they handle basic inquiries and guide users through common issues, freeing up human agents for more complex tasks. Banks also use chatbots to assist with account inquiries and transactions, offering quick and efficient service without the need for a phone call.
D - Deep Learning
Deep learning involves neural networks with many layers, allowing AI to recognise patterns and make decisions. Imagine a system that learns to identify cats in pictures by analyzing thousands of images and understanding subtle features like whiskers and ears. This technology powers Google Photos’ ability to organise your snapshots by recognising people and places. It’s also behind Siri and Alexa’s ability to understand and process voice commands, enabling them to perform tasks or answer questions seamlessly.
E - Expert System
Expert systems are AI programs that emulate the decision-making ability of a human expert. Think of them as digital consultants. For example, IBM’s Watson can analyse vast amounts of medical data to assist doctors in diagnosing diseases, suggesting potential treatments based on the latest research. These systems are invaluable in fields like finance and law, where they help professionals sift through complex data to make informed decisions quickly.
F - Fuzzy Logic
Fuzzy logic deals with reasoning that’s approximate rather than fixed, much like how you decide how much salt to add to a dish based on taste. It’s used in various control systems, such as the automatic gearboxes in cars that adapt to driving conditions for a smoother ride. In climate control, fuzzy logic helps maintain a comfortable environment by adjusting heating and cooling systems in response to changing conditions, providing a balance between efficiency and comfort.
G - Generative AI
Generative AI can create new content, from images to music and text, by learning from existing patterns. It’s like having a creative partner that never runs out of ideas. OpenAI’s DALL-E generates images from textual descriptions, enabling artists and designers to visualise concepts quickly. ChatGPT, another generative AI, assists writers by drafting articles or brainstorming ideas, making it a versatile tool for content creators across industries.
H - Heuristic
Heuristics are strategies or approaches that help in problem-solving and decision-making, often using shortcuts. They’re like rules of thumb that guide quick decisions, such as chess engines like Stockfish, which use heuristics to evaluate positions and make strong moves without calculating every possible outcome. This approach is also used in search algorithms, helping AI systems efficiently navigate large datasets or complex problems without exhaustive analysis.
I - Inference
Inference is the process by which AI systems apply learned knowledge to make predictions or decisions on the fly. It’s akin to a self-driving car deciding to stop or turn based on the data it’s continually processing from its surroundings. Inference allows AI to function in real-time scenarios, such as chatbots interpreting user queries or recommendation systems suggesting products based on browsing history, making interactions seamless and efficient.
J - Joint Probability
Joint probability involves calculating the likelihood of two events happening at the same time. It’s used in AI models like Bayesian networks, which help in decision-making processes by evaluating various outcomes, such as predicting health risks based on genetic and lifestyle factors. This approach is crucial in fields like finance and insurance, where understanding the interplay between multiple variables can lead to better risk management and decision-making.
K - Knowledge Graph
A knowledge graph is a network of interconnected data points that represents real-world entities and their relationships. Picture it as a massive web of information, where each node is a piece of data and the connections are how they relate. Google uses knowledge graphs to enhance search results, providing users with rich, contextual information about their queries by linking related facts and presenting them in a coherent way, such as showing a celebrity’s filmography alongside their biography.
Various data points are brought together in Zendaya's Google Search result.
L - Large Language Model (LLM)
Large Language Models are AI systems trained on vast amounts of text data to understand and generate human-like language. Imagine having a super linguist that can write essays, translate languages, or even help with coding. OpenAI’s GPT-3 and GPT-4 are prime examples, used in applications ranging from chatbots to content creation tools, where they assist with generating ideas, drafting copy, or answering complex questions with surprising coherence and depth.
M - Machine Learning
Machine learning is about teaching computers to learn from data, improving over time without explicit programming. Think of your favourite playlist evolving over time as it learns your musical tastes. This technology powers spam filters that adapt to new types of junk email, recommendation engines like Spotify’s that suggest new songs based on your listening habits, and fraud detection systems that identify suspicious transactions by spotting anomalies in spending patterns.
N - Neural Network
Neural networks are AI systems inspired by the human brain, designed to recognise patterns and solve complex problems. They’re the magic behind facial recognition, like Apple’s Face ID, which identifies you by analysing unique facial features. Google Assistant also uses neural networks to process and comprehend voice commands, enabling it to perform tasks like setting reminders or answering questions almost as naturally as a human would.
O - Overfitting
Overfitting happens when an AI model learns its training data too well, like a student memorising answers rather than understanding concepts. This can lead to poor performance on new, unseen data. To combat this, techniques like cross-validation and regularisation are employed, ensuring the model generalises well and performs reliably, whether it’s in recognising objects in new images or predicting future trends from real-world data.
P - Predictive Analytics
Predictive analytics uses AI to analyse data and forecast future events, helping businesses make informed decisions. It’s like having a crystal ball that spots trends before they happen. Companies use it for sales forecasting, optimising supply chains, and predicting customer behavior, allowing them to anticipate demand, reduce costs, and enhance customer satisfaction by tailoring experiences based on predictive insights.
Q - Quantum Computing
Quantum computing is a cutting-edge field that harnesses quantum mechanics to process information in fundamentally new ways. It’s like having a supercomputer that can solve problems classical computers struggle with. Companies like IBM and Google are exploring how quantum computing can revolutionise AI, potentially enabling breakthroughs in areas like cryptography, complex simulations, and machine learning optimisation.
Neural Networks allow AI to act as an effective customer support assistant.
R - Reinforcement Learning
Reinforcement learning is a type of machine learning where an agent learns by interacting with its environment, receiving rewards for desired actions. Think of it as training a dog with treats. This approach is used in robotics for automating tasks and by companies like DeepMind, which developed AI that can play games like Go and Chess at superhuman levels, learning strategies through trial and error.
S - Supervised Learning
Supervised learning involves training AI models on labelled datasets, where the desired output is known. A helpful tutor guiding you through your coursework problems, if you like. This method is used in email filtering to distinguish between spam and legitimate messages, and in medical imaging to help diagnose diseases by classifying scans, making it a powerful tool in both everyday applications and critical fields like healthcare.
T - Transfer Learning
Transfer learning allows AI models to apply knowledge from one task to another, much like how knowing to play the guitar can make learning the ukulele easier. It’s used in natural language processing, where models like BERT are fine-tuned for specific applications, such as sentiment analysis or translation, leveraging pre-existing knowledge to quickly adapt to new tasks with minimal additional training.
U - Unsupervised Learning
Unsupervised learning involves AI finding patterns in data without explicit labels, akin to discovering a hidden structure in a puzzle. It’s used for market segmentation, categorising customers based on behavior without predefined categories, and for anomaly detection in network security, where it identifies unusual patterns that might indicate a breach, proving invaluable in fields that require insightful data analysis without pre-existing guidance.
Chatbots, like the one on the IMI website, help to answer questions and enhance the user experience.
V - Vision Processing
Vision processing enables AI to interpret and understand visual information, similar to how humans see and comprehend the world. It’s used in applications like Tesla’s Autopilot, which detects and responds to objects on the road, and in security systems that recognise faces or suspicious activities, highlighting the versatility of AI in interpreting complex visual data for practical, real-world applications.
W - Weak AI
Weak AI, or narrow AI, refers to systems designed for specific tasks, unlike general AI that would possess broader cognitive abilities. Examples include voice assistants like Alexa and Google Home, which perform tasks like setting alarms or playing music. These systems are highly effective within their defined scope, providing convenience and efficiency in everyday tasks without possessing true understanding or consciousness.
X - Explainable AI (XAI)
Explainable AI aims to make AI decisions transparent and understandable, ensuring users know why decisions are made. It’s like having a teacher breaking down the steps required to solve a math problem, rather than just giving you the answer. Tools like LIME help break down and illustrate AI decisions, making it easier for developers and users to trust and verify AI outcomes, especially in sensitive areas like healthcare or finance where understanding the rationale behind decisions is crucial.
Y - YOLO (You Only Look Once)
YOLO is a real-time object detection system that allows AI to identify and classify objects in images quickly and efficiently. Imagine giving AI a pair of eagle eyes that can spot and label items instantly. It’s widely used in applications ranging from security surveillance, where it helps identify potential threats in video feeds, to autonomous vehicles, where it assists in navigation by recognising pedestrians, vehicles, and road signs with remarkable speed and accuracy.
Z - Zero-shot Learning
Zero-shot learning enables AI to recognise and categorise data it hasn’t explicitly been trained on, much like a detective piecing together clues to solve a mystery. This capability is what makes models like GPT-3 so versatile, allowing them to perform a wide range of tasks without needing extensive task-specific data. It’s used in scenarios where rapid adaptability is required, such as classifying new types of content or adapting to evolving user needs, showcasing AI’s ability to generalise from limited information.
"The AI lecture really got us thinking about the future—how AI could shape the industry and the amazing possibilities it brings. We left feeling inspired and motivated to explore how we, too, can leverage AI to create meaningful change in hospitality.
Jethro Andre Jolliffe, MSc in International Hospitality and Events Management student
While AI, and its associated solutions, can feel all pervasive in today's society, this perhaps gives us a false sense of familiarity.
As we come to rely more heavily on these tools at both work and play, it is worth reflecting on how much we really understand about the underlying technology (and those promoting it to us).
By educating ourselves on the terminology of AI and the principles behind it, we can better evaluate the opportunities and threats it poses to us both as individuals and on a societal level.
We have come too far down the road to turn back on this AI-generated journey.
Our responsibility now is to not sprint on blindly, but rather plot out the way ahead as best possible through education, curiosity and questioning.
It's a brave new world, and we still have a say in how it looks...
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