As we continue to develop and refine new AI language models and tools at an unbelievable (worrying?) pace, Artificial Intelligence has the potential to revolutionise the way we work, forever.
So, get ready to delve into a realm where machines meet us, ingenious marketers, all while we keep our cool, professional, and friendly vibe intact. Let's ditch the standard marketing playbook and look at the key AI terms you should know as a modern marketer:
A: Artificial Intelligence (AI)
Artificial Intelligence refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, writing, decision-making, and problem-solving. AI is becoming mainstream due to recent advancements made by organisations like OpenAI through technologies like ChatGPT. These have greatly improved the capabilities of AI in understanding and generating human-like text, making it more accessible and usable for a wide range of applications in sales & marketing among many (many) other fields.
Bard is Google’s own take on OpenAI’s ChatGPT. This advanced chatbot system is designed to generate content by receiving prompts and undertaking text-related tasks. These tasks include delivering answers and summaries, as well as generating diverse forms of textual content – which Google plans to integrate in its wide offer of products imminently (think Gmail or Google Meet).
ChatGPT is an AI language model developed by OpenAI, introduced in November 2022. It is designed to generate human-like responses to text prompts and engage in conversation with users. ChatGPT is trained on a diverse range of internet text and can provide information, answer questions, and generate creative text – making it immediately useful as a co-pilot solution for marketers needing to draft or edit content such as blog posts, emails or ads.
OpenAI have released several versions of ChatGPT to date. These include GPT-3, GPT-3.5 Turbo and GPT-4, with each version representing an iteration and improvement upon the previous one.
For instance, with GPT-4, OpenAI introduced Plugins which allow ChatGPT to access up-to-date information on the web*, run computations and use third-party services ranging from Zapier to Expedia.
*The previous versions of ChatGPT were trained on data predating September 2021.
DALL·E is an AI model developed by OpenAI that generates images from textual descriptions. It combines techniques from both language models and generative adversarial networks (GANs) to create unique and realistic images based on textual prompts.
Deepfake technology uses AI to create fake media content that can be misleading, compromising privacy, and dangerous if exploited. It's a growing concern because of its ability to produce authentic and deceiving content.
Embeddings refer to a technique that assists AI in comprehending and extracting meaning from intricate data. OpenAI's text embeddings, for instance, enable the measurement of similarity between text strings, making them valuable for AI-driven tasks such as search, clustering, recommendations and classification of data.
Read this post to learn about some practical implementations of embeddings for chatbots.
Fine-tuning is the process of taking a pre-trained AI model and adapting it to a specific task or domain. By training the model on task-specific data, it can learn to perform that task more effectively, leveraging the general knowledge it has gained from its pre-training.
G: GodMode AI
God Mode is an AI tool that can create tasks, follow user instructions, and work on new tasks until the main goal is achieved. It helps automate complex tasks that would otherwise take a long time to do manually. GodMode was one of the first to explore the power of generative agents, which are AI algorithms that are designed to replicate human responses and, most interestingly, actions.
HuggingFace is a community and data science platform that aims to democratise AI by providing a central space for sharing and exploring a vast collection of models and datasets. It also serves as a collaborative hub for data scientists, researchers, and ML engineers to share ideas and contribute to open-source projects.
I: Inflection AI
Inflection AI is a machine learning startup founded by Reid Hoffman (founder of LinkedIn) and Mustafa Suleyman (founding member of DeepMind). They’re the company behind Pi, a ChatGPT-like personal AI system that can develop into a virtual co-pilot over time as it learns from your personal interactions.
J: Jasper AI
Jasper.ai is an AI writing tool designed to generate marketing copy, such as blog posts, product descriptions, company bios, ad copy, and social media captions. It gained popularity as one of the very first AI writing tools before OpenAI launched ChatGPT.
Refers to the core computational component of OpenAI's technology, responsible for executing machine learning algorithms and processing data efficiently.
LLM stands for "Language Model". LLMs are designed to understand and generate human-like text, making them capable of a wide range of language-based tasks such as translation, summarisation, and conversational interactions. OpenAI's GPT-3 is a renowned LLM example.
LaMDA (Language Model for Dialogue Applications) is a family of conversational language models developed by Google. It serves as the foundational technology behind dialogue-based applications, enabling the generation of human-like, natural-sounding language during conversations. Google’s goal is to integrate the AI system as a core component across various Google systems, enabling Google products to engage in realistic conversations with users.
Midjourney is an AI service that allows users to generate images based on textual descriptions, creating a wide range of art forms, from realistic to abstract styles. Midjourney’s AI is especially known for its high-quality, well-structured, and detailed images.
Natural Language Processing (NLP) is a branch of AI that deals with the interaction between computers and human language. It involves the development of algorithms and models to understand, interpret, and generate natural language, enabling computers to process and respond to human speech or text.
OpenAI is an artificial intelligence research organisation that aims to ensure that artificial general intelligence (AGI) benefits all of humanity. It develops advanced AI models, conducts research, and promotes the responsible and ethical development of AI technologies. OpenAI has successfully captured mainstream attention and brought the world of AI to the masses through groundbreaking products like ChatGPT and DALL-E.
A prompt refers to the input provided to an AI model to generate a desired output. It can be a text snippet, question, or instruction that guides the model's response. Choosing an effective prompt is crucial for obtaining accurate and relevant results from AI models, which is leading to the emergence of new job roles in the AI field, such as Prompt Engineers, as well as services like Promptbase where people can search, buy and sell prompts.
P: PaLM 2
PaLM 2 is the latest Large Language Model (LLM) released by Google that is highly capable in advanced reasoning, coding, and mathematics. Trained on a diverse dataset including scientific papers and web pages, PaLM 2 is aimed at enhancing popular Google apps like Gmail, Google Docs, and Bard.
In AI, a query refers to a request or question posed to a machine learning system or a database to retrieve specific information or perform a task. It involves seeking relevant data or insights based on the user's input or search criteria.
Regulation in AI is meant to ensure accountability, ethics, and safety as it continues to become mainstream and more accessible. Different governments across the US and Europe are weighing up options around the regulation of AI, which may involve certification requirements for models before release, preventing unchecked operations, and addressing potential risks or biases.
S: Stability AI
Stability AI is the company behind Stable Diffusion, a popular image generation model that primarily generates images from text.
AI training is the process of teaching and refining AI models by exposing them to extensive data, enabling them to learn patterns, make predictions, and perform a wide range of tasks. Through iterative iterations, AI training enhances the model's capabilities and improves its accuracy and performance.
As AI technologies advance, there is apprehension about automation and machine learning systems replacing human labour in various industries. Efforts are underway to address this issue, including reskilling programs and fostering a balance between technological advancement and job creation to mitigate the potential negative impact on employment.
Also referred to as ‘Co-pilots’, virtual assistants are AI-powered software programs or apps that can interact with users and assist them with tasks such as answering queries, providing information, or performing basic actions. Virtual assistants are a key use case in marketing and sales, as they can be used in customer service, sales support, and other business applications to enhance user experiences.
Watermarking is a technique used in AI-generated images to embed identifiable information directly into the content. Its purpose is to distinguish between AI-generated and real images. Alongside watermarking, metadata provides additional context and information about the original files. These measures are aimed at ensuring responsible AI practices and facilitating the recognition of synthetically generated content.
Explainability refers to the ability of an AI system to provide clear and understandable explanations for its decisions and actions. In a marketing context, explainability is crucial for gaining trust and ensuring transparency in AI-powered solutions.
In the context of AI, yield refers to the performance or accuracy of a model or system. It indicates the proportion of correct or usable outputs generated by the system, often measured in terms of precision, recall, accuracy, or other relevant metrics.
Z: Zero-shot Learning
Zero-shot learning is a machine learning approach where models can recognise and classify new objects or concepts they haven't seen before. It achieves this by leveraging existing knowledge from known classes and using additional information or attributes. By understanding the relationships between different classes, zero-shot learning enables models to make predictions on unseen data.
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