Key terminology for non-technical people
Table of Contents
- What is AI?
- What is Natural Language Processing?
- What is a Large Language Model (LLM)?
- How does AI know what content to create?
- What is GPT?
- What is GPT-3?
- What is ChatGPT?
- What is Bard?
- What is the difference between GPT and ChatGPT?
- Can ChatGPT be used to generate marketing content?
- Why is AI content biased?
According to the definition by Oracle, "In the simplest terms, AI refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect."
AI systems typically process large amounts of labeled training data to search for correlations and patterns. These are then used to predict the output of a given input – such as a chatbot conversing with humans in a lifelike manner or an image recognition tool describing objects in pictures from millions of examples.
Generative AI is gaining momentum and is widely used for content creation. Some of the most famous examples of generative AI include ChatGPT, a Large Language Model (GPT-3.5) trained to have a conversation, or Midjourney or Dall-e, the text-to-image models.
According to IBM, "Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can."
NLP is a crucial technology for many AI projects. It is widely used in chatbots, virtual assistants, search engines, and other applications.
A large language model (LLM) is a type of artificial neural network that has been trained on large amounts of text data in order to generate natural language text or perform language-related tasks. LLMs typically consist of multiple layers of artificial neurons that process information about the relationships between words and phrases in a text corpus, allowing the model to learn patterns and generate text that resembles human language.
LLMs have been trained on massive amounts of data, often consisting of billions of words or sentences, using techniques such as unsupervised learning and deep learning. These models have shown remarkable performance in a wide range of natural language processing (NLP) tasks, including language translation, text summarization, sentiment analysis, and language generation.
LLMs are often pre-trained on large datasets, such as Wikipedia or Common Crawl, and then fine-tuned on specific tasks or domains, such as medical language or legal documents. This allows the models to adapt to specific linguistic nuances and produce more accurate results for specific applications.
Overall, LLMs are a powerful technology that have revolutionized the field of NLP and enabled new applications in fields such as chatbots, voice assistants, and machine translation.
This is the answer created by a GPT-3.5-powered application: "So imagine you're trying to learn a new language, like French. You might start by learning some basic words and phrases like "Bonjour" (which means hello) or "Comment ça va?" (which means how are you?). A large language model is kind of like a super smart computer that has been taught to understand language just like you're learning French. But instead of just learning a few words and phrases, it has learned millions and millions of words and sentences from books, articles, and websites. With all that knowledge, the computer can do really cool things like answer questions, write stories, or even translate languages! It's kind of like having a really smart language tutor that can help you learn any language you want."
AI is trained through listening/understanding, much like humans. A person is involved, but AI does most of the work. If you train an AI model to know a brand or a business, the first step is to "teach" it as much as possible about the subject matter and a particular brand. This is oversimplified, but think about it to be similar to onboarding a new employee – you provide the AI model all the information you'd like it to use in its work. Then, the model can generate relevant, engaging, on-brand content that aligns with the marketing and communication goals.
AI is "a polymath" with vast amounts of knowledge on many topics, far greater than any of us can remember. Its context and syntax comprehension at a huge scale allows it to create content that's indistinguishable from what a human would write.
At Intentful, we train AI to understand our client's industry, brand voice, and other company-specific information. This enables us to generate content tailored to your business and achieve the quality you would expect from a human, much faster and more cost-effectively. This frees up your content teams to work on strategic and more complex tasks.
GPT, also referred to as generative pre-trained transformer, is a natural language processing (NLP) model based on the Transformer architecture.
It is a deep learning model trained on a massive volume of text data to become capable of generating human-like text.
People can use it to generate natural language from a prompt, complete a sentence, answer a question, create summaries, and more.
GPT-3, also called Generative Pre-trained Transformer 3, is a large language model (LLM) created by OpenAI.
It is a deep-learning autoregressive LM that uses unsupervised learning to predict the next word in a sentence based on all the previous words.
GPT-3 is trained on a 45TB of text data dataset.
It can be used to generate text, complete tasks like translation and question answering, and has been applied to various natural language processing tasks, including text-to-image and text-to-code.
ChatGPT is a Large Language Model trained to have a conversation. It is powered by OpenAI’s GPT-3, a cutting-edge artificial intelligence (AI) system.
The chatbot is designed to engage in conversations with users in a natural way, rather than responding with pre-programmed responses.
It also can emulate a human conversationalist and perform various tasks, such as writing and debugging computer programs and composing music. It attempts to reduce harmful and deceitful responses and uses filters to prevent offensive outputs.
However, it suffers from multiple limitations, such as "hallucination" (generating deceptive data) and algorithmic bias inherited from the data it was trained on.
Intentful launched the DEI in AI project in the summer of 2021 to identify bias in AI-generated content.
Bard is Google's experimental AI chatbot, similar to ChatGPT. It is based on LaMDA (Google's Language Model for Dialogue Applications), which was originally revealed in 2021.
Google has been testing Bard with a limited group of trusted testers. Both internal and external feedback will be considered to guarantee the Bard meets Google's AI responsibility as well as their search quality standards before the chatbot is released to the public.
The distinction is akin to that between a base and what is constructed upon it.
GPT (Generative Pre-trained Transformer) is an LLM trained on large datasets to generate new text using its understanding of language.
ChatGPT is a version of GPT trained to have a conversation, a chatbot specifically tailored for dialogue. It was designed to generate more natural and coherent conversations. It also maintains a level of short-term memory that allows it to respond to previous user input and then use that knowledge to respond to the current one.
ChatGPT can help overcome writer's block.
It was trained with 175 billion parameters from the web but is unaware of your company or your brand.
Due to how LLMs, or large language models, produce content (by predicting the next word), ChatGPT may be making things up. This phenomenon is called AI hallucination.
To produce marketing content with the help of AI, it is essential to train the model to know your brand. Contact Intentful to learn how to get started.
GPT has been trained on 45 TB of text data, some of it dating back as far as centuries and inheriting multiple biases. While more recent content is also available in the training dataset, there is a risk that the product created with the help of AI will be biased against women, people of color, and other groups. Considering the extent and speed of AI deployment, this can have large-scale effects.
At Intentful, our objective is to create resources to aid those working with AI to detect and alert businesses and persons employing AI of possible bias in content. Learn about Intentful's DEI in AI. We're building a dictionary to help anyone working with AI identify potential bias in content and flag it. The dictionary will be available for anyone for free on the web through Github and other channels.
Intentful invites contributors to help do the following:
- Expand the topics
- Build out the dictionary
- Have experts review its contents
- Promote the project and its accessibility