AI 101 for non-technical people

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.

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.

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.

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.

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.

Conversational AI is the technology that enables computers to understand, interpret, and generate human-like conversations using natural language processing (NLP), machine learning, and other AI techniques. The primary goal of this technology is to facilitate seamless and effective communication between humans and machines, simulating human-like interactions naturally and intuitively.

Conversational AI systems are designed to recognize and process various languages, dialects, accents, and slang, allowing them to understand users' intentions and context better. These systems can engage in text or voice-based conversations, making them highly versatile and suitable for various applications.

Typical applications of conversational AI include chatbots, virtual assistants, and customer support systems. In these roles, the technology can help users answer frequently asked questions, provide product or service information, book appointments, and offer personalized recommendations. By automating many routine interactions, conversational AI can significantly reduce the workload on human agents and improve customer experience.

The advanced forms of Conversational AI can evolve by learning from past interactions, becoming more intelligent and responsive over time. This includes ability to handle complex conversations, manage multiple languages and contexts, and even recognize indirect and implicit queries.

Generative AI is a subfield of artificial intelligence that focuses on creating new content, data, or output based on the patterns and structures it has learned from existing data. These AI systems utilize machine learning algorithms (especially deep learning techniques) to analyze and understand the underlying features and relationships within the data and generate novel outputs that follow similar patterns.

Some of the most common generative AI techniques include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, such as the Generative Pre-trained Transformer (GPT) series. These models can be applied to various data types, including text, images, audio, and video.

Generative AI has numerous practical applications across various industries.

In the creative domain, it can generate visuals, music, or written content, enabling creators and writers to explore new ideas and styles.
In natural language processing, it can power chatbots, virtual assistants, and language translation systems that create human-like responses and interactions.
In computer vision and design, it can generate realistic images, 3D models, or even complete virtual environments.

AI-generated content is any creative output, such as text, images, audio, or video, produced by artificial intelligence systems rather than human creators. These AI systems employ machine learning algorithms, particularly deep learning techniques, to analyze and understand the patterns, structures, and relationships within existing data. Based on this understanding, AI can generate novel content that adheres to similar characteristics or styles.

The following types of content can be generated with the help of AI:

Images, video, music, and written content.
Human-like dialogues for chatbots, virtual assistants, and language translation systems.
3D models, and even complete virtual environments.

AI-generated content can streamline content creation processes, save time and resources, and enable personalized user experiences. For instance, it can help marketers and advertisers create customized promotional materials tailored to the preferences of audience segments. In education, it can provide personalized learning materials, adapting to each student's unique needs and progress.

AI 101

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