The Ultimate Guide to Generative AI

Introduction

Generative AI is a new technology that has many people excited. In this article, we'll define Generative AI is, its history, how it works, its advantages, challenges, and its numerous applications. Understanding Generative AI is pivotal in today's rapidly changing world. It holds the potential to change many industries and redefine how we interact with technology.

Definition of Generative AI

Basic Definition

Generative AI refers to a subset of artificial intelligence that focuses on creating new data that closely mimics a given dataset. AI algorithms have focused on performing specific tasks, like finding patterns within data sets. Generative AI models actually generate new (or novel) content that has never existed before. These models learn from existing data, detect patterns, and then produce new content that are similar to the original data.

Many different companies have trained generative AIs to write text, compose a new melody, generate images, and even create video. We have now entered a vast new world of generative AI-generated content.

Generative vs Discriminative Models

To grasp the concept of Generative AI fully, it's helpful to compare it with Discriminative Models. Generative AI models generate new content based on training data. On the other hand, discriminative AI models categorize or label existing data.

For example, a discriminative AI model can identify whether a given email is spam or not. A generative AI model cannot automatically label an email as spam (or not). On the other hand, a discriminative AI model cannot write an email for scratch. A generative AI model can write an email from scratch (with a sufficient prompt).

The key difference lies in their key objective: generative models are creators, whereas discriminative models are classifiers.

Examples of Generative AI

Generative AIs are appearing everywhere in our daily lives. For example:

• Large Language Models like GPT-3.5 and GPT-4, which can generate text that is almost identical human-written articles.
• Character.ai allows users to create characters and interact with them.
• Generative Adversarial Networks (GANs) like DALL-E and Midjourney create realistic images—from portraits of non-existent people to intricate artwork. These examples underscore the impressive capabilities of Generative AI, demonstrating its potential across a multitude of domains.
• Wal-Mart, McKinsey, Ernst and Young have already deployed generative AIs for their employees to search internal knowledge and generate content.
• GitHub Copilot is a generative AI that helps software developers write code faster by generating code for them.

History of Generative AI

Early Generative AI Attempts

Generative AI started in the 1960s, during the early days of machine learning models and neuroscience with computers.

The concept of computers mimicking human-like learning has been around for decades. Initial models were simple and lacked the capability to generate high-quality, realistic data. These early, basic algorithms proved that machines could learn from data and make predictions or decisions.

Early models like perceptrons were limited, but they paved the way for more complex architectures. Advances in computing power and algorithms allowed researchers to develop more sophisticated generative models. These models were more adept at mimicking real-world data patterns.

Generative AI Milestone Projects and Research

A number of milestone projects and research papers have significantly shaped the field of Generative AI.

In 1988, Recurrent Neural Networks (RNNs) improved the capture of sequential information in text data. RNNs were a major improvement, but they could not handle longer sentences.

In 1995, two German researchers introduced Long Short-Term Memory (LSTM). LSTM made significant progress in handling longer sentences, but still had several limitations like overfitting, complexity, and being a black box.

Another landmark was the introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow in 2014. GANs represented a breakthrough in the ability to generate high-quality and realistic images. At the same time, Google's DeepDream project showed how neural networks could generate odd, psychedelic images, capturing the public's imagination.

In 2017, researchers at Google published the influential paper, “Attention Is All You Need.” This paper introduced a the transformer architecture, which permanently changed the natural language processing or NLP landscape.

Large Language Models, such as GPT-2 (2019) and GPT-3 (2020) by OpenAI, use transformers and redefined what was achievable by AIs. These projects pushed the boundaries of Generative AI and provided the research community with solid frameworks for further innovation.

Generative AI Evolution Over Time

Over time, Generative AI has undergone a remarkable transformation.

Computational power -- i.e., the ability for computers to quickly process a lot of data faster -- has skyrocketed over time. This increase in power has enabling the training of models that are not just larger but also more nuanced. Generative AI algorithms have evolved to become more efficient and capable, making more and more practical applications.

For instance, the quality of text generated by the latest language models is often almost identical to human-written text. Likewise, GANs now create lifelike images and have found applications in fields ranging from art to medicine. This rapid advancement suggests a promising future for generative AI. We have yet to discover many applications.

How Generative AI Works

A Brief Introduction to LLMs

Large Language Models (LLMs) are a type of generative model trained on vast amounts of text data. Models like GPT (Generative Pre-Trained Transformer) and BERT (Bi-Directional Encoder Representations from Transformers) are prime examples. Their function is to understand and generate human-like text based on training data.

How does an LLM work?

An LLM takes "prompt" text as input and produces a response based on statistical properties learned from training. It examines the relationships between words, phrases, and sentences in both the prompt and the training data. The LLM learns syntactical rules of language and some level of semantic understanding in words.

LLMs Use Neural Networks

The foundational technology behind LLMs is neural networks, specifically transformer architectures. These neural networks consist of layers of connected nodes that work together to identify patterns and relationships in the data. Each layer transforms the text into a more abstract representation.

For example:

Input sentence: "The cow jumped over the moon."

An overly-simplified abstraction of this sentence based on parts of speech: Definite article, noun, verb, preposition, definite article, noun.

Each of these parts of speech can then be further transformed. For example, the LLM could transform the first noun into:

• Proper or common noun
• What type of noun ("animal")
• What type of animal ("mammal")
• Which species of mammal ("beauvine")

These abstractions help the model fully "understand" what the sentence means and the sentiment behind the sentence. For example, was the sentence a statement or a question? Was the sentence a statement of fact, or statement of disbelief and surprise?

Ultimately, each of these abstractions becomes a numerical value, or parameter. Leading LLMs liek GPT-3 have 175 billion parameters. LLMs also have weights which define the strength of connections between the neurons in the LLM.

Entity Recognition

Entity recognition identifies specific categories of words or phrases within the user's input, such as names, dates, products, or locations. Recognizing entities enables the chatbot to understand what the user is referring to and help it generate a more accurate response. With generative AI chatbots, the underlying LLM does entity recognition.

LLMs Need Training

LLMs require extremely large datasets for training. For example, OpenAI crawled the entire internet to create the training data set for GPT-2, GPT-3, GPT-3.5, and GPT-4. LLM training datasets can range from millions to billions of words.

The large data set also needs pre-processing or cleaning. For example, removing redundant data, misspellings, and code, and converting emojis to text improve the quality of a (text-based) LLM.

Training an LLM first involves feeding it the large dataset, or pre-training, into an unweighted LLM and determining weights. Then it involves comparing LLM predictions to actual data, and adjusting weights to better simulate actual data. The LLM creator repeats this process countless times on different subsets of the data until the model performs sufficiently.

• Creating an LLM that simulates human written text requires significant resources.
• You need several terabytes of content to train the LLM.
• You need millions of dollars in capital to hire personnel to write the LLM algorithms. You also need lots of people to perform supervised learning of the model.
• You also need millions in capital to rent or buy a highly distributed, parallel computing infrastructure. For example, if GPT-3 used only 1 graphical processing unit (GPU), training would have taken 355 years.

Knowledge becomes “Embeddings”

Embeddings are a string of numerical values that represent phrases or sentences. These strings -- or vectors -- capture the semantic essence of the data and serve as an input layer to the LLM.

An LLM converts all knowledge or content fed to it into an embeddings file.

How an LLM works from the end user's perspective

An end user typically interacts with an LLM thru a front-end interface like a chatbot. The user enters their question or prompt into the chatbot. The chatbot sends the prompt or question to the LLM, and the LLM converts this input into embeddings.

The LLM then predicts the most probable next word in the conversation, based on the embeddings and its trained knowledge base. For example if the user asks:

"Tell me a knock knock joke."

A good LLM will most likely predict that the next word in the conversation is "Knock" followed by "knock.", then "Who's", and then "there?"

The LLM prediction is an iterative process. The model continues to generate one word at a time the LLM predicts the the response is complete.

Thru this process, the LLM is able to produce responses that have correct grammar and syntax. In addition, the response should also be relevant to the user, providing the user with meaningful and informative replies.

The chatbot displays the LLM's response, and the end user can than enter another question or prompt. The chatbot can also maintain the full conversation between the user and the LLM. This way, the user can ask the LLM to elaborate or provide a more specific response without repeating prior prompts.

Advantages of Generative AI

Ability to Handle Complex Questions

A noteworthy advantage of Large Language Models (LLMs) is their ability to comprehend complex questions.

Traditional query-based systems often struggle to understand nuanced or multi-faceted questions. As a result, they tend to return answers that are overly simplistic or irrelevant answers.

LLMs are able to dissect various elements of a complex question, thanks to their deep neural networks and extensive training data. They can understand the context of the question, identify its key components, and generate a response that addresses its complexity.

Whether the query involves multiple questions or combines abstract concepts, LLMs can often deliver a coherent and context-appropriate answer. For example, when I enter the following question into ChatGPT:

"Is the moon made of cheese and is there a God?"

I receive the following response:

"These are two very different questions, and I'll address them separately.

1- Is the moon made of cheese?

No, the moon is not made of cheese. This is a folk tale or myth. We know the moon is composed mainly of rock... [etc.]

2 - Is there a God?

The existence of God is a deeply philosophical, theological, and personal question. It's one of the most fundamental inquiries humans have made throughout history.... [etc.]"

LLMs change the way people access and communicate knowledge. As such, a wide array of industries and use cases for LLMs will arise. In the long term, LLMs have the capability to displace search engines as the primary method of accessing knowledge.

Ability to Generate New/Novel Content

The most defining trait of Generative AI is its capacity to generate entirely new and previously unseen data or content. This capability goes beyond mere replication of existing patterns. Generative AI draws on the vast amounts of data to make novel content. This novel content can be images, text, or even new molecular structures for potential drugs.

Ability to Scale and Automation

One of the most prominent advantages of Generative AI is its ability to scale and potential for automation.

In traditional systems, scaling often requires extensive manual effort, whether it's designing, coding, or entering data. Generative AI models can generate vast amounts of content or perform numerous tasks without the need for continuous human intervention. Generative AI will see huge adoption in fields like content creation, where it can be impractical to manually produce large datasets.

Applications that require consistent automation, such as customer support chatbots, will also extensively embrace generative AI.

Ability to Customize

You can easily customize and adapt Generative AIs. Unlike rigid algorithms, these models can be fine-tuned to cater to specific needs or preferences.

For example, you can train a generative AI that produces music to generate jazz, classical, or any other musical genre. To accomplish this, you simply need to change the training data. This flexibility allows businesses and researchers to tailor AI solutions to a variety of unique challenges and scenarios.

Applications in Various Fields

The flexibility of Generative AI means that its applications span a number of fields.

In healthcare, generative AI can generate models of how proteins will fold and new synthetic drugs.

In the arts, generative AI can create new pieces of music, artwork, and literature.

In finance and business, generative AI models can simulate economic scenarios or produce reports. Generative AI can also analyze large data sets, potentially becoming the primary interface for Business Intelligence (BI) software.

The breadth of generative AI application underscores its potential across different sectors.

Multi-Lingual Sharing of Knowledge

An often under-appreciated advantage is Generative AI's ability to facilitate multi-lingual sharing of knowledge. LLMs trained on diverse languages can generate content in multiple languages or offer translations, bridging communication gaps.

Language will no longer confine knowledge, innovations, and solutions. Global collaboration and understanding can occur without manual translation or fluency in a foreign language.

Potential Challenges with Generative AI

Ethical Concerns with Generative AI

While Generative AI holds immense promise, it also presents a set of ethical challenges.

One major concern is data privacy and misuse. Companies often train these models on vast datasets that can include sensitive or personally identifiable information. As a result, the model's outputs can then mistakenly include PII.

Generative AI may also produced biased content. If the training data of a generative AI is biased, it will likely produce biased content.

Generative AI Costs

On the technical front, Generative AI models, particularly the larger ones, require substantial computing resources for both training and inference. This increases both financial and environmental costs, given the energy consumption of data processing.

Hallucination

Hallucination is when a generative AI model generates outputs that are incorrect or nonsensical.

Hallucination is not a bug or defect in generative AI -- it's a built-in feature. By definitions, LLMs only predict the next most probable word in a conversation. The next word -- or even the full sentence or full paragraph, may have no relationship to established fact.

Hallucinations can be highly problematic when accuracy is extremely important, like in healthcare, financial services, customer service, and legal decision-making.

Some generative AI leaders believe that LLMs will never be able to stop hallucinating. Other generative AI leaders believe LLMs will prevent hallucination in the next 2-3 years. Other companies, like Gleen.ai, have built ML/AI systems around the LLM to minimize or de-facto eliminate LLM hallucination.

Generative AI and Societal Challenges

Societal challenges from generative AI loom large.

Generative AI may cause job loss in sectors and functions that automation can easily handle. This raises important economic and social questions about the future of work and income distribution.

Generative AI is already wreaking havoc in education. Teachers and professors are trying to prevent use of generative AI on essays and term papers. Researchers are using generative AI to produce academic papers. The use of generative AI may cause the global "dumbing down" of students.

Generative AI's ability to generate realistic content creates a ethical concern over deepfakes, fraud, and fake news. These risks necessitate robust ethical guidelines and safeguards to prevent misuse. Several companies are already creating technologies that can tell the difference between genuine content an AI-generated content. Other companies are spearheading initiatives to include "watermarks" in AI-generated photos and videos.

There's a pressing need for regulatory frameworks that can govern the use and impact of this technology. All the above challenges call for a multi-disciplinary approach. Technologists, policy makers, and ethicists will need to work together to navigate the complex landscape generative AI.

Commercial Applications of Generative AI

Text-based Applications for Generative AI

Generative AI is becoming an highly valuable tool across multiple domains.

One area where it's making a considerable impact is customer support. Customer support chatbots powered by Generative AI respond in real-time and often indistinguishable-from-human support. These generative AI chatbots can transform how businesses interact with their customers.

Text-based generative AI will also rapidly transform journalism. AI can quickly generate news reports or summaries, allowing human journalists to focus on more complex tasks like investigative reporting.

Text-based generative AI will also transform marketing. Generative AI can write product descriptions, emails, white papers, and eBooks. In addition, generative AI can engage with audience members on social media platforms and community members on platforms like Discord.

Visual Applications for Generative AI

Image generation technologies can create realistic visuals for everything from virtual real estate tours to design mockup. Video synthesis tools can generate videos from scratch or modify existing videos, creating new possibilities in filmmaking and content creation. These capabilities are not just novelties but are finding real-world applications in industries ranging from advertising to architecture.

Generative AI in Scientific Research

Generative AI holds great promise in scientific research. One area it's already making strides in is drug discovery. By generating possible molecular structures for new medications, AI drastically cuts down the time and resources traditionally required in pharmaceutical research.

Climate modeling is also deploying generative AI. Generative AI simulates different environmental conditions. This provides important data for sustainability efforts and policy decisions.

Generative AI in Entertainment and Fine Arts

In music, creators are using generative AI to compose original scores or assist musicians in creating complex arrangements.

In visual arts, digital artists are using generative AI to create novel paintings and illustrations. This is not just a fad but a growing movement that raises interesting questions about creativity and authorship. New generative AIs allow artists to "opt out" of the training set, retain copyright, and minimize generative AI-based derived works.

The Future of Generative AI

Upcoming Trends in Generative AI

Several trends will shape the future of generative AI.

First, algorithms will continue to improve. Generative AI algorithms will produce even more realistic and nuanced content.

Second, generative AI will become more efficient. New training methodologies, hardware accelerators, and specialized AI chips are likely to make these powerful models more accessible and environmentally sustainable.

Third, generative AI will become more capable. Currently, text-based LLMs focus on answering users' questions. In the future, text-based LLMs will also focus on taking actions on behalf of the user.

Speculation about Generative AI's Future

Generative AI is likely to usher in a new era of automation across various sectors. This will change the way we interact with technology.

Furthermore, a closer integration of AI into our daily lives is inevitable. Imagine a future where your personal AI assistant:

• Schedules your day
• Generates personalized reading material for you
• Replies to business emails you your behalf
• Designs your home interior
• Creates meal plans and writes new recipes, and
• Eventually orders food and even automatically cooks it for you.

While these scenarios may seem like science fiction, they are rapidly approaching reality given the current trajectory of Generative AI.

Conclusion

Generative AI is a fascinating and rapidly evolving field that has the potential to transform many aspects of our lives.

Generative AI offers numerous advantages. At the same time, it's important to understand the ethical, technical, and societal challenges that come with its widespread adoption.

We should embrace the incredible possibilities of generative AI and be prepared to navigate the challenges that arise.