What is generative AI explained simply?

Introduction

Generative AI has become a transformative force in the world of artificial intelligence, enabling machines to generate high-quality text, images, and various forms of content based on the data they were trained on. The recent advancements in generative AI, exemplified by models like ChatGPT, have revolutionized the way we interact with AI systems. These models are capable of creating poems, jokes, essays, and even mimicking the artistic style of renowned human creators. In this extensive exploration, we will delve into the concept of generative AI, its historical context, and the mechanisms behind its operation.

The Emergence of Generative AI

The landscape of artificial intelligence has experienced numerous cycles of excitement and skepticism. However, the release of ChatGPT signifies a pivotal moment in the field. Developed by OpenAI, ChatGPT showcases the capacity of large language models to produce content that closely resembles human-generated text. By providing a simple prompt, this AI system can produce love poems in the form of Yelp reviews or song lyrics in the style of a famous artist like Nick Cave.

Historically, the most prominent breakthroughs in generative AI were in the realm of computer vision. This was evident as selfies were transformed into Renaissance-style portraits, and social media feeds were flooded with images of prematurely aged faces. However, the focus has shifted in recent years towards natural language processing, particularly the ability of large language models to generate diverse text on various topics. Importantly, generative models are not limited to text; they can also learn the grammar of software code, molecules, natural images, and a wide range of other data types.

Applications of Generative AI

The applications of generative AI are expanding rapidly, and we are just scratching the surface of its potential. At IBM Research, efforts are underway to harness generative models to accelerate software code development, discover new molecules for pharmaceutical research, and create trustworthy conversational chatbots grounded in enterprise data. Generative AI is even being utilized to generate synthetic data, which serves as a valuable resource for training robust AI models while respecting privacy and copyright regulations.

Understanding Generative AI

Generative AI, at its core, encompasses deep-learning models that have the ability to generate new content based on the patterns and information contained within their training data. These models encode a simplified representation of the data they were trained on and use this encoding to generate new content that is similar to, but not identical to, the original data.

The foundation for generative AI was laid by variational auto encoders (VAEs), introduced in 2013. VAEs were instrumental in extending generative modeling beyond numerical data to complex data types, such as images and speech. These models work by encoding raw data into a compressed representation and then decoding it back into its original form. VAEs introduced the critical feature of generating variations on the original data, a capability that ignited a wave of innovation in generative AI.

Transformers: A Game-Changer

The introduction of transformers in 2017, through Google’s groundbreaking paper “Attention Is All You Need,” revolutionized the training of language models. Transformers combined the encoder-decoder architecture with an attention mechanism for processing text. Encoders converted raw, unannotated text into embeddings, while decoders used these embeddings, along with previous model outputs, to predict each word in a sentence.

Transformers were particularly impactful because they allowed text to be processed in parallel, significantly speeding up training compared to earlier techniques like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks, which processed words sequentially. Transformers also learned the positions of words and their contextual relationships, enabling them to infer meaning and disambiguate words in lengthy sentences.

The scalability of transformers paved the way for pre-training language models on vast amounts of raw text, eliminating the need to label specific task-related features. Once pre-trained on a massive dataset, these models could be fine-tuned on smaller amounts of labeled data to perform various tasks. Transformers have become known as foundation models due to their adaptability and versatility.

Diverse Applications of Language Transformers

Language transformers have found utility in both non-generative and generative tasks. They fall into three primary categories:

1. Encoder-Only Models: Encoder-only models, like BERT, are employed in search engines, customer service chatbots, and classification tasks. For instance, IBM’s Watson Assistant utilizes an encoder-only model. These models excel at non-generative tasks such as classifying customer feedback and extracting information from lengthy documents. In collaboration with NASA, IBM is developing an encoder-only model to extract knowledge from earth-science journals.
2. Decoder-Only Models: Decoder-only models, such as the GPT family, are trained to predict the next word without relying on an encoded representation. GPT-3, a model with 175 billion parameters, was a significant milestone when OpenAI released it in 2020. Other massive models like Google’s PaLM and open-access BLOOM have since joined the landscape. These models are well-suited for generative tasks, including dialogue generation, essay writing, and more.
3. Encoder-Decoder Models: Models like Google’s Text-to-Text Transfer Transformer (T5) combine features of both BERT and GPT-style models. They are capable of performing many generative tasks while being more compact, making them faster and cost-effective for fine-tuning and serving.

Supervised Learning Resurgence

While generative AI has relied heavily on unsupervised learning, supervised learning has made a resurgence in recent developments. AI developers are increasingly utilizing supervised learning to refine the interactions between humans and generative models, enhancing their usability and practicality.

Instruction-tuning, introduced with Google’s FLAN series of models, allows generative models to move beyond basic tasks and engage interactively. By providing instructions alongside responses on a wide range of topics, these models can generate not only statistically probable text but also human-like answers to questions and requests. This approach often requires minimal or no labeled data, facilitating rapid AI solution development.

Zero-shot and few-shot learning techniques further expedite AI development, reducing the need for extensive data gathering. However, these methods have limitations, such as sensitivity to prompt formatting, leading to the emergence of a new discipline known as prompt-engineering. Effective prompts can significantly influence a model’s performance but can be challenging to craft consistently.

Overcoming the Challenge of Proprietary Data

In enterprise settings, incorporating proprietary data into generative models poses challenges. Fine-tuning a large generative model on enterprise-specific data can be prohibitively expensive. To address this issue, techniques like prompt-tuning and adaptors have emerged. These methods allow users to adapt models without modifying their massive parameter counts, achieving desired behavior modifications without significant cost.

Parameter-efficient tuning methods empower users to leverage the capabilities of large pre-trained models while incorporating their proprietary data. This combination of prompt engineering and parameter-efficient tuning provides a robust toolkit for tailoring models to specific tasks without the need for extensive deep-learning solutions.

Human Supervision for Alignment

Another significant advancement in generative AI involves aligning model behavior with human expectations. Alignment refers to shaping a generative model’s responses to align more closely with human preferences. Reinforcement learning from human feedback (RLHF) is a method, popularized by OpenAI, that plays a crucial role in endowing models like ChatGPT with human-like conversational abilities. In RLHF, models generate candidate responses that are rated by humans for correctness and quality. Through reinforcement learning, models adapt to produce responses that align with human standards, resulting in high-quality conversational text.

Future Directions for Generative AI

The future of generative AI is both exciting and uncertain, as researchers explore different directions and dimensions. Traditionally, the dominant trend has been to create larger models trained on ever-expanding datasets, leading to improved performance. Scaling laws enable researchers to estimate the capabilities of new, larger models based on previous advancements.

However, recent research challenges the assumption that bigger is always better. Several studies have demonstrated that smaller models trained on domain-specific data can outperform larger, general-purpose models in specific tasks. For example, Stanford researchers trained the relatively small model PubMedGPT 2.75B on biomedical abstracts, and it outperformed a generalist model of the same size in answering medical questions. This suggests that specialization and domain-specific focus may be preferable when performance in a particular area is paramount.

The concept of model distillation further complicates the notion of model size. Researchers from Stanford attempted to distill the capabilities of OpenAI’s large language model, GPT-3.5, into a much smaller model known as Alpaca. By generating thousands of instruction-response pairs, they used instruction-tuning to impart Alpaca with ChatGPT-like conversational abilities. This approach raises questions about whether large models are essential for emergent capabilities. Some models are even bypassing the distillation step, gathering instruction-response data directly from humans, suggesting a potential shift towards more compact models for practical use cases.

Challenges and Ethical Considerations

While generative AI offers immense potential for innovation and value creation in enterprise settings, it also introduces challenges. One significant concern is the generation of inaccurate or biased information, often referred to as “hallucinations.” Generative models, including ChatGPT, can produce content that sounds authoritative but is factually incorrect or objectionable. Addressing these issues is an ongoing endeavor in AI research and development.

Additionally, generative models may inadvertently include personal or copyrighted information from their training data in their outputs, raising privacy and intellectual property concerns. Striking a balance between leveraging generative AI’s capabilities and mitigating these risks is a crucial challenge for both researchers and practitioners in the field.

Conclusion

Generative AI has evolved rapidly, transforming the way we interact with machines and creating new opportunities across various domains. From its origins in variational auto encoders to the emergence of transformers and beyond, generative AI has shown remarkable progress. Recent developments in instruction-tuning, zero-shot learning, and human feedback reinforcement have enhanced the usability and practicality of generative models.

The future of generative AI holds promise and uncertainty. While larger models have traditionally been the focus, domain-specific models and model distillation are challenging the assumption that size is everything. As the field continues to evolve, addressing challenges related to accuracy, bias, and privacy will be essential to harnessing the full potential of generative AI while mitigating its risks. Generative AI’s impact on enterprise and society at large will undoubtedly continue to shape the landscape of artificial intelligence in the years to come.

FAQ Related to Generative AI