Top-10 considerations when deploying enterprise scale Generative-AI at your organization

My previous blog discussed practical steps to kick off a Generative-AI practice at your organization. As a next step, I have been thinking about how to deploy and scale a GenAI model at an enterprise level, and as I began to draft a playbook I realized that there are many considerations that an AI team should keep in mind before venturing deep into the implementation process.

Here are my Top-10 things to consider for a successful GenAI implementation:

1. Hallucinations - in the context of Generative AI, it refers to instances where the AI model produces outputs that are unexpected, unrealistic, or otherwise divergent from what was intended or anticipated. Text, images, and other forms of content generated based on the patterns that an underlying model has learned from its training data are not always perfect, and so such output might be considered hallucinatory.? Some examples include "Inaccurate information", "Plausible-sounding untrue text", "Imaginative storytelling", "Misinterpretation of prompts and contextual Errors", etc.
2. Corpus - refers to a large collection of text, audio, or other data used to train and evaluate AI models. It serves as the input data for the model to learn patterns, structures, and relationships within the data. GenAI models like GPT-3 and its iterations, rely on massive amounts of text data to learn patterns, syntax, and semantics from various sources. This text data is often collected from a diverse range of documents, books, articles, websites, and other written materials which are collectively referred to as a "corpus."
3. Inference - this refers to the process of using a trained Generative AI model to generate new content or make predictions based on the patterns it learned during training through "inference". Generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are designed to learn patterns and distributions within a dataset and then generate new data samples that resemble the training data. Inference is a crucial step in utilizing these models for various creative and practical applications.
4. Discriminator - a component of a GAN, it evaluates the content generated by the generator and determines how similar it is to real data. The generator and discriminator work in tandem to improve the quality of the generated content. The discriminator plays a critical role in driving the training process by providing a feedback signal to the generator, helping it refine its output over time. This adversarial setup has led to significant advancements in various domains, including image synthesis, style transfer, and augmenting data.
5. Overfitting / Underfitting - concepts commonly encountered in the realm of Machine Learning (ML) they refer to a situation where a Gen-AI model performs very well on the data it was trained on but fails to generalize its performance to new, unseen data. In other words, the model becomes too specialized in learning the specific details of the training data and is not able to capture the underlying patterns that would allow it to generate new data accurately. ?Underfitting on the other hand refers to a situation where a model fails to capture the underlying patterns and complexities of the training data. This may lead to models performing poorly on both the training data and new, unseen data because it oversimplifies the relationships within the data.

Balancing between overfitting and underfitting is a crucial challenge in generative AI. While some level of specialization is necessary for generating high-quality outputs, the model mustn't become so specialized that it fails to capture the broader distribution of the data.

1. Attention Mechanism - a component used in certain Generative AI models, like Transformers, to focus on different parts of the input data when generating output and the model weighs the importance of different elements in the input.? The attention mechanism has significantly advanced the capabilities of Generative AI models by allowing them to effectively capture long-range dependencies and context in input data. This has led to breakthroughs in various natural language processing and computer vision tasks, where understanding context and relationships is crucial for generating meaningful and accurate outputs.
2. Beam Search - a search algorithm often used in text generation tasks to explore multiple possible sequences of words and select the most likely sequence based on the model's probabilities. Beam search helps improve the coherence and grammaticality of generated sequences by considering a range of possibilities instead of just selecting the single most probable word at each step. However, it's important to note that beam search may not always produce the most creative or diverse outputs, as it tends to favor highly probable sequences, potentially leading to repetitive or conservative outputs.
3. Perplexity - used as a measure to evaluate how well a Generative AI model predicts a given sequence of data, it indicates how surprised the model is by the data and serves as a measure of the model's performance. In the context of evaluating generative AI models like GPT, lower perplexity values are generally desired, as they indicate that the model has a better understanding of the language and can make more accurate predictions about what comes next in a given sequence. However, it's important to note that while perplexity is a useful metric for comparing models and tracking progress, it's not always a perfect reflection of the quality of generated text in more nuanced aspects like coherence, context, and factual correctness.
4. Fine-tuning - this is the process of taking a pre-trained model and further training it on a smaller, domain-specific dataset to adapt it to perform specific tasks or generate content in a particular style. It's important to note that fine-tuning with generative AI also poses challenges, such as the potential for the model to generate biased or inappropriate content if not carefully managed. Ethical considerations and responsible AI practices should be adhered to when fine-tuning models to ensure their outputs align with societal values and norms.
5. Latent Space - a hypothetical space where a Generative AI model maps input data. It's a lower-dimensional representation of the data where similar items are closer together, allowing for manipulation and interpolation of content. It refers to a multi-dimensional vector space that captures the underlying representations of data in a condensed and meaningful way. This space is often referred to as "latent" because the dimensions themselves may not have a direct interpretation but instead represent abstract features or attributes.

Hope the above items provide a foundation for your understanding of the concepts and principles of Generative AI that can help you plan a model deployment at your organization.

As always, please comment on topics you would like to discuss and share your thoughts on this very important technology revolution of our lifetime.