Generative AI
Chapter 1: Introduction to Generative AI
Generative AI is a game-changer in the field of technology, holding the potential to pivot a company's strategic trajectory. It's not just a tech novelty but an innovation that can revolutionize various aspects of business operations and decision-making processes.
- 1.1 Understanding Generative AI
- Generative AI is a subset of Machine Learning (ML) and Deep Learning. It's about creating fresh, new content, which can range from text like articles or answers to questions, to images, videos, even 3-D representations for video games.
- Generative AI models are fundamentally probabilistic models. They generate predictions based on what they perceive to be the most likely next word or pixel in a sequence, given the data they've been trained on.
Chapter 2: Evaluating Generative AI Models
Evaluating the effectiveness of Generative AI models involves understanding the technical aspects, but more importantly, it requires domain expertise to ensure that the models are trained and tested on appropriate data and evaluated using the most fitting metrics.
- 2.1 Training and Testing Generative AI Models
- Generative AI models are trained on vast amounts of data, both public and private. For instance, OpenAI partnered with Shutterstock to train its image model on Shutterstock’s proprietary images.
- Generative AI models use a method called Self-Supervised Learning, which is a blend of supervised and unsupervised learning. Here, labels are "created" that are intrinsic to the data itself, enabling the models to learn from the data itself to predict what comes next.
Chapter 3: The Future of Generative AI
The future of Generative AI holds significant potential, with advancements expected in model size, agency, embodiment, and autocracy.
- 3.1 Bigger Models
- The size of Generative AI models is expected to increase, although there is a limit to this growth due to the amount of training data available.
- 3.2 Model Agency
- AI won't just react to inputs—it will exhibit agency, making autonomous decisions based on contexts, history, and programmed objectives.
- 3.3 Model Embodiment
- Generative AI will transition from tools to interactive agents, understanding, learning from, and responding to their environment.
- 3.4 Model Autocracy
- The control of these large generative AI models does not lie in the hands of a few mega tech companies. The barrier to entry for training and experimentation has dropped significantly, enabling more players to enter the field.
Chapter 4: Implications of Generative AI for Organizations
Generative AI has significant implications for organizations, particularly in terms of how they can leverage it to derive value.
- 4.1 Generative AI in Various Domains
- Generative AI is already delivering value for companies in various domains, including information technology, marketing and sales, customer service, and product development.
- For instance, automated coders have boosted developer productivity by over 50 percent. In marketing and sales, 30 percent of all outbound marketing messages are expected to be crafted with the help of generative AI systems within the next two years.
- In customer service, natural-sounding, personalized chatbots and virtual assistants are handling customer inquiries and guiding customers to the information they need. In product development, Generative AI is being used for rapid prototyping of product designs.
- 4.2 Using Existing Foundation Models or Creating Your Own
- Companies can either leverage existing foundation models with some degree of customization or create their own fine-tuned foundation models. Fine-tuning these models is a more manageable task as it requires less data, is less expensive, and can be done in days.
- 4.3 Reimagining End-to-End Domains
- Companies need to evolve beyond seeking incremental improvement to become visionaries, crafting a resonant purpose, and boldly imagining and pursuing the future. This involves reimagining end-to-end domains and leveraging generative AI in a strategic manner.