The Future of Generative Models

In this book, so far, we have discussed generative models for building applications, and we have implemented a few simple ones – for example, for semantic search, applications for content creation, customer service agents, and assistants for developers and data scientists. We have explored techniques such as tool use, agent strategies, semantic search with retrieval augmented generation, and the conditioning of models with prompts and fine-tuning.

In this chapter, we’ll deliberate on where this leaves us and where the future leads us. We’ll consider weaknesses and socio-technical challenges of generative models, and strategies for mitigation and improvement. We’ll focus on value creation opportunities, where unique customization of foundation models for specific use cases stands out. It remains uncertain which entities – big tech firms, start-ups, or foundation model developers – will capture the most upsides...

The current state of generative AI

As discussed in this book, in recent years, generative AI models have attained new milestones in producing human-like content across modalities including text, images, audio, and video. Leading models like OpenAI’s GPT-4 and DALL-E 2, and Anthropic’s Claude display impressive fluency in content generation, be it textual or creative visual artistry.

Between 2022 and 2023, models have progressed in strides. If generative models were previously capable of producing barely coherent text or grainy images, now we see high-quality 3D images, videos, and the generation of coherent and contextually relevant prose and dialogue, rivaling or even surpassing the fluency levels of humans. These AI models leverage gargantuan datasets and computational scale, enabling them to capture intricate linguistic patterns, display a nuanced understanding of knowledge about the world, translate texts, summarize content, answer natural language questions...

Economic consequences

Integrating generative AI promises immense productivity gains through automating tasks across sectors – albeit risking workforce disruptions given the pace of change. Assuming computing scales sustainably, projections estimate 30–50% of current work activities will be automatible by 2030, adding $6–8 trillion annually to global GDP. Sectors like customer service, marketing, software engineering, and R&D may see over 75% of use case value. However, past innovations ultimately spawned new occupations, suggesting long-term realignment.

Developed regions are likely to witness faster uptake, displacing administrative, creative, and analytical roles initially. Yet automation extends beyond employment loss – at present, under 20% of US worker tasks seem automatable directly through LLMs. But LLM-enhanced software could transform 50% of tasks, affirming the force multiplication from complementary innovations.

Thus automation’...

Societal implications

As generative models continue to develop and add value to businesses and creative projects, generative AI will shape the future of technology and human interaction across domains. While their widespread adoption brings forth numerous benefits and opportunities for businesses and individuals, it is crucial to address the ethical and societal concerns that arise from increasing reliance on AI models in various fields.

Generative AI offers immense potential benefits across personal, societal, and industrial realms if deployed thoughtfully. At a personal level, these models can enhance creativity and productivity, and increase accessibility to services like healthcare, education, and finance. By democratizing access to knowledge resources, they can help students learn or aid professionals in making decisions by synthesizing expertise. As virtual assistants, they provide instant, customized information to facilitate routine tasks.

From a consumer standpoint...

The road ahead

The forthcoming era of generative AI models offers a plethora of intriguing opportunities and unparalleled progression, yet it is interspersed with numerous uncertainties. As discussed in this book, many breakthroughs have been accomplished in recent months, but successive challenges continue to linger, mainly pertaining to precision, reasoning ability, controllability, and entrenched bias within these models. While grandiose claims of superintelligent AI on the horizon may seem hyperbolic, consistent trends predict sophisticated capabilities sprouting within a few decades.

On a technical level, generative models like ChatGPT often function as black boxes, with limited transparency into their decision-making processes. A lack of model interpretability makes it difficult to fully understand model behavior or to control outputs. There are also concerns about potential biases that could emerge from imperfect training data. On a practical level, generative models require...

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