Introduction

If we ever achieve Artificial General Intelligence (AGI), it will not happen solely thanks to Large Language Models (LLMs). While these models have made remarkable progress in natural language processing (NLP) and have given us the impression that machines can finally 'talk' to us, language by itself is simply not enough – and sometimes even unnecessary – for an intelligent creature. Language, in this sense, is not what directly contributes to intelligence; rather, it serves as a sign for us – self-acclaimed intelligent beings – that we are 'talking the same language' with the models.

If AGI ever becomes possible, language will once again play a more significant role in communication, allowing us to understand the models better. However, it is essential to consider how children and animals learn and develop intelligence. Children initially learn by sight, by ear, and by touch, relying on their senses to understand the world around them. Similarly, animals can understand us through speech recognition and/or visual perception without relying on text, emphasizing the importance of non-linguistic cues in intelligence.

While not dismissing the importance of advancements in language models and acknowledging the significant progress in NLP (we even have an amazing series about The History of LLMs), as exemplified by models like GPT (Generative Pre-trained Transformer), it is crucial to highlight the importance of computer vision (CV) in understanding the physical world. In this adventure series about CV, you will learn about all the struggles the researchers went through from the late 50s up to nowadays; the main discoveries and major roadblocks for real-life implementation, dead ends and breakthroughs; and how much computer vision has changed our lives. But first, let's explore what computer vision entails.

What is computer vision?

Conclusion or Why should you follow this series?

In this series, we focus on the role of computer vision in the development of AGI because a comprehensive understanding of the world requires more than just language processing. We believe that looking back at history helps us understand what worked, what didn't, and can contribute to cross-pollination of ideas for our readers. We want to answer these questions: What were the challenges of the past and how they were solved? Could it help researchers solve the current challenges? What are some unexpected intersections of ideas that could lead to innovative solutions?

In the upcoming articles, we will dive deeper into the early inspirations and theoretical foundations laid down by pioneers in the field of computer vision, explore key milestones and breakthroughs, discuss recent advancements and their applications, and examine the challenges and future directions of this rapidly evolving field. By understanding the past and present of computer vision, we can better appreciate its significance in the pursuit of AGI and anticipate the exciting possibilities that lie ahead.

Next episode: The Pioneering Era: 1950s to 1970s - How it all started? Or The Dawn of Computer Vision: From Concept to Early Models

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