PptxGenJS Presentation

[Virtual Presenter] We are about to embark on a fascinating journey through the history of artificial intelligence. From its humble beginnings to the groundbreaking advancements that shape our world today, we will delve into the key milestones and pioneers who laid the foundation for this revolutionary technology. Get ready to discover how AI has evolved and transformed our lives in countless ways..

[Audio] Alan Turing, a renowned mathematician, asked a groundbreaking question in 1950: Can machines think? This inquiry sparked the creation of the Turing Test, a pioneering benchmark for artificial intelligence..

[Audio] In 1956, the Dartmouth Conference marked the beginning of Artificial Intelligence as a distinct field of study. Four pioneers, McCarthy, Minsky, Newell, and Simon, gathered together to lay the groundwork for decades of AI research. These individuals, including John McCarthy from Stanford University, Marvin Minsky from MIT, Alan Newell, and Herbert Simon from Carnegie-Mellon, would go on to lead the way in AI research for years to come. Their work set the stage for the advancements we see today..

[Audio] Frank Rosenblatt's invention of the perceptron in 1957 marked a significant milestone in the history of artificial intelligence. His work, inspired by the human brain, introduced a single-layer neural network that could learn and process information. This breakthrough demonstrated the potential for machines to mimic human-like intelligence. The perceptron's architecture, as depicted in the diagram, involves taking inputs, multiplying them by weights, summing them, and applying an activation function. Although a simplified model, it laid the foundation for the development of more sophisticated neural networks and the advancements in deep learning that followed..

[Audio] Despite the surge of interest in artificial intelligence in the late 1950s and early 1960s, the technology at the time was not advanced enough to produce significant results in complex areas like chess and Go..

[Audio] David Rumelhart, Ronald J. Williams, and Geoffrey Hinton introduced the backpropagation algorithm in 1986, which enabled efficient learning in multi-layer networks. By calculating errors at the output layer and adjusting weights accordingly, this innovation significantly improved the neural network's performance. This breakthrough paved the way for the resurgence of deep learning, driving many of the AI advancements we see today..

[Audio] Researchers continued building upon the foundation laid by backpropagation in the late 1980s. David Rumelhart introduced the multi-layer perceptron in 1988, a significant breakthrough that enabled the creation of deeper, more complex neural networks. This innovation allowed these networks to tackle increasingly difficult tasks, expanding the possibilities for artificial intelligence and paving the way for greater achievements in the years to come..

[Audio] Deep Blue, an IBM chess-playing supercomputer, defeated world champion Garry Kasparov in 1996, showcasing the capabilities of specialized AI systems and marking a substantial advancement in the development of artificial intelligence. This achievement served as a testament to the rapid progress being made in the field, as Deep Blue became the first computer to win a game and a match against a reigning world champion..

[Audio] The year 1998 witnessed a major milestone in the evolution of artificial intelligence when Yann LeCun, Geoffrey Hinton, and Yoshua Bengio pioneered the concept of the Convolutional Neural Network, or CNN. Inspired by the human brain's visual cortex, this innovative architecture enabled the efficient processing of image data. By automatically learning spatial hierarchies of features, the CNN proved highly effective in tasks such as image recognition and classification. This groundbreaking achievement not only propelled advancements in deep learning but also established a solid foundation for various computer vision applications that remain prominent today..

[Audio] By the year 2000, a significant turning point emerged as advances in microprocessor speeds and memory sizes had finally reached a level where machine learning could be successfully applied to large datasets. This hardware evolution was pivotal, providing the computational power necessary to train increasingly sophisticated AI models and unlock their full potential..

[Audio] Artificial intelligence had been making slow but steady progress since the 1950s. However, building on the advancements in computing power, neural networks began to demonstrate real-world value around 2005. This marked a significant turning point, as training these networks was finally taking off. With this newfound capability, researchers were able to achieve breakthroughs in various fields, including facial recognition and medical diagnosis. For instance, neural networks were used to detect tumors on X-rays, paving the way for more accurate diagnoses and treatments..

[Audio] The era of big AI started around 2012, driven by the massive use of GPUs, particularly from NVIDIA, and billions of dollars invested by Silicon Valley. This led to a rush for competitive advantage, characterized by the pursuit of larger neural networks, trained on vast amounts of data, and utilizing immense computing power. This era continues to push the boundaries of what's possible with artificial intelligence..

[Audio] The groundbreaking paper "Attention is All You Need" published by eight researchers at Google Research Lab in 2017 introduced the Transformer architecture, a pioneering design tailored for "Large Language Models." The Transformer leverages an attention mechanism, permitting it to assess the significance of distinct segments within the input sequence. This innovation has been transformative, shifting away from traditional recurrent networks and empowering the creation of cutting-edge AI models..

[Audio] Another significant breakthrough in the development of artificial intelligence occurred in 2020 when OpenAI released GPT-3, a large language model with a staggering 175 billion parameters. This remarkable achievement marked a dramatic leap in capability compared to previous systems, demonstrating the potential of AI to process and analyze vast amounts of information. With its ability to learn from the entirety of the World Wide Web, GPT-3 showcased the incredible advancements being made in this field, further solidifying AI's position as a transformative technology..

[Audio] The field of AI in 2024 is primarily driven by Generative AI and Large Language Models such as GPT, LLaMA, Gemini, and Phi2, developed by tech giants like OpenAI, Meta, Google, and Microsoft. These models combine multiple AI disciplines, including machine learning and computer vision. Notably, universities lack the resources to build these models, whereas only large technology companies possess the necessary computational power and data to train these complex systems..

[Audio] The sheer scale of the infrastructure involved in Generative AI is truly remarkable. Advanced models, including Large Language Models, Large Multimodal Models, Vision Language Models, and technologies like Text-to-Speech and Speech-to-Text, rely on a global network of data centers. This infrastructure represents a staggering investment of approximately 1 trillion US dollars, highlighting the immense resources required to fuel the ongoing AI revolution..

[Audio] The trajectory of Artificial Intelligence is set to continue its rapid ascent, with each passing year bringing breakthroughs that were previously unimaginable. The impact of AI on humanity will be profound, transforming industries, revolutionizing healthcare, and redefining the way we live and work. As we gaze into the crystal ball of the future, we wonder what wonders await us..