Section 1.1: 2013 - The Rise of Deep Learning

The year 2013 is often considered the turning point for deep learning, driven by substantial advancements in computer vision. Geoffrey Hinton noted that by then, "almost all computer vision research had transitioned to neural networks." This transformation was largely triggered by a groundbreaking achievement in image recognition the previous year.

In September 2012, AlexNet, a deep convolutional neural network (CNN), achieved remarkable success at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), showcasing the power of deep learning in image recognition. It recorded a top-5 error rate of 15.3%, significantly outperforming its nearest rival by 10.9%.

The architectural innovations behind AlexNet were pivotal for AI’s future trajectory, shifting perceptions of deep learning. The researchers employed a deep CNN with five convolutional layers and three fully connected layers—an approach many once deemed impractical. They also utilized two GPUs to parallelize training, illustrating how to accelerate processes for large datasets. The switch from traditional activation functions to the more efficient rectified linear unit (ReLU) further reduced training time.

These collective advancements represented a watershed moment in AI history, igniting significant interest in deep learning within both academic and tech sectors.

Section 1.2: 2014 - Introduction of GANs

In June 2014, the advent of generative adversarial networks (GANs) by Ian Goodfellow and his team marked another significant stride in deep learning.

GANs consist of two neural networks—the generator and the discriminator—trained together in a competitive manner. The generator fabricates synthetic data, while the discriminator assesses its authenticity. This innovative method has been utilized not only for image and video generation but also for creating music and art, while advancing unsupervised learning by demonstrating the ability to produce high-quality samples without explicit labels.

The first video, "Ten Years Hence Lecture: AI Ethics – Past, Present, and Future," explores the ethical considerations and historical context of AI advancements.

Section 2.1: 2015 - Breakthroughs in Computer Vision and NLP

The year 2015 saw remarkable progress in both computer vision and natural language processing (NLP). Kaiming He and his collaborators introduced residual neural networks (ResNets), which facilitated smoother information flow in networks by incorporating shortcut connections. This innovation addressed the vanishing gradient problem, enabling the training of much deeper networks and significantly enhancing image classification and recognition tasks.

At the same time, recurrent neural networks (RNNs) and long short-term memory (LSTM) models gained traction due to larger datasets and enhanced computational power. These architectures improved context understanding in text, leading to advancements in translation, text generation, and sentiment analysis, ultimately paving the way for today's large language models (LLMs).

Section 2.2: 2016 - AlphaGo's Historic Victory

In 2016, Google’s AlphaGo made headlines by defeating world champion Go player Lee Sedol, marking a significant milestone in AI's evolution. Using deep reinforcement learning and Monte Carlo tree search techniques, AlphaGo demonstrated that machines could surpass human intelligence in complex games once deemed too intricate for AI.

The second video, "What Will AI Look Like in 10 Years?" speculates on future developments and implications of AI technology.

Section 2.3: 2017 - The Transformer Architecture

The introduction of the transformer architecture in December 2017 was a game-changer, as it utilized self-attention mechanisms to efficiently process sequential data. This method enabled better handling of long-range dependencies, overcoming traditional RNN limitations.

Transformers consist of encoders and decoders, with the encoder processing input data—such as word sequences—and the decoder generating output sequences. This architecture has become fundamental to the success of LLMs, enhancing various NLP applications like translation, modeling, and question answering.

Section 2.4: 2018 - Advances in NLP with GPT-1 and BERT

Shortly after the transformer architecture's introduction, OpenAI unveiled GPT-1 in June 2018, demonstrating the efficacy of unsupervised pre-training followed by task-specific fine-tuning. Google responded with BERT, which utilized a bidirectional approach to process text, significantly improving performance on numerous NLP benchmarks.

Additionally, graph neural networks (GNNs) emerged as a powerful tool for analyzing graph-structured data, paving the way for advancements in social network analysis and recommendation systems.

Section 2.5: 2019 - GPT-2 and Enhanced Generative Models

2019 witnessed the launch of GPT-2, which excelled in various NLP tasks and generated impressively realistic text. Other notable advancements included DeepMind's BigGAN, producing high-quality images, and NVIDIA's StyleGAN, allowing for refined control over image features.

Section 2.6: 2020 - The Arrival of GPT-3

The release of GPT-3 marked a significant leap in the scale and capabilities of LLMs, boasting 175 billion parameters. This massive parameter space allowed for the generation of coherent text across various tasks, highlighting the potential of self-supervised learning, enabling models to learn from vast amounts of unlabeled data.

Section 2.7: 2021 - Innovations in AI Applications

2021 was notable for the introduction of AlphaFold 2, which tackled the protein folding problem, and DALL·E, a model that generates images from textual descriptions. GitHub Copilot also emerged, providing code suggestions based on natural language comments.

Section 2.8: 2022 - The Chatbot Revolution

The launch of OpenAI's ChatGPT in November 2022 represented a significant advancement in conversational AI, with capabilities spanning coherent responses, creative suggestions, and problem-solving assistance. The user-friendly interface broadened AI's accessibility across various professional fields.

Stable diffusion also gained attention for generating photorealistic images from text prompts, showcasing the potential of multimodal generative AI.

Section 2.9: 2023 - The Era of Advanced LLMs

This year has seen an explosion of new models and applications, with Meta AI's LLaMA outperforming GPT-3 and OpenAI introducing GPT-4. Various companies are integrating these models into their products, demonstrating the increasing presence of AI in everyday life, including automated customer support and personalized language learning.