Transformers: The best idea in AI | Andrej Karpathy and Lex Fridman

Lex Clips
1 Nov 202208:38

TLDRThe Transformer architecture is hailed as a revolutionary concept in AI, offering a general-purpose, differentiable computing model that efficiently handles various sensory modalities. Since its introduction in 2016 with the paper 'Attention is All You Need,' it has proven to be highly expressive, optimizable through backpropagation, and well-suited for parallel computation on modern hardware like GPUs. Its design, including residual connections and message-passing mechanisms, allows it to learn complex algorithms, beginning with simpler ones and scaling up. Despite attempts to modify it, the Transformer's core structure remains resilient, and its potential for solving a wide array of problems is vast, making it a central focus in the progression of AI.


  • 🚀 The Transformer architecture is a standout idea in AI, having a profound and broad impact since its introduction in 2016.
  • 🌐 Transformers represent a convergence point for various neural network architectures, handling modalities like vision, audio, speech, and text.
  • 🎯 It functions as a general-purpose, differentiable computer that is efficient and trainable on current hardware.
  • 📈 The 'Attention is All You Need' paper may have underestimated the transformative impact of the model.
  • 🤖 The design of Transformers allows for powerful expression in the forward pass through a message-passing mechanism.
  • 🛠️ Transformers are highly optimizable due to features like residual connections and layer normalizations, making them easy to train with backpropagation and gradient descent.
  • 🌟 The architecture is efficient, taking advantage of the parallel processing capabilities of modern hardware like GPUs.
  • 📈 Transformers learn 'short algorithms' efficiently due to the residual connections, which allow gradients to flow uninterrupted.
  • 🧩 The original Transformer model from 2016 remains remarkably resilient, with only minor adjustments like pre-norm formulations.
  • 🔍 Future discoveries in Transformers may involve enhancing memory and knowledge representation aspects.
  • 🌐 The AI community continues to scale up datasets and refine evaluations while maintaining the core Transformer architecture.

Q & A

  • What does Andrej Karpathy find particularly fascinating about the Transformer architecture?

    -Andrej Karpathy finds the Transformer architecture fascinating because it has become a general-purpose, differentiable computer that is efficient to run on current hardware and can process various sensory modalities like video, images, speech, and text.

  • How does the Transformer architecture function in terms of its expressiveness?

    -The Transformer architecture is expressive in the forward pass, allowing it to represent general computation through a message-passing scheme where nodes store vectors, communicate with each other, and update based on interesting information.

  • What are some of the key architectural components of the Transformer?

    -The Transformer includes several key components such as the attention mechanism, residual connections, layer normalizations, soft max attention, and a multi-layer perceptron, all arranged in a way that optimizes for expressiveness, optimizability, and efficiency.

  • Why is the Transformer architecture considered efficient for current hardware?

    -The Transformer is efficient because it is designed with high parallelism, which aligns with the throughput capabilities of modern hardware like GPUs, avoiding sequential operations and instead performing many operations in parallel.

  • How does the residual connection in the Transformer contribute to its learning capabilities?

    -The residual connections allow the Transformer to learn short algorithms quickly and then gradually extend them during training. This is because the gradients flow along the residual pathway uninterrupted, enabling optimization from the top down through the layers.

  • What is the significance of the 'Attention is All You Need' paper in the history of Transformers?

    -The 'Attention is All You Need' paper introduced the foundational concepts of the Transformer architecture in 2016. Despite its impact, the title suggests that the authors may not have fully anticipated the extent of the Transformer's influence on AI.

  • How has the Transformer architecture evolved since its introduction in 2016?

    -While the core Transformer architecture has remained remarkably stable since 2016, there have been adjustments such as the reshuffling of layer norms and player normalizations to a pre-norm formulation.

  • What are some potential areas of future discovery or improvement for the Transformer architecture?

    -Potential areas for future discoveries or improvements include advancements in memory handling, knowledge representation, and the development of even more efficient or powerful architectures beyond the current Transformer model.

  • How has the Transformer architecture influenced the progression of AI over the past few years?

    -The Transformer architecture has significantly influenced AI by becoming a convergent point for various AI tasks, leading to a focus on scaling up datasets, refining evaluations, and optimizing within the unchanged architecture framework.

  • What is the role of the soft max attention mechanism in the Transformer architecture?

    -The soft max attention mechanism in the Transformer plays a crucial role in enabling the model to weigh the importance of different inputs and adjust its focus accordingly, which contributes to its powerful expressiveness and adaptability.



🤖 The Emergence of Transformer Architecture in AI

This paragraph discusses the impact and significance of the Transformer architecture in the field of deep learning and AI. The speaker reflects on the evolution of neural network architectures and highlights the Transformer's ability to handle various sensory modalities efficiently. The paper 'Attention Is All You Need' is mentioned as a pivotal work that introduced the Transformer, despite its underestimation of the technology's potential impact. The speaker also touches on the meme-like title of the paper, suggesting it might have contributed to its memorable status. The Transformer's versatility as a general-purpose, differentiable computer is emphasized, along with its efficiency and trainability on modern hardware.


🧠 Resilience and Evolution of the Transformer Architecture

The second paragraph delves into the Transformer's resilience and adaptability during training, focusing on the concept of learning short algorithms and the role of residual connections. The speaker explains how the Transformer's design allows for gradients to flow uninterrupted along the residual pathway, facilitating efficient learning. The paragraph also discusses the architecture's stability since its introduction in 2016, with minor adjustments but no major overhauls. The potential for future improvements and the current trend of scaling up datasets and evaluations without altering the core architecture is mentioned. The speaker acknowledges the Transformer's dominance in AI and speculates on possible future discoveries related to memory and knowledge representation within this framework.




Transformers, in the context of the video, refers to a revolutionary neural network architecture that has significantly impacted the field of AI. It is a general-purpose model capable of handling various types of data inputs such as text, images, and speech. The architecture is notable for its efficiency and ability to be trained on a wide range of tasks, from language translation to image recognition. The term 'Transformers' is used to highlight the versatility and profound influence this model has had on the progression of AI technologies, as discussed by Andrej Karpathy and Lex Fridman.

💡Deep Learning

Deep Learning is a subset of machine learning that involves the use of artificial neural networks to learn and make decisions. It is characterized by the use of multiple layers of neural networks, which allows the system to learn complex patterns and representations from large amounts of data. In the video, deep learning is presented as a field that has seen explosive growth and has led to the development of innovative ideas, such as the Transformer architecture. The term underscores the depth and complexity of the algorithms that drive modern AI systems.

💡Attention Mechanism

The Attention Mechanism is a key component of the Transformer architecture. It allows the model to focus on different parts of the input data when making predictions, similar to how humans pay attention to specific details in a scene. The attention mechanism is crucial for the model's ability to understand and process sequences of data, such as sentences or paragraphs, by assigning different weights to different parts of the input. This concept is highlighted in the video as a critical innovation that has contributed to the success of the Transformer architecture.

💡General-Purpose Computer

In the video, a 'General-Purpose Computer' metaphor is used to describe the versatility of the Transformer architecture. It implies that just like a general-purpose computer can run a variety of software programs to perform different tasks, the Transformer model can be trained on a multitude of AI tasks. This highlights the model's flexibility and adaptability, as it can process and learn from different types of data, making it a powerful tool in the AI field.


Efficiency, in the context of the video, refers to the Transformer architecture's ability to perform computations in an optimal manner, utilizing minimal resources and time. This is particularly important as it allows the model to run effectively on modern hardware like GPUs, which are designed for parallel processing. The efficiency of the Transformer is highlighted as a key reason for its widespread adoption and success in various AI applications.


Backpropagation is a fundamental algorithm used in training artificial neural networks, including the Transformer architecture. It involves the calculation of gradients, which are used to update the weights of the network in a way that minimizes the error. This process is essential for the learning capabilities of the model, allowing it to improve its performance over time. The video emphasizes the importance of backpropagation as it enables the Transformer to be optimized, making it a powerful tool for a wide range of tasks.

💡Residual Connections

Residual Connections are a critical architectural feature of the Transformer model. They allow the output of one layer to be added back to the input of the next layer, which helps in preventing the vanishing gradient problem and enables the training of deeper networks. This concept is highlighted in the video as it contributes to the model's ability to learn complex functions and its resilience during training, allowing for the effective transfer of learned algorithms across different layers.

💡Message Passing

Message Passing is a communication scheme used within the Transformer architecture. It involves nodes (or layers) within the network sharing and updating information with each other. This process is likened to nodes broadcasting their needs and other nodes responding with relevant information. Message passing is crucial for the model's ability to process and understand complex relationships within the data, as it allows for a dynamic and interactive flow of information that contributes to the model's overall understanding and predictive capabilities.


The term 'Differentiable' in the context of the video refers to the mathematical property of a function that allows for the calculation of its derivatives. In the case of the Transformer architecture, being differentiable means that the model's functions can be optimized using gradient-based methods like backpropagation. This property is essential for the training process of neural networks, as it enables the model to learn and adjust its parameters to improve performance on specific tasks.

💡High Parallelism

High Parallelism refers to the ability of a system to perform multiple operations simultaneously. In the context of the Transformer architecture, this is a key feature that allows it to be highly efficient on hardware like GPUs. The model is designed to take advantage of the parallel processing capabilities of modern hardware, which enables it to handle large-scale computations quickly. This is highlighted in the video as a significant factor in the Transformer's success and its suitability for a wide range of AI applications.


Optimization in the context of the video pertains to the process of improving the performance of the Transformer model through the adjustment of its parameters. This is achieved using techniques like backpropagation and gradient descent, which are essential for training neural networks. The term emphasizes the iterative process of refining the model to minimize error and enhance its predictive capabilities. The video discusses the importance of the Transformer's design, which not only allows for powerful expression in the forward pass but also makes it easily optimizable in the backward pass.


The Transformer architecture is the most beautiful and surprising idea in AI.

Transformers have become a general-purpose computer that is efficient and trainable on our hardware.

The paper 'Attention is All You Need' marked the beginning of the Transformer era in 2016.

The title 'Attention is All You Need' is memeable and may have contributed to the paper's impact.

Transformers are expressive in the forward pass, allowing for general computation through message passing.

The design of Transformers includes residual connections and layer normalizations, making them optimizable.

Transformers are efficient due to their high parallelism, which is ideal for hardware like GPUs.

The residual connections in Transformers allow for learning short algorithms before extending them.

The Transformer architecture has remained remarkably stable since its introduction in 2016.

Despite attempts to improve upon it, the original Transformer architecture has proven to be resilient.

The Transformer's ability to solve a wide range of problems signifies a convergence in AI.

Current AI advancements involve scaling up datasets and improving evaluations without changing the Transformer architecture.

The future of Transformers may involve surprising discoveries related to memory and knowledge representation.

The Transformer's success story is a testament to the power of a well-designed neural network architecture.

The generality of Transformers has led to their dominance in the field of AI.

The Transformer architecture continues to be a focal point of innovation and research in AI.