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Introduction
Artificial Intelligence (AI) has transformed our ability to process and understand sequential data, a critical component in fields like natural language processing, time series analysis, and speech recognition. Over the past years, continued advancement in these neural networks has revolutionized the way be process language. More specifically, large language models like GPT, Llama, Mistral, and Gemini.
But how did we get here? The journey from Recurrent Neural Networks (RNNs) & Long Short-Term Memory (LSTM) all the way to Transformers encapsulates a remarkable evolution in AI, demonstrating how each step builds upon the last to address unique challenges and unlock new possibilities.
Recurrent Neural Networks - The Start
Recurrent Neural Networks (RNNs) are an artificial neural network designed for processing sequential data by maintaining a form of memory developed pre-2000s. RNNs have connections that loop back on themselves instead of input straight to output, allowing information to persist across time steps.
This recursive nature creates a form of internal state or memory, enabling RNNs to "remember" past information. However, this design also leads to challenges, notably the problem of vanishing and exploding gradients. As sequences grow longer, the gradients used in backpropagation can diminish to near-zero or escalate uncontrollably, affecting the network's ability to learn long-term dependencies.
Advantages | Disadvantages |
|---|---|
RNNs are a simple by nature and therefore easy to run on commodity hardware quickly. Suitable for variable length sequences, favoring short bursts. | Suffers from longer sequences due to gradient loss. Has no way of deciding what to remember resulting in large memory usage, with less-than-ideal organization of the memory. |
The disadvantages of RNNs were apparent from the start and has been addressed by more advanced models over time. These improved models addressed speed and memory efficiency. RNNs now serve as the foundational model for various LSTM models.
Long Short Tern Memory - The Evolution
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to process sequences of data and retain information over extended periods. LSTMs have a unique architecture that allows them to remember important information and forget less relevant data.
Instead of a simple feedback loop found in traditional RNNs, LSTMs have multiple gates that control the flow of information: the input gate, the forget gate, the output gate, and the cell state. These gates enable LSTMs to decide which information to keep and which to discard, allowing them to capture long-range dependencies in the data effectively.
Advantages | Disadvantages |
|---|---|
Mitigate the vanishing/exploding gradient problem in RNN. With its Forget Gate architecture, LSTMS retain tokens that have higher value. LSTMs are more suitable for longer sequences than RNN. | The additional gates in LSTM and classifying importance makes computations using LSTM more complex. Due to complexity, LSTMs require higher computational cost and time. |
There are various LSTM models that tackle different challenges. Quick overview of a couple different popular LSTM models:
- BiLSTM
- BiLSTMs process input sequences in both forward and backward directions. This bidirectional approach allows them to capture context from both past and future time steps, which can be useful for tasks like speech recognition and machine translation.
- GRUs
- GRUs are similar to LSTMs but have a simplified architecture. They use fewer gates (reset and update gates) compared to LSTMs, making them computationally more efficient while still capturing long-term dependencies.
- ConvLSTM
- ConvLSTM combines the LSTM architecture with convolutional layers. It is particularly useful for spatiotemporal data, such as video sequences or image sequences, where both spatial and temporal dependencies need to be modeled.
- Attention-Based LSTM
- Attention mechanisms enhance the LSTM’s ability to focus on relevant parts of the input sequence. By assigning different weights to different time steps, attention-based LSTMs can improve performance in tasks like machine translation and natural language understanding.
What are Transformers - The Next Level
Transformer networks are a type of neural network architecture designed to handle sequential data but departs from traditional recurrent neural networks. Transformer networks rely entirely on a mechanism called self-attention to model dependencies between elements in a sequence while also employing positional encoding.
This design enables Transformer networks to process sequences in a non-sequential parallelized manner while also accounting for the position of each element in a sequence to help determine intent, a significant advantage over RNNs. Instead of relying on memory from previous steps, each part of a sequence can directly "attend to" or reference any other part, enabling the model to capture relationships over long distances.
Advantages | Disadvantages |
|---|---|
Positional Encoding allows for better attention-based memory. Transformers process sequencies in parallel for greater speed. Highly efficient on large scale datasets and long-range dependencies. | Transformers are more complex and less intuitive which suffers when there's is lack of finetuning. Due to complexity, transformers require even more computational cost and time. |
Transformer networks are groundbreaking architecture characterized by its self-attention mechanism emphasized its revolutionary and acclaimed paper “Attention is All You Need” written in 2017. The approach changed modern AI, first for translation, and now for large language models and generative AI.
Models like BERT (Bidirectional Encoder Representation from Transformers) and GPT (Generative Pretrained Transformers) erupted in popularity. In 2024, transformers dominate as the most impactful and most used architecture for various LLMs, multi-modal models, and generative AI such as GPT-4, Mistral, Llama, Gemini and more.
Choosing a Model - Is There a Right Choice?
Right is subjective, something almost generative AI chatbots will denounce. Like a tool, each architecture is having their designated purpose. While you can drive a nail in a 2x4 with the butt of your screwdriver or dropping an anvil on it, you might be better off using a hammer. Nothing is concrete and therefore picking the right choice is up to your specific task and dataset.
For lightweight shorter tasks, perhaps an RNN or LSTM. But for longer range dependencies and large corpuses of text, an LSTM or transformer architecture would be preferred. For developing an LLM that can tie complex relationships between training and prompt while requiring parallelism, transformer networks are your tool of choice.
Either way, training complex AI with any neural network model requires some level of computing. SabrePC stocks high performance hardware including server CPUs and enterprise grade GPUs. Our sales engineers are here to help you choose the appropriate hardware for your next workstation or server.