BERT stands for Bidirectional Encoder Representations from Transformers.
It is a type of deep learning model developed by Google in 2018, primarily used in natural language processing tasks such as text generation, question-answering, and language translation.
Despite sharing core transformer technology, BERT operates in a completely way to GPT based AI systems from companies like Anthropic and OpenAI.
The key difference lies in two words, bidirectional and autoregressive.
BERT uses a bidirectional approach to understanding text, which means it looks much more deeply at the whole context, rather than just reading and predicting words in one direction.
This quote from the EU’s EITC explains what this means;
“For example, consider the sentence: “The quick brown fox jumps over the lazy dog.” If the word “fox” is masked, BERT will use the context from both “The quick brown” and “jumps over the lazy dog” to predict the masked word. This bidirectional context enables BERT to generate more accurate and contextually relevant representations of words…”
In contrast, GPT-4 will read the sentence from left to right in a unidirectional manner, which makes it faster and more effective at generating relevant and coherent conversation flows.
In short GPT is ideally suited for more creative and generalized tasks, while BERT excels in tasks such as sentiment analysis, where the model is trying to identify underlying meaning from words.
BERT predates GPT technology by a number of years, and this has made it historically a more popular choice for researchers, who needed the power of natural language processing way before chatbots arrived on the scene.
While ChatGPT has garnered most of the headlines in recent years, BERT continues to have a role to play in specialized applications where analyzing meaning from words is important.
Its ability to understand relationships between words and phrases also makes it a good choice for applications which involve direct interaction with users, such as answering questions.
In practice BERT and GPT are often used together in user facing applications. GPT models, with their huge data resources are perfect for wide ranging generalized utility, while BERT can provide the kind of deep analysis of word structures that GPT lacks.
BERT-based models are also widely used in machine translation, where they can help bridge gaps between source and target languages.
Researchers particularly like the fact that BERT can be fine tuned on modest computing hardware, and also excels at the kind of classification tasks that are common in research circles.
Various versions of BERT have been introduced over time to address specific needs and improve performance in different application domains.
BERT-Large and BERT-Tiny are two commonly used versions, differing mainly in the size of their pre-trained models and the scope of their training data.
These variations allow developers to choose the most suitable model for their particular applications. These models can be fine-tuned, or distilled using a teacher model to enhance the knowledge base of the target.
Despite the growing dominance of the GPT AI ecosystem, BERT continues to provide specialized and popular utility for a variety of research and general applications.
The ongoing work to develop its capabilities and provide more valuable use cases in research, should ensure a long and healthy lifespan for this veteran AI technology.
As machine learning and AI technologies evolve, BERT and its descendants will likely continue to play an important role in enabling intuitive interactions between humans and machines.
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