What is Google BERT?
Google BERT is a cutting-edge AI language model that is now being applied to search results to provide a more contextually accurate result. This complex model gives Google the power to analyze every word in a search query concerning all of the other words and helps it to better understand the context around the search. Previously, Google would only process words one-by-one in order, but BERT takes the whole sentence into account, including prepositions and context clues that can change the search’s meaning. This natural language processing (NLP) and natural language understanding (NLU) allows for search results to be catered around the context of the search phrase. Google estimates that BERT will have an effect on about 10% of American searches and that the language model within it can also understand languages other than English, meaning its effect will only become wider. As many now search for information using natural language, BERT provides a more complete understanding to produce the desired results.
Is BERT created by Google?
In 2018, Google made a breakthrough in natural language understanding with the introduction of BERT. Its unique characteristics such as tackling challenges such as ambiguity, semantic role labeling, context analysis, and more, have enabled it to rival human “common sense” understanding. In October 2019, Google announced that it would deploy BERT to its production search algorithms for the United States. BERT distinguished itself from word2vec and GloVe, as it understands context and polysemous words. This breakthrough enables the algorithm to correctly interpret words with multiple meanings. Moreover, it is capable of sensing the most challenging of obstacles, ambiguity. Natural language comprehension scientists express the complexity of the task as being the foremost challenge. Given the tremendous successes of BERT, it was no surprise that Google would apply the framework to their search algorithms. Utilizing this technology will certainly add invaluable information on what users are after when making inquiries. It remains to be seen whether or not the BERT-powered algorithms will fulfill the high expectations Google has for the technology.
What is BERT used for?
- Bert is a powerful language processing tool that provides wide capabilities to solve many language tasks:
Sentiment Analysis
- Sentiment Analysis can be used to assess how positive or negative reviews are.
Question Answering
- Question Answering aids chatbots in responding to user queries.
Text Prediction
- Gmail has used the Text Prediction feature to accurately suggest the next text.
Text Generation
- Text Generation can craft articles from just a few sentences.
Summarization
- Summarization helps contract documents take up less space.
Polysemy resolution
- Polysemy resolution allows for differentiating words with multiple definitions based on context.
What data is BERT trained on?
The BERT framework underwent extensive training on vast textual sources such as Wikipedia, which contained approximately 2.5 billion words, and the Google Books Corpus, comprising around 800 million words. These expansive and informative datasets equipped BERT with profound knowledge of not only the English language but also our world’s intricacies. The training process for BERT was a time-consuming endeavor that became feasible due to the revolutionary Transformer architecture and the utilization of Tensor Processing Units (TPUs). These TPUs are custom circuits devised by Google explicitly for handling large-scale ML models. Employing approximately 64 TPUs, BERT’s training duration amounted to roughly four days, a remarkable achievement given the immense scale of the task. Initially, Google introduced two versions of BERT: BERTlarge and the comparatively smaller BERTbase. While BERTbase exhibited slightly lower accuracy, it remained on par with other cutting-edge models in terms of performance. This ensured that BERT, in its various iterations, represented a significant advancement in language understanding and processing capabilities.
Why do we need BERT?
Effective language representation plays a pivotal role in enabling machines to comprehend language comprehensively. Traditional models like word2vec or GloVe produce a solitary word embedding for each word in their vocabulary, disregarding contextual nuances. This means that a word like “Rock” would have the same representation in both “Rock Music” and “River Rock.” Conversely, contextual models generate word representations based on the surrounding words in a sentence. BERT, as a contextual model, adeptly captures these intricate relationships in a bidirectional manner. BERT’s development draws inspiration from a range of pre-training techniques, such as Semi-supervised Sequence Learning, Generative Pre-Training, ELMo, the OpenAI Transformer, ULMFit, and the Transformer. While these models are primarily unidirectional or shallowly bidirectional, BERT stands out as a fully bidirectional framework. By employing diverse methodologies and innovative ideas, BERT emerges as a cutting-edge approach to achieving comprehensive language understanding. Its contextual nature allows for a more nuanced and accurate representation of words, enhancing the overall performance and versatility of language processing models.
Disadvantages of BERT
The size of BERT, while advantageous in enhancing prediction and learning capabilities through extensive training on large datasets, also brings forth certain limitations. These drawbacks are inherently tied to its scale and encompass the following:
Large Size
- Primarily, the model’s large size can be attributed to its training structure and the corpus used for training.
Slow Training Process
- Due to its magnitude and numerous weights that require updating, BERT’s training process is notably slow.
Implementation Cost
- Moreover, the sheer size of BERT necessitates substantial computational resources, resulting in higher costs for implementation.
It is essential to note that BERT is specifically designed as an input for other systems rather than a standalone program. Consequently, fine-tuning becomes imperative for downstream tasks, which can be a meticulous and intricate process.
In summary, the drawbacks associated with BERT primarily stem from its considerable size. Despite the manifold benefits it offers, such as improved prediction and learning capabilities, the trade-offs include slower training speed, higher computational requirements, increased costs, and the need for meticulous fine-tuning when integrated into other systems. Awareness of these limitations is crucial in determining the optimal implementation of BERT for specific use cases.
