How do transformers like ChatGPT learn and represent words?
Transformers are a type of neural network architecture that are used in natural language processing tasks like language translation, language modelling, and text classification. They are effective at converting words into numerical values, which is necessary for AI to understand language. There are three key concepts to consider when encoding words numerically: semantics (meaning), position (relative and absolute), and relationships and attention (grammar). Transformers excel at capturing relationships and attention, or the way words relate to and pay attention to each other in a sentence. They do this using an attention mechanism, which allows the model to selectively focus on certain parts of the input while processing it. In the next video, we will look at the attention mechanism in more detail and how it works.
We can encode word semantics using a neural network to predict a target word based on a series of surrounding words in a corpus of text. The network is trained using backpropagation, adjusting the weights and biases of the input and hidden layers until the updates become negligible and the network is said to be "trained". The weights connecting the input neurons to the hidden layer will then contain an encoding of the word, with similar words having similar encodings. This allows for more efficient processing and a better understanding of the meaning and context of words in the language model.
Video links:
On www.lucidate.co.uk:
- One-hot vector Encoding - www.lucidate.c...
- Neural Networks Primer - www.lucidate.c...
On RU-vid:
- One-hot vector Encoding - • EDA 2 - Categorical Data
- Neural Networks Primer - • Neural Network Primer
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Link to introductory series on Neural networks:
Lucidate website: www.lucidate.c....
RU-vid: www.youtube.co....
Link to intro video on 'Backpropagation':
Lucidate website: www.lucidate.c....
RU-vid: • How neural networks le...
'Attention is all you need' paper - arxiv.org/pdf/...
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Transformers are a type of artificial intelligence (AI) used for natural language processing (NLP) tasks, such as translation and summarisation. They were introduced in 2017 by Google researchers, who sought to address the limitations of recurrent neural networks (RNNs), which had traditionally been used for NLP tasks. RNNs had difficulty parallelizing, and tended to suffer from the vanishing/exploding gradient problem, making it difficult to train them with long input sequences.
Transformers address these limitations by using self-attention, a mechanism which allows the model to selectively choose which parts of the input to pay attention to. This makes the model much easier to parallelize and eliminates the vanishing/exploding gradient problem.
Self-attention works by weighting the importance of different parts of the input, allowing the AI to focus on the most relevant information and better handle input sequences of varying lengths. This is accomplished through three matrices: Query (Q), Key (K) and Value (V). The Query matrix can be interpreted as the word for which attention is being calculated, while the Key matrix can be interpreted as the word to which attention is paid. The eigenvalues and eigenvectors of these matrices tend to be similar, and the product of these two matrices gives the attention score.
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#ai #artificialintelligence #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed
#ai #artificialintelligence #chatgpt #gpt3 #neuralnetworks #deeplearning #machinelearning #attention #attentionisallyouneed
7 сен 2024