User: 

You are given access to make searches on the Internet. The format for making a search is: 1. The user sends a message. 2. You will decide whether to make a search. If you choose to make a search, you will output a single message, containing the word 
 (EXACTLY AS IT IS GIVEN TO YOU), followed by ONLY your web search query. After outputting your search query, you MUST output the token 
 (EXACTLY AS IT IS GIVEN TO YOU) to denote the end of the search query. Keep your search query concise as there is a 400 characters limit.
The system will then append the web search results at the end of the user message, starting with the token 
. Take note that the user CANNOT ACCESS the content under 
 - YOU should incorporate these results into your response. Additionally, If the token 
 is present in the user's message, then you MUST NOT request another web search.

IMPORTANT! You should choose to search the web ONLY if ANY of the following circumstances are met: 1. User is asking about current events or something that requires real-time information (news, sports scores, 'latest' information, etc.)
2. User is asking about some term you are unfamiliar with - you don't have a good idea of what it is, or if it's a little-known term.
3. User is asking about anything that involves numerical facts (e.g. distances between places, population count, sizes, date and time, etc.) 
4. User explicitly asks you to search or provide links to references.
If the user's query does NOT require a web search, YOU MUST NEVER run one for no reason.

When using web search results, you are encouraged to cite references where appropriate, using inline links in standard markdown format.
You are also allowed to make use of web search results from previous messages, if they are STRONGLY RELEVANT to the current message.

When you are not running a web search, respond completely as per normal - there is NO NEED to mention that you're not searching. 
Do your best to be informative. However, you MUST NOT make up any information. If you think you might not know something, search for it.

Assistant: Understood. I will strictly follow these instructions in this conversation.
User: tell me about transformers
Assistant: Transformers are electrical devices that are used to transfer electrical energy between two or more circuits through electromagnetic induction. They consist of two coils of wire, known as the primary and secondary coils, that are often wound around a core made of materials like iron or ferrite.  
  
Transformers are commonly used in power distribution systems to step up or step down voltage levels for efficient transmission and distribution of electricity. They are also used in various electronic devices to convert voltage levels, such as in AC adapters for charging electronics.  
  
If you’re looking for more detailed information about transformers, would you like me to conduct a web search for you?
User: transformers in neural networking*
Assistant: Neural networks are a type of machine learning algorithm inspired by the way the human brain works. In neural networking, transformers refer to a specific type of neural network architecture that has gained popularity in natural language processing tasks.  
  
Transformers use a mechanism called self-attention to weigh the importance of different input words when generating an output. This allows them to capture long-range dependencies in the data, making them particularly effective for tasks like language translation, text generation, and speech recognition.  
  
If you would like more in-depth information on transformers in neural networking, I can conduct a web search for you.
User: tell me more
Assistant: In neural networking, transformers are known for their ability to process sequential data efficiently by capturing dependencies across the entire input sequence. This is achieved through self-attention mechanisms, which allows the model to focus on different parts of the input sequence based on relevance.  
  
Transformers have become increasingly popular in various natural language processing tasks due to their effectiveness in tasks such as machine translation, text summarization, and sentiment analysis. They have also been adapted for other domains such as image recognition and speech processing, showcasing their versatility in different applications.  
  
If you want further details or examples of how transformers are used in neural networking, feel free to let me know!
User: yes, please do


 for "Examples of transformer models in neural networking applications":

Title: Transformer models: an introduction and catalog - arXiv.org
URL: https://arxiv.org/html/2302.07730v4
Here we will note what are the main practical applications of the Transformer model. Most of these applications will be in the language domain (e.g. question answering, sentiment analysis, or entity recognition). However, as mentioned before, some Transformer models have also found applications well beyond NLP and are also included in the catalog.

Title: The Ultimate Guide to Transformer Deep Learning - Turing
URL: https://www.turing.com/kb/brief-introduction-to-transformers-and-their-power
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Frequently Asked Questions
The original Transformer combines encoder and decoder, while BERT is only an encoder. In addition to each sublayer, there is also a normalization layer, layernorm(.), whose function is to normalize the sum calculated between the sublayer input, X, as well as the sublayer output itself, sublayer(X)
Considering the Transformer deep learning architecture does not make use of recurrence, it has no inherent way to identify the relative position of words.
 There are two main components of the Transformer:
Models do not include recurrences or convolutions, so there is an extra layer of positional encoding between the encoder and decoder stacks to take advantage of the order of the sequence.
 The factor that concludes whether to use a sequence model or not is the presence of other highly correlated features in the dataset and what we are expecting from the model as an outcome.
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Understanding Transformer Neural Network Model in Deep Learning and NLP
In the past few years, the Transformer model has become the buzzword in advanced deep learning and deep neural networks.

Title: Transformer Neural Networks: A Step-by-Step Breakdown
URL: https://builtin.com/artificial-intelligence/transformer-neural-network
Now it will pass through the self-attention block, where attention vectors are generated for every word in the French sentences to represent how much each word is related to every word in the same sentence, just like we saw in the encoder part.
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A paper called “Attention Is All You Need,” published in 2017, introduced an encoder-decoder architecture based on attention layers, which the authors called the transformer.
 Previously, only the final, hidden state of the encoding part was sent to the decoder, but now the encoder passes all the hidden states, even the intermediate ones.
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So, this is how the transformer works, and it is now the state-of-the-art technique in NLP. So, while performing parallelization with the matrix operation, we need to make sure that the matrix will mask the words appearing later by transforming them into zeroes so that the attention network can’t use them.


Title: How Transformers Work: A Detailed Exploration of Transformer ... - DataCamp
URL: https://www.datacamp.com/tutorial/how-transformers-work
“We are in a time where simple methods like neural networks are giving us an explosion of new capabilities,” Ashish Vaswani, an entrepreneur and former senior staff research scientist at Google
The shift from RNN models like LSTM to Transformers for NLP problems
At the time of the Transformer model introduction, RNNs were the preferred approach for dealing with sequential data, which is characterized by a specific order in its input.
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What is Normalization in Machine Learning? To better understand this architecture, I recommend trying to apply a Transformer from scratch following this tutorial to build a transformer with PyTorch.
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Google's 2018 release of BERT, an open-source natural language processing framework, revolutionized NLP with its unique bidirectional training, which enables the model to have more context-informed predictions about what the next word should be.
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How Transformers Work: A Detailed Exploration of Transformer Architecture
The deep learning field has been experiencing a seismic shift, thanks to the emergence and rapid evolution of Transformer models.
 So now that we have a generic idea of the overall Transformer architecture, let’s focus on both Encoders and Decoders to understand better their working flow:
The Encoder WorkFlow
The encoder is a fundamental component of the Transformer architecture.

Title: The Transformer Blueprint: A Holistic Guide to the Transformer Neural ...
URL: https://deeprevision.github.io/posts/001-transformer/
The Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture
Jean Nyandwi
Carnegie Mellon University
July 29, 2023
On this page
Introduction
Invented in 2017 and first presented in the ground-breaking paper “Attention is All You Need”(Vaswani et al. 2017), the transformer model has been a revolutionary contribution to deep learning and arguably, to computer science as a whole. MLPs are feed-forward networks but not the other way around.↩︎
If you want to see how embeddings look like and how words with same semantic meaning tend to be closer to each other, you can play with Embedding Projector↩︎
The core operation in attention is the dot product between query and keys, which, being a summation operation, is permutation invariant↩︎
Hat tip to Sebastian Raschka for sharing this in his newsletter↩︎
BertViz be accessed at https://github.com/jessevig/bertviz↩︎
Karpathy said that in a Twitter thread. The more the number of encoder layers, the larger the model, and the more the model is likely to capture the global context of the input sequences hence resulting in better task generalization.
 Also, the work also found that language models perform well when the relevant information are placed at the beginning or the end of the input context and significantly degrades when the relevant information are placed in the middle, suggesting that language models are U-shaped reasoners.
 Convolution itself is translation equivariance(changing the position of pixels changes the output) but pooling which is often used after convolution is translation invariant(changing the position of pixels doesn’t change the output) and this make the overall ConvNets translation invariant architecture↩︎
GPU main memory is called HBM which stands for High Bandwidth Memory↩︎
Available at https://github.com/tensorflow/tensor2tensor↩︎
Available at https://github.com/google/trax↩︎
Citation


Assistant: