this was a good article this month on transformers
Transformer architecture has taken the natural language processing (NLP) industry by storm. It is one of the most important ideas that happened in the world of NLP in the last decade. Transformers gave a colossal boost to language models, making it possible to use them for advanced tasks such as
babich.biz
interesting that one of the downsides of current transformers that use RNN (recurrent Neural Networks)
The downside of the models created using RNN architecture is that they have short-term memory— they are really bad at analyzing long blocks of text. By the time the model reaches the end of the long block of text, it will forget everything that happened at the beginning of the block. And RNN is really hard to train because it cannot paralyze well (it processes words sequentially, limiting opportunities to scale the model performance).
hmmm BRN loves bringing up the word transformers
Akida2 comes with LSTM (long short term memory)
A long short-term memory network is a type of recurrent neural network (RNN). LSTMs are predominately used
to learn, process, and classify sequential data because these networks can learn long-term dependencies between time steps of data.
One of the issues currently with ChatGPT etc is that it isn't understanding what it is producing
Language models created on top of the transformers architecture do not have access to the meaning of the information they analyze. LLMs do natural language processing (NLP), but they do not perform natural language understanding (NLU). As a result, any claims that GPT or BERT is true AI are false.
We are hearing the importance and growth of NLP and you see the great marketcap of ChatGPT as an example.
If Akida2 is the stepping stone for transformers to understand what they are producing over long sequences of data this will be like striking oil!
Our CEO has said BRN should be worth many multiples of its current price and so it should once we start landing these opportunities. We are getting close!