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olegranmo OP t1_j2wc7vz wrote

Reply to comment by SatoshiNotMe in [R] Do we really need 300 floats to represent the meaning of a word? Representing words with words - a logical approach to word embedding using a self-supervised Tsetlin Machine Autoencoder. by olegranmo

Hi u/SatoshiNotMe! To relate the Tsetlin machine to well-known techniques and challenges, I guess the following excerpt from the book could work:

"Recent research has brought increasingly accurate learning algorithms and powerful computation platforms. However, the accuracy gains come with escalating computation costs, and models are getting too complicated for humans to comprehend. Mounting computation costs make AI an asset for the few and impact the environment. Simultaneously, the obscurity of AI-driven decision-making raises ethical concerns. We are risking unfair, erroneous, and, in high-stakes domains, fatal decisions. Tsetlin machines address the following key challenges:

  • They are universal function approximators, like neural networks.
  • They are rule-based, like decision trees.
  • They are summation-based, like Naive Bayes classifier and logistic regression.
  • They are hardware-near, with low energy- and memory footprint.

As such, the Tsetlin machine is a general-purpose, interpretable, and low-energy machine learning approach."

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