Slices_of_me

  Here are several ways in which tokenization can impact the accuracy of these models: 1.  Granularity of Tokens Word vs. Subword vs. Character Tokenization : The choice of tokenization method affects how the model interprets language. For instance, word tokenization may lose nuances in compound words or phrases, while subword tokenization (like Byte Pair Encoding or WordPiece) can handle rare words and morphological variations better. Character tokenization captures every detail but may lead to longer sequences that are harder for models to process effectively. Impact on Context : The granularity of tokens can influence how well the model understands context. For example, splitting "New York" into two tokens ("New" and "York") may lead to a loss of meaning, affecting the model's ability to understand references to the city. 2.  Handling of Special Cases Punctuation and Special Characters : How a tokenizer handles punctuation, special characters, and w...

  Artificial Intelligence (AI) is one of the most fascinating advancements of modern times, but its roots go back decades—even centuries. In this quick journey, we’ll explore how AI evolved from an ambitious idea to a transformative reality. The Early Concepts: AI’s Philosophical Foundations The dream of creating intelligent machines dates back to ancient history. Greek myths like Talos, a giant automaton, show humanity’s early fascination with synthetic life. In the 17th century, thinkers like René Descartes and Gottfried Leibniz laid mathematical and logical foundations that would inspire future AI. 1950s: The Dawn of Artificial Intelligence The term "Artificial Intelligence" was coined in 1956 at the Dartmouth Conference. Key players like John McCarthy and Marvin Minsky imagined computers that could mimic human intelligence. Alan Turing’s groundbreaking "Turing Test" became a benchmark for evaluating machine intelligence. Key Developments: 1950: Alan Tur...