Oncotelic partners with TechForce on AI robotics platform - Investing.com
Oncotelic partners with TechForce on AI robotics platform Investing.com
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Truth Technology and the Architecture of Digital Trust
The digital economy has entered a credibility crisis. Across industries, borders, and institutions, systems now move information at extraordinary speed, yet too often fail at the more fundamental task of proving what that information actually means. Credentials can be duplicated. Professional claims can be inflated. Identity can be fragmented across platforms. In this environment, the central challenge is no longer access to data, but confidence in its validity. This is not a peripheral issue. It is one of the defining infrastructure problems of the modern technological era. My work sits precisely at this intersection. As a Data Scientist and Full-Stack Developer, I have come to view trust not as a social abstraction, but as a systems problem that must be solved through rigorous engineerin
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Understanding Attention Mechanisms – Part 6: Final Step in Decoding
In the previous article , we obtained the initial output, but we didn’t receive the EOS token yet. To get that, we need to unroll the embedding layer and the LSTMs in the decoder , and then feed the translated word “vamos” into the decoder’s unrolled embedding layer. After that, we follow the same process as before. But this time, we use the encoded values for “vamos” . The second output from the decoder is EOS , which means we are done decoding. When we add attention to an encoder-decoder model, the encoder mostly stays the same. However, during each step of decoding, the model has access to the individual encodings for each input word. We use similarity scores and the softmax function to determine what percentage of each encoded input word should be used to predict the next output word.
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