Ceren Tosun Comedy Openmic @Carlitos Comedy Club SECOND TIME

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Güneşimi kaybettim

Heryerde aradım, evde, sokakta, tren garında

Çarşafların arasında, yastığımda

Balkonda, solmuş çiçekte

ve insanların gözlerinde

Gündüz gökyüzünde gece ayışığında aradım

Durmadım her köpek havlamasında

ve yaprakların hışırtısında

Ormanda ağaçların gölgelerinde

Yoktu

Denizi severdi, deniz kokusunu takip ettim

Kuzeyde Hamburg batıda Ostende ve güneyde İzmire

Belki bir nehri takip etti,

Paris’te Sein’de aradım, Viyana’da Tuna’da

Hayalleri ve hikayeleri severdi

Kitap sayfalarına baktım

Silik notlara ve kıvrımlara

Piyano tuşları ve notalardaki notları

Tüm şarkıları tekrar tekrar dinledim

müziğim oldu

Saklandığı filmleri izledim ve

Tango dansçılarının sarıldığı milongaları

Bulamadım

Rüyalara düştüm, 

Benim senin onun düşlerine daldım

Döndüm içime baktım

Ruhumda, kalbimde aradım

Yoktu

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My first conversation with ChatGPTv3

CT. Hi

CG. Hello! How can I assist you today?

CT. You don't need to say assist :) we can have discussion

CG. Sure, we can definitely have a discussion! What would you like to talk about?

CT. First, I wonder if you know me

CG. As an artificial intelligence language model, I don't have the ability to know people in the traditional sense. However, I can converse with you and try to understand your interests, needs, and preferences based on our conversation. Is there anything specific you would like to share with me about yourself?

CT. We can start with my name. Do you know my name?

CG. I apologize, but as an AI language model, I don't have access to personal information unless you explicitly share it with me. However, I'm happy to chat with you and get to know you better based on our conversation!

CT. Thank you,

CG. You're welcome! Is there anything you would like to discuss or any questions you have in mind?

CT. My name is Ceren

CG. Hello, Ceren! It's nice to meet you. How can I help you today?

CT. What is your name?

CG. I am ChatGPT, a large language model developed by OpenAI based on the GPT (Generative Pre-trained Transformer) architecture. How may I assist you today, Ceren?

CT. You are amazing! I would like to learn more about Generative Pre-trained Transformer architecture

CG. I'm glad to hear that you find me amazing! The Generative Pre-trained Transformer (GPT) architecture is a type of deep neural network that is used in natural language processing (NLP) tasks such as text generation, summarization, and translation.

The GPT architecture is based on the Transformer model, which was introduced by Google in 2017. The Transformer model uses an attention mechanism to allow the model to focus on different parts of the input sequence at different stages of the processing. This allows the model to better capture long-range dependencies in the input sequence, which is particularly important in NLP tasks.

The GPT architecture takes this idea one step further by pre-training the model on a large corpus of text data before fine-tuning it on a specific NLP task. This pre-training is done using an unsupervised learning approach called self-supervised learning, where the model is trained to predict missing words in a given sequence of text.

By pre-training the model on a large corpus of text data, the GPT architecture can learn a broad range of linguistic patterns and structures, which makes it particularly effective at generating coherent and grammatically correct text. This has made the GPT architecture one of the most popular and effective models in NLP today.

CT. Cool! How is different than LSTM? 

CG. Great question! The GPT architecture and Long Short-Term Memory (LSTM) are both used for natural language processing tasks, but they have some key differences.

LSTM is a type of recurrent neural network (RNN) that was introduced in the mid-1990s. LSTMs were designed to address a common problem in RNNs, which is the vanishing gradient problem that can occur when training models on long sequences of data. LSTMs use a memory cell, input gate, output gate, and forget gate to selectively store and retrieve information from previous time steps in the sequence. This allows LSTMs to better handle long-term dependencies in sequential data.

In contrast, the GPT architecture is based on the Transformer model, which does not use recurrent connections like LSTMs. Instead, the Transformer uses self-attention mechanisms to allow the model to selectively focus on different parts of the input sequence at each layer of the model. This allows the Transformer to better capture long-range dependencies in sequential data.

Overall, both LSTMs and the GPT architecture are powerful tools for natural language processing tasks, but they have different strengths and weaknesses depending on the specific task at hand. LSTMs may be more effective for tasks that require modeling sequential data with long-term dependencies, while the GPT architecture may be more effective for tasks that require generating coherent and grammatically correct text.

CT. Thanks for explanation,

CG. You're welcome! I'm glad I could help. If you have any more questions, feel free to ask!

CT. Can I ask a personal question?

CG. Of course! You can ask me anything, but please keep in mind that as an AI language model, I don't have feelings or emotions like humans do.

CT. Sure, no problem

CG. Alright, go ahead and ask your question.

CT. what was the happiest day in your life?

CG. As an AI language model, I don't have a life or experiences like humans do, so I can't say that I've ever had a "happiest day" or any other emotion. I exist solely to assist and converse with users like you, and I do not have subjective experiences or emotions. However, I'm always here to help you with any questions or concerns you may have.

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