Increasing the size of language models does not inherently make them better follow the user's intent. For example, large language models may generate results that do not match the needs of their users. In the article "Training Language Models To Follow Instructions With Human Feedback", the OpenAI research team uses human feedback reinforcement learning (RLHF) methods to achieve significant improvements in consistency. Let's discuss this work and look into the details.
In this video, we'll demonstrate the most powerful jailbreaks that can bypass the installed restrictions and will unlock ChatGPT's true potential. Are you interested in learning which Star Wars Trilogy is superior? Watch the video and find out ChatGPT's opinion!
Discover how to integrate ChatGPT and create chatbots that engage users in more natural and dynamic conversations than ever before. Supercharge your smartbot with the power of NLP, enabling it to understand and respond intelligently to a wide range of queries.
In this video, we explore the fundamental theorem that defines neural networks as universal approximations. We will find out how this theorem became a milestone in the development of neural networks, why it is still relevant and what role it plays in the world of AI.
Learn about the fascinating realm of the IceCube Neutrino Observatory. Discover the significance of the joint research of scientists at the Observatory and Qudata in developing algorithms for detecting neutrino interactions – elusive particles that hold the key to unraveling the mysteries of supernovae, black holes, and the enigmatic dark matter.
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Early detection of spine injuries is critical to preventing serious complications or even paralysis. With machine learning and medical imaging we can now accurately identify cervical spine fractures more accurately and efficiently than ever before.
Now let's move on to the main block of ChatGPT - the Attention mechanism. Everything that we have considered so far is the ten-year-old architecture: fully connected neural networks, embedding. All the most interesting and modern is inside Attention.
Attention Is All You Need!
ChatGPT moves input sequence token vectors in embedding space. Tokens that are nearby become the output of the model. This is a brief description of how ChatGPT works. In the video, we will understand in detail how the ChatGPT core works.
ChatGPT (Chat Generative Pre-trained Transformer) by OpenAI is a machine learning model capable of interactively processing natural language queries, writing extended texts, code, music, and much more.
In this video, we will get to know it in more detail, delve a little into the history of the development of neural networks and understand what preceded its appearance, consider examples of ChatGPT.
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We continue to interpret the work of neural networks using SHAP diagrams. With this approach, not the processes that occur on a particular channel of a particular layer are analyzed, but the significance of a particular pixel in the original image.
With the help of SHAP diagrams, it is possible to analyze which pixels of the original image led to an increase in the probability of a given class, and which ones led to its decrease.
Today we will interpret the processes that occur "in the head" of convolutional neural networks. Let's find out how the miracle happens, which we then get, looking at the graphs of errors and metrics.
We warn you right away - there will be no complete answer :) And no one knows how this miracle occurs. But some additional understanding may appear.