Learning
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Gemini: Automated Feedback for Theoretical Computer Scientists
Read Full Article: Gemini: Automated Feedback for Theoretical Computer Scientists
Gemini, an innovative tool designed to provide automated feedback, was introduced at the Symposium on Theory of Computing (STOC) 2026 to assist theoretical computer scientists. The project was spearheaded by Vincent Cohen-Addad, Rajesh Jayaram, Jon Schneider, and David Woodruff, with significant input from Lalit Jain, Jieming Mao, and Vahab Mirrokni. This tool aims to enhance the quality of research by offering constructive feedback and suggestions, thereby streamlining the review process for researchers and conference participants. The development of Gemini was a collaborative effort involving numerous contributors, including the Deep Think team, which played a crucial role in its creation. The project also received valuable insights and discussions from several prominent figures in the field, such as Mohammad Taghi Hajiaghayi, Ravi Kumar, Yossi Matias, and Sergei Vassilvitskii. By leveraging the collective expertise of these individuals, Gemini was designed to address the specific needs and challenges faced by theoretical computer scientists, ensuring that the feedback provided is both relevant and actionable. This initiative is significant as it represents a step forward in utilizing technology to improve academic research processes. By automating feedback, Gemini not only saves time for researchers but also enhances the overall quality of submissions, fostering a more efficient and productive academic environment. This matters because it supports the advancement of theoretical computer science by ensuring that researchers receive timely and precise feedback, ultimately contributing to the field's growth and innovation.
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Pretraining BERT from Scratch: A Comprehensive Guide
Read Full Article: Pretraining BERT from Scratch: A Comprehensive Guide
Pretraining a BERT model from scratch involves setting up a comprehensive architecture that includes various components like the BertConfig, BertBlock, BertPooler, and BertModel classes. The BertConfig class defines the configuration parameters such as vocabulary size, number of layers, hidden size, and dropout probability. The BertBlock class represents a single transformer block within BERT, utilizing multi-head attention, layer normalization, and feed-forward networks. The BertPooler class is responsible for processing the [CLS] token output, which is crucial for tasks like classification. The BertModel class serves as the backbone of the BERT model, incorporating embedding layers for words, types, and positions, as well as a series of transformer blocks. The forward method processes input sequences through these components, generating contextualized embeddings and a pooled output for the [CLS] token. Additionally, the BertPretrainingModel class extends the BertModel to include heads for masked language modeling (MLM) and next sentence prediction (NSP), essential tasks for BERT pretraining. The model is trained using a dataset, with a custom collate function handling variable-length sequences and a DataLoader to batch the data. Training involves setting up an optimizer, learning rate scheduler, and loss function, followed by iterating over multiple epochs to update the model parameters. The MLM and NSP tasks are optimized using cross-entropy loss, with the total loss being the sum of both. The model is trained on a GPU if available, and the state of the model is saved after training for future use. Understanding the process of pretraining a BERT model from scratch is crucial for developing custom language models tailored to specific datasets and tasks, enhancing the performance of natural language processing applications. This matters because pretraining a BERT model from scratch allows for customized language models that can significantly improve the performance of NLP tasks on specific datasets and applications.
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Google Research 2025: Bolder Breakthroughs
Read Full Article: Google Research 2025: Bolder Breakthroughs
The current era is being hailed as a golden age for research, characterized by rapid technical breakthroughs and scientific advancements that quickly translate into impactful real-world solutions. This cycle of innovation is significantly accelerating, driven by more powerful AI models, new tools that aid scientific discovery, and open platforms. These developments are enabling researchers, in collaboration with Google and its partners, to advance technologies that are beneficial across diverse fields. The focus is on leveraging AI to unlock human potential, whether it be assisting scientists in their research, helping students learn more effectively, or empowering professionals like doctors and teachers. Google Research is committed to maintaining a rigorous dedication to safety and trust as it progresses in AI development. The aim is to enhance human capacity by using AI as an amplifier of human ingenuity. This involves utilizing the full stack of Google's AI infrastructure, models, platforms, and talent to contribute to products that impact billions of users worldwide. The commitment is to continue building on Google's legacy by addressing today's biggest questions and enabling tomorrow's solutions. The approach is to advance AI in a bold yet responsible manner, ensuring that the technology benefits society as a whole. This matters because the advancements in AI and research spearheaded by Google have the potential to significantly enhance human capabilities across various domains. By focusing on safety, trust, and societal benefit, these innovations promise to create a more empowered and informed world, where AI serves as a tool to amplify human creativity and problem-solving abilities.
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Understanding Token Journey in Transformers
Read Full Article: Understanding Token Journey in Transformers
Large language models (LLMs) rely on the transformer architecture, a sophisticated neural network that processes sequences of token embeddings to generate text. The process begins with tokenization, where raw text is divided into discrete tokens, which are then mapped to identifiers. These identifiers are used to create embedding vectors that carry semantic and lexical information. Positional encoding is added to these vectors to provide information about the position of each token within the sequence, preparing the input for the deeper layers of the transformer. Inside the transformer, each token embedding undergoes multiple transformations. The first major component is multi-headed attention, which enriches each token's representation by capturing various linguistic relationships within the text. This component is crucial for understanding the role of each token in the sequence. Following this, feed-forward neural network layers further refine the token features, applying transformations independently to each token. This process is repeated across multiple layers, progressively enhancing the token embeddings with more abstract and long-range linguistic information. At the final stage, the enriched token representation is processed through a linear output layer and a softmax function to produce next-token probabilities. The linear layer generates unnormalized scores, or logits, which the softmax function converts into normalized probabilities for each possible token in the vocabulary. The model then selects the next token to generate, typically the one with the highest probability. Understanding this journey from input tokens to output probabilities is crucial for comprehending how LLMs generate coherent and context-aware text. This matters because it provides insight into the inner workings of AI models that are increasingly integral to various applications in technology and communication.