​[[{“value”:” Natural language processing (NLP) has experienced significant growth, largely due to the recent surge in the size and strength of large language models. These models, with their exceptional performance and unique characteristics, are rapidly making a significant impact in real-world applications. These considerations have… Read More »How Valuable is Interpretability and Analysis Work for NLP Research? This Paper Investigate the Impact of Interpretability and Analysis Research on NLP Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers achieved great success in many visual tasks, such as image classification, object detection, and semantic segmentation. However, their ability to handle different changes in data is still a big concern, especially for use… Read More »Comprehensive Analysis of The Performance of Vision State Space Models (VSSMs), Vision Transformers, and Convolutional Neural Networks (CNNs) Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​Can Transformers predict new syllogisms by composing established ones? More generally, what type of targets can be learned by such models from scratch? Recent works show that Transformers can be Turing-complete in terms of expressivity, but this does not address the learnability objective. This paper… Read More »How Far Can Transformers Reason? The Locality Barrier and Inductive Scratchpad Apple Machine Learning Research

​The inability to linearly classify XOR has motivated much of deep learning. We revisit this age-old problem and show that linear classification of XOR is indeed possible. Instead of separating data between halfspaces, we propose a slightly different paradigm, equality separation, that adapts the SVM… Read More »Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection Apple Machine Learning Research

​[[{“value”:”This paper was accepted at the Natural Language Reasoning and Structured Explanations workshop at ACL 2024. Reinforcement Learning from AI Feedback (RLAIF) has demonstrated significant potential across various domains, including mitigating harm in LLM outputs, enhancing text summarization, and mathematical reasoning. This paper introduces an… Read More »Applying RLAIF for Code Generation with API-usage in Lightweight LLMs Apple Machine Learning Research

​[[{“value”:” Photography has been around for more than a century. There are many old photos around, and probably your family has some, too. Limited by the camera and film of the time, you may have photos of low resolution, blurry, or with folds or scratches.… Read More »Stable Diffusion Project: Reviving Old Photos Kanwal Mehreen MachineLearningMastery.com

​[[{“value”:” As artificial intelligence (AI) technology continues to advance and permeate various aspects of society, it poses significant challenges to existing legal frameworks. One recurrent issue is how the law should regulate entities that lack intentions. Traditional legal principles often rely on the concept of… Read More »The Human Factor in Artificial Intelligence AI Regulation: Ensuring Accountability Aabis Islam Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Understanding how LLMs comprehend natural language plans, such as instructions and recipes, is crucial for their dependable use in decision-making systems. A critical aspect of plans is their temporal sequencing, which reflects the causal relationships between steps. Planning, integral to decision-making processes, has been… Read More »CAT-BENCH: Evaluating Language Models’ Understanding of Temporal Dependencies in Procedural Texts Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) face a critical challenge in their training process: the impending scarcity of high-quality internet data. Predictions suggest that by 2026, the available pool of such data will be exhausted, forcing researchers to turn to model-generated or synthetic data for training.… Read More »This AI Paper from CMU and Google DeepMind Studies the Role of Synthetic Data for Improving Math Reasoning Capabilities of LLMs Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Claude 3.5 Sonnet by Anthropic AI has heralded a new era, surpassing its predecessors and contemporaries with unprecedented capabilities. This iteration of large language models (LLMs) demonstrates versatility and sophistication that exceed expectations and opens doors to applications previously deemed impractical or beyond reach.… Read More »10 Use Cases of Claude 3.5 Sonnet: Unveiling the Future of Artificial Intelligence AI with Revolutionary Capabilities Asif Razzaq Artificial Intelligence Category – MarkTechPost