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Does the Turing test no longer work?
A new study proposes a three-step system to evaluate artificial intelligence’s ability to reason like a human, acknowledging the limitations of the Turing test due to AI’s capacity to imitate human responses.
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What’s next for AI in 2024
In 2023, predictions about the future of AI, Big Tech, and AI’s impact on industries were partly accurate. Looking forward to 2024, specific trends include the rise of customized chatbots for non-tech users, advancements in generative video models, the spread of AI-generated election disinformation, and the development of robots with multitasking abilities.
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Meet SPACEL: A New Deep-Learning-based Analysis Toolkit for Spatial Transcriptomics
A group of researchers led by Prof. Qu Kun has developed SPACEL, a deep-learning toolkit consisting of Spoint, Splane, and Scube modules, to overcome limitations in spatial transcriptomics analysis. By accurately predicting cell types, identifying spatial domains, and constructing 3D tissue architecture, SPACEL outperforms existing techniques, offering a powerful solution for comprehensive spatial transcriptomic analysis.
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This Paper from MBZUAI Introduces 26 Guiding Principles Designed to Streamline the Process of Querying and Prompting Large Language Models
Large Language Models (LLMs) have revolutionized processing multimodal information, leading to breakthroughs in multiple fields. Prompt engineering, introduced by researchers at MBZUAI, focuses on optimizing prompts for LLMs. Their study outlines 26 principles for crafting effective prompts, emphasizing conciseness, context relevance, task alignment, and advanced programming-like logic to improve LLMs’ responses.
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Philosophy and data science — Thinking deeply about data
The article explores the intersection of philosophy and data science, focusing on causality. It delves into different philosophical theories of causality, such as deterministic vs probabilistic causality, regularity theory, process theory, and counterfactual causation. The author emphasizes the importance of understanding causality in data science to provide valuable recommendations.
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10+ Open-Source Tools for LLM Applications Development
Large Language Models (LLMs) are crucial in enabling machines to understand and generate human-like text. The open-source frameworks for LLM application development include LangChain, Chainlit, Helicone, LLMStack, Hugging Face Gradio, FlowiseAI, LlamaIndex, Weaviate, Semantic Kernel, Superagent, and LeMUR. These frameworks offer diverse tools to simplify LLM application development, enhancing flexibility, transparency, and usability.
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Nvidia Researchers Developed and Open-Sourced a Standardized Machine Learning Framework for Time Series Forecasting Benchmarking
Nvidia researchers developed TSPP, a benchmarking tool for time series forecasting in finance, weather, and demand prediction. It standardizes machine learning evaluation, integrates all lifecycle phases, and demonstrates the effectiveness of deep learning models. TSPP offers efficiency and flexibility, marking a significant advance in accurate forecasting for real-world applications. [50 words]
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A Winding Road to Parameter Efficiency
The text can be summarized as follows: The article discusses the use of LoRA (Low-Rank Adaptation) for fine-tuning language models. The summary highlights the practical strategies for achieving good performance and parameter efficiency using LoRA. It also addresses the impact of hyperparameters and design decisions on performance, GPU memory utilization, and training speed. The article…
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This AI Paper Tests the Biological Reasoning Capabilities of Large Language Models
Researchers from the University of Georgia and Mayo Clinic tested the proficiency of Large Language Models (LLMs), particularly OpenAI’s GPT-4, in understanding biology-related questions. GPT-4 outperformed other AI models in reasoning about biology, scoring an average of 90 on 108 test questions. The study highlights the potential applications of advanced AI models in biology and…
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Statistical analysis of rounded or binned data
The article “On the Statistical Analysis of Rounded or Binned Data” discusses the impact of rounding or binning on statistical analyses. It explores Sheppard’s corrections and the total variation bounds on the rounding error in estimating the mean. It also introduces bounds based on Fisher information. The article highlights the importance of addressing errors when…