What if the Next Medical Breakthrough is Hidden in Plain Text? Meet NATURAL: A Pipeline for Causal Estimation from Unstructured Text Data in Hours, Not Years
Causal effect estimation is crucial for understanding the impact of interventions in various domains, such as healthcare, social sciences, and economics. This area of research focuses on determining how changes…
CompeteAI: An Artificial Intelligence AI Framework that Understands the Competition Dynamics of Large Language Model-based Agents
Competition significantly shapes human societies, influencing economics, social structures, and technology. Traditional research on competition, relying on empirical studies, is limited by data accessibility and lacks micro-level insights. Agent-based modeling…
The Impact of Questionable Research Practices on the Evaluation of Machine Learning (ML) Models
Evaluating model performance is essential in the significantly advancing fields of Artificial Intelligence and Machine Learning, especially with the introduction of Large Language Models (LLMs). This review procedure helps understand…
Emergence AI Proposes Agent-E: A Web Agent Achieving 73.2% Success Rate with a 20% Improvement in Autonomous Web Navigation
Autonomous web navigation focuses on developing AI agents capable of performing complex online tasks. These tasks range from data retrieval and form submissions to more intricate activities like finding the…
RogueGPT: Unveiling the Ethical Risks of Customizing ChatGPT
Generative Artificial Intelligence (GenAI), particularly large language models (LLMs) like ChatGPT, has revolutionized the field of natural language processing (NLP). These models can produce coherent and contextually relevant text, enhancing…
Researchers at Stanford Introduce Contrastive Preference Learning (CPL): A Novel Machine Learning Framework for RLHF Using the Regret Preference Model
Aligning models with human preferences poses significant challenges in AI research, particularly in high-dimensional and sequential decision-making tasks. Traditional Reinforcement Learning from Human Feedback (RLHF) methods require learning a reward…
Llama 3.1 vs GPT-4o vs Claude 3.5: A Comprehensive Comparison of Leading AI Models
The landscape of artificial intelligence has seen significant advancements with the introduction of state-of-the-art language models. Among the leading models are Llama 3.1, GPT-4o, and Claude 3.5. Each model brings…
Optimizing Artificial Intelligence Performance by Distilling System 2 Reasoning into Efficient System 1 Responses
Large Language Models (LLMs) can improve their final answers by dedicating additional computer power to intermediate thought generation during inference. System 2 strategies are used in this procedure to mimic…
IBM Researchers Propose a New Training-Free AI Approach to Mitigate Hallucination in LLMs
Large language models (LLMs) are used in various applications, such as machine translation, summarization, and content creation. However, a significant challenge with LLMs is their tendency to produce hallucinations—statements that…
Google DeepMind’s AlphaProof and AlphaGeometry-2 Solves Advanced Reasoning Problems in Mathematics
In a groundbreaking achievement, AI systems developed by Google DeepMind have attained a silver medal-level score in the 2024 International Mathematical Olympiad (IMO), a prestigious global competition for young mathematicians.…