Large language model
Researchers in Japan have developed a two-legged biohybrid robot inspired by human gait, using a combination of muscle tissues and artificial materials. The robot is capable of walking, pivoting, and efficiently converting energy into movement, harnessing the flexibility and fine movements of the human body.
Chemists have created ‘RoboChem’, an autonomous chemical synthesis robot with integrated AI and machine learning capabilities. This benchtop device surpasses human chemists in speed, accuracy, and innovation. It has the potential to greatly expedite chemical discovery for pharmaceutical and various other purposes.
The article discusses the challenges of aligning Large Language Models (LLMs) with human preferences in reinforcement learning from human feedback (RLHF), focusing on the phenomenon of reward hacking. It introduces Weight Averaged Reward Models (WARM) as a novel, efficient strategy to mitigate these challenges, highlighting its benefits and empirical results. Reference: https://arxiv.org/pdf/2401.12187.pdf
The development of large language models (LLMs) like GPT and LLaMA has led to significant advances in natural language processing. A cost-effective alternative to creating these models from scratch is the fusion of existing pre-trained LLMs, as demonstrated by the FuseLLM approach. This method has shown superior performance in various tasks and offers promising advancements…
Researchers propose three measures to increase visibility into AI agents for safer functioning: agent identifiers, real-time monitoring, and activity logs. They identify potential risks, including malicious use, overreliance, delayed impacts, multi-agent risks, and sub-agents. The paper stresses the need for governance structures and improved visibility to manage and mitigate these risks.
The EU AI Act Summit 2024, held in London on February 6, 2024, focuses on the groundbreaking EU AI Act, offering practical guidance for stakeholders. The Act introduces comprehensive AI regulations, categorized by risk levels, and revolving around compliance responsibilities and opportunities for the industry. The summit features notable speakers, sessions, and registration discounts. Visit…
The spread of explicit and fake AI-generated images of Taylor Swift on social media platform X has raised concerns about the challenge of controlling such content online. Despite platform rules, the images spread widely, leading to potential legal action by Swift and criticism of X’s response. Fans have used hashtags to share real content in…
Tensoic introduced Kannada Llama (Kan-LLaMA), aiming to overcome limitations of language models (LLMs) by emphasizing the importance of open models for natural language processing and machine translation. The paper presents the solution for enhancing efficiency of Llama-2 vocabulary for processing Kannada texts through low-level optimization, dataset pretraining, and collaboration for broader accessibility.
The post highlights the best ChatGPT alternatives and their key features. It covers GitHub Copilot’s code automation, Writesonic’s content marketing bots, Claude AI’s contextual writing, Perplexity AI’s research capabilities, Microsoft Copilot’s Microsoft 365 integration, and Poe AI’s diverse AI models. Each alternative’s pricing, best use, and unique features are outlined to aid in selecting a…
The recent RAND report concludes that current Large Language Models (LLMs) do not significantly increase the risk of a biological attack by non-state actors. Their research, conducted through a red-team exercise, found no substantial difference in the viability of plans generated with or without LLM assistance. However, the study emphasized the need for further research…
The latest advancement in AI, Large Language Models (LLMs), has shown great language production improvement but faces increased inference latency due to model size. To address this, researchers developed MEDUSA, a method that enhances LLM inference efficiency by adding multiple decoding heads. MEDUSA offers lossless inference acceleration and improved prediction accuracy for LLMs.
This week’s AI news highlights AI excelling in math tests and stirring debate about fake truths. Google unveiled its text-to-video model, while OpenAI ventured into education and faced criticism for data practices. Other developments include legal regulations for AI hiring and Samsung’s collaboration with Google in AI-rich mobile phones. Meanwhile, AI’s impact on healthcare and…
Significant progress has been made in utilizing Large Language Models like GPT-4 and Llama 2 in Artificial Intelligence, showing potential for various sectors. While challenges persist in integrating AI into agriculture due to limited specialized training data, the introduction of a pioneering pipeline by Microsoft researchers, combining Retrieval-Augmented Generation (RAG) and fine-tuning methods, has notably…
The text discusses challenges in model-based reinforcement learning (MBRL) due to imperfect dynamics models. It introduces COPlanner, an innovation using uncertainty-aware policy-guided model predictive control (UP-MPC) to address these challenges. Through comparisons and performance evaluations, COPlanner is shown to substantially improve sample efficiency and asymptotic performance in handling complex tasks, advancing the understanding and practical…
Background Oriented Schlieren (BOS) imaging is an effective, low-cost method for visualizing fluid flow. A new approach using Physics-Informed Neural Networks (PINNs) has been developed to accurately deduce complete 3D velocity and pressure fields from Tomo-BOS imaging, showing promise for experimental fluid mechanics. The versatility and potential of this method suggest advancements in fluid dynamics.
RAGxplorer is an interactive AI tool that visualizes document chunks and queries in a high-dimensional space, supporting the understanding and improvement of retrieval augmented generation (RAG) applications. Its unique approach provides an interactive map of the document’s semantic landscape, allowing users to assess RAG model comprehension, identify biases, and enhance overall comprehension.
Text-to-image diffusion models have revolutionized AI image generation, simulating human creativity. Orthogonal Finetuning enhances control over these models, maintaining semantic generation ability. It enables subject-driven image generation, improves efficiency, and has applications in digital art, advertising, gaming, education, automotive, and medical research. Challenges include scalability and parameter efficiency. This breakthrough heralds a new era in…
Scientists face a challenge in understanding the unique composition of cells, notably peptide sequences, crucial for personalized treatments, such as immunotherapy. Traditional methods create gaps in sequencing, hindering accuracy. However, GraphNovo, a new program developed by researchers at the University of Waterloo, utilizes machine learning to significantly enhance accuracy, offering promising potential for personalized medicine…
Recent advancements in language models have led to the development of semi-autonomous agents like WebGPT, AutoGPT, and ChatGPT plugins for real-world use. However, the transition from text interactions to real-world actions brings risks. To address this, a new framework called ToolEmu utilizes language models to simulate tool executions and evaluate risks, aiming to enhance agent…
Recent advancements in machine learning show potential in understanding Theory of Mind (ToM), crucial for human-like social intelligence in machines. MIT and Harvard introduced a Multimodal Theory of Mind Question Answering (MMToMQA) benchmark, assessing machine ToM on both multimodal and unimodal data types related to household activities. A novel method called BIP-ALM integrates Bayesian inverse…