Artificial Intelligence
DiffMoog, a differentiable modular synthesizer, integrates commercial instrument modules for AI-guided sound synthesis. Its modular architecture facilitates custom signal chain creation and automation of sound matching. DiffMoog’s open-source platform combines it with an end-to-end system, introducing a unique signal-chain loss for optimization. Challenges in frequency estimation persist, but the research suggests potential for stimulating additional…
The demand for bilingual digital assistants in the modern digital age is growing. Current large language models face challenges in understanding and interacting effectively in multiple languages. A new open-source model named ‘Yi’ is tailored for bilingual capabilities, showcasing exceptional performance in language tasks and offering versatile applications, making it a significant breakthrough in language…
Large-scale pre-trained vision-language models like CLIP exhibit strong generalizability but struggle with out-of-distribution (OOD) samples. A novel approach, OGEN, combines feature synthesis for unknown classes and adaptive regularization to address this, yielding improved performance across datasets and settings. OGEN showcases potential for addressing overfitting and enhancing both in-distribution (ID) and OOD performance.
Researchers at Google Deepmind and the University of Toronto propose Generative Express Motion (GenEM), using Large Language Models (LLMs) to generate expressive robot behaviors. The approach leverages LLMs to create adaptable and composable robot motion, outperforming traditional methods and demonstrating effectiveness in user studies and simulation experiments. This research signifies a significant advancement in robotics…
CDAO Financial Services 2024 in New York gathers industry leaders in data and analytics to drive innovation in the financial sector, heavily influenced by AI. The event hosts over 40 experts, panel discussions, and networking sessions, and delves into AI’s potential in finance. Key speakers include JoAnn Stonier, Mark Birkhead, and Heather Tubbs. Visit the…
Recent advancements in machine learning and artificial intelligence have facilitated the development of advanced AI systems, particularly large language models (LLMs). A recent study by MIT and Harvard researchers delves into predicting and influencing human brain responses to language using an LLM-based encoding model. The implications extend to neuroscience research and real-world applications, offering potential…
Dify.AI addresses AI development challenges by emphasizing self-hosting, multi-model support, and flexibility. Its unique approach ensures data privacy and compliance by processing data on independently deployed servers. With features like the RAG engine and easy integration, Dify offers a robust platform for businesses and individuals to customize and optimize their AI applications.
Research from ETH Zurich and Microsoft introduces SliceGPT, a post-training sparsification scheme for large language models (LLMs). It reduces the embedding dimension, leading to faster inference without extra code optimization. The method utilizes computational invariance in transformer networks and has been shown to outperform SparseGPT, offering significant speedups across various models and tasks.
A groundbreaking development in AI and machine learning presents intelligent agents that adapt and evolve by integrating past experiences into diverse tasks. The ICE strategy, developed by researchers, shifts agent development paradigms by enhancing task execution efficiency, reducing computational resources, and improving adaptability. This innovative approach holds great potential for the future of AI technology.
MambaTab is a novel machine learning method developed by researchers at the University of Kentucky to process tabular data. It leverages a structured state-space model to streamline data handling, demonstrating superior efficiency and scalability compared to existing models. MambaTab’s potential to simplify analytics and democratize advanced techniques marks a significant advancement in data analysis.
Researchers have developed a new, sleek 3D surface imaging system with simpler optics that can recognize faces just as effectively as existing smartphone systems. This advancement could replace cumbersome facial recognition technology currently in use for unlocking devices and accessing accounts.
Generative AI, particularly Large Language Models (LLMs), has shown remarkable progress in language processing tasks but has struggled to significantly impact molecule optimization in drug discovery. A new model, DrugAssist, developed by Tencent AI Lab and Hunan University, exhibits impressive human-interaction capabilities and achieved promising results in multi-property optimization, showcasing great potential for enhancing the…
New York University researchers trained an AI system using 60 hours of first-person video recordings from children aged 6 months to 2 years. The AI employed self-supervised learning to understand actions and changes like a child. The study’s findings suggest AI can efficiently learn from limited, targeted data, challenging conventional AI training methods.
Researchers work to optimize large language models (LLMs) like GPT-3, which demand substantial GPU memory. Existing quantization techniques have limitations, but a new system design, TC-FPx, and FP6-LLM provide a breakthrough. FP6-LLM significantly enhances LLM performance, allowing single-GPU inference of complex models with higher throughput, representing a major advancement in AI deployment. For more details,…
Auto-regressive decoding in large language models (LLMs) is time-consuming and costly. Speculative sampling methods aim to solve this issue by speeding up the process, with EAGLE being a notable new framework. It operates at the feature level and demonstrates faster and more accurate draft accuracy compared to other systems. EAGLE improves LLM throughput and can…
Nightshade, a tool from the University of Chicago, gained over 250,000 downloads within five days of its release. It combats unauthorized use of artwork by AI models by poisoning them at the pixel level, rendering them unable to replicate images accurately. The team is overwhelmed by its success, with potential future integration and cloud hosting.
US lawmakers have proposed the DEFIANCE Act to address the growing problem of AI-generated explicit images. Prompted by a series of deep fake AI-generated images of Taylor Swift, the bipartisan bill aims to empower individuals to sue for damages if they are depicted in “digital forgeries” without consent. This legislation expands the legal framework to…
Mastercard has developed a new generative AI fraud detection tool, called Decision Intelligence Pro (DI Pro), powered by a recurrent neural network. It analyzes cardholders’ purchasing histories and scans data points to predict transaction authenticity in less than 50 milliseconds. Initial modeling suggests a potential 20-300% boost in fraud detection rates. The tool is expected…
This week’s AI news features the following highlights: 1. Taylor Swift’s battle against explicit AI deep fake images and the concerning ease of generating such content using AI. 2. The rise of political deep fakes showcasing AI’s capabilities in replicating voices with convincing realism and the challenges of detecting these fakes. 3. OpenAI’s evolving transparency…
The CMMMU benchmark has been introduced to bridge the gap between powerful Large Multimodal Models (LMMs) and expert-level artificial intelligence in tasks involving complex perception and reasoning with domain-specific knowledge. It comprises 12,000 Chinese multimodal questions across six core disciplines and employs a rigorous data collection and quality control process. The benchmark evaluates LMMs, presents…