Software development teams often grapple with the complexities of product insights and monitoring, testing, end-to-end analytics and surfacing errors. These tasks could consume significant development time often due to developers having to build internal tools for addressing these issues. Focus has mainly been on numerical metrics like concerning click through rate (CTR) and conversion rates.…
Data handling and analytics, especially large volumes extracted from a variety of documents, have always been a challenging task that has predominantly required proprietary solutions. Open Contracts aims to revolutionize this by providing a free, open-source platform for democratizing document analytics.
The platform, licensed under Apache-2, uses AI and Large Language Models (LLMs) to enable…
In recent years, the advancement of technology has allowed for the development of computer-verifiable formal languages, further advancing the field of mathematical reasoning. One of these languages, known as Lean, is an instrument employed to validate mathematical theorems, thereby ensuring accuracy and consistency in mathematical outcomes. Scholars are increasingly using Large Language Models (LLMs), specifically…
Chinese AI tech giant, SenseTime, announced a major upgrade for their flagship product SenseNova 5.5 at the 2024 World Artificial Intelligence Conference & High-Level Meeting on Global AI Governance. The update incorporates the first real-time multimodal model in China, SenseNova 5o, and demonstrates a commitment to providing innovative and practical applications in various industries.
SenseNova 5o…
Large Language Models (LLMs) are advanced Artificial Intelligence tools designed to understand, interpret, and respond to human language in a similar way to human speech. They are currently used in various areas such as customer service, mental health, and healthcare, due to their ability to interact directly with humans. However, recently, researchers from the National…
Artificial Intelligence (AI) projects require a high level of processing power to function efficiently and effectively. Traditional hardware often struggles to meet these demands, resulting in higher costs and longer processing times. This presents a significant challenge for developers and businesses that are seeking to harness the impact and potential of AI application. Previous options…
AI integration into low-powered Internet of Things (IoT) devices such as microcontrollers has been enabled by advances in hardware and software. Holding back deployment of complex Artificial Neural Networks (ANNs) to these devices are constraints such as the need for techniques such as quantization and pruning. Shifts in data distribution between training and operational environments…
Data curation, particularly high-quality and efficient data curation, is crucial for large-scale pretraining models in vision, language, and multimodal learning performances. Current approaches often depend on manual curation, making it challenging to scale and expensive. An improvement to such scalability issues lies in model-based data curation that selects high-quality data based on training model features.…
In the field of software development, large coding projects often come with their fair share of difficulties. Common problems include battling with unfamiliar technology, managing extensive backlogs, and spending significant time on repetitive tasks. Current tools and methods often fall short when it comes to efficiently handling these challenges, causing delays and frustration for developers.
Existing…
Traditional protein design, which relies on physics-based methods like Rosetta can encounter difficulties in creating functional proteins due to parametric and symmetric constraints. Deep learning tools such as AlphaFold2 have revolutionized protein design by providing more accurate prediction abilities and the capacity to analyze large sequence spaces. With these advancements, more complex protein structures can…
Generative Domain Adaptation (GDA) is a machine learning technique used to adapt a model trained in one domain (source) using a few examples from another domain (target). This is beneficial in situations where it is expensive or impractical to obtain substantial labeled data from the target domain. While existing GDA solutions focus on enhancing a…