GraphStorm, a low-code enterprise graph machine learning (GML) framework designed for building, training, and deploying GML solutions swiftly on complex, large-scale graphs, announces the launch of GraphStorm 0.3. The new version includes native support for multi-task learning on graphs, enabling users to define multiple training targets on different nodes and edges within a single training…
In this article, co-authors Vicente Cruz Mínguez, Head of Data and Advanced Analytics at Cepsa Química, and Marcos Fernández Díaz, Senior Data Scientist at Keepler, discuss the implementation of generative Artificial Intelligence (AI) as a transformative force in business industries. They key focus is on the how it impacts the energy sector, specifically, Cepsa Química…
This week's news roundup includes OpenAI's search for cash and release of more AI products, Meta's continued distribution of free cutting-edge AI tools, and the potential negative impact of US politics on AI development.
OpenAI is struggling with profitability as it continually innovates and unveils new technologies. Its latest product is "SearchGPT," which is designed to…
As technology continues to advance, the prospects for automation in our daily digital lives are expanding. There's a rise in the ability of large language models (LLMs) to follow instructions, code, and use tools effectively. Many everyday digital tasks involve complex activities across multiple applications, requiring reasoning and decision-making based on intermediate results. A key…
Large Language Models (LLMs) have transformed natural language processing, demonstrating impressive performance across an assortment of tasks. The Scaling Law suggests that increased model size enhances LLMs' capability to comprehend context and handle long sequences. Applications such as document summarization, code generation, and conversational AI leverage these properties. However, the increased cost and efficiency associated…
The field of software vulnerability detection has seen significant strides thanks to the integration of deep learning models. These models assess code to unearth patterns and irregularities that could point to vulnerabilities. Despite their efficacy, these models are not invulnerable to attacks. In particular, adversarial attacks that manipulate input data to trick the model pose…
Composio offers a powerful solution to the often daunting task of integrating AI solutions with other applications and tools. Traditional methods, such as utilizing individual APIs or creating custom solutions, can be laborious due to their lack of consistency, need for extensive coding and ongoing maintenance, as well as potential for errors in tool calls…
The integration of AI agents with various applications and tools can be a significant challenge, traditionally approached using individual APIs or custom solutions. However, these methods come with considerable drawbacks, including a lack of consistency, intricate coding and maintenance, and the potential for errors in tool calls and data handling. Another challenge is managing different…
Artificial Intelligence (AI) is significantly impacting various medical fields by automating complex tasks, increasing efficiency, and improving patient care. AI, including Machine Learning (ML) and Deep Learning (DL), processes large datasets to identify patterns and build adaptive models. It has applications in medical imaging, remote medical advice, telemedicine, electronic health records, and decision support systems.
AI…
Researchers from MIT and University of Washington have developed a novel method that utilizes a good model of human behaviour, specifically involving the computational constraints in decision-making, in order to improve the collaboration between AI and humans. The unique technique of their new model permits an automatic inference regarding an agent's computational constraints solely based…
Researchers from MIT and the MIT-IBM Watson AI Lab have designed a machine-learning accelerator that can improve the security of health-monitoring apps. These applications can be slow and inefficient due to the large machine-learning models that need to be transferred between a smartphone and a central memory server. Instead, the team developed a chip that…