BrainBox AI Introduces ARIA: The Globe’s Initial Virtual Building Assistant Powered by Generative AI
The introduction of ARIA, the Generative AI-Powered Virtual Building Assistant by BrainBox AI, is bringing about a significant transformation in the way buildings are managed. ARIA caters to facility managers and building operators by providing advanced solutions to streamlining operations, enhancing energy efficiency, and reducing pollution.
ARIA plays the role of an intelligent building assistant, handling…
BrainBox AI has developed an innovative Generative AI-Powered Virtual Building Assistant, ARIA. Created to revolutionize building management, ARIA applies advanced AI technology to facilitate smoother running of buildings, significant energy savings, and decreased pollution. Overall, ARIA operates like a smart assistant for buildings, handling aspects from energy use optimization to daily operations.
Crucial to ARIA's operation…
Artificial Intelligence (AI) is reshaping numerous aspects of life in the modern world, and the medical field is no exception. A remarkable breakthrough in this area has been achieved by OpenEvidence, a medical AI created under the Mayo Clinic Platform Accelerate. The AI has set a benchmark by scoring an impressive 90% on the United…
The ability of large Multimodal Language Models (MLLMs) to tackle visual math problems is currently the subject of intense interest. While MLLMs have performed remarkably well in visual scenarios, the extent to which they can fully understand and solve visual math problems remains unclear. To address these challenges, frameworks such as GeoQA and MathVista have…
The increased adoption and integration of large Language Models (LLMs) in the biomedical sector for interpretation, summary and decision-making support has led to the development of an innovative reliability assessment framework known as Reliability AssessMent for Biomedical LLM Assistants (RAmBLA). This research, led by Imperial College London and GSK.ai, puts a spotlight on the critical…
As the advancements in Large Language Models (LLMs) such as ChatGPT, LLaMA, and Mistral continue, there are growing concerns about their vulnerability to harmful queries. This has caused an immediate need to implement robust safeguards. Techniques such as supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and direct preference optimization (DPO) have been useful…
Generative Language Models (GLMs) are now ubiquitous in various sectors, including customer service and content creation. Consequently, handling potential harmful content while keeping linguistic diversity and inclusivity has become important. Toxicity scoring systems aim to filter offensive or hurtful language, but often misidentify harmless language as harmful, especially from marginalized communities. This restricts access to…
Optimizing efficiency in complex systems is a significant challenge for researchers, particularly in high-dimensional spaces commonly found in machine learning. Second-order methods like the cubic regularized Newton (CRN) method demonstrate rapid convergence; however, their application in high-dimensional problems has been limited due to substantial memory and computational requirements.
To counter these challenges, scientists from UT…
In today's ever-evolving financial universe, investors often feel inundated by the sheer volume of data and information that needs to be analyzed while examining investment prospects. Without the right tools and guidance, investors often struggle to make sound financial decisions. Traditional approaches or financial advisor services, although resourceful, can often turn out to be time-consuming…
In recent years, natural language processing (NLP) has seen significant advancements due to the transformer architecture. However, as these models grow in size, so do their computational costs and memory requirements, limiting their practical use to a select few corporations. Increasing model depths also present challenges, as deeper models need larger datasets for training, which…