Scientists from The Hong Kong University of Science and Technology, and the University of Illinois Urbana-Champaign, have presented ScaleBiO, a unique bilevel optimization (BO) method that can scale up to 34B large language models (LLMs) on data reweighting tasks. The method relies on memory-efficient training technique called LISA and utilizes eight A40 GPUs.
BO is attracting…
After a successful call for proposals on AI studies in the summer, MIT issued a second call for papers that resulted in 53 submissions. Subsequently, 16 of these were selected to receive exploratory funding. The selected proposals offer insights and perspectives on the potential impact of AI across various domains.
The MIT President, Sally Kornbluth, and…
Amazon's new product, Amazon Q Business, is a robust, artificially intelligent assistant capable of analyzing various types of documents such as receipts, health plans, tax statements, and more from industries like finance, insurance, healthcare, and life sciences. Unlike other software, Amazon Q Business eliminates the need to extract text from scanned PDF documents before it…
Researchers from Shanghai Jiaotong University, Shanghai AI Laboratory, and Nanyang Technological University's S-Lab have developed an advanced multi-modal large language model (MLLM) called MG-LLaVA. This new model aims to overcome the limitations of current MLLMs when interpreting low-resolution images.
The main challenge with existing MLLMs has been their reliance on low-resolution inputs which compromises their…
Large Language Models (LLMs) have demonstrated impressive performances in numerous tasks, particularly classification tasks, in recent years. They exhibit a high degree of accuracy when provided with the correct answers or "gold labels". However, if the right answer is deliberately left out, these models tend to select an option from the available choices, even when…
Generative AI (GenAI) is rapidly transforming industries such as healthcare, finance, entertainment, and customer service. The efficiency of GenAI systems by and large depends on the successful integration of four critical constituents: Human, Interface, Data, and large language models (LLMs).
Starting with the human element, it is fundamental for two reasons. Firstly, humans are the ones…
Generative AI (GenAI) has made significant impacts across various industries, including healthcare, finance, entertainment, and customer service, largely due to a successful integration of four key components: Human, Interface, Data, and Large Language Models (LLMs).
The human element is the most defining aspect of GenAI networks. Humans are not only the end-users of these systems,…
In light of the increased interest in the development of generative Artificial Intelligence (AI), MIT President Sally Kornbluth and Provost Cynthia Barnhart issued a call for papers. They sought submissions that could provide effective roadmaps, policy recommendations, and action plans across the broad field of generative AI. The first call for papers received an enthusiastic…
In response to a call for proposals issued by MIT President Sally Kornbluth and Provost Cynthia Barnhart that aimed at outlining efficient strategies, policies, and actions in the field of generative AI, the university received 75 submissions last summer. 27 of those submissions were selected for seed funding due to their exceptional potency. After such…
Traditionally, sourcing relevant information from documents is laborious and often frustrating. This inefficiency not only affects productivity but also leads to the risk of missing critical information hidden within the document. The advent of chatbots in the conversational artificial intelligence (AI) domain has transformed this process. Using natural language processing (NLP) and machine learning (ML),…