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The Launch of Nephilim v3 8B: A Groundbreaking AI Solution for Combining Models to Improve Roleplay and Creativity

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 22, 202436Views0Likes0Comments
Hugging Face has introduced two new innovative models named llama-3-Nephilim-v3-8B and llama-3-Nephilim-v3-8B-GGUF. Despite not being explicitly trained for roleplays, these models have demonstrated outstanding proficiency in this area, illuminating the possibilities of "found art" strategies in the domain of artificial intelligence (AI) development. To create these models, several pre-trained language models were converged. The merger was…

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An improved, quicker method to stop an AI chatbot from providing harmful responses.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 22, 202440Views0Likes0Comments
Artificial Intelligence (AI) Chatbots like OpenAI's ChatGPT are capable of performing tasks from generating code to writing article summaries. However, they can also potentially provide information that could be harmful. To prevent this from happening, developers use a process called red-teaming, where human testers write prompts to identify unsafe responses in the model. Nevertheless, this…

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New AI technique encapsulates ambiguity in healthcare pictures.

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Health care, Imaging, Machine learning, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedJuly 22, 202439Views0Likes0Comments
In the realm of biomedicine, segmentation is a process where certain areas or pixels within a medical image, such as an organ or cell, are annotated or highlighted. This primarily assists clinicians in pinpointing areas showing signs of diseases or abnormalities. However, there is often a gray area since different experts can have differing interpretations…

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This AI research article from NYU and Meta presents Neural Optimal Transport using Lagrangian Expenses: Effective Representation of Intricate Transport Dynamics.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Deep Learning, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 21, 202432Views0Likes0Comments
Optimal transport is a mathematical field focused on the most effective methods for moving mass between probability distributions. It has a broad range of applications in disciplines such as economics, physics, and machine learning. However, the optimization of probability measures in optimal transport frequently faces challenges due to complex cost functions influenced by various factors…

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A more efficient and improved method to inhibit AI chatbots from producing harmful responses.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 21, 202436Views0Likes0Comments
AI chatbots like ChatGPT, trained on vast amounts of text from billions of websites, have a broad potential output which includes harmful or toxic material, or even leaking personal information. To maintain safety standards, large language models typically undergo a process known as red-teaming, where human testers use prompts to elicit and manage unsafe outputs.…

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A fresh approach to artificial intelligence measures ambiguity in health-related imagery.

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Health care, Imaging, Machine learning, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedJuly 21, 202437Views0Likes0Comments
Biomedical segmentation pertains to marking pixels from significant structures in a medical image like cells or organs which is crucial for disease diagnosis and treatment. Generally, a single answer is provided by most artificial intelligence (AI) models while making these annotations, but such a process is not always straightforward. In a recent paper, Marianne Rakic, an…

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Could “anxiety” be crucial in developing more flexible, robust, and organic AI systems?

Bio-inspired AI, Biology, Industry, Machine learning, UncategorizedJuly 21, 202433Views0Likes0Comments
The pursuit of artificial general intelligence (AGI), where an AI can perform tasks similar to a human, is at the forefront of research. This involves complex systems mimicking behaviors observed in natural organisms. Despite this, the belief that AI cannot obtain natural intelligence is prevalent. Some limitations of AI include its inability to navigate unpredictable…

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Google Research introduces a new AI strategy for genetic exploration which can utilize concealed information in highly dimensional medical data.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 21, 202434Views0Likes0Comments
Harnessing high-dimensional clinical data (HDCD) – health care datasets with significantly higher variables than patients – for genetic discovery and disease prediction poses a considerable challenge. HDCD analysis and processing demands immense computational resources due to its rapidly expanding data space. This further complicates interpreting models based on this data, potentially hindering clinical decisions. Traditional…

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Efficient Quantization-Aware Training (EfficientQAT): A New Approach to Quantification in Machine Learning for Compressing Large Language Models (LLMs).

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 21, 202432Views0Likes0Comments
Large Language Models (LLMs) have become increasingly important in AI and data processing tasks, but their superior size leads to substantial memory requirements and bandwidth consumption. Standard procedures such as Post-Training Quantization (PTQ) and Quantized Parameter-Efficient Fine-Tuning (Q-PEFT) can often compromise accuracy and performance, and are impractical for larger networks. To combat this, researchers have…

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Researchers from the University of Texas at Austin have launched PUTNAMBENCH, a thorough AI benchmarking tool for assessing the performance of Neural Theorem-Provers on Putnam Mathematical Problems.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 21, 202435Views0Likes0Comments
Researchers at the University of Texas (UT) in Austin have introduced a new benchmark designed to evaluate the effectiveness of artificial intelligence in solving complex mathematical problems. PUTNAMBENCH is aimed at solving a key issue facing the sector as current benchmarks are not sufficiently rigorous and mainly focus on high-school level mathematics. Automating mathematical reasoning…

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An improved and speedier method to stop AI chatbot from providing harmful responses.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 21, 202436Views0Likes0Comments
AI chatbots pose unique safety risks—while they can write computer programs or provide useful summaries of articles, they can also potentially generate harmful or even illegal instructions, including how to build a bomb. To address such risks, companies typically use a process called red-teaming. Human testers aim to generate unsafe or toxic content from AI…

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A new technique in artificial intelligence accurately recognizes uncertainty in health imagery.

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Health care, Imaging, Machine learning, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedJuly 21, 202437Views0Likes0Comments
A research team from MIT, the Broad Institute of MIT and Harvard, and Massachusetts General Hospital has developed an artificial intelligence (AI) tool, named Tyche, that presents multiple plausible interpretations of medical images, highlighting potentially important and varied insights. This tool aims to address the often complex ambiguity in medical image interpretation where different experts…

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