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A New AI Research Introduces REV: A Game-Changer in AI Research – A New Information-Theoretic Measure Evaluating Novel, Label-Relevant Information in Free-Text Rationales Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

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​ Model explanations have proved essential for trust and interpretability in natural language processing (NLP). Free-text rationales, which provide a natural language explanation of a model prediction, have gained popularity because of their adaptability in eliciting the thought process that went into the model’s choice,… Read More »A New AI Research Introduces REV: A Game-Changer in AI Research – A New Information-Theoretic Measure Evaluating Novel, Label-Relevant Information in Free-Text Rationales Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

Google AI Researchers Introduce Pic2Word: A Novel Approach To Zero-Shot Composed Image Retrieval (ZS-CIR) Bhoumik Mhatre Artificial Intelligence Category – MarkTechPost

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​ Image Retrieval is a complex process if we try to represent it accurately. Many research scientists are working on this process to ensure minimum loss from the actual image given. Researchers found a way to represent an image through text embeddings. But formatting an… Read More »Google AI Researchers Introduce Pic2Word: A Novel Approach To Zero-Shot Composed Image Retrieval (ZS-CIR) Bhoumik Mhatre Artificial Intelligence Category – MarkTechPost

Meet PoisonGPT: An AI Method To Introduce A Malicious Model Into An Otherwise-Trusted LLM Supply Chain Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

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​ Amidst all the buzz around artificial intelligence, businesses are beginning to realize the many ways in which it may help them. However, as Mithril Security’s latest LLM-powered penetration test shows, adopting the newest algorithms can also have significant security implications. Researchers from Mithril Security,… Read More »Meet PoisonGPT: An AI Method To Introduce A Malicious Model Into An Otherwise-Trusted LLM Supply Chain Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

Google Research Introduces SPAE: An AutoEncoder For Multimodal Generation With Frozen Large Language Models (LLMs) Tanya Malhotra Artificial Intelligence Category – MarkTechPost

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​ Large Language Models (LLMs) have rapidly gained enormous popularity by their extraordinary capabilities in Natural Language Processing and Natural Language Understanding. This recent development in the field of Artificial Intelligence has revolutionized the way humans and computers interact with each other. The recent model… Read More »Google Research Introduces SPAE: An AutoEncoder For Multimodal Generation With Frozen Large Language Models (LLMs) Tanya Malhotra Artificial Intelligence Category – MarkTechPost

A Research Group From CMU, AI2 and University of Washington Introduces NLPositionality: An AI Framework for Characterizing Design Biases and Quantifying the Positionality of NLP Datasets and Models Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

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​ Researchers’ positionality—their perspectives formed by their own experience, identity, culture, and background—influences their design decisions while developing NLP datasets and models.  Latent design choices and the researcher’s positionality are two sources of design bias in producing datasets and models. This leads to discrepancies in… Read More »A Research Group From CMU, AI2 and University of Washington Introduces NLPositionality: An AI Framework for Characterizing Design Biases and Quantifying the Positionality of NLP Datasets and Models Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

This AI Paper Introduces a Novel Class of Simulation-Free Objectives for Learning Continuous-Time Stochastic Generative Models between General Source and Target Distributions Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

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​ A potent family of generative models that can depict complicated distributions over high-dimensional spaces is score-based generative models (SBGMs), which include diffusion models. The development of a source density, almost always Gaussian, is commonly simulated using SBGMs using a stochastic differential equation (SDE) to… Read More »This AI Paper Introduces a Novel Class of Simulation-Free Objectives for Learning Continuous-Time Stochastic Generative Models between General Source and Target Distributions Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

Training Diffusion Models with Reinforcement Learning The Berkeley Artificial Intelligence Research Blog

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Training Diffusion Models with Reinforcement Learning

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Diffusion models have recently emerged as the de facto standard for generating complex, high-dimensional outputs. You may know them for their ability to produce stunning AI art and hyper-realistic synthetic images, but they have also found success in other applications such as drug design and continuous control. The key idea behind diffusion models is to iteratively transform random noise into a sample, such as an image or protein structure. This is typically motivated as a maximum likelihood estimation problem, where the model is trained to generate samples that match the training data as closely as possible.

However, most use cases of diffusion models are not directly concerned with matching the training data, but instead with a downstream objective. We don’t just want an image that looks like existing images, but one that has a specific type of appearance; we don’t just want a drug molecule that is physically plausible, but one that is as effective as possible. In this post, we show how diffusion models can be trained on these downstream objectives directly using reinforcement learning (RL). To do this, we finetune Stable Diffusion on a variety of objectives, including image compressibility, human-perceived aesthetic quality, and prompt-image alignment. The last of these objectives uses feedback from a large vision-language model to improve the model’s performance on unusual prompts, demonstrating how powerful AI models can be used to improve each other without any humans in the loop.

Read More »Training Diffusion Models with Reinforcement Learning The Berkeley Artificial Intelligence Research Blog

Google AI Introduces ArchGym: An Open-Source Gymnasium for Machine Learning that Connects a Diverse Range of Search Algorithms To Architecture Simulators Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

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​ Research into computer architecture has a long history of producing simulators and tools for assessing and influencing computer system design. For instance, in the late 1990s, the SimpleScalar simulator was developed to let scientists test new microarchitecture concepts. Research in computer architecture has made… Read More »Google AI Introduces ArchGym: An Open-Source Gymnasium for Machine Learning that Connects a Diverse Range of Search Algorithms To Architecture Simulators Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

Top Tools for Machine Learning (ML) Experiment Tracking and Management (2023) Prathamesh Ingle Artificial Intelligence Category – MarkTechPost

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​ One thing is getting good results from a single model-training run when working on a machine learning project. It’s another thing to keep your machine learning trials well-organized and to have a method for drawing reliable conclusions from them. Experiment tracking provides the solution… Read More »Top Tools for Machine Learning (ML) Experiment Tracking and Management (2023) Prathamesh Ingle Artificial Intelligence Category – MarkTechPost