​[[{“value”:” Large language models (LLMs) are expanding in usage, posing new cybersecurity risks. These risks emerge from their core traits: heightened capability in code generation, heightened deployment for real-time code generation, automated execution within code interpreters, and integration into applications handling untrusted data. This poses… Read More »Meta AI Introduces CyberSecEval 2: A Novel Machine Learning Benchmark to Quantify LLM Security Risks and Capabilities Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The advent of generative artificial intelligence (AI) marks a significant technological leap, enabling the creation of new text, images, videos, and other media by learning from vast datasets. However, this innovative capability brings forth substantial copyright concerns, as it may utilize and repurpose the… Read More »Balancing Innovation and Rights: A Cooperative Game Theory Approach to Copyright Management in Generative AI Technologies Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Charts have become indispensable tools for visualizing data in information dissemination, business decision-making, and academic research. As the volume of multimodal data grows, a critical need arises for automated chart comprehension, which has garnered increasing attention from the research community. Recent advancements in Multimodal… Read More »This AI Paper from China Introduces TinyChart: An Efficient Multimodal Large Language Models MLLMs for Chart Understanding with Only 3B Parameters Mohammad Arshad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) signify a revolutionary leap in numerous application domains, facilitating impressive accomplishments in diverse tasks. Yet, their immense size incurs substantial computational expenses. With billions of parameters, these models demand extensive computational resources for operation. Adapting them to specific downstream tasks… Read More »Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Extracting information quickly and efficiently from websites and digital documents is crucial for businesses, researchers, and developers. They require specific data from various online sources to analyze trends, monitor competitors, or gather insights for strategic decisions. Collecting this data can be time-consuming and prone… Read More »ScrapeGraphAI: A Web Scraping Python Library that Uses LLMs to Create Scraping Pipelines for Websites, Documents, and XML Files Niharika Singh Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Edge artificial intelligence (Edge AI) involves implementing AI algorithms and models on local devices like sensors or IoT devices at the network’s periphery. This setup allows for immediate data processing and analysis, reducing dependence on cloud infrastructure. Consequently, it empowers devices to make intelligent… Read More »Edge AI and It’s Advantages over Traditional AI Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) are advancing at a very fast pace in recent times. However, the lack of adequate data to thoroughly verify particular features of these models is one of the main obstacles. An additional layer of complication arises when evaluating the precision… Read More »This AI Research from Cohere Discusses Model Evaluation Using a Panel of Large Language Models Evaluators (PoLL) Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take as input text instructions and visual egocentric observations and output… Read More »Large Language Models as Generalizable Policies for Embodied Tasks Apple Machine Learning Research

​In this paper, we introduce a novel approach to automatically assign entity labels to images from existing noisy image-text pairs. The approach employees a named entity recognition model to extract entities from text, and uses a CLIP model to select the right entities as the… Read More »MOFI: Learning Image Representation from Noisy Entity Annotated Images Apple Machine Learning Research

​[[{“value”:”This paper was accepted at the How Far Are We from AGI? workshop at ICLR 2024. Vision-Language Models (VLMs) such as GPT-4V have recently demonstrated incredible strides on diverse vision language tasks. We dig into vision-based deductive reasoning, a more sophisticated but less explored realm,… Read More »How Far Are We from Intelligent Visual Deductive Reasoning? Apple Machine Learning Research