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Multimodal large language models (MLLMs) are cutting-edge innovations in artificial intelligence that combine the capabilities of language and vision models to handle complex tasks such as visual question answering & image captioning. These models utilize large-scale pretraining, integrating multiple data modalities to enhance their performance significantly across various applications. The integration of language and vision…
Machine Translation (MT) is a significant field within Natural Language Processing (NLP) that focuses on automatically translating text from one language to another. This technology leverages large language models (LLMs) to understand and generate human languages, facilitating communication across linguistic boundaries. MT aims to bridge global communication gaps by continuously improving translation accuracy supporting multilingual…
Large Language Models (LLMs) are stepping into clinical and medical fields as they grow in capability and versatility. These models have a number of benefits, including the capacity to supplement or even replace the work that doctors typically do. This include providing medical information, keeping track of patient information, and holding consultations with patients. In…
The rapid growth of large language models (LLMs) has catalyzed the development of numerous NLP applications, such as chatbots, writing assistants, and programming aids. However, these applications often require unlimited input length and robust memory capabilities, which current LLMs lack. Extending pre-training text length is impractical, necessitating research into enabling LLMs to handle infinite input…
Recently, Meta has been at the forefront of Open Source LLMs with its Llama series. Following the success of Llama 2, Meta has introduced Llama 3, which promises substantial improvements and new capabilities. Let’s delve into the advancements from Llama 2 to Llama 3, highlighting the key differences and what they mean for the AI…
Managing and deploying Retrieval-Augmented Generation (RAG) systems has recently become a significant challenge, especially when moving from experimental setups to production environments. While tools like Langchain and LlamaIndex offer convenient abstractions for initial development and prototyping, they often need to catch up regarding modularity, scalability, and extensibility required for production. As a result, organizations need…
The popularity of AI is soaring, with businesses across industries harnessing its innovation potential. AWS is pivotal in this trend, offering robust AI solutions and services. AWS courses on AI topics provide invaluable knowledge and skills, empowering individuals to leverage AI effectively enabling them to stay ahead in today’s competitive landscape. This article lists the…
Local image feature matching techniques help identify fine-grained visual similarities between two images. Although there is a lot of progress in this area, these advancements don’t account for the generalization capability of image-matching models. Many models that focus on specific visual domains with lots of training data, show lower performance on out-of-domain data compared to…
The rapid growth of AI and complex neural networks drives the need for efficient hardware that suits power and resource constraints. In-memory computing (IMC) is a promising solution for developing various IMC devices and architectures. Designing and deploying these systems requires a comprehensive hardware-software co-design toolchain that optimizes across devices, circuits, and algorithms. The Internet…
Financial analysis has always been crucial for interpreting market trends, predicting economic outcomes, and providing investment strategies. This field, traditionally rooted in data, has increasingly turned to artificial intelligence (AI) and algorithmic methods to handle the vast and complex data generated daily. AI’s role in finance has grown significantly, automating tasks once performed by human…