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Deploying large language models (LLMs) on resource-constrained devices presents significant challenges due to their extensive parameters and reliance on dense multiplication operations. This results in high memory demands and latency bottlenecks, hindering their practical application in real-world scenarios. For instance, models like GPT-3 require immense computational resources, making them unsuitable for many edge and cloud…
Developers spend much of their time and effort trying to make attractive products and websites. Users’ expectations have never been greater with all the available products and websites. Meet CodeParrot AI, an AI-powered startup with super cool AI tools designers and developers can use to make coding easier. The main purpose of this application is…
Large Generative Models (LGMs) like GPT, Stable Diffusion, Sora, and Suno have recently made remarkable strides in creating creative and meaningful content, greatly boosting the efficiency of real-world applications. Unlike earlier models like Bert/Bart in Natural Language Processing (NLP) and Unet in Image Segmentation, which were trained on small datasets from specific areas and for…
Generative AI has made remarkable progress in revolutionizing fields like image and video generation, driven by innovative algorithms, architectures, and data. However, the rapid proliferation of generative models has highlighted a critical gap: the absence of trustworthy evaluation metrics. Current automatic assessments such as FID, CLIP, and FVD often fail to capture the nuanced quality…
Gboard, Google’s mobile keyboard app, operates on the principle of statistical decoding. This approach is necessary due to the inherent inaccuracy of touch input, often referred to as the ‘fat finger’ problem, on small screens. Studies have shown that without decoding, the error rate for each letter can be as high as 8 to 9…
In recent years, the integration of ML and AI into biomedicine has become increasingly pivotal, particularly in digital health. The explosion of high-throughput technologies, such as genome-wide sequencing, extensive libraries of medical images, and large-scale drug perturbation screens, has resulted in vast and complex biomedical data. This multi-omics data offers a wealth of information that…
Document understanding is a critical field that focuses on converting documents into meaningful information. This involves reading and interpreting text and understanding the layout, non-textual elements, and text style. The ability to comprehend spatial arrangement, visual clues, and textual semantics is essential for accurately extracting and interpreting information from documents. This field has gained significant…
Predicting the scaling behavior of frontier AI systems like GPT-4, Claude, and Gemini is essential for understanding their potential and making decisions about their development and use. However, it is difficult to predict how these systems will perform on specific tasks as they scale up, despite the well-established relation between parameters, data, compute, and pretraining…
Analogical reasoning, fundamental to human abstraction and creative thinking, enables understanding relationships between objects. This capability is distinct from semantic and procedural knowledge acquisition, which contemporary connectionist approaches like deep neural networks (DNNs) typically handle. However, these techniques often need help to extract relational abstract rules from limited samples. Recent advancements in machine learning have…
Large Language Models (LLMs) have become increasingly prominent in natural language processing because they can perform a wide range of tasks with high accuracy. These models require fine-tuning to adapt to specific tasks, which typically involves adjusting many parameters, thereby consuming substantial computational resources and memory. The fine-tuning process of LLMs presents a significant challenge…