The primary goal of AI is to create interactive systems capable of solving diverse problems, including those in medical AI aimed at improving patient outcomes. Large language models (LLMs) have demonstrated significant problem-solving abilities, surpassing human scores on exams like the USMLE. While LLMs can enhance healthcare accessibility, they still face limitations in real-world clinical…
Large language models (LLMs), including GPT-4, LLaMA, and PaLM are pushing the boundaries of artificial intelligence. The inference latency of LLMs plays an important role because of LLMs integration in various applications, ensuring a positive user experience and high service quality. However, the LLM service operates within an AR paradigm, generating one token at a…
Natural language processing (NLP) has advanced significantly thanks to neural networks, with transformer models setting the standard. These models have performed remarkably well across a range of criteria. However, they pose serious problems because of their high memory requirements and high computational expense, particularly for applications that demand long-context work. This persistent problem motivates the…
The evaluation of artificial intelligence models, particularly large language models (LLMs), is a rapidly evolving research field. Researchers are focused on developing more rigorous benchmarks to assess the capabilities of these models across a wide range of complex tasks. This field is essential for advancing AI technology as it provides insights into the strengths &…
Feedback is crucial for student success, especially in large computing classes facing increasing demand. Automated tools, incorporating analysis techniques and testing frameworks, are gaining popularity but often need more helpful suggestions. Recent advancements in large language models (LLMs) show promise in offering rapid, human-like feedback. However, concerns about the accuracy, reliability, and ethical implications of…
Large Language Models (LLMs) like GPT 3.5 and GPT 4 have recently gained a lot of attention in the Artificial Intelligence (AI) community. These models are made to process enormous volumes of data, identify patterns, and produce language that resembles that of a human being in response to cues. One of their primary characteristics is…
Recently, there’s been a surge in the adoption of Integrated Speech and Large Language Models (SLMs), which can understand spoken commands and generate relevant text responses. However, concerns linger regarding their safety and robustness. LLMs, with their extensive capabilities, raise the need to address potential harm and guard against misuse by malicious users. Although developers…
Google has released a new family of vision language models called PaliGemma. PaliGemma can produce text by receiving an image and a text input. The architecture of the PaliGemma (Github) family of vision-language models consists of the image encoder SigLIP-So400m and the text decoder Gemma-2B. A cutting-edge model that can comprehend both text and visuals…
Artificial neural networks (ANNs) show a remarkable pattern when trained on natural data irrespective of exact initialization, dataset, or training objective; models trained on the same data domain converge to similar learned patterns. For example, for different image models, the initial layer weights tend to converge to Gabor filters and color-contrast detectors. Many such features…
In the last year, artificial intelligence’s advancement and appeal reached unprecedented heights. There is also a lot of talk on the moral implications of this technology. Still, deep-learning models can do much more than produce social media posts with made-up words or amusing false photos of celebrities. If you’re a filmmaker looking to streamline your…