Author: Only AI Stuff

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP). LLMs, though lacking a universal definition, are regarded as multi-functional machine learning models capable of handling various NLP tasks effectively. The introduction of transformer architecture marked an important phase in the evolution of these models. LLMs majorly perform four tasks: natural language

Self-supervised learning (SSL) has shown its indispensability in AI by pre-training representations on large, unlabeled datasets, lessening the need for labeled data. Still, a major hindrance remains in SSL, primarily in Joint Embedding (JE) architectures. The challenge lies in appraising the quality of learned representations without relying on downstream tasks or annotated datasets. The evaluation

The rise of Generative AI, particularly large language models (LLMs), has transformed various sectors, enhancing tools that aid in search-based interactions, program synthesis, and chat, among others. LLMs have facilitated connections between different modalities, initiating transformations like text-to-code, text-to-3D, and more, emphasizing the impact of language-based interactions on future human-computer interactions. Despite these advancements, issues

CroissantLLM, an innovative language model offering robust bilingual capabilities in both English and French, is bridging linguistic divides. Developed through a collaborative effort involving multiple prestigious institutions and firms, including Illumina Technology, Unbabel and INESC-ID Lisboa, this initiative represents a dramatic shift from the English-focused bias of traditional models. CroissantLLM was borne out of the

The UK Government has disclosed its stance on AI innovation and regulation in response to its consultations. In March 2023, a white paper was published outlining a “pro-innovation regulatory framework for AI,” followed by a 12-week discussion period with international stakeholders. The main focus areas of the white paper were safety, security, robustness, transparency, explainability,

Meta has committed to increasing transparency around AI-generated content in their platform by labelling such images to distinguish between human-created and synthetic content. Nick Clegg, Meta’s President for Global Affairs, highlighted this in a blog post, stating that as human and synthetic content become increasingly indistinguishable, it becomes crucial to indicate when content is AI-generated.

Microsoft has partnered with media platform Semafor to incorporate AI into journalism. Their goal is to disrupt the news industry further following transformations due to the internet. Co-founded by Ben Smith, ex-BuzzFeed News editor, and Justin Smith, former CEO of Bloomberg Media, Semafor aims to use AI to increase efficiency and broaden perspectives in news

Researchers from The University of Texas at Austin and JPMorgan Chase have created a novel algorithm for machine unlearning in image-to-image (I2I) generative models. In today’s digital era where privacy is of utmost importance, the ability of artificial intelligence (AI) systems to erase specific data upon request is a societal necessity and technical challenge. I2I

Recent progress in large language models (LLMs) has advanced our ability to interpret and implement instructions. However, LLMs still struggle with recall and composition of world knowledge which results in inaccurate responses. A suggested approach to improve reasoning is the integration of auxiliary tools such as search engines or calculators during inference. Existing tool-augmented LLMs

Welcome to our exploration of Chat GPT and its functionality, specifically its application in reading and extracting data from PDF documents. This guide outlines a simple process that allows Chat GPT, a text-based language model by OpenAI, to process PDF files. It’s an ideal tool for students, researchers, or anyone who needs to turn chunks