Skip to content Skip to sidebar Skip to footer

News

Introducing Symbolicai: An Integration of Generative Models and Solvers in a Logic-Based Approach to Machine Learning Frameworks

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 like…

Read More

Say Goodbye to Language Prejudice! The Balanced Bilingual Method of CroissantLLM is here for the Long Haul!

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…

Read More

Zyphra Releases BlackMamba as Open Source: An Innovative Structure Merging Mamba SSM and MoE to Reap the Advantages of Both

Processing long linguistic data sequences can be challenging due to computational and memory demands. Traditional transformer models often struggle due to quadratic complexity, a factor that increases as sequence length increases. State Space Models (SSMs) and mixture-of-experts (MoE) models have showed promise by making computational complexity linear. However, memory requirements are still high. Zyphra researchers have…

Read More

UK progresses in AI regulation through consultation on white paper

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,…

Read More

The figures on AI’s energy consumption and carbon emissions could be exaggerated

The Information Technology and Innovation Foundation (ITIF) has published a report challenging the narrative that AI’s energy consumption is dangerously high. The report suggests that such alarmist depictions are frequently misleading and overblown. ITIF argues that concerns like these have arisen with new technologies in the past, citing a 1990s-era Forbes report that claimed half…

Read More

Meta escalates efforts to combat AI-generated deep fake content

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.…

Read More

A New Algorithm for Machine Unlearning in Image-to-Image Generative Models – A Joint Innovation by UT Austin and JPMorgan Chase in an AI Research Paper

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…

Read More

Scientists from EPFL and Meta AI Suggest Chain-of-Abstraction (CoA): A Fresh Approach for LLMs to More Effectively Utilize Tools in Multi-Step Reasoning

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…

Read More

Introducing Time-LLM: A Redesigning Machine Learning Structure for Reutilizing LLMs for Broad Time Series Prediction, Preserving the Core Language Models

In the dynamic domain of data analytics, a quest for robust forecasting models has given rise to TIME-LLM, a groundbreaking framework by institutions like Monash University and Ant Group. TIME-LLM uses Large Language Models (LLMs) traditionally used for natural language processing to predict future trends in time series data. Unlike conventional models requiring extensive domain…

Read More