Memory and new controls for ChatGPT

The dependence on external servers for AI applications can pose risks, including data security and reliance on a stable internet connection. While some alternatives exist, most of them still require an internet connection for functioning. This leaves users searching for a solution that combines the power of AI with the comfort of offline usage. Currently,…

Large Language Models (LLMs) are increasingly employed for various domains, with use cases including creative writing, chatbots, and semantic search. Many of these applications are inherently subjective and require generations catering to different demographics, cultural and societal norms, or individual preferences. Through their large-scale training, current language models are exposed to diverse data that allows…

With the growth of large language models, natural language processing has been revolutionized. Many LLMs, like GPT-3.5, LLaMA, and Mixtral, came up last year, which helped tackle diverse language tasks. Even though there are many such LLMs now, open-source models have no reliable models for translation tasks. Thorough research has been done to tackle this…

Powered by getessentialspro.com Welcome Interested in sponsorship opportunities? Join the AI conversation and transform your advertising strategy with AI weekly sponsorship https://ads.aiweekly.co/ In the News AI’s impact on elections is being overblown This year, close to half the world’s population has the opportunity to participate in an election. And according to a steady stream of…

Speech recognition technology has become a cornerstone for various applications, enabling machines to understand and process human speech. The field continuously seeks advancements in algorithms and models to improve accuracy and efficiency in recognizing speech across multiple languages and contexts. The main challenge in speech recognition is developing models that accurately transcribe speech from various…

Evaluating LLMs as versatile agents is crucial for their integration into practical applications. However, existing evaluation frameworks face challenges in benchmarking diverse scenarios, maintaining partially observable environments, and capturing multi-round interactions. Current assessments often focus on a simplified final success rate metric, providing limited insights into the complex processes. The complexity of agent tasks, involving…