Large Language Models (LLMs) are valuable in many areas, especially when it comes to generating texts or responding to queries. However, they face a significant challenge - they consume vast amounts of memory for efficient functioning. This memory is utilized to store information on previously encountered words and phrases, which aids the model in generating…
A team of AI researchers has developed a new series of open-source large language models (LLMs) called WizardLM-2, signaling a significant breakthrough in artificial intelligence. Consisting of three models, WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B, each model is designed to handle different complex tasks, aiming to enhance machine learning capabilities.
The introduction of WizardLM-2…
Artificial Intelligence's powerful autoregressive (AR) large language models (LLMs), like the GPT series, have made significant progress in achieving general artificial intelligence (AGI). These models use self-supervised learning to predict the next token in a sequence, allowing them to adapt to a diverse range of unseen tasks through zero-shot and few-shot learning. This adaptability makes…
Climate change is an impending threat to planet earth and the life on it. Luckily, the integration of machine learning (ML) and artificial intelligence (AI) into related fields offers promising solutions to predict and deal with its impacts more efficiently. ML aids in countering climate challenges by enhancing data analysis, forecasting, system efficiency, and driving…
Language model-based machine learning systems, or LLMs, are reaching beyond their previous role in dialogue systems and are now actively participating in real-world applications. There is an increasing belief that many web interactions will be facilitated by systems driven by these LLMs. However, due to the complexities involved, humans are presently needed to verify the…
Large Language Models (LLMs) like those used in Microsoft Bing or Google Search are capable of providing natural language responses to user queries. Traditional search engines often struggle to provide cohesive responses, only offering relevant page results. LLMs improve upon this by compiling results into understandable answers. Yet, issues arise with keeping LLMs current with…
Pretrained language models (LMs) are essential tools in the realm of machine learning, often used for a variety of tasks and domains. But, adapting these models, also known as finetuning, can be expensive and time-consuming, especially for larger models. Traditionally, the solution to this issue has been to use Parameter-efficient finetuning (PEFT) methods such as…
Generative Artificial Intelligence (AI) has seen significant advancement in different fields like art, content creation, and entertainment by leveraging machine learning algorithms. AI programs can now generate various forms of content, such as images, music, text, and videos. This paradigm shift has enabled a novel, realistic, and diverse range of outputs, transforming the creative process.
Concerning…
Developers, project managers, and business owners often face the challenge of swiftly converting conceptual ideas into interactive, tangible prototypes. This process typically requires extensive programming knowledge, even with the aid of tools such as integrated development environments (IDEs) and software development kits (SDKs), and can be time-consuming and excluding for non-technical stakeholders. This lack of…
The rapid improvement of large language models and their role in natural language processing has led to challenges in incorporating less commonly spoken languages. Embedding the majority of artificial intelligence (AI) systems in well-known languages inevitably forces a technological divide across linguistic communities that remains mostly unaddressed.
This paper introduces the SambaLingo system, a novel…
Digital agents, or software designed to streamline interactions between humans and digital platforms, are becoming increasingly popular due to their potential to automate routine tasks. However, a consistent challenge with these agents is their frequent misunderstanding of user commands or inability to adapt to new or unique environments—problems that can lead to errors and inefficiency.…