We thank the reviewer s for the careful feedback and appreciate the time spent reading our paper. Detailed responses are as below: Literature on online L P (OLP) and the contribution of our work: (i) From the algorithmic perspective, our algorithm has a strongly polynomial O(nnz(A)) flop complexity (linear in the number of non - zero entries in A), while the previous OLP algorithms all require solving O(log n) or O(n) of LP s ( increasing to the full size over time) . For example, Agrawal e t al. (2014) solved O(log n) LPs and Kesselheim et a l . As far as we know, the algorithm is the first of its kind and the most efficient OLP algorithm so far. As mentioned by the reviewer, our algorithms share similarity with the network control algorithm in Neely, M.J. (2010), but our analysis extends their analysis (in i.i.

In qualitative research, data transcription is often labor-intensive and time-consuming. To expedite this process, a workflow utilizing artificial intelligence (AI) was developed. This workflow not only enhances transcription speed but also addresses the issue of AI-generated transcripts often lacking compatibility with standard content analysis software. Within this workflow, automatic speech recognition is employed to create initial transcripts from audio recordings, which are then formatted to be compatible with content analysis software such as ATLAS.ti or MAXQDA. Empirical data from a study of 12 interviews suggests that this workflow can reduce transcription time by up to 46.2%. Furthermore, by using widely used standard software, this process is suitable for both students and researchers while also being adaptable to a variety of learning, teaching, and research environments. It is also particularly beneficial for non-native speakers. In addition, the workflow is GDPR-compliant and facilitates local, offline transcript generation, which is crucial when dealing with sensitive data.

The process of annotating data within the legal sector is filled with distinct challenges that differ from other fields, primarily due to the inherent complexities of legal language and documentation. The initial task usually involves selecting an appropriate raw dataset that captures the intricate aspects of legal texts. Following this, extracting text becomes a complicated task, as legal documents often have complex structures, footnotes, references, and unique terminology. The importance of data cleaning is magnified in this context, ensuring that redundant information is eliminated while maintaining crucial legal details and context. Creating comprehensive yet straightforward annotation guidelines is imperative, as these guidelines serve as the road map for maintaining uniformity and addressing the subtle nuances of legal terminology. Another critical aspect is the involvement of legal professionals in the annotation process. Their expertise is valuable in ensuring that the data not only remains contextually accurate but also adheres to prevailing legal standards and interpretations. This paper provides an expanded view of these challenges and aims to offer a foundational understanding and guidance for researchers and professionals engaged in legal data annotation projects. In addition, we provide links to our created and fine-tuned datasets and language models. These resources are outcomes of our discussed projects and solutions to challenges faced while working on them.

Although large language models (LLMs) have gained widespread attention for their performance in English language We investigate the effectiveness of GPT-3.5 and applications, recent studies have shown that they GPT-4, two large language models, as Grammatical can produce good results for other languages. While the Error Correction (GEC) tools for Brazilian amount of data available for training LLMs in languages Portuguese and compare their performance other than English is often more limited, the success of against Microsoft Word and Google Docs. We introduce these models in tasks such as translation, language modeling, a GEC dataset for Brazilian Portuguese and sentiment analysis demonstrates their potential for with four categories: Grammar, Spelling, Internet, improving language processing across a range of different and Fast typing. Our results show that languages.

As the world becomes more reliant on technology, artificial intelligence (AI) is becoming more prevalent in various industries. Writing is no exception, with AI writing assistants becoming increasingly popular among writers, bloggers, and content creators. In this blog, we'll explore five AI writing assistants that can help improve your writing productivity and quality. Grammarly is one of the most popular AI writing assistants available today. It is an all-in-one writing tool that checks your grammar, spelling, punctuation, and sentence structure. Grammarly has a free version and a premium version, with the latter offering more advanced features such as plagiarism detection, genre-specific writing style checks, and a readability score.

You can prompt the chat to give you milestones of a project you're working on, and it will search across apps and documents to present you with answers, offering citations of where it got those answers, for example. You can then continue the conversation and ask it to summarize its answer or infer what risks might be related to the project. Then you can turn that into an action by asking the chat to draft an email to specific colleagues including the summary and risks. If you like the draft, you can click to make the chat open Outlook and transfer all the data to the right fields, so that the subject moves to the subject line and the content is automatically pasted into the body of the email.

Microsoft plans to make OpenAI's generative AI technology such as ChatGPT available to billions of users by integrating it into all of its products, CEO Satya Nadella said this week. That means that ChatGPT's ability to generate text through short prompts is likely on its way to the Office 365 product suite, including Microsoft Word, PowerPoint and Outlook. Using OpenAI's models, Microsoft Word's autocomplete and autocorrect features could carry out more advanced tasks than style and grammar correction and generate longer chunks of text based on a few words. Though the company hasn't announced any specific features yet, users could potentially be able to input prompts and generate complete PowerPoint presentations and emails. These kinds of features could help Microsoft attract younger users. While Microsoft Office 365 has been a de facto standard for millions of enterprises, analysts say the tech giant has fallen behind in attracting those who gravitate toward collaborative-first products like Google Docs and Sheets.

Quickly and easily translate multiple documents with just a few simple steps. With Eden AI, you can start translating your documents in seconds and save valuable time and resources. While Machine Translation refers to the translation of a text into another language using rules, statics or ML technics, Document Translation can be used to translate multiple and complex documents into all supported languages and dialects while maintaining the original document structure and data format. Document Translation API can be used to support multi-lingual websites, chatbot, mobile applications etc. It can translate the document in real-time or as a batch process.

Who owns these fabulous works of art generated by systems and models like OpenAI's DALL-E or Stability.ai's What about blog content created by tools like GoCharlie or Copy.ai? To engage Ruth's services as an attorney, visit their website at GeekLawFirm.com. This interview does not constitute legal advice or create a client-attorney relationship with anyone. The information contained in this interview is presented on an "as is" basis with no guarantee of completeness, accuracy, usefulness, timeliness, or of the results obtained from the use of this information and without warranty of any kind, express or implied, including, but not limited to warranties of performance, merchantability, or fitness for a particular purpose. While we have taken every reasonable precaution to insure that the content is accurate, errors can occur. In all cases you should consult with a qualified professional familiar with your particular situation for advice concerning specific matters. What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video. Please note the following warning disclosure and disclaimer, this interview does not constitute legal advice or create a client attorney relationship with anyone.

Dictation software has come a long way in recent years. It used to be a bit of a gimmick, but today it is changing the way companies do business. Dictation software makes it easier to take notes in meetings, keep track of important conversations, or transcribe documents while on the go. It can also empower persons with disabilities who are unable to type using conventional methods. As the software continues to improve, the number of business applications of this technology is rapidly increasing.