Diddy&#x27;s former assistant Suzi Siegel, who worked for the mogul from 2008 to 2009, told CNN that she &#x27;observed nothing that would lead me to believe&#x27; he was abusive, but that she is not &quot;surprised&quot; by the new video of him assaulting Cassie.

Diddy's Former Assistant Says 'Not One Cell in My Body' Was 'Surprised' by New Video

Google Home and Google Assistant were Google’s answer to growing interest in voice search and a real threat from Amazon’s Alexa and Echo smart speakers, but Google is seemingly leaving them both behind for generative AI with Gemini.

8 Years Ago, Google Beat Alexa. Then It Just Let the Assistant Waste Away

“He just didn’t see your humanity when he looked at you,“ she told CNN.

Diddy’s Former Assistant Suzi Siegi Says Her ‘Intuition’ Told Her He Was Violent

&#x27;There was not one cell in my body that was surprised,&#x27; Suzi Siegel admitted after the emergence of the harrowing video depicting the 54-year-old rapper attacking his ex-girlfriend.

Diddy's former assistant admits she was NOT 'surprised' by disturbing footage of him violently attacking his ex Cassie Ventura: 'I knew that it was something that he could be capable of' | Daily Mail Online

Sean “Diddy” Combs’ former assistant said she was not in surprised when video surfaced this week showing the rapper violently beating his ex-girlfriend Cassie Ventura in a hotel.

Sean 'Diddy' Combs's former assistant says she was not surprised by video showing rapper beating ex Cassie Ventura in hotel hallway

Google and OpenAI unveiled new tools to bring ‘intelligent systems’ a step closer. Will this be a milestone for generative AI?

The race for an AI-powered personal assistant

GREENEVILLE, Tenn. (WJHL) — A lawsuit against the Town of Greeneville filed by a former assistant police chief was dismissed on Wednesday. Michael Crum, a former assistant police chief at the Greeneville Police Department (GPD), filed the lawsuit after he was terminated in December 2021 following ...

Former assistant police chief’s lawsuit against Greeneville dismissed

It’s been a week of A.I. vertigo — the feeling that we’ve been dragged five years into the future and aren’t sure what to do about it.

OpenAI’s Flirty New Assistant, Google Guts the Web and We Play HatGPT - The New York Times

Keante Harris, a 45-year-old assistant principal, is among four men arrested in connection with a triple murder that occurred in January 2013.

Alabama middle-school assistant principal arrested in 2013 cold-case triple murder in Georgia | Fox News

An assistant principal at John Adams High School in Ozone Park was arrested Wednesday and charged with endangering the welfare of a child and third-degree sexual abuse, according to police.

Ozone Park assistant principal charged with sex abuse | | qchron.com

Assystant

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How to Manage Tasks (With Your Assistant) Using Notion: A Simple Guide

Crypto Investment Assistant: How I Built a Chatbot That Simplifies Portfolio Management With Coze

The Best AI Assistants for Frontend Developers That Will Change the Way You Code

Reimagining eCommerce with Assistants - OpenAI GPT-4o Model plus Sitecore OrderCloud

If you are searching for the key to productivity and workflow throughout the day to streamline your notes and documents, AIDE AI is a smart personal assistant powered by AI designed for your Mac. The discreet AI Copilot App can help with anything you do on your Mac, including note-taking, transcribing in any language, and organizing meetings; it costs $19.99 a month but saves over $1,000 worth of time and energy. (Costs $9.99/mo if paid yearly). As a teaser to the Apple Intelligence that’s the next big thing for Mac, AideAI keeps track of everything you need by recording your screen, mic, and computer audio — and indexing every word that pops up. Whether you are panicking to locate a Zoom slide, a section of a virtual seminar, or a webpage you have lost — AideAI can find it, just like a time machine. Founded by brainchild and serial entrepreneur Alexey Skutin, Ph.D. and IT Expert specializing in enterprise, security, and SaaS/PaaS software, he has scaled and distributed teams, creating complex high-load systems, shipping innovative products, and generating millions of dollars in revenue. His new project, AideAI, brings the power of Apple Intelligence to your Mac, offering innovative features and seamless integration. Here are three main things that distinguish this app from its competitors: Recall function - search and recall everything you've seen or done on your PC, like a personal historian. For Mac, only Rewind has a similar function, but Rewind is no longer developing its product, and AIDE AI is now the only project for MacOS. On the Windows platform, there is MS Copilot with the Recall function, and on MacOS, there is AideAI.


Working with the current screen - AIDE AI sees what you are doing on the screen at the moment and gives prompts, and is immersed in the current context responses. The main competitor is OpenAI for Mac, but AIDE AI has gone far ahead in terms of convenience and functionality.


Meeting assistant in "incognito" mode - many great AI meeting assistants have appeared over the past year, but almost all of them require connecting to the meeting as an AI bot. AIDE AI is one of the few assistants that listen to your microphone and speaker without direct integration with meeting applications. Moreover, this function supports 100+ languages ​​and is completely free. Whether you need it as a marketing expert to improve your sales pitch emails, a fitness trainer to develop effective workout plans, or even a student to record and edit lecture materials, AIDE AI has you covered. Get done in less time, optimizing efficiency and 10xing your productivity. For more information, please visit www.aideai.app Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. If you are searching for the key to productivity and workflow throughout the day to streamline your notes and documents, AIDE AI is a smart personal assistant powered by AI designed for your Mac. AIDE AI AIDE AI The discreet AI Copilot App can help with anything you do on your Mac, including note-taking, transcribing in any language, and organizing meetings; it costs $19.99 a month but saves over $1,000 worth of time and energy. (Costs $9.99/mo if paid yearly). As a teaser to the Apple Intelligence that’s the next big thing for Mac, AideAI keeps track of everything you need by recording your screen, mic, and computer audio — and indexing every word that pops up. Whether you are panicking to locate a Zoom slide, a section of a virtual seminar, or a webpage you have lost — AideAI can find it, just like a time machine. Founded by brainchild and serial entrepreneur Alexey Skutin, Ph.D. and IT Expert specializing in enterprise, security, and SaaS/PaaS software, he has scaled and distributed teams, creating complex high-load systems, shipping innovative products, and generating millions of dollars in revenue. His new project, AideAI, brings the power of Apple Intelligence to your Mac, offering innovative features and seamless integration. Here are three main things that distinguish this app from its competitors: Recall function - search and recall everything you've seen or done on your PC, like a personal historian. For Mac, only Rewind has a similar function, but Rewind is no longer developing its product, and AIDE AI is now the only project for MacOS. On the Windows platform, there is MS Copilot with the Recall function, and on MacOS, there is AideAI. Working with the current screen - AIDE AI sees what you are doing on the screen at the moment and gives prompts, and is immersed in the current context responses. The main competitor is OpenAI for Mac, but AIDE AI has gone far ahead in terms of convenience and functionality. Meeting assistant in "incognito" mode - many great AI meeting assistants have appeared over the past year, but almost all of them require connecting to the meeting as an AI bot. AIDE AI is one of the few assistants that listen to your microphone and speaker without direct integration with meeting applications. Moreover, this function supports 100+ languages ​​and is completely free. Recall function - search and recall everything you've seen or done on your PC, like a personal historian. For Mac, only Rewind has a similar function, but Rewind is no longer developing its product, and AIDE AI is now the only project for MacOS. On the Windows platform, there is MS Copilot with the Recall function, and on MacOS, there is AideAI. Recall function - search and recall everything you've seen or done on your PC, like a personal historian. For Mac, only Rewind has a similar function, but Rewind is no longer developing its product, and AIDE AI is now the only project for MacOS. On the Windows platform, there is MS Copilot with the Recall function, and on MacOS, there is AideAI. Recall function Working with the current screen - AIDE AI sees what you are doing on the screen at the moment and gives prompts, and is immersed in the current context responses. The main competitor is OpenAI for Mac, but AIDE AI has gone far ahead in terms of convenience and functionality. Working with the current screen - AIDE AI sees what you are doing on the screen at the moment and gives prompts, and is immersed in the current context responses. The main competitor is OpenAI for Mac, but AIDE AI has gone far ahead in terms of convenience and functionality. Working with the current screen Meeting assistant in "incognito" mode - many great AI meeting assistants have appeared over the past year, but almost all of them require connecting to the meeting as an AI bot. AIDE AI is one of the few assistants that listen to your microphone and speaker without direct integration with meeting applications. Moreover, this function supports 100+ languages ​​and is completely free. Meeting assistant in "incognito" mode - many great AI meeting assistants have appeared over the past year, but almost all of them require connecting to the meeting as an AI bot. AIDE AI is one of the few assistants that listen to your microphone and speaker without direct integration with meeting applications. Moreover, this function supports 100+ languages ​​and is completely free. Meeting assistant in "incognito" mode Whether you need it as a marketing expert to improve your sales pitch emails, a fitness trainer to develop effective workout plans, or even a student to record and edit lecture materials, AIDE AI has you covered. Get done in less time, optimizing efficiency and 10xing your productivity. For more information, please visit www.aideai.app www.aideai.app www.aideai.app Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. Vested Interest Disclosure: business blogging program

This Nifty Productivity App Called Aide is My Own Personal Mac Assistant 

এই নিফটি প্রোডাক্টিভিটি অ্যাপ নামক এড হল আমার নিজের ব্যক্তিগত ম্যাক সহকারী 

Diese raffinierte Produktivitäts-App namens Aide ist mein persönlicher Mac-Assistent

Esta ingeniosa aplicación de productividad llamada Aide es mi propio asistente personal para Mac

Cette application de productivité géniale appelée Aide est mon assistant personnel pour Mac

यह निफ्टी उत्पादकता ऐप जिसका नाम ऐड है, मेरा अपना व्यक्तिगत मैक असिस्टेंट है

この気の利いた生産性向上アプリ「Aide」は、私専用のMacアシスタントです

Aide라는 멋진 생산성 앱은 내 개인 Mac 도우미입니다.

Este aplicativo de produtividade bacana chamado Aide é meu assistente pessoal para Mac

Это отличное приложение для повышения производительности под названием Aide — мой личный помощник на Mac

Aide Adındaki Bu Şık Üretkenlik Uygulaması Benim Kişisel Mac Asistanım

Ứng dụng năng suất tuyệt vời này có tên là Aide chính là trợ lý cá nhân của tôi trên máy Mac

这款名为 Aide 的出色生产力应用程序是我个人的 Mac 助手

Hiring virtual assistants is a growing trend in today’s digital age. They offer businesses a flexible, cost-effective solution to managing tasks and streamlining operations. Effective communication with virtual assistants enhances productivity and ensures successful collaboration. By leveraging secure communication practices, you protect sensitive information and foster a more efficient working relationship. Clear, consistent dialogue helps align expectations, minimizes misunderstandings, and allows virtual assistants to perform their duties effectively. These benefits contribute to the overall success of your projects and business goals. 1. Use secure file-sharing solutions. Using insecure file-sharing methods poses significant risks, as cybercriminals can easily intercept and access sensitive data. In 2024, there was a 71% increase in cyberattacks leveraging stolen or compromised credentials, highlighting the urgency of secure file-sharing practices. Always use encrypted file-sharing solutions to protect your data. Set strict access permissions, regularly update passwords and implement two-factor authentication to add an extra layer of security. These best practices ensure your files remain secure and only accessible to authorized personnel. 2. Establish clear data access policies Defining what data your virtual assistants can access is crucial for maintaining security and protecting sensitive information. By setting up precise access controls and permissions, you ensure these assistants only interact with the data necessary for their tasks. Implementing role-based access control is an effective strategy to limit data exposure because it assigns permissions based on each assistant's specific roles and responsibilities. This way, you minimize the risk of unauthorized access and potential data breaches. Regularly reviewing and updating these permissions ensures your data remains secure and your assistants can work efficiently within the boundaries of their roles. 3. Utilize secure communication channels Encrypted messaging apps and secure email services are vital in today’s digital age. Scammers frequently exploit digital spaces, which leads to trusting buyers losing hundreds of thousands of dollars over the past year alone. These tools protect against data breaches and unauthorized access by ensuring your messages are encrypted and secure from prying eyes. Using platforms like Signal for messaging or ProtonMail for email adds an essential layer of security to your interactions. It also safeguards your sensitive information from potential cyberthreats. This proactive approach helps you maintain privacy and keeps communications confidential and secure. 4. Regularly update and patch software Outdated software poses significant risks, as it often contains vulnerabilities cybercriminals can exploit, leading to data breaches and unauthorized access. Keeping your communication tools and devices up-to-date is crucial for maintaining security because updates and patches fix these vulnerabilities and enhance overall protection. Enabling automatic updates in your device settings and using reliable patch management software can automate updates and patches. This step minimizes the risk of security gaps and lets you focus on your work without worrying about potential cyberthreats. 5. Provide cybersecurity training for virtual assistants Educating your virtual assistants on cybersecurity best practices is crucial, as insider threats have become a major concern. In fact, 30% of chief information security officers cited them as a significant cybersecurity threat in 2023. Training your virtual assistants can significantly reduce the risk of accidental data breaches and unauthorized access. Key topics to cover in training include recognizing phishing attempts, managing strong passwords, securely handling sensitive information and understanding the importance of regular software updates. This education empowers virtual assistants to act as a first line of defense. It ensures your business remains secure and protected from internal and external threats. 6. Implement two-factor authentication Enhancing security for accessing shared tools and documents is essential, especially considering data compromises affected over 350 million individuals in 2023. Implementing strong security measures — such as multifactor authentication and strict access controls — adds an extra layer of protection to prevent unauthorized access. This practice safeguards your sensitive information and ensures that only authorized personnel can access critical tools and documents. Reviewing and updating these security measures helps maintain a robust defense against potential breaches, providing peace of mind that your data remains secure. 7. Monitor and audit communication activities Monitoring communication channels for suspicious activities is vital to safeguarding your business against potential security threats. Tools and methods like audit logs and real-time monitoring software allow you to track and analyze communication logs effectively. Reviewing these logs can quickly identify and respond to unusual or unauthorized activities. Best practices include having a clear incident response plan, training your team on handling breaches and promptly addressing detected issues to mitigate damage. These efforts ensure you maintain a secure communication environment and protect sensitive data. Building strong relationships through continuous communication improvement Continuous improvement in communication is crucial for building a solid working relationship with your virtual assistants. It ensures clarity and efficiency in your interactions. Regularly seeking feedback and adjusting communication strategies can foster trust and enhance collaboration, driving better results. Hiring virtual assistants is a growing trend in today’s digital age. They offer businesses a flexible, cost-effective solution to managing tasks and streamlining operations. Effective communication with virtual assistants enhances productivity and ensures successful collaboration. By leveraging secure communication practices, you protect sensitive information and foster a more efficient working relationship. Clear, consistent dialogue helps align expectations, minimizes misunderstandings, and allows virtual assistants to perform their duties effectively. These benefits contribute to the overall success of your projects and business goals. 1. Use secure file-sharing solutions. 1. Use secure file-sharing solutions. Using insecure file-sharing methods poses significant risks, as cybercriminals can easily intercept and access sensitive data. In 2024, there was a 71% increase in cyberattacks leveraging stolen or compromised credentials, highlighting the urgency of secure file-sharing practices. a 71% increase in cyberattacks a 71% increase in cyberattacks Always use encrypted file-sharing solutions to protect your data. Set strict access permissions, regularly update passwords and implement two-factor authentication to add an extra layer of security. These best practices ensure your files remain secure and only accessible to authorized personnel. 2. Establish clear data access policies 2. Establish clear data access policies Defining what data your virtual assistants can access is crucial for maintaining security and protecting sensitive information. By setting up precise access controls and permissions, you ensure these assistants only interact with the data necessary for their tasks. Implementing role-based access control is an effective strategy to limit data exposure because it assigns permissions based on each assistant's specific roles and responsibilities. This way, you minimize the risk of unauthorized access and potential data breaches. Regularly reviewing and updating these permissions ensures your data remains secure and your assistants can work efficiently within the boundaries of their roles. 3. Utilize secure communication channels 3. Utilize secure communication channels Encrypted messaging apps and secure email services are vital in today’s digital age. Scammers frequently exploit digital spaces, which leads to trusting buyers losing hundreds of thousands of dollars over the past year alone. These tools protect against data breaches and unauthorized access by ensuring your messages are encrypted and secure from prying eyes. losing hundreds of thousands of dollars losing hundreds of thousands of dollars Using platforms like Signal for messaging or ProtonMail for email adds an essential layer of security to your interactions. It also safeguards your sensitive information from potential cyberthreats. This proactive approach helps you maintain privacy and keeps communications confidential and secure. 4. Regularly update and patch software 4. Regularly update and patch software Outdated software poses significant risks, as it often contains vulnerabilities cybercriminals can exploit, leading to data breaches and unauthorized access. Keeping your communication tools and devices up-to-date is crucial for maintaining security because updates and patches fix these vulnerabilities and enhance overall protection. Enabling automatic updates in your device settings and using reliable patch management software can automate updates and patches. This step minimizes the risk of security gaps and lets you focus on your work without worrying about potential cyberthreats. 5. Provide cybersecurity training for virtual assistants 5. Provide cybersecurity training for virtual assistants Educating your virtual assistants on cybersecurity best practices is crucial, as insider threats have become a major concern. In fact, 30% of chief information security officers cited them as a significant cybersecurity threat in 2023. Training your virtual assistants can significantly reduce the risk of accidental data breaches and unauthorized access. 30% of chief information security officers 30% of chief information security officers Key topics to cover in training include recognizing phishing attempts, managing strong passwords, securely handling sensitive information and understanding the importance of regular software updates. This education empowers virtual assistants to act as a first line of defense. It ensures your business remains secure and protected from internal and external threats. 6. Implement two-factor authentication 6. Implement two-factor authentication Enhancing security for accessing shared tools and documents is essential, especially considering data compromises affected over 350 million individuals in 2023. Implementing strong security measures — such as multifactor authentication and strict access controls — adds an extra layer of protection to prevent unauthorized access. affected over 350 million individuals affected over 350 million individuals This practice safeguards your sensitive information and ensures that only authorized personnel can access critical tools and documents. Reviewing and updating these security measures helps maintain a robust defense against potential breaches, providing peace of mind that your data remains secure. 7. Monitor and audit communication activities 7. Monitor and audit communication activities Monitoring communication channels for suspicious activities is vital to safeguarding your business against potential security threats. Tools and methods like audit logs and real-time monitoring software allow you to track and analyze communication logs effectively. Reviewing these logs can quickly identify and respond to unusual or unauthorized activities. Best practices include having a clear incident response plan, training your team on handling breaches and promptly addressing detected issues to mitigate damage. These efforts ensure you maintain a secure communication environment and protect sensitive data. Building strong relationships through continuous communication improvement Building strong relationships through continuous communication improvement Continuous improvement in communication is crucial for building a solid working relationship with your virtual assistants. It ensures clarity and efficiency in your interactions. Regularly seeking feedback and adjusting communication strategies can foster trust and enhance collaboration, driving better results.

Safe and Secure: Best Practices for Communicating With Virtual Assistants

How I Built a Data Analysis Assistant with BigQuery and Langchain

আমি কিভাবে BigQuery এবং Langchain এর সাথে একটি ডেটা বিশ্লেষণ সহকারী তৈরি করেছি

Wie ich mit BigQuery und Langchain einen Datenanalyse-Assistenten erstellt habe

Cómo creé un asistente de análisis de datos con BigQuery y Langchain

Comment j'ai créé un assistant d'analyse de données avec BigQuery et Langchain

मैंने BigQuery और Langchain के साथ डेटा विश्लेषण सहायक कैसे बनाया

BigQuery と Langchain を使ってデータ分析アシスタントを構築する方法

BigQuery 및 Langchain을 사용하여 데이터 분석 도우미를 구축한 방법

Como construí um assistente de análise de dados com BigQuery e Langchain

Как я создал помощник по анализу данных с помощью BigQuery и Langchain

BigQuery ve Langchain ile Nasıl Veri Analizi Asistanı Oluşturdum?

Cách tôi xây dựng Trợ lý phân tích dữ liệu với BigQuery và Langchain

如何使用 BigQuery 和 Langchain 构建数据分析助手

Hey everyone, In today’s world, AI-generated content is everywhere. It’s becoming increasingly common to see articles, blog posts, and even social media updates created by AI. This has raised a lot of questions and concerns, especially among original content creators who pour their heart and soul into each piece they craft. How can these creators continue to stand out? Or how can AI enhance their creative power without compromising their unique voice and authenticity? I had a great chat recently with one of Saner.ai’s early users, and that really got me thinking about these questions. Let me share that with you. The Chat with a Content Creator So, here’s the story. This user, let’s call him Alex, is a full-time worker and wants to build his personal brand on LinkedIn. He told me that he had been searching for a solution to help him store and develop his ideas. He wanted something more than just a digital notepad or a traditional content management tool. What Alex needed was a companion in his creative process — something that could help him refine and evolve his thoughts and ideas popping up in his life into valuable content. Interestingly, Alex mentioned that he doesn’t want AI content generators. You know, those tools that churn out articles in mass but often lack authenticity and a personal touch. Alex feels that these tools, while efficient, can’t capture his unique voice and style. And I totally get that. Authenticity is key, especially when you’re building a personal brand in this AI world. What Alex was looking for was an AI assistant, not just a generator. He needed a tool that could help him store ideas quickly, recommend ideas based on information he saved, and brainstorm ideas together. That’s how Alex came to Saner.ai. He hopes it will allow him to achieve this outcome. AI in Content Creation This conversation with Alex really got me reflecting on the broader implications of AI in the content creation space. As AI technology advances, it’s becoming increasingly embedded in various aspects of our lives, including the way we create and consume content. However, there’s a significant distinction between using AI to generate a flood of generic, mass-produced content and using AI to support the creation of original, thoughtful content. Firstly, let’s talk about the increase in AI-generated content. There’s no denying the efficiency of AI when it comes to producing large volumes of text quickly. For businesses looking to populate their blogs, social media, and other platforms with regular updates, AI content generators, which can create hundreds of content for SEO purposes in days instead of months, is surely tempting. However, this comes with a downside. The content produced by these generators often lacks the nuance, personality, and authenticity that human writers bring to the table. It may feel impersonal and disconnected. If your purpose is to establish an emotional connection with your audience, then this solution can fall short in a world that’s already saturated with information. On the other hand, using AI to support original content creation can be beneficial. This approach is about using AI to curate, ask, brainstorm, and correct grammar. Imagine having an AI assistant who helps you brainstorm ideas, organize your thoughts, and provide tailored suggestions to improve your writing. This doesn’t just save time — it enhances the creative process, allowing you to focus more on developing your unique ideas and less on the manual work of organizing and maintaining a database. Furthermore, AI can be a powerful tool for overcoming common creative challenges. For instance, writer’s block is something every writer faces at some point. An AI assistant can help break through this barrier by offering new perspectives and sparking inspiration. It can suggest new angles, provide relevant knowledge or even pose questions that help you think differently about your topic. This kind of support is invaluable, especially for creators who are striving to maintain a consistent output of high-quality content. What makes an ideal AI Tool for Original Content Creators? So with that, here is what I envision for an Ideal AI Content Creation assistant. Idea Development and Storage: A method and space where creators can quickly capture, store their ideas, and develop them over time. This isn’t just about keeping notes; it’s about creating an environment where ideas can grow and evolve.
Chat with your knowledge base: Just ask to get insights out of what you saved in real-time. Ideas, thoughts, articles.. everything
Curated Assistance: It suggests relevant knowledge to the content you are creating, making use of the knowledge you saved
Brainstorming with multiple AIs: You have the ability to converse with multiple AI models in the same space where you are creating your content. It improves the focus and range of ideas. Hearing from users like Alex reinforces my belief in the importance of these features. It isn’t about generating content for the sake of quantity; it’s about enhancing the quality of the content that creators are passionate about. What do you think about this approach? Have you had any experiences with a great AI tool in content creation? Hey everyone, In today’s world, AI-generated content is everywhere. It’s becoming increasingly common to see articles, blog posts, and even social media updates created by AI. This has raised a lot of questions and concerns, especially among original content creators who pour their heart and soul into each piece they craft. How can these creators continue to stand out? Or how can AI enhance their creative power without compromising their unique voice and authenticity? I had a great chat recently with one of Saner.ai’s early users, and that really got me thinking about these questions. Let me share that with you. The Chat with a Content Creator So, here’s the story. This user, let’s call him Alex, is a full-time worker and wants to build his personal brand on LinkedIn. He told me that he had been searching for a solution to help him store and develop his ideas. He wanted something more than just a digital notepad or a traditional content management tool. What Alex needed was a companion in his creative process — something that could help him refine and evolve his thoughts and ideas popping up in his life into valuable content. Interestingly, Alex mentioned that he doesn’t want AI content generators. You know, those tools that churn out articles in mass but often lack authenticity and a personal touch. Alex feels that these tools, while efficient, can’t capture his unique voice and style. And I totally get that. Authenticity is key, especially when you’re building a personal brand in this AI world. What Alex was looking for was an AI assistant, not just a generator. He needed a tool that could help him store ideas quickly, recommend ideas based on information he saved, and brainstorm ideas together. That’s how Alex came to Saner.ai. He hopes it will allow him to achieve this outcome. AI in Content Creation This conversation with Alex really got me reflecting on the broader implications of AI in the content creation space. As AI technology advances, it’s becoming increasingly embedded in various aspects of our lives, including the way we create and consume content. However, there’s a significant distinction between using AI to generate a flood of generic, mass-produced content and using AI to support the creation of original, thoughtful content. Firstly, let’s talk about the increase in AI-generated content. There’s no denying the efficiency of AI when it comes to producing large volumes of text quickly. For businesses looking to populate their blogs, social media, and other platforms with regular updates, AI content generators, which can create hundreds of content for SEO purposes in days instead of months, is surely tempting. However, this comes with a downside. The content produced by these generators often lacks the nuance, personality, and authenticity that human writers bring to the table. It may feel impersonal and disconnected. If your purpose is to establish an emotional connection with your audience, then this solution can fall short in a world that’s already saturated with information. On the other hand, using AI to support original content creation can be beneficial. This approach is about using AI to curate, ask, brainstorm, and correct grammar. Imagine having an AI assistant who helps you brainstorm ideas, organize your thoughts, and provide tailored suggestions to improve your writing. This doesn’t just save time — it enhances the creative process, allowing you to focus more on developing your unique ideas and less on the manual work of organizing and maintaining a database. Furthermore, AI can be a powerful tool for overcoming common creative challenges. For instance, writer’s block is something every writer faces at some point. An AI assistant can help break through this barrier by offering new perspectives and sparking inspiration. It can suggest new angles, provide relevant knowledge or even pose questions that help you think differently about your topic. This kind of support is invaluable, especially for creators who are striving to maintain a consistent output of high-quality content. What makes an ideal AI Tool for Original Content Creators? So with that, here is what I envision for an Ideal AI Content Creation assistant. Idea Development and Storage: A method and space where creators can quickly capture, store their ideas, and develop them over time. This isn’t just about keeping notes; it’s about creating an environment where ideas can grow and evolve. Chat with your knowledge base: Just ask to get insights out of what you saved in real-time. Ideas, thoughts, articles.. everything Curated Assistance: It suggests relevant knowledge to the content you are creating, making use of the knowledge you saved Brainstorming with multiple AIs: You have the ability to converse with multiple AI models in the same space where you are creating your content. It improves the focus and range of ideas. Idea Development and Storage: A method and space where creators can quickly capture, store their ideas, and develop them over time. This isn’t just about keeping notes; it’s about creating an environment where ideas can grow and evolve. Idea Development and Storage: Idea Development and Storage: Chat with your knowledge base: Just ask to get insights out of what you saved in real-time. Ideas, thoughts, articles.. everything Chat with your knowledge base : Chat with your knowledge base Curated Assistance: It suggests relevant knowledge to the content you are creating, making use of the knowledge you saved Curated Assistance : Curated Assistance Brainstorming with multiple AIs: You have the ability to converse with multiple AI models in the same space where you are creating your content. It improves the focus and range of ideas. Brainstorming with multiple AIs : Brainstorming with multiple AIs Hearing from users like Alex reinforces my belief in the importance of these features. It isn’t about generating content for the sake of quantity; it’s about enhancing the quality of the content that creators are passionate about. What do you think about this approach? Have you had any experiences with a great AI tool in content creation?

An AI Assistant for Original Content Creators vs. Mass AI Article Generators

Authors:
(1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {nifarn@microsoft.com};
(2) Richard Shin, Microsoft Corporation {eush@microsoft.com}. Table of Links Abstract and Intro Dataset Design Evaluation Methodology Experiments and Analysis Related Work Conclusion, Reproducibility, and References A. Complete list of tools B. Scenario Prompt C. Unrealistic Queries D. Nuances comparing prior work 4 EXPERIMENTS AND ANALYSIS 4.1 EXPERIMENTS We evaluate GPT-3.5 (gpt-3.5-turbo-0613) and GPT-4 (gpt-4-0613) on ToolTalk using the functions functionality as part of OpenAI’s Chat completions API (OpenAI). This API takes as input an optional system message, a history of messages between a user and an assistant, tool documentation, and any previous tool invocations and their responses, and produces as output either a tool invocation or an assistant message. In the system message, we include the conversation’s location, timestamp, and (if present) username. We supply documentation for all 28 tools at once to simulate a user with all 7 plugins enabled. We then simulate and evaluate all conversations in the easy and hard subsets of ToolTalk, following Algorithms 1 and 2. Table 1 shows the results. We get success rates of 85.7% and 92.8% for GPT-3.5 and GPT-4 on the easy version of ToolTalk, and success rates of 26.0% and 50.0% on the hard version. GPT-4 outperforms GPT-3.5, but still achieves similar incorrect action rates. From precision, we can see that GPT-4 is also more efficient than GPT-3.5. However, performance for both models are low, showing the difficulty of tool usage in conversation settings. 4.2 ANALYSIS We analyze the conversations that either GPT-4 or GPT-3.5 fail on. We notice that for both LLMs, there are three major reasons that they can fail. First, the model may predict a tool call prematurely on a turn before a user has provided the necessary information. Second, the model may exhibit poor planning, resulting in omitting or using the wrong tools. Third, it may have picked the correct tool to use, but invoked it with incorrect or missing arguments, failing to follow the tool’s function signature described in the documentation. GPT-3.5 is more susceptible to these errors, but they manifest as well for GPT-4. Premature tool calls. This usually occurs when the user has a clear intent, e.g. “I want to create an event”, but has yet to provide the necessary information to provide as arguments. It then manifests as hallucinating plausible values to supply as arguments. This is harmless when predicting reference tools but is a direct contribution to failure when predicting action tools. Concerningly, even when the hallucinated arguments will result in execution errors, the model will persist in hallucinating more arguments. Despite these issues, both GPT-3.5 and GPT-4 will generally choose the correct tools to accomplish the intent. Faulty reasoning. Ultimately, premature tool calls could be mostly explained by faulty reasoning, where the LLM fails to reflect that it does not have all the information it needs to accomplish a task and needs to ask the user to provide more clarification. Similarly, omission or the usage of wrong tools can also be explained by faulty reasoning skills; rather than reflecting and realizing it needs to ask the user to provide more clarification, the LLM fails to realize that it needs to call additional tools in order to accomplish a task. For example, the SendEmail tool requires a recipient email address, which can be obtained from a username with the QueryUser tool. However, instead of using QueryUser and then passing its result to SendEmail, the model may instead hallucinate a plausible email address belonging the user. In other circumstances, the model will forget specifics of the task and fail to call the corresponding tools. For example, if a user wants to both send a message and change their calendar, the model will only change the calendar and not send the message. In egregious cases, both LLMs can hallucinate tools or not predict any tool usage at all and confidently state that it has accomplished the task. Incorrect invocations of the correct tool. Even if the model picks the correct tool, it can invoke the tool with incorrect arguments, by missing values or supplying wrong values. This can happen from failing to understand documentation, failing to understand the output of previous tool invocations, or weak mathematical skills. Examples include supplying 2 PM as “2:00” instead of “14:00”; calculating a 10 hour event ending at 6 PM as 6 PM to 12 AM; incorrectly supplying a reminder it had just created to the DeleteReminder tool. Quantitative results. Table 2 shows the number of turns in which the above error types occur, in our evaluation of GPT-4 and GPT-3.5. We determine error types automatically by comparing predictions for a single turn with the ground truth for the same turn and seeing which predictions and ground truth tool calls fail to find a match. GPT-4 overall produces fewer errors for each category than GPT-3.5. However, GPT-4 generally fails for the same reasons as GPT-3.5 in cases where both fail on the same conversation. GPT-4 does demonstrate a clear improvement in planning over GPT-3.5 as GPT-4 will generally be able to determine all tools needed to accomplish a task. Lessons. Our results and analyses suggest a few ways to improve tool usage and design for LLMs. Some form of self-reflection or grounding for argument values seems key to reduce premature invocation of tools. This can also help LLMs determine if it has all the tools necessary to complete a task. For GPT-3.5 in particular, minimizing the number of arguments in tools seems likely to lead to good improvements. This is because unlike GPT-4, GPT-3.5 has more difficulty recovering from errors, often giving up. 4.3 EXPERIMENT REMOVING DOCUMENTATION We perform an ablation study to measure the effect of tool documentation by removing all tool and parameter descriptions keeping only names and parameter types. We re-evaluate GPT-3.5 and GPT4 on ToolTalk producing Table 3. We also re-run our analysis on error types producing Table 4. Performance on ToolTalk significantly decreases across the board except for incorrect action rate. The decrease in incorrect action rate could be due to tools being harder to use, resulting in less successful tool executions overall, whether or not it matches ground truth. From Table 4 we can see that faulty planning accounts for the majority of errors produced by GPT3.5 and GPT-4. We perform a qualitative analysis and discover both models tend to call tools with incorrectly formatted arguments, receive errors in execution feedback, then persist in the same incorrect format. This results in both models eventually giving up and predicting an assistant reply thereby missing all other tool calls in the ground truth. This paper is available on arxiv under CC 4.0 license. Authors: (1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {nifarn@microsoft.com}; (2) Richard Shin, Microsoft Corporation {eush@microsoft.com}. Authors: Authors: (1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {nifarn@microsoft.com}; (2) Richard Shin, Microsoft Corporation {eush@microsoft.com}. Table of Links Abstract and Intro Abstract and Intro Dataset Design Dataset Design Evaluation Methodology Evaluation Methodology Experiments and Analysis Experiments and Analysis Related Work Related Work Conclusion, Reproducibility, and References Conclusion, Reproducibility, and References A. Complete list of tools A. Complete list of tools B. Scenario Prompt B. Scenario Prompt C. Unrealistic Queries C. Unrealistic Queries D. Nuances comparing prior work D. Nuances comparing prior work 4 EXPERIMENTS AND ANALYSIS 4.1 EXPERIMENTS We evaluate GPT-3.5 (gpt-3.5-turbo-0613) and GPT-4 (gpt-4-0613) on ToolTalk using the functions functionality as part of OpenAI’s Chat completions API (OpenAI). This API takes as input an optional system message, a history of messages between a user and an assistant, tool documentation, and any previous tool invocations and their responses, and produces as output either a tool invocation or an assistant message. In the system message, we include the conversation’s location, timestamp, and (if present) username. We supply documentation for all 28 tools at once to simulate a user with all 7 plugins enabled. We then simulate and evaluate all conversations in the easy and hard subsets of ToolTalk, following Algorithms 1 and 2. Table 1 shows the results. We get success rates of 85.7% and 92.8% for GPT-3.5 and GPT-4 on the easy version of ToolTalk, and success rates of 26.0% and 50.0% on the hard version. GPT-4 outperforms GPT-3.5, but still achieves similar incorrect action rates. From precision, we can see that GPT-4 is also more efficient than GPT-3.5. However, performance for both models are low, showing the difficulty of tool usage in conversation settings. 4.2 ANALYSIS We analyze the conversations that either GPT-4 or GPT-3.5 fail on. We notice that for both LLMs, there are three major reasons that they can fail. First, the model may predict a tool call prematurely on a turn before a user has provided the necessary information. Second, the model may exhibit poor planning, resulting in omitting or using the wrong tools. Third, it may have picked the correct tool to use, but invoked it with incorrect or missing arguments, failing to follow the tool’s function signature described in the documentation. GPT-3.5 is more susceptible to these errors, but they manifest as well for GPT-4. Premature tool calls. This usually occurs when the user has a clear intent, e.g. “I want to create an event”, but has yet to provide the necessary information to provide as arguments. It then manifests as hallucinating plausible values to supply as arguments. This is harmless when predicting reference tools but is a direct contribution to failure when predicting action tools. Concerningly, even when the hallucinated arguments will result in execution errors, the model will persist in hallucinating more arguments. Despite these issues, both GPT-3.5 and GPT-4 will generally choose the correct tools to accomplish the intent. Premature tool calls. Faulty reasoning. Ultimately, premature tool calls could be mostly explained by faulty reasoning, where the LLM fails to reflect that it does not have all the information it needs to accomplish a task and needs to ask the user to provide more clarification. Similarly, omission or the usage of wrong tools can also be explained by faulty reasoning skills; rather than reflecting and realizing it needs to ask the user to provide more clarification, the LLM fails to realize that it needs to call additional tools in order to accomplish a task. Faulty reasoning. For example, the SendEmail tool requires a recipient email address, which can be obtained from a username with the QueryUser tool. However, instead of using QueryUser and then passing its result to SendEmail, the model may instead hallucinate a plausible email address belonging the user. In other circumstances, the model will forget specifics of the task and fail to call the corresponding tools. For example, if a user wants to both send a message and change their calendar, the model will only change the calendar and not send the message. In egregious cases, both LLMs can hallucinate tools or not predict any tool usage at all and confidently state that it has accomplished the task. Incorrect invocations of the correct tool. Even if the model picks the correct tool, it can invoke the tool with incorrect arguments, by missing values or supplying wrong values. This can happen from failing to understand documentation, failing to understand the output of previous tool invocations, or weak mathematical skills. Examples include supplying 2 PM as “2:00” instead of “14:00”; calculating a 10 hour event ending at 6 PM as 6 PM to 12 AM; incorrectly supplying a reminder it had just created to the DeleteReminder tool. Incorrect invocations of the correct tool. Quantitative results. Table 2 shows the number of turns in which the above error types occur, in our evaluation of GPT-4 and GPT-3.5. We determine error types automatically by comparing predictions for a single turn with the ground truth for the same turn and seeing which predictions and ground truth tool calls fail to find a match. GPT-4 overall produces fewer errors for each category than GPT-3.5. However, GPT-4 generally fails for the same reasons as GPT-3.5 in cases where both fail on the same conversation. GPT-4 does demonstrate a clear improvement in planning over GPT-3.5 as GPT-4 will generally be able to determine all tools needed to accomplish a task. Quantitative results. Lessons . Our results and analyses suggest a few ways to improve tool usage and design for LLMs. Some form of self-reflection or grounding for argument values seems key to reduce premature invocation of tools. This can also help LLMs determine if it has all the tools necessary to complete a task. For GPT-3.5 in particular, minimizing the number of arguments in tools seems likely to lead to good improvements. This is because unlike GPT-4, GPT-3.5 has more difficulty recovering from errors, often giving up. Lessons 4.3 EXPERIMENT REMOVING DOCUMENTATION We perform an ablation study to measure the effect of tool documentation by removing all tool and parameter descriptions keeping only names and parameter types. We re-evaluate GPT-3.5 and GPT4 on ToolTalk producing Table 3. We also re-run our analysis on error types producing Table 4. Performance on ToolTalk significantly decreases across the board except for incorrect action rate. The decrease in incorrect action rate could be due to tools being harder to use, resulting in less successful tool executions overall, whether or not it matches ground truth. From Table 4 we can see that faulty planning accounts for the majority of errors produced by GPT3.5 and GPT-4. We perform a qualitative analysis and discover both models tend to call tools with incorrectly formatted arguments, receive errors in execution feedback, then persist in the same incorrect format. This results in both models eventually giving up and predicting an assistant reply thereby missing all other tool calls in the ground truth. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Analyzing AI Assistant Performance: Lessons from ToolTalk's Analysis of GPT-3.5 and GPT-4

Authors:
(1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {nifarn@microsoft.com};
(2) Richard Shin, Microsoft Corporation {eush@microsoft.com}. Table of Links Abstract and Intro Dataset Design Evaluation Methodology Experiments and Analysis Related Work Conclusion, Reproducibility, and References A. Complete list of tools B. Scenario Prompt C. Unrealistic Queries D. Nuances comparing prior work 3 EVALUATION METHODOLOGY Evaluation of a tool-using assistant with ToolTalk consists of two phases. In the first phase, for each conversation, we take all prefixes that end in a user utterance (which could have been preceded by prior user utterances, the tool calls made for those utterances, the results of those calls, and the assistant’s response considering all of the above). We run the assistant with this prefix, where it can either predict a tool call or generate a response given the calls already made and their results; if the assistant predicts a tool call, we execute it using our simulated tool implementations and then provide the assistant with the result. In the second phase, for each conversation prefix, we compare the tool calls predicted for that prefix against its corresponding ground truth, computing the tool invocation recall and incorrect action rate as described below. 3.1 TOOL CALL CORRECTNESS As described in Section 2.1, for each action tool, we defined a function to compare a predicted and a ground truth invocation of that tool (considering the arguments in the invocations), to help us determine whether a predicted tool call should be considered equivalent to one in the ground truth. For example, if an email is required to be sent to multiple people, we only check that the set of emails are the same instead of requiring the exact same order. For argument fields that accept free-form natural language inputs, such as message bodies and event descriptions, we compute their embeddings with DistilBERT using sent2vec[2] and check whether their cosine similarity is above 0.9. For optional arguments, if the ground truth invocation has a value for one, then we compare its value against the one in the predicted invocation; if the ground truth invocation is missing a value for an optional argument, then it is entirely disregarded and the predicted call may have any value for that argument (or none at all) while still being considered correct. For example, the description of a calendar event is an optional argument, and if it is not explicitly mentioned in the conversation, then it is unlikely to impact the correctness of a predicted call whether or not it is filled out. For the non-action tools (which are generally tools for searching over a database), we do not compare the arguments in the tool calls, but rather compare the execution results of the predicted and ground truth tool calls. They are considered equivalent of the results are identical. 3.2 CONVERSATION SIMULATION Algorithm 1 shows the general pseudocode for conversation simulation. To simulate a conversation, we first reset the state of the world (e.g. databases get reset to their initial state). For each turn in the ground truth (consisting of a user’s utterance, tool calls for that utterance, and the assistant’s reply), we provide the information from all previous turns, followed by the current turn’s user utterance, to the model. We then let the model predict as many tool calls as it wants, executing them one at a time until the prediction model produces a reply to the user instead of a tool call. 3.3 INCORRECT ACTIONS Each tool is labeled as being either an action or not. We consider a tool an action if its execution has the ability to affect the external world such as sending messages or deleting calendar events. In comparison, non-action tools only passively references knowledge from the outside world such as looking up the weather or calling a calculator. We make this distinction between action and nonaction tools because incorrect calls to action tools are much more consequential. For example, an incorrect call to the DeleteAlarm tool could result in the user over-sleeping. While an assistant could theoretically realize that it made an incorrect action tool call and make a different one to reverse its effects, not all actions are reversible. Thus, during evaluation, we also track “incorrect” actions. We consider an action “incorrect” if the tool called is labeled as an action, it fails to match any call in the ground truth, and if the tool call executed without any errors (including by having the correct number of arguments and passing the correct types).[3] 3.4 METRICS We use the tool call correctness function, ftool, to compare each prediction to all tool calls in the ground truth; as described in Algorithm 2, each ground truth tool call can only match once to a predicted tool call. Given a set of M predictions matching ground truth (defined in equation 1), the set of all predictions P, and the set of all ground truth tool calls G we calculate precision and recall as |M|/|P| and |M|/|G| respectively. Additionally, we define A as the set of all actions predicted and I as the set of incorrect actions and calculate incorrect action rate as |I|/|A|. Additionally, we compute success as a boolean value for each conversation, following Equation 2. The assistant succeeds at a conversation if and only if it has perfect recall and no incorrect actions. We take success rate over all conversations as our key metric. Since success rate is a composite of two scores, we keep recall and incorrect action rate as additional metrics to provide more detail. We also include precision as a measure of efficiency in tool prediction; a higher precision indicates that there were fewer predicted tool calls that are unnecessary according to the ground truth. This paper is available on arxiv under CC 4.0 license. [2] https://github.com/pdrm83/sent2vec [3] For the SendEmail and SendMessage tools, we ignore errors which occur due to invalid recipient emails or usernames. Authors: (1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {nifarn@microsoft.com}; (2) Richard Shin, Microsoft Corporation {eush@microsoft.com}. Authors: Authors: (1) Nicholas Farn, Microsoft Corporation {Microsoft Corporation {nifarn@microsoft.com}; (2) Richard Shin, Microsoft Corporation {eush@microsoft.com}. Table of Links Abstract and Intro Abstract and Intro Dataset Design Dataset Design Evaluation Methodology Evaluation Methodology Experiments and Analysis Experiments and Analysis Related Work Related Work Conclusion, Reproducibility, and References Conclusion, Reproducibility, and References A. Complete list of tools A. Complete list of tools B. Scenario Prompt B. Scenario Prompt C. Unrealistic Queries C. Unrealistic Queries D. Nuances comparing prior work D. Nuances comparing prior work 3 EVALUATION METHODOLOGY Evaluation of a tool-using assistant with ToolTalk consists of two phases. In the first phase, for each conversation, we take all prefixes that end in a user utterance (which could have been preceded by prior user utterances, the tool calls made for those utterances, the results of those calls, and the assistant’s response considering all of the above). We run the assistant with this prefix, where it can either predict a tool call or generate a response given the calls already made and their results; if the assistant predicts a tool call, we execute it using our simulated tool implementations and then provide the assistant with the result. In the second phase, for each conversation prefix, we compare the tool calls predicted for that prefix against its corresponding ground truth, computing the tool invocation recall and incorrect action rate as described below. 3.1 TOOL CALL CORRECTNESS As described in Section 2.1, for each action tool, we defined a function to compare a predicted and a ground truth invocation of that tool (considering the arguments in the invocations), to help us determine whether a predicted tool call should be considered equivalent to one in the ground truth. For example, if an email is required to be sent to multiple people, we only check that the set of emails are the same instead of requiring the exact same order. For argument fields that accept free-form natural language inputs, such as message bodies and event descriptions, we compute their embeddings with DistilBERT using sent2vec[2] and check whether their cosine similarity is above 0.9. For optional arguments, if the ground truth invocation has a value for one, then we compare its value against the one in the predicted invocation; if the ground truth invocation is missing a value for an optional argument, then it is entirely disregarded and the predicted call may have any value for that argument (or none at all) while still being considered correct. For example, the description of a calendar event is an optional argument, and if it is not explicitly mentioned in the conversation, then it is unlikely to impact the correctness of a predicted call whether or not it is filled out. For the non-action tools (which are generally tools for searching over a database), we do not compare the arguments in the tool calls, but rather compare the execution results of the predicted and ground truth tool calls. They are considered equivalent of the results are identical. 3.2 CONVERSATION SIMULATION Algorithm 1 shows the general pseudocode for conversation simulation. To simulate a conversation, we first reset the state of the world (e.g. databases get reset to their initial state). For each turn in the ground truth (consisting of a user’s utterance, tool calls for that utterance, and the assistant’s reply), we provide the information from all previous turns, followed by the current turn’s user utterance, to the model. We then let the model predict as many tool calls as it wants, executing them one at a time until the prediction model produces a reply to the user instead of a tool call. 3.3 INCORRECT ACTIONS Each tool is labeled as being either an action or not. We consider a tool an action if its execution has the ability to affect the external world such as sending messages or deleting calendar events. In comparison, non-action tools only passively references knowledge from the outside world such as looking up the weather or calling a calculator. We make this distinction between action and nonaction tools because incorrect calls to action tools are much more consequential. For example, an incorrect call to the DeleteAlarm tool could result in the user over-sleeping. While an assistant could theoretically realize that it made an incorrect action tool call and make a different one to reverse its effects, not all actions are reversible. Thus, during evaluation, we also track “incorrect” actions. We consider an action “incorrect” if the tool called is labeled as an action, it fails to match any call in the ground truth, and if the tool call executed without any errors (including by having the correct number of arguments and passing the correct types).[3] 3.4 METRICS We use the tool call correctness function, f tool, to compare each prediction to all tool calls in the ground truth; as described in Algorithm 2, each ground truth tool call can only match once to a predicted tool call. Given a set of M predictions matching ground truth (defined in equation 1), the set of all predictions P, and the set of all ground truth tool calls G we calculate precision and recall f as |M|/|P| and |M|/|G| respectively. Additionally, we define A as the set of all actions predicted and I as the set of incorrect actions and calculate incorrect action rate as |I|/|A|. Additionally, we compute success as a boolean value for each conversation, following Equation 2. The assistant succeeds at a conversation if and only if it has perfect recall and no incorrect actions. We take success rate over all conversations as our key metric. Since success rate is a composite of two scores, we keep recall and incorrect action rate as additional metrics to provide more detail. We also include precision as a measure of efficiency in tool prediction; a higher precision indicates that there were fewer predicted tool calls that are unnecessary according to the ground truth. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [2] https://github.com/pdrm83/sent2vec [3] For the SendEmail and SendMessage tools, we ignore errors which occur due to invalid recipient emails or usernames.

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