Tool-augmented large language models (LLMs) leverage tools, often in the form of APIs, to improve their reasoning capabilities on complex tasks. This enables them to act as intelligent agents interacting with the real world. The recently introduced ToolLLaMA model by Qin et al. [ 2023 ] utilizes the depth-first search-based decision tree (DFSDT) mechanism for multi-step reasoning with 16000+ real-world APIs, effectively enhancing the performance of tool-augmented LLMs compared to traditional chain reasoning mechanisms. However, their approach only employs successful paths from decision trees (also called inference trees) for supervised fine-tuning (SFT), missing out on the potential learning opportunities from failed paths. Inspired by this, we propose an inference trajectory optimization framework based on preference learning to address this limitation.

Below we describe the steps for calculating metrics. To avoid overfitting on our dataset, we used already fitted image-and video-quality-assessment models with public source code. Below are the steps for calculating different versions of such metrics. We used mean temporal pooling as a way to aggregate scores from multiple frames. We intend to include more data on this research in future publications.

Tool-augmented large language models (LLMs) leverage tools, often in the form of APIs, to improve their reasoning capabilities on complex tasks. This enables them to act as intelligent agents interacting with the real world. The recently introduced ToolLLaMA model by Qin et al. [ 2023 ] utilizes the depth-first search-based decision tree (DFSDT) mechanism for multi-step reasoning with 16000+ real-world APIs, effectively enhancing the performance of tool-augmented LLMs compared to traditional chain reasoning mechanisms. However, their approach only employs successful paths from decision trees (also called inference trees) for supervised fine-tuning (SFT), missing out on the potential learning opportunities from failed paths. Inspired by this, we propose an inference trajectory optimization framework based on preference learning to address this limitation.

Below we describe the steps for calculating metrics. To avoid overfitting on our dataset, we used already fitted image-and video-quality-assessment models with public source code. Below are the steps for calculating different versions of such metrics. We used mean temporal pooling as a way to aggregate scores from multiple frames. We intend to include more data on this research in future publications.

Social and assistive robotics have vastly increased in popularity in recent years. Due to the wide range of usage, robots executing such tasks must be highly reliable and possess enough functions to satisfy multiple scenarios. This article describes a mobile, artificial intelligence-driven, robotic platform Rico. Its prior usage in similar scenarios, the number of its capabilities, and the experiments it presented should qualify it as a proper arm-less platform for social and assistive circumstances.

Vimeo is one of the latest companies to launch AI-powered tools of its own, and as you'd expect, they're geared towards making it easier for creators to edit their videos. The video hosting platform says most people "lack the skills, time, or resources to effectively create and edit videos," and these features are meant to eliminate those barriers. Perhaps the most useful of the three new AI tools is the text-based video editor that can automatically delete long pauses and parts of the video with filler words, such as "um" and "ah," with just a single click. Users will also be able to easily remove any part of the video they want by searching for certain words in the transcript that the tool generates and then clicking delete. If they want to create short clips for social media, they can search the transcript for a specific word, highlight and right-click on the word, sentence or paragraph, and then select "keep only this."

Vimeo is looking for an experienced Data Engineer II, Analytics to join our Data Architecture and Analytics Engineering team and work closely with Data Analysts and Data Scientists to create and maintain robust, scalable, and sustainable data models that provide decision-making insights for senior leadership including executives. The ideal candidate is a self-starter with a bias for action and results, with experience in a fast-paced, data-driven environment. Vimeo (NASDAQ: VMEO) is the world's leading all-in-one video software solution. Our platform enables any professional, team, and organization to unlock the power of video to create, collaborate and communicate. We proudly serve our growing community of over 260 million users -- from creatives to entrepreneurs to the world's largest companies.

Vimeo is looking for a Data Scientist to join the Video Insights team. Vimeo's platform provides the highest quality video tools for filmmakers, businesses, and video lovers around the globe. Video Insights team delivers services and products that help Vimeo users make effective videos, by applying Artificial intelligence (AI) technologies connecting between video content and it's online performance . It is a great opportunity to join a core founding team and take part in shaping the AI movement within Vimeo. Vimeo (NASDAQ: VMEO) is the world's leading all-in-one video software solution.

A few months ago Sam Altman wrote a blog post called Moore's Law for Everything. In it, he spoke about what the world could look like as AI becomes more advanced. First what is an API and GPT-3? We will start with an API. An application programming interface (API) is a connection that allows computers or computer programmes to communicate with one another.

Date: 07.30.2018 1. Glass Enterprise Edition 2. HomeCourt 3. Electronics Resurgence Initiative (ERI) 4. General Magic impact on how we use technology today 5. Joanna routinely to speaks and keynotes at conferences, corporations, non-profits, educational and professional organizations. Her subject matter expertise is customized to meet the needs of each audience. Ai is the tool of the modern magician. At the nascent stages of the another industrial and social revolution, magic math, multiplied by design makes what is invariable hard -- seem remarkably easy.