ChatGPT is an AI chatbot created by OpenAI that uses a fine-tuned GPT-3.5 language model to engage in natural conversations. It was trained using reinforcement learning with a reward model to generate helpful, harmless, and honest responses. The document discusses ChatGPT and how it compares to other AI technologies like AI painting, AI chatbots, and goals towards artificial general intelligence.
ChatGPT is a large language model chatbot created by OpenAI to have human-like conversations. It was trained on a vast corpus of text which allows it to provide quick, scalable, and cost-effective responses. However, as an AI system, ChatGPT lacks common sense, has limited domain knowledge, and cannot be truly creative in its responses.
Seminar on ChatGPT Large Language Model by Abhilash Majumder(Intel)
This presentation is solely for reading purposes and contains technical details about ChatGPT fundamentals
Tech adoption for AI ML has been rapidly growing over the globe and ChatGPT is the game changer. Artificial intelligence and Machine learning are uplifting internet era with swift solutions for users. https://www.9series.com/blog/revolutionary-chatgpt/
ChatGPT is a large language model chatbot created by OpenAI to have human-like conversations. It was trained on a vast corpus of text which allows it to provide quick, scalable, and cost-effective responses. However, ChatGPT lacks common sense, has limited contextual understanding and domain knowledge, and is unable to be creative in its responses.
ChatGPT is a chatbot developed by OpenAI and launched in November 2022.
Useful to all the school and college going
Kindly use ChatGPT to enhance your knowledge
ChatGPT is a natural language processing model created by OpenAI that can generate human-like responses to text-based conversations. It uses deep learning and was pre-trained on vast amounts of text to understand language. Performance is evaluated using metrics like perplexity, accuracy, fluency and human evaluation. There are ethical concerns around copyright, personal data, bias and how the training data was obtained. OpenAI has introduced a paid ChatGPT Plus subscription with additional features while maintaining the free version.
ChatGPT is an AI chatbot created by OpenAI that interacts conversationally by answering questions in natural language. It was trained on millions of texts to understand language. While it can understand questions in multiple languages and provide relevant responses, it has limitations such as possibly providing incorrect or biased information. It is currently free for users but may become monetized in the future.
ChatGPT is an AI assistant created by OpenAI to have natural conversations. It was trained on a large text dataset to recognize patterns and generate responses in different styles. Since its release, ChatGPT has gained over 1 million users in its first week and demonstrated abilities like answering follow-up questions, admitting mistakes, and rejecting inappropriate requests. While ChatGPT shows promise for more human-like conversations, experts note it still has limitations like potential for incorrect answers and bias issues due to limitations in its training data.
ChatGPT is a highly advanced language model developed by OpenAI. Its ability to understand and respond to natural language input can be a valuable tool for mobile application developers looking to streamline their workflow and improve their app development process.
This document discusses Peter Purgathofer's presentation on chatGPT and the implications of conversational AI. It includes sections on Ludwig Wittgenstein's work at TU Wien, a worksheet, and a comparison of two abstracts. The document concludes with a question about where current conversational AI technology falls in relation to future progress.
ChatGPT 101 - Vancouver ChatGPT ExpertsAli Tavanayan
This document discusses using ChatGPT to plan a meetup session. It provides an agenda for exploring ChatGPT's capabilities, including finding a title, writing marketing copies, social posts, an email sequence, and presentation slides. Attendees are invited to share their experiences interacting with ChatGPT. The next event is announced as focusing on using ChatGPT for email marketing.
This document discusses ChatGPT, an AI model developed by OpenAI that can carry on human-like conversations. It was trained on over 8 million web pages. The document argues that ChatGPT has the potential to revolutionize human-machine interactions by allowing more natural communication. However, some are concerned about potential misuse, but with proper regulation it could be used responsibly. Examples of current uses include virtual assistants and customer service agents.
As an AI language model, ChatGPT is a program consisting of a large neural network that has been trained on vast amounts of textual data. Specifically, ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) family of models developed by OpenAI.
ChatGPT is a conversational AI model developed by OpenAI that is capable of generating human-like text responses. It is trained on a large dataset of human conversations which allows it to have natural, coherent conversations on a wide range of topics. Key features include generating long-form paragraphs, handling diverse inputs, and maintaining context across multiple conversation turns. While not designed for SEO, ChatGPT could potentially be used to generate website content, but may not optimize for search engines and could harm a site's rankings.
Use Chat GPT online and for free. ChatGPT is a language model developed by OpenAI, designed to respond to text-based queries and generate natural language .
OpenAI is an AI research company dedicated to developing safe and beneficial artificial intelligence. Their mission is to ensure AI benefits humanity. OpenAI conducts research across various AI domains and develops technologies like ChatGPT, a large language model capable of answering questions and generating human-like responses. The company also offers developers access to its models and tools through an API.
If you are considering using either language model, but aren’t quite sure which one’s the best fit for your intended purpose, read on for a ChatGPT vs. GPT-3 head-to-head comparison where we evaluate every aspect of the language models, right from their emergence, how they work, and their suitability in different applications.
ChatGPT is a natural language processing model created by OpenAI that can generate human-like responses to text-based conversations. It uses deep learning and was pre-trained on vast amounts of text to understand language. Performance is evaluated using metrics like perplexity, accuracy, fluency and human evaluation. While very powerful, ChatGPT and other large language models raise legal and ethical concerns regarding copyright, privacy, bias and how training data was obtained. The future potential includes using conversational AI to streamline operations in areas like data entry, scheduling and customer service.
In the US, people are already implementing the use of converstaionl AI, ChatGPT in everydy mundane tasks. Implementation is not only limited to that. Various industries are also using this revolutionary technology for maintaining a superior customer experience. People are also criticizing ChatGPT for creating employment threats and also being unethical in it's answers. The technology is being widely applauded but everything has certain pain points associated with it.
During the presentation, I will provide an overview of Stable Diffusion, including other similar projects. I will also demonstrate how to set up Stable Diffusion locally and generate images using different models, which you can find on GitHub (https://github.com/AUTOMATIC1111/stable-diffusion-webui).
Bring-your-ML-Project-into-Production-v2.pdfLiang Yan
Machine Learning (ML) is quite popular today in the academic/research world. However, it is quite difficult to put into a product, especially a product with a huge customer base. This session will give Kubeflow, the open source ML toolkit on top of Kubernetes, a deep look from the MLOps perspective. Furthermore, we will have a brief look at Distributed MLSys and how Kubeflow copes with scalability. Last, a demo of the Kubeflow stack setup and ML project pipeline deployment will be demonstrated in the cloud.
This document describes an upstream fix tracking (UFT) workflow. It lists the stable branches and development branches of various open source projects. The workflow takes the 6.1.x branch as an example, explaining the process of reading commits between releases and the master branch, filtering them based on keywords and blacklists, and sending the results to a Slack channel. It also provides some tasks for future improvement, such as adding hyperlinks to commits, supporting different repositories, and separating CVEs from fixes.
This document discusses GPU virtualization in openSUSE. It covers:
1. An overview of GPU virtualization, its use cases and market potential.
2. The critical techniques used, including SRIOV vs MDEV approaches.
3. A demo of GPU virtualization in action.
4. The current status and future plans, including support from vendors like Nvidia, AMD and Intel.
Giant VM aims to optimize virtualization by using a single large virtual machine (VM) that utilizes all available resources of the host system, rather than multiple smaller VMs. This approach is explored for scenarios like VDI, SAP HANA, and edge computing where full host resources are needed. Optimization techniques discussed include CPU pinning, huge pages, direct device assignment, and avoiding overcommit of resources.
a-new-playground-for-spdk-dpdk-on-arm64.pdfLiang Yan
This document discusses progress on developing a $500 ARM64 server using a MacchiatoBin board for hardware acceleration and virtualization. The server has been set up with an Intel NVME SSD, Mellanox network card, and DPDK/SPDK. Next steps include building an install image, adding PPV2 drivers, integrating VPP for packet processing, and developing it into a fast NAS storage server. Questions are welcomed.
Accelerate-your-AI-Cloud-infrastructure.pdfLiang Yan
This document summarizes a presentation about accelerating AI workloads using NVIDIA GPU virtualization. It discusses using GPUs for machine learning tasks in the cloud, hardware acceleration options like GPUs and FPGAs, and NVIDIA's GPU virtualization technology. Test results show vGPU performance is similar to GPU passthrough and scales effectively. The presentation demonstrates installing drivers and running a TensorRT benchmark inside a VM with a vGPU. Future plans include supporting newer NVIDIA GPUs and virtualization in other environments like Kubernetes.
A-Journney-to-support-vgpu-in-firecracker.pdfLiang Yan
1) The document discusses supporting virtual GPU (vGPU) functionality in the Firecracker microvirtual machine project.
2) It provides background on GPU virtualization technologies, Firecracker, and the motivation to extend Firecracker's computing power through GPU support.
3) The author describes their methodology for backporting and refactoring GPU passthrough and vGPU support from Cloud Hypervisor into Firecracker, though challenges remain around graphics initialization.
A journay to do AI research in the cloud.pdfLiang Yan
This document discusses the author's journey doing AI research in the cloud using open-source tools. It provides an overview of AI concepts and frameworks like PyTorch. It then describes a project called Habitat that predicts deep learning training times in the cloud. Next, it discusses AI accelerators like GPUs, distributed training, and considerations for AI cloud implementation and providers. The author shares lessons learned around dependencies, costs, and setup experiences with various cloud platforms.
This document provides an overview of GPU virtualization including:
1. Defining GPU virtualization and classifying different approaches like passthrough and full virtualization.
2. Describing use cases for GPU virtualization in providing GPU as a service and for applications like machine learning.
3. Highlighting critical techniques used in GPU virtualization like SR-IOV, mediated devices, and managing resources like memory.
This document discusses Linux, Linux distributions like SUSE, and the author's work on virtualization teams. It provides an overview of Linux and how it was developed based on Unix. It then discusses key aspects of various Linux distributions like Ubuntu, SUSE, Red Hat Enterprise Linux and how they differ in version numbers, package systems and support lifecycles. The document outlines the author's role on SUSE's virtualization team working on technologies like Xen, KVM, libvirt and QEMU. It provides examples of how virtualization improves hardware utilization and allows multiple guest operating systems to run concurrently on a single physical host.
This document provides an overview of GPUs including their history, uses, and key technologies. It discusses how GPUs have evolved from specialized graphics processors to massively parallel processors used for applications like cryptocurrency mining, AI, and gaming. The document also covers GPU development in Linux, including graphics architectures like Xorg and Wayland, and technologies like DRM/DRI that provide hardware acceleration and GPU memory management. Emerging technologies like GPU virtualization that allow more efficient sharing of GPU resources across VMs are also examined.
The document discusses how to boot Linux on system-on-chip (SOC) boards. It begins with background information on different types of SOC boards like those using ARM V7 and ARM V8 processors. It then covers the hardware requirements including storage media and RAM, as well as the software components involved like bootloaders and kernels. The boot process is explained in multiple stages, starting from the initial bootloader loading subsequent stages from storage until the kernel is loaded and initialized.
Generative AI The Key to Smarter, Faster IT Development.pdfayushiqss
Discover how generative AI is transforming IT development in this blog. Learn how using AI software development, artificial intelligence tools, and generative AI tools can lead to smarter, faster, and more efficient software creation. Explore real-world applications and see how these technologies are driving innovation and cutting costs in IT development.
Bring Strategic Portfolio Management to Monday.com using OnePlan - Webinar 18...OnePlan Solutions
Unlock the full potential of your projects with OnePlan’s seamless integration with monday.com. Join us to discover how OnePlan enhances monday.com by aligning your portfolio of projects with your organization’s strategic goals, optimizing resource allocation, and streamlining performance tracking. Learn how this powerful combination can drive efficiency, cost savings, and strategic success within your organization.
Crowd Strike\Windows Update Issue: Overview and Current Statusramaganesan0504
Crowd Strike\Windows Update Issue: Overview and Current Status
Discover the latest on the CrowdStrike Windows update issue, including an overview, current status, and support steps for affected customers. Learn about the identified defect, its impact on Windows hosts, and CrowdStrike's committed actions to ensure ongoing security and stability.
What is CrowdStrike?
CrowdStrike is a prominent cybersecurity technology company that specializes in providing advanced threat intelligence and endpoint protection solutions. Founded in 2011 by George Kurtz, Dmitri Alperovitch, and Gregg Marston, CrowdStrike has quickly established itself as a leader in the cybersecurity industry. Here are some key aspects of
Contractor PMS Software is intended to make the work process of a group dealing with a similar Software simpler. It is proper for new companies, enormous prepared ones, and, surprisingly, various groups that should team up to execute a task. The product empowers you to dole out and reassign occupations, helpfully track cutoff times to guarantee they are met, and, obviously, control the whole interaction from an centralised management interface.
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Old Tools, New Tricks: Unleashing the Power of Time-Tested Testing ToolsBenjamin Bischoff
In the rapidly evolving landscape of software development and testing, it is tempting to chase the latest tools and technologies. However, some of the most effective solutions have been in existence for decades. In this talk, we’ll delve into the enduring value of these timeless testing tools.
We’ll explore how established tools like Selenium, GNU Make, Maven, and Bash remain vital in today’s software development and testing toolkit even though they have been around for a long time (some were even invented before I was born). I’ll share examples of how these tools have addressed our testing and automation challenges, showcasing their adaptability, versatility, and reliability in various scenarios. I aim to demonstrate that sometimes, the “old” ways can indeed be the best ways.
Unified customer experience management tools integrate various customer interaction channels, data, and analytics into a single platform. They streamline customer service, enhance personalization, and provide insights for improving customer satisfaction and loyalty across all touchpoints.
Fix Production Bugs Quickly - The Power of Structured Logging in Ruby on Rail...John Gallagher
Rails apps can be a black box. Have you ever tried to fix a bug where you just can’t understand what’s going on? This talk will give you practical steps to improve the observability of your Rails app, taking the time to understand and fix defects from hours or days to minutes. Rails 8 will bring an exciting new feature: built-in structured logging. This talk will delve into the transformative impact of structured logging on fixing bugs and saving engineers time. Structured logging, as a cornerstone of observability, offers a powerful way to handle logs compared to traditional text-based logs. This session will guide you through the nuances of structured logging in Rails, demonstrating how it can be used to gain better insights into your application’s behavior. This talk will be a practical, technical deep dive into how to make structured logging work with an existing Rails app.
I talk about the Steps to Observable Software - a practical five step process for improving the observability of your Rails app.
Drona Infotech is one of the Best Mobile App Development Company in Noida. Discover the latest mobile app development frameworks to streamline your app creation process. Choose from a variety of tools and technologies to build innovative apps.
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Innoit Labs - Flutter App Development Company In Hyderabad.pptxofficialinnoitlabs
Innoit Labs is a leading Flutter app development company in Hyderabad, we specialize in creating high-quality, cross-platform mobile applications that offer a seamless user experience. Our expert team leverages the power of Flutter to deliver visually stunning and performance-optimized apps tailored to your business needs. With a strong focus on innovation and customer satisfaction, we help businesses in Hyderabad and beyond transform their ideas into successful mobile solutions. Choose us for reliable, efficient, and scalable Flutter app development services.
Predicting Test Results without Execution (FSE 2024)andrehoraa
As software systems grow, test suites may become complex, making it challenging to run the tests frequently and locally. Recently, Large Language Models (LLMs) have been adopted in multiple software engineering tasks. It has demonstrated great results in code generation, however, it is not yet clear whether these models understand code execution. Particularly, it is unclear whether LLMs can be used to predict test results, and, potentially, overcome the issues of running real-world tests. To shed some light on this problem, in this paper, we explore the capability of LLMs to predict test results without execution. We evaluate the performance of the state-of-the-art GPT-4 in predicting the execution of 200 test cases of the Python Standard Library. Among these 200 test cases, 100 are passing and 100 are failing ones. Overall, we find that GPT-4 has a precision of 88.8%, recall of 71%, and accuracy of 81% in the test result prediction. However, the results vary depending on the test complexity: GPT-4 presented better precision and recall when predicting simpler tests (93.2% and 82%) than complex ones (83.3% and 60%). We also find differences among the analyzed test suites, with the precision ranging from 77.8% to 94.7% and recall between 60% and 90%. Our findings suggest that GPT-4 still needs significant progress in predicting test results.
Laravel has quickly become one of the leading PHP frameworks. Its elegant syntax, powerful features, and strong community backing make it a top choice for developers. This article delves into what makes Laravel development stand out and why it is considered the best PHP framework for modern web applications.
AI is revolutionizing DevOps by advancing algorithmic optimizations in pipelines, elevating efficiency levels, and introducing predictive functionalities. This article examines how AI is reshaping continuous integration, deployment strategies, monitoring practices, and incident management within DevOps ecosystems, ultimately amplifying efficiency and dependability.
