Visit the website hereThis tutorial addresses the urgent need for responsible research and development in the rapidly evolving field of Generative AI. Designed for AI researchers and practitioners, it offers a comprehensive exploration of Responsible AI principles tailored specifically to Generative AI technologies. Participants will gain a deep understanding of the ethical implications of advancements in large language models and generative systems, including critical issues such as bias mitigation, privacy preservation and beyond. The tutorial goes beyond theoretical discussions by offering practical, research-oriented strategies, innovative methodological frameworks and hands-on labs. Participants will learn techniques for detecting and mitigating biases in Generative AI models and gain experience applying Responsible AI principles in real-world research scenarios.
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Abstract
According to the recent market analysis by Global Market Insight (GMI), computer vision market size will be anticipated to cross USD $40 billion in 2032. With the fast advance of modern computer vision models and AI technologies, more and more computer vision applications are developed for real deployment and applications. Many quality testing engineers run into challenge issues in testing and automation of smart computer vision systems while applying existing quality validation methods. This tutorial first covers in-depth discussion on issues, challenges, and needs in testing and automation of computer vision systems. Then, it addresses several hot topics, including computer vision intelligence/features validation focuses, AI test modeling and analysis, test methods and approaches, and AI-based test generation and augmentation. Moreover, it shares innovative 3D AI test models for different computer vision intelligence, and 3D decision table generation. Furthermore, quality testing coverage criteria and standard process are discussed. Finally, a test automation tool and demos are presented.
Who should attend this tutorial?
Test engineers, quality assurance engineers, and managers who are responsible for quality testing and assurance for modern intelligent systems and AI-powered smart computer vision systems, including mobile and online applications built-in based on modern computer vision models and techniques. In addition, researchers and students are encouraged if they are interested in AI system testing, automation, and quality assurance.
What you learned from this tutorial? What is the coverage of this tutorial?
Table of contents (outline):
Adequate quality needs
In addition, Dr. Gao will provide two show-cases and project demos on sample computer vision test automation.
Tutorial Speaker BIO:
Visit the website hereThis tutorial delves into the evolution of AI-assisted programming, tracing its roots to E.W.Dijkstra’s seminal idea of computer-assisted programming and to Natural Language Processing (NLP) and probabilistic language models. It highlights the recent transformative impact of modern transformerbased large language models (LLMs) trained on Big Code, leveraging software naturalness to revolutionize tasks like code generation, completion, translation, and defect detection. Pioneering examples include GitHub Copilot (powered by OpenAI Codex), GPT models, Meta’s Code Llama, Google’s Gemini Code Assist, Amazon CodeWhisperer, Alibaba’s Qwen, and Codeium. Participants will explore advancements in contextual-aware, multilingual programming models that enhance the adaptability of both local and cloud-based LLMs in diverse ecosystems. Core LLM architectures, their downstream applications, and challenges in integrating NLP methodologies with software naturalness will be examined. The tutorial highlights reinforcement learning with human feedback, focusing on alignment techniques to enhance fairness, safety, and performance in code generation by large language models. The session demonstrates AI-assisted programming extensions to Apple’s Xcode and LLM agent development, showcasing tools like Copilot to streamline mobile development and empower participants to evaluate, benchmark, and deploy LLMs effectively. The tutorial will also focus on general techniques for benchmarking and evaluation of LLMs for AIassisted programming. Models are assessed using code-specific benchmarks such as HumanEval and CodeNet, providing standardized datasets for evaluating code generation and completion. Performance metrics like Pass@k, BLEU, CodeBLEU, and functional correctness are analyzed to quantify the quality of generated code. Real-world effectiveness is gauged through human evaluations and deployment case studies, which provide valuable insights into user experiences and practical challenges. Additionally, advanced evaluation methodologies are discussed, including fine-grained analysis to identify common errors, assess model robustness, and measure performance on adversarial inputs. Comparative studies across different programming languages and domains illustrate the adaptability and limitations of various models, including emerging LLM coding agent players, which demonstrate cutting-edge advancements in multilingual programming and cross-domain functionality. Lastly, LLMs and LLM agents have profound implications for computer science, driving advancements in the search for efficient algorithms and automating problem-solving in competitive programming. By tackling complex programming challenges, they open new avenues for understanding algorithm design, optimization, and the theoretical foundations of computation.
Visit the website hereThis tutorial provides a step-by-step, hands-on approach to building a Retrieval-Augmented Generation (RAG) system using popular AI tools such as LangChain, OpenAI’s ChatGPT-4, FAISS, and Streamlit. Participants will learn how to design and implement an end-to-end RAG system that efficiently retrieves information from a custom knowledge base and generates insightful responses using advanced natural language generation models. The tutorial is geared towards data scientists, machine learning engineers, and AI practitioners interested in developing interactive, intelligent applications that require sophisticated question-answering and document retrieval capabilities. By the end of the session, attendees will have a fully functional RAG application that integrates seamlessly with a user-friendly interface.
Visit the website here Sensor content in electronic devices is growing, and an increasing number of applications involve batterypowered devices. The application of sensors is typically always-on, and this requires large power efficiency within the sense-process-act chain. However, today, the processors available for handling sensors and processing sensor data are characterized by high power-per-inference consumption. Much of the inefficiency lies within how sensor data is acquired from sensors, and how the information is relayed within processing subsystems.
The architectural enhancements needed for efficiency improvements cannot be achieved without hardware-software co-design. In this tutorial, we formulate requirements for hierarchical, modular neuromorphic framework that enables concurrent hardware-software co-design in smart sensing System-on-Chip. We exploit the synergy of hardware and software to examine omnidirectional dependencies of the entire design stack (from the application, neural
network algorithm, and mapper level towards system-on-chip, sub-systems and technology options level) with the goal to optimize and/or satisfy smart sensing design constraints such as energy-efficiency, performance, cost and time-to-market frame. In particular, we highlight advantages of the concurrent design, and emphasize the synergy between i) scalable reconfigurable segmented architecture that enables real-time always-on inference
of sensor data, essential for most pervasive sensing tasks, and ii) software development kit that enables the user to build and run an end-to-end application pipeline comprising multiple processing stages, with spiking neural network accelerators being one of them. The tutorial aims to delve into the contemporary trends of neuromorphic computing, explore its capabilities and challenges, and contemplate its future directions and broader impact within the AI community, industry, and society. The target audience includes research students, early-stage researchers, and practitioners with a background in AI.
Amir Zjajo
Innatera Nanosystems B.V.