Hans-Dirk Walter’s Post

I just came across an interesting article about a new program by the National Institute of Standards and Technology (NIST) called GenAI 🎉. GenAI aims to evaluate generative AI technologies 🤖, which is a field that's been growing rapidly in recent years. The program will include challenges that measure a system's ability to differentiate between synthetic and human-generated content. This will be done in text-to-text and text-to-image formats 📄 🖼️. I think this is a really important initiative, as it will help us to better understand the capabilities of generative AI and how it can be used responsibly. The findings from this pilot program will be used to inform future research on generative AI technologies 💡 What are your thoughts on generative AI? #generativeAI #AI #NIST #GenAI #technology #future https://lnkd.in/dA723caT

🚀 Excited to share our latest work, "Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models," soon to be published in the CHI '24 Proceedings! 🌟 https://lnkd.in/er2KaUkg 🔍 Our study, led by researchers Marvin Pafla, Kate Larson, and Mark Hancock from the University of Waterloo, dives deep into the intricacies of explainable AI (XAI), particularly focusing on the effectiveness of human and machine explanations in the context of AI errors. Our findings show that participants found human saliency maps to be more helpful in explaining AI answers than machine saliency maps, but performance negatively correlated with trust in the AI model and explanations. This finding hints at the dilemma of AI errors in explanation, where helpful explanations can lead to lower task performance when they support wrong AI predictions. Key contributions:  - A comprehensive analysis of 156 human-generated explanations, revealing a nuanced understanding of human explanation strategies in a question-answering task.  - Empirical evaluation of human vs. machine explanations with 136 participants across various metrics, including trust, satisfaction, and task performance.  - A discussion on the risks of explaining incorrect AI answers and how explanation confirmation bias can impact how humans evaluate explanations.  - Practical recommendations for designing and using saliency maps in AI systems to mitigate the risks of confirmation bias. 🌟 This research not only advances our understanding of XAI, but also poses critical questions about the efficacy of explaining LLMs to users. A must-read for anyone in in the fields of AI and HCI! 🔗 Read the comprehensive study here: https://lnkd.in/er2KaUkg   #AI #XAI #HumanComputerInteraction #MachineLearning #SaliencyMaps #CHI2024 #UniversityOfWaterloo 

The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks In a study conducted by PhD students at the University of Arkansas, 151 people were pitted against ChatGPT-4 with the aim of measuring divergent thinking. BUT “AI, unlike humans, does not have agency,” and is “dependent on the assistance of a human user. Therefore, the creative potential of AI is in a constant state of stagnation unless promoted. Want to check the study ? https://lnkd.in/errVimGD

Mastering Prompt Engineering involves the skillful design and optimization of prompts to guide AI models, like ChatGPT, in generating accurate and useful responses. By crafting clear, precise, and contextually relevant inputs, we can shape the behavior and output of the AI, enhancing its effectiveness in tasks like content creation, problem-solving, and conversation. Key aspects of prompt engineering include: Clarity: Defining the problem or task clearly. Specificity: Providing detailed context or instructions. Iteration: Testing and refining prompts for better results. As AI continues to evolve, mastering prompt engineering will be crucial for improving AI-human interactions and harnessing AI's full potential across industries. For a deeper dive into prompt engineering techniques and best practices, check out the brief overview in the PDF. Unlock AI's Potential with Prompt Engineering! #PromptEngineering #AI #MachineLearning #NLP #ArtificialIntelligence #DataScience #TechInnovation #AITrends

🌌 Is AGI Closer Than We Think? 🌌 Artificial General Intelligence (AGI) is often portrayed as the next giant leap for technology, but are we really on the verge of creating machines that think like humans? 🤖💭 In this article, I dive deep into the progress, challenges, and philosophical debates surrounding AGI. From breakthroughs in large language models to the limitations in context understanding and common sense, we explore if AGI is just around the corner or still a distant dream. Curious about the road to human-level AI? 🚀 Check out the full article and join the conversation! 👇 🔗 https://lnkd.in/eQb84afe #ArtificialIntelligence #AGI #FutureOfAI #TechTrends #chatgpt #AI

https://lnkd.in/g-cWUa9G Amazing to see progress on interactive and cross teaching AI models will be a reality to experience shortly. Hope they will not ensure Humans to fully RIP 😊 #interactiveai #networkingai #crossteachingai

GPT-4 is more creative than humans on divergent thinking tasks Publicly accessible AI large language models like ChatGPT have sparked global discussions on AI's capabilities, challenging the belief that creativity is exclusively human. A study comparing the creative potential of humans (151 participants) and GPT-4 using the Alternative Uses Task, Consequences Task, and Divergent Associations Task found that AI displayed significantly greater creativity across all measures of divergent thinking compared to humans. Specifically, AI's responses were more original and elaborate, even when accounting for response fluency, indicating that current AI language models possess higher creative potential than humans. https://lnkd.in/g8tWSrVe

Just finished the course “Introduction to Prompt Engineering for Generative AI” by Ronnie Sheer! One of the challenges was to generate a movie poster about the future of AI using both ChatGPT and Open Art.ai. Check it out: https://lnkd.in/gHA6XJCj #generativeai #naturallanguageprocessing.

ARTIFICIAL INTELLIGENCE // So much AI jargon gets thrown around that we were finding it all a bit dizzying. So, we decided to clear up the most important terms for you in our latest report https://lnkd.in/gtbCNvUA And for fun…we asked ChatGPT to help us define AI (a bit chicken and egg, right?): “AI, or Artificial Intelligence, refers to computer systems that simulate human intelligence by learning from data, recognizing patterns, and making decisions. It encompasses machine learning, where algorithms evolve with experience. AI aims to perform tasks that typically require human intelligence, fostering innovation across diverse fields.” #specialtypharmaceuticals #Canada #AI #artificialintelligenc #patients #patientsfirst #RWE #RWD #realworlddata #realworldevidence #healthoutcomes #data #pharmaceuticalindustry #outcomes #innovation #access #drug #drugaccess

Explored cutting-edge topics in Artificial Intelligence (AI), including: ChatGPT: Leveraging large language models (LLM) for conversational AI Natural Language Generation: Generating human-like text using AI algorithms Introduction to Generative AI: Understanding the principles and applications of generative models Practical applications of Generative AI in various fields, including art, design, and content creation Mastered the latest techniques and tools in AI to drive innovation and solve complex problems.