Generative Artificial Intelligence (AI) models such as OpenAI's ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers. There is a limited understanding of how AI may perform in group decision-making. This paper presents a wizard-of-oz study in which two participants and an AI form a committee to rank three English essays. One novelty of our study is that we adopt a speculative design by endowing AI equal power to humans in group decision-making.We enable the AI to discuss and vote equally with other human members. We find that although the voice of AI is considered valuable, AI still plays a secondary role in the group because it cannot fully follow the dynamics of the discussion and make progressive contributions. Moreover, the divergent opinions of our participants regarding an "equal AI" shed light on the possible future of human-AI relations.

Last week, both Microsoft and Google announced that they would incorporate AI programs similar to ChatGPT into their search engines--bids to transform how we find information online into a conversation with an omniscient chatbot. One problem: These language models are notorious mythomaniacs. In a promotional video, Google's Bard chatbot made a glaring error about astronomy--misstating by well over a decade when the first photo of a planet outside our solar system was captured--that caused its parent company's stock to slide as much as 9 percent. The live demo of the new Bing, which incorporates a more advanced version of ChatGPT, was riddled with embarrassing inaccuracies too. Even as the past few months would have many believe that artificial intelligence is finally living up to its name, fundamental limits to this technology suggest that this month's announcements might actually lie somewhere between the Google Glass meltdown and an iPhone update--at worst science-fictional hype, at best an incremental improvement accompanied by a maelstrom of bugs. The trouble arises when we treat chatbots not just as search bots, but as having something like a brain--when companies and users trust programs like ChatGPT to analyze their finances, plan travel and meals, or provide even basic information.

Safety and security are everyone's number one priority. Cyberguy lists some of the best tech to keep you safe at home or on the go. Artificial intelligence software is growing quickly in popularity, especially among tech companies. This has led to many wondering if this is the end of human interaction as we know it, and some are fearful that these AI robots could begin taking away jobs from you and me. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER AI is the computer systems simulation of human intelligence processes, which include learning, reasoning, and self-correction.

S. S. Rajamouli was born in 1973, in the South Indian state of Karnataka, to a family from a dominant caste. He learned how to make movies from various odd jobs and apprenticeships, including a years-long stint working for his father, the successful screenwriter Koduri Viswa Vijayendra Prasad. In the past two decades, Rajamouli has earned a reputation among Indian moviegoers for a series of formally ambitious blockbusters, including the spectacular "Baahubali: The Beginning," from 2015, which inspired a new wave of Indian historic epics. But he has found a new level of global success with his latest film, the joyously over-the-top action-fantasy "RRR"--short for "Rise Roar Revolt"--which is among the highest-grossing Indian movies of all time. "RRR" was first released last March but caught on with American viewers over the summer, after an unusual U.S.-wide theatrical rerelease organized by the distributor Variance Films and the film consultant Josh Hurtado.

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.

A fundamental problem in signal processing is to denoise a signal. While there are many well-performing methods for denoising signals defined on regular supports, such as images defined on two-dimensional grids of pixels, many important classes of signals are defined over irregular domains such as graphs. This paper introduces two untrained graph neural network architectures for graph signal denoising, provides theoretical guarantees for their denoising capabilities in a simple setup, and numerically validates the theoretical results in more general scenarios. The two architectures differ on how they incorporate the information encoded in the graph, with one relying on graph convolutions and the other employing graph upsampling operators based on hierarchical clustering. Each architecture implements a different prior over the targeted signals. To numerically illustrate the validity of the theoretical results and to compare the performance of the proposed architectures with other denoising alternatives, we present several experimental results with real and synthetic datasets.

Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising direction to tackle this problem is to generate synthetic dialogues by prompting large language models. In this work, we use a small set of expert-written conversations as in-context examples to synthesize a social conversation dataset using prompting. We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations. This includes various dimensions of conversation quality with human evaluation directly on the synthesized conversations, and interactive human evaluation of chatbots fine-tuned on the synthetically generated dataset. We additionally demonstrate that this prompting approach is generalizable to multi-party conversations, providing potential to create new synthetic data for multi-party tasks. Our synthetic multi-party conversations were rated more favorably across all measured dimensions compared to conversation excerpts sampled from a human-collected multi-party dataset.

This article was written by The Kid, the 12-year old writer of If Lisa Simpson Had A Substack. If you enjoy reading it, or think you learned something, please like it. If you want to read more posts like this, hit the subscribe button. The Third Edition New Oxford American Dictionary defines Artificial Intelligence (or AI) as "the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." When we think of AI, we usually think of Issac Asimov's I, Robot -- or, if you're like me, you'll think of Data or Lore from Star Trek.

Emotional support conversation (ESC) task can utilize various support strategies to help people relieve emotional distress and overcome the problem they face, which has attracted much attention in these years. However, most state-of-the-art works rely heavily on external commonsense knowledge to infer the mental state of the user in every dialogue round. Although effective, they may suffer from significant human effort, knowledge update and domain change in a long run. Therefore, in this article, we focus on exploring the task itself without using any external knowledge. We find all existing works ignore two significant characteristics of ESC. (a) Abundant prior knowledge exists in historical conversations, such as the responses to similar cases and the general order of support strategies, which has a great reference value for current conversation. (b) There is a one-to-many mapping relationship between context and support strategy, i.e.multiple strategies are reasonable for a single context. It lays a better foundation for the diversity of generations. Taking into account these two key factors, we propose Prior Knowledge Enhanced emotional support model with latent variable, PoKE. The proposed model fully taps the potential of prior knowledge in terms of exemplars and strategy sequence and then utilizes a latent variable to model the one-to-many relationship of strategy. Furthermore, we introduce a memory schema to incorporate the encoded knowledge into decoder. Experiment results on benchmark dataset show that our PoKE outperforms existing baselines on both automatic evaluation and human evaluation. Compared with the model using external knowledge, PoKE still can make a slight improvement in some metrics. Further experiments prove that abundant prior knowledge is conducive to high-quality emotional support, and a well-learned latent variable is critical to the diversity of generations.