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.

The exceptional ability of ChatGPT to engage in human-like conversations and generate outputs ranging from code to music has caused it to gain widespread popularity as an online phenomenon in recent months.However, despite the extensive discussions and attention the chatbot has received, people know relatively little about the individual who created it – Sam Altman, who is also a co-founder of OpenAI.Thus, even as ChatGPT continues to capture the public's imagination, the question of who Sam Altman is remains unanswered. Sam Altman, who is currently 37 years old, was born in 1985 in Chicago, Illinois. He spent his childhood in St. Louis, Missouri. When he was just eight years old, he got gift as Macintosh computer. He quickly learned to program and disassemble due to his precociousness and efficiency.

Senior Researcher Chris Dede isn't overly worried about growing concerns over generative artificial intelligence, like ChatGPT, in education. As a longtime researcher on emerging technologies, he's seen many decades where new technologies promised to upend the field. Instead, Dede says artificial intelligence requires educators to get smarter about how they teach in order to truly take advantage of what AI has to offer."The But if you educate them for what AI can't do, then you've got IA [Intelligence Augmentation]," he says. Dede, the associate director of research for the National AI Institute for Adult Learning and Online Education, says AI raises the bar and it has the power to significantly impact learning in powerful ways. In this episode of the Harvard EdCast, Dede talks about how the field of education needs to evolve and get smarter, in order to work with -- not against -- artificial intelligence. This is the Harvard EdCast. Chris Dede thinks we need to get smarter about using artificial intelligence and education. He has spent decades exploring emerging learning technologies as a Harvard researcher.

This is part 1 of a three-part article series covering the recent news on AI involving OpenAI, Microsoft, Google, ChatGPT, the New Bing, and Bard, and how the events will unfold into a new era for AI, search, and the web (I'll add here part 2 and part 3 once they're published). That's the best way to describe what the AI industry has lived since Monday. Google and Microsoft are at the hottest point in their long-term relationship and everything is because of ChatGPT. In a surprise event on Tuesday, Microsoft announced the New Bing, powered by a "next-generation" AI model by OpenAI, just one day after Google disclosed they will soon release a ChatGPT competitor named Bard, and one day before its official presentation. We're living through a paradigm change in the way companies do AI with OpenAI, Microsoft, and Google (among others) as the leading characters of what promises to be an epic play. AI has turned into a product-focused blooming landscape that keeps the foot on the gas pedal. Many predicted this outcome the very moment OpenAI released ChatGPT--which recently topped off a record-breaking ascent to being the "fastest-growing consumer application" ever. Even zooming out, these events--from ChatGPT's release on Nov 30 to Microsoft's and Google's announcements this week--reflect a salient milestone in the history of AI (even if not technically speaking, at least at every other level). The AI industry will be deeply marked by them for the foreseeable future. This is my attempt to frame the facts objectively and hypothesize on the impending future that's looming over us.

ChatGPT has become one of the most popular artificial intelligence (AI) platforms ever. In just two months since its launch, it had amassed more than 100 million users. And people have been using it for a variety of tasks from coding, finding research material for college assignments, generating content, getting recipes and more. Since ChatGPT has made its name for providing answers to people's tough queries, I decided to ask it one of the most difficult questions ever presented in modern civilization? If you too are looking for a date this Valentine's day, then take a look and it just might be able to give you some Valentine's Day inspiration.