Articles for author: ikayaniaamirshahzad@gmail.com

Why genAI-powered intelligent document processing is a big deal

With LLMs, the processing can be more dynamic. First, prompts and examples can steer LLMs toward the information extraction goals and help them work around document complexities. Second, the same LLMs can be used for ad hoc querying, and feedback mechanisms can be instrumented to improve the information extractions based on end-user prompts. “The advancement of genAI and LLMs is allowing us to use natural language to describe a desired program, expression, or result, and they are particularly good at extracting data from unstructured and multimodal sources,” says Greg Benson, professor of computer science at the University of San Francisco

Users Report Emotional Bonds With Startlingly Realistic AI Voice Demo

An anonymous reader quotes a report from Ars Technica: In late 2013, the Spike Jonze film Her imagined a future where people would form emotional connections with AI voice assistants. Nearly 12 years later, that fictional premise has veered closer to reality with the release of a new conversational voice model from AI startup Sesame that has left many users both fascinated and unnerved. “I tried the demo, and it was genuinely startling how human it felt,” wrote one Hacker News user who tested the system. “I’m almost a bit worried I will start feeling emotionally attached to a voice

Introducing Muse: Our first generative AI model designed for gameplay ideation

Today, the journal Nature (opens in new tab) is publishing our latest research, which introduces the first World and Human Action Model (WHAM). The WHAM, which we’ve named “Muse,” is a generative AI model of a video game that can generate game visuals, controller actions, or both. The paper in Nature offers a detailed look at Muse, which was developed by the Microsoft Research Game Intelligence (opens in new tab) and Teachable AI Experiences (opens in new tab) (Tai X) teams in collaboration with Xbox Games Studios’ Ninja Theory (opens in new tab). Simultaneously, to help other researchers explore these models

TSMC diversifies from Taiwan with record US investment in chip production

Summary Taiwan Semiconductor Manufacturing Co (TSMC), the world’s leading contract chipmaker, has announced a massive expansion of its US operations. According to a company press release, TSMC will invest an additional $100 billion in American semiconductor production on top of the $65 billion already committed, bringing the total investment to $165 billion – the largest foreign direct investment in US history. The new investment will fund the construction of three production facilities, two advanced packaging plants, and a research and development center. TSMC expects to create approximately 40,000 construction jobs over the next four years, with tens of thousands of

Curating High-Quality Customer Identities with Databricks and Amperity

When we think of use cases like product recommendations, churn predictions, advertising attribution and fraud detection, a common denominator is they all require us to consistently identify our customers across various interactions. Failing to recognize that the same person is browsing online, purchasing in-store, opening a marketing email and clicking on an advertisement, leaves us with an incomplete view of the customer, limiting our ability to recognize their needs, preferences and predict their future behavior. Despite its importance, accurately identifying the customer across these interactions is incredibly difficult. People often interact with us without providing explicit identifying details, and when

Reddit – Dive into anything

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Reddit – Dive into anything

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

How to Build a MERN Stack To-Do App

This guide will walk you through building a full-stack MERN To-Do application. It covers setting up the environment, writing code to demonstrate core CRUD (Create, Read, Update, Delete) operations, and connecting the application to MongoDB Atlas, a free cloud database. Before diving into this article, I recommend that you have a foundational understanding of HTML, CSS, and JavaScript, as well as some knowledge of frontend and backend frameworks and libraries. My primary focus will be on functionality, allowing you to customize the design as you see fit. The commands I’ll use here are tailored for Windows, so if you’re using

Eerily realistic AI voice demo sparks amazement and discomfort online

An example argument with Sesame’s CSM created by Gavin Purcell. An example argument with Sesame’s CSM created by Gavin Purcell. Gavin Purcell, co-host of the AI for Humans podcast, posted an example video on Reddit where the human pretends to be an embezzler and argues with a boss. It’s so dynamic that it’s difficult to tell who the human is and which one is the AI model. Judging by our own demo, it’s entirely capable of what you see in the video. “Near-human quality” Under the hood, Sesame’s CSM achieves its realism by using two AI models working together (a

Understanding Convolutions on Graphs

Contents This article is one of two Distill publications about graph neural networks. Take a look at A Gentle Introduction to Graph Neural Networks for a companion view on many things graph and neural network related. Many systems and interactions – social networks, molecules, organizations, citations, physical models, transactions – can be represented quite naturally as graphs. How can we reason about and make predictions within these systems? One idea is to look at tools that have worked well in other domains: neural networks have shown immense predictive power in a variety of learning tasks. However, neural networks have been