During my initial interactions with ChatGPT Plus, I was not fully convinced that OpenAI's $20-a-month subscription was worth it. While it was quite fun to test the upgraded chatbot powered by GPT-4, the free version seemed good enough for most prompts. I'm not a software developer who needs a deft coding assistant; I'm a nerd who uses chatbots to have entertaining conversations with artificial intelligence and brainstorm a little. On May 12, OpenAI announced that users who pay for ChatGPT Plus would be able to access beta versions of its chatbot with web browsing and plugins. Curious about the new features, I eschewed an evening of takeout, ate some gross leftovers, and spent money on finally upgrading my personal ChatGPT account.

Apple is famous for being protective of its projects and for expecting secrecy from its workers. Now, according to The Wall Street Journal, the tech giant is concerned about the possibility of its employees inadvertently leaking proprietary data while using ChatGPT. To prevent that scenario from happening, Apple has reportedly restricted the use of ChatGPT and other AI tools, such as GitHub's Copilot that can autocomplete code. The Journal also says that Apple is working on large language models of its own. In early April, The Economist Korea reported that three Samsung employees shared confidential information with ChatGPT.

New York attorney and writer Alexander Zubatov weighs in on how A.I. is rapidly changing society and says he's concerned about A.I. being used as a weapon against dissent on'The Ingraham Angle.' ChatGPT is a sophisticated artificial intelligence chatbot developed by AI research company OpenAI. The AI technology was added to Microsoft products including Bing, the corporation's search engine. ChatGPT is a generative AI that is capable of producing content from text to images, having conversations with humans, suggesting edits to computer programming code and more. The chatbot has the ability to answer questions or assist humans in queries or tasks through its vast training using social media, websites, articles, datasets, books and other forms of text on the internet. ChatGPT is set to be one of the most disruptive forces in Big Tech, specific industries like education and business, and for the future of the human workforce in coming years.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' You've probably heard of some of the biggest artificial intelligence chatbots being used and explored today, like ChatGPT and Google Bard. One artificial intelligence tool that may be new to you is Auto-GPT, an AI tool released at the end of March that is more advanced than both ChatGPT and Google Bard. Auto-GPT is a step closer to creating what is known as "strong AI," a type of AI that is likely what we pictured when we thought of AI in the past. These depictions often feature robots with human-like capabilities that were only seen in futuristic science-fiction movies.

In each case, the students confessed to using such systems and agreed to rewrite the assignments themselves. With all the panic about how students might use these systems to get around the burden of actually learning, we often forget that as of 2023, the systems don't work well at all. It was easy to spot these fraudulent essays. They used text that did not respond to the prompt we had issued to students. Or they just sounded unlike what a human would write.

Evidence retrieval is a core part of automatic fact-checking. Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: either no access to evidence, access to evidence curated by a human fact-checker, or access to evidence available long after the claim has been made. In this work, we present the first fully automated pipeline to check real-world claims by retrieving raw evidence from the web. We restrict our retriever to only search documents available prior to the claim's making, modeling the realistic scenario where an emerging claim needs to be checked. Our pipeline includes five components: claim decomposition, raw document retrieval, fine-grained evidence retrieval, claim-focused summarization, and veracity judgment. We conduct experiments on complex political claims in the ClaimDecomp dataset and show that the aggregated evidence produced by our pipeline improves veracity judgments. Human evaluation finds the evidence summary produced by our system is reliable (it does not hallucinate information) and relevant to answering key questions about a claim, suggesting that it can assist fact-checkers even when it cannot surface a complete evidence set.

This paper looks at the ability of large language models to participate in educational guided reading. We specifically, evaluate their ability to generate meaningful questions from the input text, generate diverse questions both in terms of content coverage and difficulty of the questions and evaluate their ability to recommend part of the text that a student should re-read based on the student's responses to the questions. Based on our evaluation of ChatGPT and Bard, we report that, 1) Large language models are able to generate high quality meaningful questions that have high correlation with the input text, 2) They generate diverse question that cover most topics in the input text even though this ability is significantly degraded as the input text increases, 3)The large language models are able to generate both low and high cognitive questions even though they are significantly biased toward low cognitive question, 4) They are able to effectively summarize responses and extract a portion of text that should be re-read.

The rise of large language models (LLMs) has unlocked various applications of this technology in software development. In particular, generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we present CodeCompose, an AI-assisted code authoring tool developed and deployed at Meta internally. CodeCompose is based on the InCoder LLM that merges generative capabilities with bi-directionality. We have scaled up CodeCompose to serve tens of thousands of developers at Meta, across 10+ programming languages and several coding surfaces. We discuss unique challenges in terms of user experience and metrics that arise when deploying such tools in large-scale industrial settings. We present our experience in making design decisions about the model and system architecture for CodeCompose that addresses these challenges. Finally, we present metrics from our large-scale deployment of CodeCompose that shows its impact on Meta's internal code authoring experience over a 15-day time window, where 4.5 million suggestions were made by CodeCompose. Quantitative metrics reveal that (i) CodeCompose has an acceptance rate of 22% across several languages, and (ii) 8% of the code typed by users of CodeCompose is through accepting code suggestions from CodeCompose. Qualitative feedback indicates an overwhelming 91.5% positive reception for CodeCompose. In addition to assisting with code authoring, CodeCompose is also introducing other positive side effects such as encouraging developers to generate more in-code documentation, helping them with the discovery of new APIs, etc.

Research publications are the primary vehicle for sharing scientific progress in the form of new discoveries, methods, techniques, and insights. Unfortunately, the lack of a large-scale, comprehensive, and easy-to-use resource capturing the myriad relationships between publications, their authors, and venues presents a barrier to applications for gaining a deeper understanding of science. In this paper, we present PubGraph, a new resource for studying scientific progress that takes the form of a large-scale knowledge graph (KG) with more than 385M entities, 13B main edges, and 1.5B qualifier edges. PubGraph is comprehensive and unifies data from various sources, including Wikidata, OpenAlex, and Semantic Scholar, using the Wikidata ontology. Beyond the metadata available from these sources, PubGraph includes outputs from auxiliary community detection algorithms and large language models. To further support studies on reasoning over scientific networks, we create several large-scale benchmarks extracted from PubGraph for the core task of knowledge graph completion (KGC). These benchmarks present many challenges for knowledge graph embedding models, including an adversarial community-based KGC evaluation setting, zero-shot inductive learning, and large-scale learning. All of the aforementioned resources are accessible at https://pubgraph.isi.edu/ and released under the CC-BY-SA license. We plan to update PubGraph quarterly to accommodate the release of new publications.

Automatic code generation has recently attracted large attention and is becoming more significant to the software development process. Solutions based on Machine Learning and Artificial Intelligence are being used to increase human and software efficiency in potent and innovative ways. In this paper, we aim to leverage these developments and introduce a novel approach to generating frontend component code for the popular Angular framework. We propose to do this using behavior-driven development test specifications as input to a transformer-based machine learning model. Our approach aims to drastically reduce the development time needed for web applications while potentially increasing software quality and introducing new research ideas toward automatic code generation.