I decided to take ChatGPT3 to a writing class. I mean a real writing class. One that takes you seriously and tries to get a story out of your soul. One that works hard to help you when you fail to express yourself and helps you find your way. ChatGPT3 is an impressive piece of technology.

OpenAI, the research lab behind the viral ChatGPT chatbot, is in talks to sell existing shares in a tender offer that would value the company at around $29 billion, according to people familiar with the matter, making it one of the most valuable U.S. startups on paper despite generating little revenue. Venture-capital firms Thrive Capital and Founders Fund are in talks to buy shares, the people said. The tender could total at least $300 million in OpenAI share sales, they said. The deal is structured as a tender offer, with the investors buying shares from existing shareholders such as employees, the people said.

The use of advanced analytics has become increasingly important in today's data-driven world. By leveraging advanced analytics techniques, organizations are able to uncover valuable insights and make better-informed decisions. However, in order to get the most out of advanced analytics, it is important to have a well-structured team in place. One effective way to structure an advanced analytics team is to have a multi-disciplinary approach. This means bringing together individuals with a range of skills and expertise, including data scientists, business analysts, and subject matter experts. This allows the team to tackle complex problems from multiple angles and to draw on a wide range of knowledge and experience.

Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: (1) How is the zero-shot capability of the models (train on seen types and test on unseen types)? (2) How to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes both the heuristic input reconstruction and the contrastive learning to improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset.

These platforms collect data about both users' and items' attributes, as well as accumulate the ratings and feedback of products and services, to develop algorithms for significant enhancement of users' experience on the marketplace. These algorithms are capable of influencing the purchasing behavior of users by (1) offering them the selection of the most relevant personalized positions, (2) reducing the individual searching costs, and (3) alleviating the information asymmetry on large commercial platforms with homogeneous sellers and products through feedback mechanisms. Since recommender systems have the power to affect the marketing decisions of users, they have become an attractive target for ratings and reviews manipulations, also known as attacks. Specifically, these attacks are aimed at inflating/deflating the ranks and text reviews of certain product positions or at simply sabotaging the efficiency and credibility of the the commercial platform in general. The current study focuses on solving the task of filtering out the deceptive opinions and detecting anomalous behavior on a platform with text reviews. The emphasis on text reviews can be explained by the fact that texts are a more informative and a more reliable source of product's and seller's quality, than a star-rating system, which is easy to manipulate (see [19], [14], [27], [28]).

We carried out a reproducibility study of InPars recipe for unsupervised training of neural rankers. As a by-product of this study, we developed a simple-yet-effective modification of InPars, which we called InPars-light. Unlike InPars, InPars-light uses only a freely available language model BLOOM and 7x-100x smaller ranking models. On all five English retrieval collections (used in the original InPars study) we obtained substantial (7-30%) and statistically significant improvements over BM25 in nDCG or MRR using only a 30M parameter six-layer MiniLM ranker. In contrast, in the InPars study only a 100x larger MonoT5-3B model consistently outperformed BM25, whereas their smaller MonoT5-220M model (which is still 7x larger than our MiniLM ranker), outperformed BM25 only on MS MARCO and TREC DL 2020. In a purely unsupervised setting, our 435M parameter DeBERTA v3 ranker was roughly at par with the 7x larger MonoT5-3B: In fact, on three out of five datasets, it slightly outperformed MonoT5-3B. Finally, these good results were achieved by re-ranking only 100 candidate documents compared to 1000 used in InPars. We believe that InPars-light is the first truly cost-effective prompt-based unsupervised recipe to train and deploy neural ranking models that outperform BM25.

Five weeks ago, OpenAI, a San Francisco artificial intelligence lab, released ChatGPT, a chatbot that answers questions in clear, concise prose. The A.I.-powered tool immediately caused a sensation, with more than a million people using it to create everything from poetry to high school term papers to rewrites of Queen songs. Now OpenAI is in the midst of a new gold rush. The lab is in talks to complete a deal that would value it at around $29 billion, more than twice its valuation in 2021, two people with knowledge of the discussions said. The potential deal -- where OpenAI would sell existing company shares in a so-called tender offer -- could total $300 million, depending on how many employees agree to sell their stock, they said.

The International Conference on Machine Learning (ICML) announced the policy earlier this week, stating, "Papers that include text generated from a large-scale language model (LLM) such as ChatGPT are prohibited unless the produced text is presented as a part of the paper's experimental analysis." The news sparked widespread discussion on social media, with AI academics and researchers both defending and criticizing the policy. The conference's organizers responded by publishing a longer statement explaining their thinking.

'The Five' panelists reacts to a new artificial intelligence bot, ChatGPT, that's capable of writing essays, books, poems and even computer code upon request. The New York City Department of Education has reportedly banned access to the popular artificial intelligence tool ChatGPT over fears it would harm students' education and in order to help prevent cheating. The controversial free writing tool can generate paragraphs of human-like text. ""Due to concerns about negative impacts on student learning, and concerns regarding the safety and accuracy of content, access to ChatGPT is restricted on New York City Public Schools' networks and devices," Education Department spokesperson Jenna Lyle first told Chalkbeat. "While the tool may be able to provide quick and easy answers to questions, it does not build critical-thinking and problem-solving skills, which are essential for academic and lifelong success."

Check out all the on-demand sessions from the Intelligent Security Summit here. Since the release of ChatGPT in November, there has been a lot of speculation about OpenAI's latest large language model (LLM) spelling doom for Google Search. The sentiment has only intensified with the recent report of Microsoft preparing to integrate ChatGPT into its Bing search engine. There are several reasons to believe that a ChatGPT-powered Bing (or any other search engine) will not seriously threaten Google's search near-monopoly. LLMs have several critical problems to solve before they can make a dent in the online search industry. Meanwhile, Google's share of the search market, its technical ability and its financial resources will help it remain competitive (and possibly dominant) as conversational LLMs start to make their mark in online search.