MOUNTAIN VIEW, Calif., Feb 22 (Reuters) - As Alphabet Inc (GOOGL.O) looks past a chatbot flub that helped erase $100 billion from its market value, another challenge is emerging from its efforts to add generative artificial intelligence to its popular Google Search: the cost. Executives across the technology sector are talking about how to operate AI like ChatGPT while accounting for the high expense. The wildly popular chatbot from OpenAI, which can draft prose and answer search queries, has "eye-watering" computing costs of a couple or more cents per conversation, the startup's Chief Executive Sam Altman has said on Twitter. In an interview, Alphabet's Chairman John Hennessy told Reuters that having an exchange with AI known as a large language model likely cost 10 times more than a standard keyword search, though fine-tuning will help reduce the expense quickly. Even with revenue from potential chat-based search ads, the technology could chip into the bottom line of Mountain View, Calif.-based Alphabet with several billion dollars of extra costs, analysts said.

Separately, in February French grocery chain Carrefour produced its first-ever video made with ChatGPT answering FAQs. The 30-second video has a robot speaking in French and answering common questions from customers like "how to eat better and cheaper via its website." Carrefour's Chief E-commerce Officer Elodie Perthuisot wrote in a LinkedIn post that Carrefour Carrefour's "data and innovation teams are currently working on the use cases of ChatGPT, and generative AI in general." Analysts and grocery tech executives that Modern Retail spoke with said while all types of retailers are excited about using ChatGPT, grocers have compelling reasons to jump into this head first, for a few reasons. For starters, grocers have among the most diverse customer base.

If you haven't heard of ChatGPT, the uncanny new AI-driven chatbot from San Francisco-based OpenAI, here is a quick primer on everything you need to know about the controversial new program. ChatGPT is an artificial intelligence tool that allows a user to generate original text. You can ask it questions, give it creative prompts, and use it to generate a whole bunch of different stuff--from poems, to songs, to essays, to short stories. ChatGPT was created by OpenAI and launched in November of last year. Partially founded by Elon Musk, OpenAI is an organization that is dedicated to the research and development of artificial intelligence.

With the above examples of its applications, we have only scratched the surface of the vast business potential that ChatGPT offers to industries across the board. Revolutionary content creation, enhanced customer experiences, and targeted product recommendations enabled by this conversational AI solution can unlock several strategies for continued growth. If you are looking for expertise on the various applications of AI, we highly recommend booking a free consultation with us. Our AI Center of Excellence (CoE) can provide you with highly advanced solutions driven by AI and machine learning approaches.

The Diproche system is an automated proof checker for texts written in a controlled fragment of German, designed for didactical applications in classes introducing students to proofs for the first time. The first version of the system used a controlled natural language for which a Prolog formalization routine was written. In this paper, we explore the possibility of prompting large language models for autoformalization in the context of Diproche, with encouraging first results.

Mobile app stores produce a tremendous amount of data in the form of user reviews, which is a huge source of user requirements and sentiments; such reviews allow app developers to proactively address issues in their apps. However, only a small number of reviews capture common issues and sentiments which creates a need for automatically identifying prominent reviews. Unfortunately, most existing work in text ranking and popularity prediction focuses on social contexts where other signals are available, which renders such works ineffective in the context of app reviews. In this work, we propose a new framework, PPrior, that enables proactive prioritization of app issues through identifying prominent reviews (ones predicted to receive a large number of votes in a given time window). Predicting highly-voted reviews is challenging given that, unlike social posts, social network features of users are not available. Moreover, there is an issue of class imbalance, since a large number of user reviews receive little to no votes. PPrior employs a pre-trained T5 model and works in three phases. Phase one adapts the pre-trained T5 model to the user reviews data in a self-supervised fashion. In phase two, we leverage contrastive training to learn a generic and task-independent representation of user reviews. Phase three uses radius neighbors classifier t o m ake t he final predictions. This phase also uses FAISS index for scalability and efficient search. To conduct extensive experiments, we acquired a large dataset of over 2.1 million user reviews from Google Play. Our experimental results demonstrate the effectiveness of the proposed framework when compared against several state-of-the-art approaches. Moreover, the accuracy of PPrior in predicting prominent reviews is comparable to that of experienced app developers.

Asking insightful questions is crucial for acquiring knowledge and expanding our understanding of the world. However, the importance of questioning has been largely overlooked in AI research, where models have been primarily developed to answer questions. With the recent advancements of large language models (LLMs) like ChatGPT, we discover their capability to ask high-quality questions when provided with a suitable prompt. This discovery presents a new opportunity to develop an automatic questioning system. In this paper, we introduce ChatCaptioner, a novel automatic-questioning method deployed in image captioning. Here, ChatGPT is prompted to ask a series of informative questions about images to BLIP-2, a strong vision question-answering model. By keeping acquiring new visual information from BLIP-2's answers, ChatCaptioner is able to generate more enriched image descriptions. We conduct human-subject evaluations on common image caption datasets such as COCO, Conceptual Caption, and WikiArt, and compare ChatCaptioner with BLIP-2 as well as ground truth. Our results demonstrate that ChatCaptioner's captions are significantly more informative, receiving three times as many votes from human evaluators for providing the most image information. Besides, ChatCaptioner identifies 53% more objects within the image than BLIP-2 alone measured by WordNet synset matching. Code is available at https://github.com/Vision-CAIR/ChatCaptioner

Incorporating prior knowledge can improve existing pre-training models in cloze-style machine reading and has become a new trend in recent studies. Notably, most of the existing models have integrated external knowledge graphs (KG) and transformer-based models, such as BERT into a unified data structure. However, selecting the most relevant ambiguous entities in KG and extracting the best subgraph remains a challenge. In this paper, we propose the LUKE-Graph, a model that builds a heterogeneous graph based on the intuitive relationships between entities in a document without using any external KG. We then use a Relational Graph Attention (RGAT) network to fuse the graph's reasoning information and the contextual representation encoded by the pre-trained LUKE model. In this way, we can take advantage of LUKE, to derive an entity-aware representation; and a graph model - to exploit relation-aware representation. Moreover, we propose Gated-RGAT by augmenting RGAT with a gating mechanism that regulates the question information for the graph convolution operation. This is very similar to human reasoning processing because they always choose the best entity candidate based on the question information. Experimental results demonstrate that the LUKE-Graph achieves state-of-the-art performance on the ReCoRD dataset with commonsense reasoning.

ChatGPT is an AI language model developed by OpenAI that has gained widespread recognition for its ability to generate human-like responses to natural language input. One of the key technologies that underlie the development of ChatGPT is Python, which is a high-level, interpreted programming language widely used in the field of artificial intelligence and machine learning. Python is an ideal language for developing AI models like ChatGPT because of its simplicity, flexibility, and vast ecosystem of libraries and frameworks. Python has become the language of choice for machine learning and natural language processing due to its ease of use, readability, and high-level syntax, which makes it easy to write and understand complex algorithms. One of the key libraries used in the development of ChatGPT is TensorFlow, an open-source machine learning library developed by Google.

Are you tired of staring at a blank screen, trying to create the perfect Twitter thread to engage your followers? Do you want to take your Twitter game to the next level? Well, we've got just the thing for you! Introducing 333 Twitter thread ChatGPT prompts resource - the ultimate toolkit to help you create engaging, share-worthy Twitter threads that will leave your followers begging for more. With my customizable prompts, you can effortlessly create eye-catching threads to grab your followers' attention.