In its August edition, Resources Policy, an academic journal under the Elsevier publishing umbrella, featured a peer-reviewed study about how ecommerce has affected fossil fuel efficiency in developing nations. But buried in the report was a curious sentence: "Please note that as an AI language model, I am unable to generate specific tables or conduct tests, so the actual results should be included in the table." The study's three listed authors had names and university or institutional affiliations--they did not appear to be AI language models. But for anyone who has played around in ChatGPT, that phrase may sound familiar: The generative AI chatbot often prefaces its statements with this caveat, noting its weaknesses in delivering some information. After a screenshot of the sentence was posted to X, formerly Twitter, by another researcher, Elsevier began investigating.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' The deputy prime minister of the United Kingdom is speculating that the proliferation of artificial intelligence will have a bigger impact on the nation than the Industrial Revolution. "This is a total revolution that is coming," deputy PM Oliver Dowden told The Times. "It's going to totally transform almost all elements of life over the coming years, and indeed, even months, in some cases." "It is much faster than other revolutions that we've seen and much more extensive, whether that's the invention of the internal combustion engine or the Industrial Revolution," he added.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' House Democrats are launching a working group aimed at crafting artificial intelligence policy, the latest attempt by federal lawmakers to wrap their heads around legislating the rapidly-advancing sector. The New Democrat Coalition, a group of nearly 100 House Democrats that touts itself as "pragmatic," unveiled the new initiative this week. Rep. Don Beyer, D-Va., one of the initiative's vice chairs, told Fox News Digital he hopes the working group will "help develop real, practicable ideas that will put guardrails in place for AI. "I continue to be focused on a variety of areas related to AI, including safety and security, transparency, the future of work, preventing civil rights abuses, health care and suicide prevention, and more, and have discussions ongoing about legislation in these areas with members of both parties," Beyer said. "Congress has to get up to speed on this issue, and I think the New Dems' AI working group will be a constructive setting for progress." The Biden administration and Congress are examining how to regulate AI. Working group Chair Rep. Derek Kilmer, D-Wash., suggested it could lay the groundwork for an AI regulatory framework in the House of Representatives. "We are already seeing how breakthroughs in this emerging technology present both great opportunities and challenges with potential disruptions for workers, for democracy, and for national security," Kilmer said. "As AI's applications expand and change, it is incumbent on lawmakers to address its unique opportunities and challenges by creating a regulatory framework that both encourages growth while guarding against potential risks." WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Rep. Seth Moulton, D-Mass., another member of the working group and a Marine veteran, said he was concerned with how AI would "transform warfare" and called on Congress to put up responsible guardrails against the technology's most devastating possibilities. "It's going to be impossible for Congress to really stay ahead of AI, but what we can and should do is to take very seriously AI's most dangerous use cases and develop solutions and safeguards that apply directly to those cases," Moulton told Fox News Digital. "I'm also particularly concerned about how AI will transform warfare.

Three decades later, Hozier's debut single Take Me to Church, which he said criticised the church's teaching of "shame about sexual orientation", reached number one in 12 countries and remains the 30th most streamed song of all time.

'Woke Inc.' author Vivek Ramaswamy called the relationship between Big Tech and the government threat to liberty because'each can do what the other cannot.' The political fable, "Animal Farm," written by visionary George Orwell, was published on this day in history, Aug. 17, 1945. The plot of "Animal Farm" is based on the story of the Russian Revolution and its betrayal by Joseph Stalin and is deemed an allegory, according to Britannica.com The novella tells the story of a group of barnyard animals that overthrow and chase off their exploitative human masters -- and set up an egalitarian society of their own, the same source chronicles. As "Animal Farm" opens, Mr. Jones, the owner of Manor Farm, is intoxicated and heading to bed.

Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability. While recent efforts have focuses on connecting LLMs with external knowledge sources, the integration of knowledge bases (KBs) remains understudied and faces several challenges. In this paper, we introduce KnowledGPT, a comprehensive framework to bridge LLMs with various knowledge bases, facilitating both the retrieval and storage of knowledge. The retrieval process employs the program of thought prompting, which generates search language for KBs in code format with pre-defined functions for KB operations. Besides retrieval, KnowledGPT offers the capability to store knowledge in a personalized KB, catering to individual user demands. With extensive experiments, we show that by integrating LLMs with KBs, KnowledGPT properly answers a broader range of questions requiring world knowledge compared with vanilla LLMs, utilizing both knowledge existing in widely-known KBs and extracted into personalized KBs.

Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through historical user-item interactions, which may potentially violate user privacy. Additionally, these approaches often overlook the significance of the temporal fluctuation in item popularity that can sway users' decision-making. To bridge this gap, we propose Popularity-Aware Recommender (PARE), which makes non-personalized recommendations by predicting the items that will attain the highest popularity. PARE consists of four modules, each focusing on a different aspect: popularity history, temporal impact, periodic impact, and side information. Finally, an attention layer is leveraged to fuse the outputs of four modules. To our knowledge, this is the first work to explicitly model item popularity in recommendation systems. Extensive experiments show that PARE performs on par or even better than sophisticated state-of-the-art recommendation methods. Since PARE prioritizes item popularity over personalized user preferences, it can enhance existing recommendation methods as a complementary component. Our experiments demonstrate that integrating PARE with existing recommendation methods significantly surpasses the performance of standalone models, highlighting PARE's potential as a complement to existing recommendation methods. Furthermore, the simplicity of PARE makes it immensely practical for industrial applications and a valuable baseline for future research.

Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale. Although detection methods for such AI-generated text exist already, we investigate ChatGPT's performance as a detector on such AI-generated text, inspired by works that use ChatGPT as a data labeler or annotator. We evaluate the zeroshot performance of ChatGPT in the task of human-written vs. AI-generated text detection, and perform experiments on publicly available datasets. We empirically investigate if ChatGPT is symmetrically effective in detecting AI-generated or human-written text. Our findings provide insight on how ChatGPT and similar LLMs may be leveraged in automated detection pipelines by simply focusing on solving a specific aspect of the problem and deriving Figure 1: We use OpenAI's ChatGPT as a detector to distinguish the rest from that solution. All code and data is available at between human-written and AI-generated text.

In this paper, we study the problem of generating obstinate (over-stability) adversarial examples by word substitution in NLP, where input text is meaningfully changed but the model's prediction does not, even though it should. Previous word substitution approaches have predominantly focused on manually designed antonym-based strategies for generating obstinate adversarial examples, which hinders its application as these strategies can only find a subset of obstinate adversarial examples and require human efforts. To address this issue, in this paper, we introduce a novel word substitution method named GradObstinate, a gradient-based approach that automatically generates obstinate adversarial examples without any constraints on the search space or the need for manual design principles. To empirically evaluate the efficacy of GradObstinate, we conduct comprehensive experiments on five representative models (Electra, ALBERT, Roberta, DistillBERT, and CLIP) finetuned on four NLP benchmarks (SST-2, MRPC, SNLI, and SQuAD) and a language-grounding benchmark (MSCOCO). Extensive experiments show that our proposed GradObstinate generates more powerful obstinate adversarial examples, exhibiting a higher attack success rate compared to antonym-based methods. Furthermore, to show the transferability of obstinate word substitutions found by GradObstinate, we replace the words in four representative NLP benchmarks with their obstinate substitutions. Notably, obstinate substitutions exhibit a high success rate when transferred to other models in black-box settings, including even GPT-3 and ChatGPT. Examples of obstinate adversarial examples found by GradObstinate are available at https://huggingface.co/spaces/anonauthors/SecretLanguage.

A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing auxiliary information for a triplet. While existing hyper-relational knowledge graph embedding methods assume that the entities are discrete objects, some information should be represented using numeric values, e.g., (J.R.R., was born in, 1892). Also, a triplet (J.R.R., educated at, Oxford Univ.) can be associated with a qualifier such as (start time, 1911). In this paper, we propose a unified framework named HyNT that learns representations of a hyper-relational knowledge graph containing numeric literals in either triplets or qualifiers. We define a context transformer and a prediction transformer to learn the representations based not only on the correlations between a triplet and its qualifiers but also on the numeric information. By learning compact representations of triplets and qualifiers and feeding them into the transformers, we reduce the computation cost of using transformers. Using HyNT, we can predict missing numeric values in addition to missing entities or relations in a hyper-relational knowledge graph. Experimental results show that HyNT significantly outperforms state-of-the-art methods on real-world datasets.