The task of Argument Mining, that is extracting argumentative sentences for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large Argument Mining datasets are rare and recognition of argumentative sentences requires expert knowledge. The task becomes even more difficult if it also involves stance detection of retrieved arguments. Given the cost and complexity of creating suitably large Argument Mining datasets, we ask whether it is necessary for acceptable performance to have datasets growing in size. Our findings show that, when using carefully composed training samples and a model pretrained on related tasks, we can reach 95% of the maximum performance while reducing the training sample size by at least 85%. This gain is consistent across three Argument Mining tasks on three different datasets. We also publish a new dataset for future benchmarking.

Elon Musk has officially introduced his xAI team to the masses with a live Twitter Spaces event - after years of claiming the tech will be the demise of humanity. The Twitter boss laid out his plans to make an artificial general intelligence (AGI) that will be'maximally curious and truth-seeking' and'won't be politically correct.' 'People will be offended,' Musk said. 'Our AI can give answers that they might find controversial even though they might be true.' But beyond the AI culture wars, Musk expressed ambitious hopes to produce an AGI with deep analytical reasoning capable of solving higher order math and science problems, including many that have eluded mankind's best thinkers. The billionaire suggested that xAI could answer questions about the nature of dark matter and dark energy: theorized but difficult to confirm components of the known universe which astrophysicists estimate constitute 95 percent of the cosmos. Musk also said he hoped xAI could help resolve the'Fermi paradox' -- a theoretical question that asks why humans have not yet encountered extraterrestrial life in a universe that is over 13 billion years old and ripe with the conditions supporting life.

Musk has been outspoken about AI for years, famously saying in 2014 that inventing super-intelligent computers would be like "summoning the demon" and could create an existential threat to humanity. He helped found ChatGPT-maker OpenAI, in 2015, but left the company in 2018 after disagreements with its other leaders. Over the past few months, he has complained about OpenAI and other AI companies scraping Twitter data to help train their bots.

Before that, he co-founded DeepMind, one of the world's leading artificial intelligence companies. AI systems are increasingly everywhere and are becoming more powerful almost by the day. But how can we know if a machine is truly "intelligent"? For decades this has been defined by the Turing test, which argues that an AI that's able to replicate language convincingly enough to trick a human into thinking it was also human should be considered intelligent. But there's now a problem: the Turing test has almost been passed--it arguably already has been.

But there's now a problem: the Turing test has almost been passed--it arguably already has been. The latest generation of large language models, systems that generate text with a coherence that just a few years ago would have seemed magical, are on the cusp of acing it. So where does that leave AI? And more important, where does it leave us? The truth is, I think we're in a moment of genuine confusion (or, perhaps more charitably, debate) about what's really happening.

The United States' competition watchdog has opened an investigation into ChatGPT creator OpenAI amid suspicions the startup broke the law by scraping public data and publishing false and defamatory information. In a 20-page letter, the US Federal Trade Commission (FTC) has requested OpenAI to provide detailed information about its technology and privacy protections, including any efforts to prevent a repeat of incidents in which its groundbreaking chatbot published false and disparaging information about members of the public. The Washington Post first reported on the "expansive" probe on Thursday. The FTC declined to comment when contacted by Al Jazeera. OpenAI chief executive Sam Altman said the leak of the regulator's probe was "disappointing" and would not help build trust.

The Associated Press is licensing a part its archive of news stories to OpenAI under a deal that will explore generative AI's use in news, the companies said Thursday, a move that could set the precedent for similar partnerships between the industries. The news publisher will gain access to OpenAI's technology and product expertise as part of the deal, the financial details of which were not disclosed. AP also did not reveal how it would integrate OpenAI's technology in its news operations. The publisher already uses AI for automating corporate earnings reports, recapping sporting events and transcription for certain live events. This could be due to a conflict with your ad-blocking or security software.

In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. Our framework alternates between updating two language models -- an elaboration generator and an answer predictor -- allowing each to influence the other. Using less than 0.5% of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap on GPT-3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high.

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

This paper proposes a ChatGPT-based zero-shot Text-to-SQL method, dubbed C3, which achieves 82.3\% in terms of execution accuracy on the holdout test set of Spider and becomes the state-of-the-art zero-shot Text-to-SQL method on the Spider Challenge. C3 consists of three key components: Clear Prompting (CP), Calibration with Hints (CH), and Consistent Output (CO), which are corresponding to the model input, model bias and model output respectively. It provides a systematic treatment for zero-shot Text-to-SQL. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposed method.