Twitter CEO Elon Musk provides insight on the consequences of developing artificial intelligence and the potential impact on elections on'Tucker Carlson Tonight.' Academic leaders from around the world penned an open letter calling on artificial intelligence developers to learn more about consciousness as artificial intelligence (AI) systems advance rapidly, giving it a prominent place in our moral landscape, raising ethnical, legal and political concerns. The Association for Mathematical Consciousness Science (AMCS), "a large community of over 150 international researchers who are spearheading mathematical and computational approaches to consciousness," published a letter Wednesday as "a wakeup call for the tech sector, the scientific community and society in general to take seriously the need to accelerate research in the field of consciousness science." The Association for Mathematical Consciousness Science published an open letter calling on "the tech sector, the scientific community and society in general to take seriously the need to accelerate research in the field of consciousness science." Its writers referenced the recent letter written by leaders in tech that called for a pause in AI experiments, noting "we are living through an exciting and uncertain time in the development of artificial intelligence (AI) and other brain-related technologies" and warned that AI is "accelerating at a pace that far exceeds our progress in understanding their capabilities and their'alignment' with human values." Signatories of the letter argue that language models like OpenAI's ChatGPT and Google's Bard are based on the neural networks of animal brains, but in the near future will be constructed to mimic "aspects of higher-level brain architecture and functioning."

This month, a local TV-news station in Arizona ran an unsettling report: A mother named Jennifer DeStefano says that she picked up the phone to the sound of her 15-year-old crying out for her, and was asked to pay a $1 million ransom for her daughter's return. In reality, the teen had not been kidnapped, and was safe; DeStefano believes someone used AI to create a replica of her daughter's voice to deploy against her family. "It was completely her voice," she said in one interview. It was the way she would have cried." DeStefano's story has since been picked up by other outlets, while similar stories of AI voice scams have surfaced on TikTok and been reported by The Washington Post.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' The use of artificial intelligence among Americans has skyrocketed since the release of platforms such as ChatGPT, and a new study found that residents of states out West are far more likely to use AI than Southern states. "The use of Artificial Intelligence in the US is on the rise, and it's clear to see why," a spokesperson for YACSS, an AI-driven company that builds websites and also conducted the study, said of the findings in a report provided to Fox News Digital. "It is frequently used to reduce time spent on tedious tasks as well as provide users with endless creative possibilities, and this is all available at the touch of a button." The study, released this month, examined Google data on keywords frequently searched by people interested in artificial intelligence over a 12-month span, and averaged each state's monthly search volume for such terms per 100,000 people.

Elon Musk caused a stir last week when he told the (recently fired) right-wing provocateur Tucker Carlson that he plans to build "TruthGPT," a competitor to OpenAI's ChatGPT. Musk says the incredibly popular bot displays "woke" bias and that his version will be a "maximum truth-seeking AI"--suggesting only his own political views reflect reality. Musk is far from the only person worried about political bias in language models, but others are trying to use AI to bridge political divisions rather than push particular viewpoints. David Rozado, a data scientist based in New Zealand, was one of the first people to draw attention to the issue of political bias in ChatGPT. Several weeks ago, after documenting what he considered liberal-leaning answers from the bot on issues including taxation, gun ownership, and free markets, he created an AI model called RightWingGPT that expresses more conservative viewpoints.

One of the most anticipated games of the year is almost here. Legend of Zelda: Tears of the Kingdom may seem to feature the same basic graphics, map layout and general mechanics as its predecessor, Breath of the Wild, but it breaks new ground with Link's new skills – Ascend (shooting to the ceiling), Recall (rewinding time for an item), Fuse (combining items and weapons for countless effects) and Ultrahand (building machines). These can seemingly help fight enemies or get you from A to B. I'm now pretty excited for May 12th. Check out all of our impressions from a 75-minute playthrough. Great deals on consumer electronics delivered straight to your inbox, curated by Engadget's editorial team.

Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. The automatic and human evaluation of our augmented data allows us to explore whether (back-)translation aid or hinder performance. Our in-depth analyses indicate that both data augmentation strategies boost performance; however, balancing human-produced and machine-generated data seems to be crucial.

Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either directly finetunes or prompts a pretrained language model (LM) to leverage the stored knowledge. However, they do not fully exploit the parameterized knowledge. To address this issue, we propose a two-stage, closed-book QA framework which employs a coarse-to-fine approach to extract relevant knowledge and answer a question. Our approach first generates a related context for a given question by prompting a pretrained LM. We then prompt the same LM for answer prediction using the generated context and the question. Additionally, to eliminate failure caused by context uncertainty, we marginalize over generated contexts. Experimental results on three QA benchmarks show that our method significantly outperforms previous closed-book QA methods (e.g. exact matching 68.6% vs. 55.3%), and is on par with open-book methods that exploit external knowledge sources (e.g. 68.6% vs. 68.0%). Our method is able to better exploit the stored knowledge in pretrained LMs without adding extra learnable parameters or needing finetuning, and paves the way for hybrid models that integrate pretrained LMs with external knowledge.

The strive to make AI applications "safe" has led to the development of safety-measures as the main or even sole normative requirement of their permissible use. Similar can be attested to the latest version of chatbots, such as chatGPT. In this view, if they are "safe", they are supposed to be permissible to deploy. This approach, which we call "safety-normativity", is rather limited in solving the emerging issues that chatGPT and other chatbots have caused thus far. In answering this limitation, in this paper we argue for limiting chatbots in the range of topics they can chat about according to the normative concept of appropriateness. We argue that rather than looking for "safety" in a chatbot's utterances to determine what they may and may not say, we ought to assess those utterances according to three forms of appropriateness: technical-discursive, social, and moral. We then spell out what requirements for chatbots follow from these forms of appropriateness to avoid the limits of previous accounts: positionality, acceptability, and value alignment (PAVA). With these in mind, we may be able to determine what a chatbot may and may not say. Lastly, one initial suggestion is to use challenge sets, specifically designed for appropriateness, as a validation method.

Large language models (LLMs) have demonstrated impressive zero-shot abilities on a variety of open-ended tasks, while recent research has also explored the use of LLMs for multi-modal generation. In this study, we introduce mPLUG-Owl, a novel training paradigm that equips LLMs with multi-modal abilities through modularized learning of foundation LLM, a visual knowledge module, and a visual abstractor module. This approach can support multiple modalities and facilitate diverse unimodal and multimodal abilities through modality collaboration. The training paradigm of mPLUG-Owl involves a two-stage method for aligning image and text, which learns visual knowledge with the assistance of LLM while maintaining and even improving the generation abilities of LLM. In the first stage, the visual knowledge module and abstractor module are trained with a frozen LLM module to align the image and text. In the second stage, language-only and multi-modal supervised datasets are used to jointly fine-tune a low-rank adaption (LoRA) module on LLM and the abstractor module by freezing the visual knowledge module. We carefully build a visually-related instruction evaluation set OwlEval. Experimental results show that our model outperforms existing multi-modal models, demonstrating mPLUG-Owl's impressive instruction and visual understanding ability, multi-turn conversation ability, and knowledge reasoning ability. Besides, we observe some unexpected and exciting abilities such as multi-image correlation and scene text understanding, which makes it possible to leverage it for harder real scenarios, such as vision-only document comprehension. Our code, pre-trained model, instruction-tuned models, and evaluation set are available at https://github.com/X-PLUG/mPLUG-Owl. The online demo is available at https://www.modelscope.cn/studios/damo/mPLUG-Owl.

Identifying the frames of news is important to understand the articles' vision, intention, message to be conveyed, and which aspects of the news are emphasized. Framing is a widely studied concept in journalism, and has emerged as a new topic in computing, with the potential to automate processes and facilitate the work of journalism professionals. In this paper, we study this issue with articles related to the Covid-19 anti-vaccine movement. First, to understand the perspectives used to treat this theme, we developed a protocol for human labeling of frames for 1786 headlines of No-Vax movement articles of European newspapers from 5 countries. Headlines are key units in the written press, and worth of analysis as many people only read headlines (or use them to guide their decision for further reading.) Second, considering advances in Natural Language Processing (NLP) with large language models, we investigated two approaches for frame inference of news headlines: first with a GPT-3.5 fine-tuning approach, and second with GPT-3.5 prompt-engineering. Our work contributes to the study and analysis of the performance that these models have to facilitate journalistic tasks like classification of frames, while understanding whether the models are able to replicate human perception in the identification of these frames.