The annual report, Freedom on the Net, scores and ranks countries according to their relative degree of internet freedom, as measured by a host of factors like internet shutdowns, laws limiting online expression, and retaliation for online speech. The 2023 edition, released on October 4, found that global internet freedom declined for the 13th consecutive year, driven in part by the proliferation of artificial intelligence. "Internet freedom is at an all-time low, and advances in AI are actually making this crisis even worse," says Allie Funk, a researcher on the report. Funk says one of their most important findings this year has to do with changes in the way governments use AI, though we are just beginning to learn how the technology is boosting digital oppression. Funk found there were two primary factors behind these changes: the affordability and accessibility of generative AI is lowering the barrier of entry for disinformation campaigns, and automated systems are enabling governments to conduct more precise and more subtle forms of online censorship.

The adoption of generative AI technologies is swiftly expanding. Services employing both linguistic and mul-timodal models are evolving, offering users increasingly precise responses. Consequently, human reliance on these technologies is expected to grow rapidly. With the premise that people will be impacted by the output of AI, we explored approaches to help AI output produce better results. Initially, we evaluated how contemporary AI services competitively meet user needs, then examined society's depiction as mirrored by Large Language Models (LLMs). We did a query experiment, querying about social conventions in various countries and eliciting a one-word response. We compared the LLMs' value judgments with public data and suggested an model of decision-making in value-conflicting scenarios which could be adopted for future machine value judgments. This paper advocates for a practical approach to using AI as a tool for investigating other remote worlds. This re-search has significance in implicitly rejecting the notion of AI making value judgments and instead arguing a more critical perspective on the environment that defers judgmental capabilities to individuals. We anticipate this study will empower anyone, regardless of their capacity, to receive safe and accurate value judgment-based out-puts effectively.

Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation. LLMs, because of their large size and pretraining on large volumes of text data, exhibit special abilities which allow them to achieve remarkable performances without any task-specific training in many of the natural language processing tasks. The era of LLMs started with OpenAI GPT-3 model, and the popularity of LLMs is increasing exponentially after the introduction of models like ChatGPT and GPT4. We refer to GPT-3 and its successor OpenAI models, including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With the ever-rising popularity of GLLMs, especially in the research community, there is a strong need for a comprehensive survey which summarizes the recent research progress in multiple dimensions and can guide the research community with insightful future research directions. We start the survey paper with foundation concepts like transformers, transfer learning, self-supervised learning, pretrained language models and large language models. We then present a brief overview of GLLMs and discuss the performances of GLLMs in various downstream tasks, specific domains and multiple languages. We also discuss the data labelling and data augmentation abilities of GLLMs, the robustness of GLLMs, the effectiveness of GLLMs as evaluators, and finally, conclude with multiple insightful future research directions. To summarize, this comprehensive survey paper will serve as a good resource for both academic and industry people to stay updated with the latest research related to GPT-3 family large language models.

Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs. Conventional training-based methods have limitations in flexibility, particularly when adapting to new domains, and they often lack explanatory power. To address this gap, we propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT). Given a text, we first truncate it in the middle and then use only the preceding portion as input to the LLMs to regenerate the new remaining parts. By analyzing the differences between the original and new remaining parts through N-gram analysis in black-box or probability divergence in white-box, we unveil significant discrepancies between the distribution of machine-generated text and the distribution of human-written text. We conducted extensive experiments on the most advanced LLMs from OpenAI, including text-davinci-003, GPT-3.5-turbo, and GPT-4, as well as open-source models such as GPT-NeoX-20B and LLaMa-13B. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text on four English and one German dataset, outperforming OpenAI's own classifier, which is trained on millions of text. Additionally, our methods provide reasonable explanations and evidence to support our claim, which is a unique feature of explainable detection. Our method is also robust under the revised text attack and can additionally solve model sourcing. Codes are available at https://github.com/Xianjun-Yang/DNA-GPT.

An "urgent issue" of gender imbalance in artificial intelligence investment must be addressed according to a government-backed body which has found that female-founded companies accounted for just 2% of AI startup deals over the past decade. The report by the Alan Turing Institute found that when female-founded companies have secured funding, they raise on average £1.3m a deal compared with £8.6m raised by all-male founder teams. In the last year, investment in AI software has grown considerably. A report by Goldman Sachs predicts that AI investment will approach $200bn (£166bn) globally by 2025, while a recent report from Bloomberg found that generative AI could become a $1.3tn market by 2032. "The recent explosion in interest and investment in AI, especially generative AI, means that there is an urgent need for women and minorities to have equal access in the tech and venture space," said Dr Erin Young, a research fellow at the Alan Turing Institute.

MosaiQ's generated images (bottom row) on a quantum computer look similar to items in the initial data (top row) and seem better than those made by other quantum methods (other rows) Artificial intelligence running on a quantum computer can now generate recognisable images of things like shoes and T-shirts, using the same methods as popular text-to-image tools like Dall-E or Midjourney. They still aren't what you would call stunning images, but if the method scales up to more powerful machines, it should lead to much higher-resolution pictures.

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, we talk to Dr Hayleigh Bosher, Associate Dean and Reader in intellectual property law at Brunel University and host of the podcast Whose Song is it Anyway?, a podcast on the intersections of intellectual property (IP) and the music industry. She tells us why AI can never create an original song, what it takes to sue a generative AI company for creating music in the style of someone, and why generative AI risks missing the point about what creativity is. Hayleigh is a Reader in Intellectual Property Law and Associate Dean (Professional Development and Graduate Outcomes) at Brunel University London, as well as, Visiting Research Fellow at the Centre for Intellectual Property, Policy and Management, a legal consultant in the creative industries, an advisor for the independent UK charity for professional musicians, Help Musicians, writer and Book Review Editor for the specialist IP blog IPKat. Her work in this area has been cited extensively in academic, practitioner and policy outputs and she is regularly interviewed by numerous national and international media outlets, including the BBC, ITV, Sky News, Channel 5 News and The Guardian, The Times and The Wall Street Journal.

Since the beginning of the generative AI boom, tech companies have been feverishly trying to come up with the killer app for the technology. First it was online search, with mixed results. Last week, OpenAI, Meta, and Google launched new features for their AI chatbots that allow them to search the web and act as a sort of personal assistant. This is a risky bet, given the limitations of the technology. Tech companies have not solved some of the persistent problems with AI language models, such as their propensity to make things.

OpenAI unveiled new ChatGPT features that include the ability to have a conversation with the chatbot as if you were making a call, allowing you to instantly get responses to your spoken questions in a lifelike synthetic voice, as my colleague Will Douglas Heaven reported. OpenAI also revealed that ChatGPT will be able to search the web. Google's rival bot, Bard, is plugged into most of the company's ecosystem, including Gmail, Docs, YouTube, and Maps. The idea is that people will be able to use the chatbot to ask questions about their own content--for example, by getting it to search through their emails or organize their calendar. Bard will also be able to instantly retrieve information from Google Search.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. Since the November 2022 launch of OpenAI's ChatGPT (Generative Pre-trained Transformers), the issue of AI technologies-related job displacement is receiving renewed economic impact scrutiny. For example, in March 2023, technology firm OpenAI released a report that found at least 80% of the U.S. labor force could have at least 10% of their work-related tasks affected by the introduction of GPT, while another 19% of employees may see at least 50% of these work-related tasks impacted. While GPT influence impacts all wage levels, the higher-income jobs potentially face the greatest exposure, concludes OpenAI. Also in March 2023, researchers at investment banker Goldman Sachs, after collecting data on occupationally-oriented tasks in Europe and the U.S., found that roughly two-thirds of current occupations are exposed to varying degrees of generative AI automation (such as found in ChatGPT), and that AI could substitute for nearly one-fourth of current work performed.