In a Statement of policy (opens in new tab) published earlier this month, the Office's Director Shira Perlmutter wrote: "In the case of works containing AI-generated material, the Office will consider whether the AI contributions are the result of'mechanical reproduction' or instead of an author's'own original mental conception, to which [the author] gave visible form.'" Perlmutter describes that the analysis would be on a "case-by-case" basis in order to assess whether the human is the true author of the content. An example of a denied application would involve an AI writer receiving a prompt and generating new "complex written, visual, or musical" content. This could involve the creative arrangement of AI-generated content or further editing whereby AI content is considered merely a template for further work. In order to help distinguish AI from human-generated content, there have been discussions of watermarking work created by machines, but so far that has proven troublesome.

On Tuesday, OpenAI announced the release of GPT-4, its latest, biggest language model, only a few months after the splashy release of ChatGPT. GPT-4 was already in action -- Microsoft has been using it to power Bing's new assistant function. The people behind OpenAI have written that they think the best way to handle powerful AI systems is to develop and release them as quickly as possible, and that's certainly what they're doing. Also on Tuesday, I sat down with Holden Karnofsky, the co-founder and co-CEO of Open Philanthropy, to talk about AI and where it's taking us. Karnofsky, in my view, should get a lot of credit for his prescient views on AI.

Building systems that are good for society in the face of complex societal effects requires a dynamic approach. Recent approaches to machine learning (ML) documentation have demonstrated the promise of discursive frameworks for deliberation about these complexities. However, these developments have been grounded in a static ML paradigm, leaving the role of feedback and post-deployment performance unexamined. Meanwhile, recent work in reinforcement learning has shown that the effects of feedback and optimization objectives on system behavior can be wide-ranging and unpredictable. In this paper we sketch a framework for documenting deployed and iteratively updated learning systems, which we call Reward Reports. Taking inspiration from various contributions to the technical literature on reinforcement learning, we outline Reward Reports as living documents that track updates to design choices and assumptions behind what a particular automated system is optimizing for. They are intended to track dynamic phenomena arising from system deployment, rather than merely static properties of models or data. After presenting the elements of a Reward Report, we discuss a concrete example: Meta's BlenderBot 3 chatbot. Several others for game-playing (DeepMind's MuZero), content recommendation (MovieLens), and traffic control (Project Flow) are included in the appendix.

Survivors of sexual harassment frequently share their experiences on social media, revealing their feelings and emotions and seeking advice. We observed that on Reddit, survivors regularly share long posts that describe a combination of (i) a sexual harassment incident, (ii) its effect on the survivor, including their feelings and emotions, and (iii) the advice being sought. We term such posts MeToo posts, even though they may not be so tagged and may appear in diverse subreddits. A prospective helper (such as a counselor or even a casual reader) must understand a survivor's needs from such posts. But long posts can be time-consuming to read and respond to. Accordingly, we address the problem of extracting key information from a long MeToo post. We develop a natural language-based model to identify sentences from a post that describe any of the above three categories. On ten-fold cross-validation of a dataset, our model achieves a macro F1 score of 0.82. In addition, we contribute MeThree, a dataset comprising 8,947 labeled sentences extracted from Reddit posts. We apply the LIWC-22 toolkit on MeThree to understand how different language patterns in sentences of the three categories can reveal differences in emotional tone, authenticity, and other aspects.

The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-parameter language model on a cluster of Ascend 910 AI processors and MindSpore framework, and present the language model with 1.085T parameters named PanGu-{\Sigma}. With parameter inherent from PanGu-{\alpha}, we extend the dense Transformer model to sparse one with Random Routed Experts (RRE), and efficiently train the model over 329B tokens by using Expert Computation and Storage Separation(ECSS). This resulted in a 6.3x increase in training throughput through heterogeneous computing. Our experimental findings show that PanGu-{\Sigma} provides state-of-the-art performance in zero-shot learning of various Chinese NLP downstream tasks. Moreover, it demonstrates strong abilities when fine-tuned in application data of open-domain dialogue, question answering, machine translation and code generation.

We have entered the brave new world of AI chatbots. This means everything from reenvisioning how students learn in school to protecting ourselves from mass-produced misinformation. It also means heeding the mounting calls to regulate AI to help us navigate an era in which computers write as fluently as people. So far, there is more agreement on the need for AI regulation than on what this would entail. Mira Murati, head of the team that created the chatbot app ChatGPT--the fastest growing consumer-Internet app in history--said governments and regulators should be involved, but she didn't suggest how.

Artificial intelligence (AI) is becoming increasingly important in the technology industry, and Advanced Micro Devices (AMD) is one of the leading companies in this space. AMD has been investing heavily in AI research and development, and its efforts are beginning to pay off. The company recently announced a new generative AI platform that could revolutionize how businesses use machine learning. Generative AI is an advanced form of artificial intelligence that can generate data from scratch without relying on existing datasets or models. This type of technology has the potential to create entirely new products and services by leveraging existing data sets for training purposes.

Abstract: AI is transforming the existing technology landscape at a rapid phase enabling data-informed decision making and autonomous decision making. Unlike any other technology, because of the decision-making ability of AI, ethics and governance became a key concern. There are many emerging AI risks for humanity, such as autonomous weapons, automation-spurred job loss, socio-economic inequality, bias caused by data and algorithms, privacy violations and deepfakes. Social diversity, equity and inclusion are considered key success factors of AI to mitigate risks, create values and drive social justice. Sustainability became a broad and complex topic entangled with AI. Many organizations (government, corporate, not-for-profits, charities and NGOs) have diversified strategies driving AI for business optimization and social-and-environmental justice.

Artists, designers, photographers, authors, actors and musicians see little humour left in jokes about AI programs that will one day do their job for less money. That dark dawn is here, they say. Vast amounts of imaginative output, work made by people in the kind of jobs once assumed to be protected from the threat of technology, have already been captured from the web, to be adapted, merged and anonymised by algorithms for commercial use. But just as GPT-4, the enhanced version of the AI generative text engine, was proudly unveiled last week, artists, writers and regulators have started to fight back in earnest. "Picture libraries are being scraped for content and huge datasets being amassed right now," says Isabelle Doran, head of the Association of Photographers.

Back in 2021, a law took effect in New York City that requires businesses to post conspicuous signs if they're collecting customers' biometric information, such as their facial scans and fingerprints. Now, Amazon is facing a proposed class-action lawsuit that accuses the company of failing to inform customers at its Go cashierless stores that it was collecting their biometrics. In the lawsuit (PDF), filed by Alfredo Alberto Rodriguez Perez, the plaintiff argues that Go stores constantly use customers' biometrics "by scanning [their palms] to identify them and by applying computer vision, deep learning algorithms, and sensor fusion that measure the shape and size of each customer's body to identify customers, track where they move in the stores, and determine what they have purchased." It said the company only put up signs about its biometric tracking activities over a year after the law went into effect. Amazon's Go stores give shoppers the option to take whatever product they have off shelves and walk out without the need to check out.