Do you know Ted Cruz, a former Texas senator to the US Senate? If not, allow me to briefly outline his outstanding background. Senator Cruz is, first and foremost, a highly educated person with degrees from both Princeton University and Harvard Law School. His successful legal career, which included his tenure as Texas' Solicitor General, when he presented nine cases to the Supreme Court, was definitely influenced by this solid educational foundation. Senator Cruz has made other achievements as well.

For the first time, the Supreme Court is considering its opinion on the brief but powerful "26 words that created the internet." Enacted in 1996, Section 230 of the Communications Decency Act immunizes online platforms from liability for anything that is posted on their site by a third party--a protection that allowed the web to bloom by encouraging experimentation and interactivity in its early years. More recently, Section 230 has been the subject of scrutiny as bipartisan critics argue that it provides powerful tech companies with too much cover and too little accountability. The Supreme Court's perspective on the issue was a mystery until this week, when justices heard oral arguments for two cases involving 230. On Tuesday, the Court was asked to consider whether Google is liable for YouTube-recommendation algorithms showing Islamic State videos to users.

Synthetic data is becoming an increasingly promising technology, and successful applications can improve privacy, fairness, and data democratization. While there are many methods for generating synthetic tabular data, the task remains non-trivial and unexplored for specific scenarios. One such scenario is survival data. Here, the key difficulty is censoring: for some instances, we are not aware of the time of event, or if one even occurred. Imbalances in censoring and time horizons cause generative models to experience three new failure modes specific to survival analysis: (1) generating too few at-risk members; (2) generating too many at-risk members; and (3) censoring too early. We formalize these failure modes and provide three new generative metrics to quantify them. Following this, we propose SurvivalGAN, a generative model that handles survival data firstly by addressing the imbalance in the censoring and event horizons, and secondly by using a dedicated mechanism for approximating time-to-event/censoring. We evaluate this method via extensive experiments on medical datasets. SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.

Text data can pose a risk of harm. However, the risks are not fully understood, and how to handle, present, and discuss harmful text in a safe way remains an unresolved issue in the NLP community. We provide an analytical framework categorising harms on three axes: (1) the harm type (e.g., misinformation, hate speech or racial stereotypes); (2) whether a harm is \textit{sought} as a feature of the research design if explicitly studying harmful content (e.g., training a hate speech classifier), versus \textit{unsought} if harmful content is encountered when working on unrelated problems (e.g., language generation or part-of-speech tagging); and (3) who it affects, from people (mis)represented in the data to those handling the data and those publishing on the data. We provide advice for practitioners, with concrete steps for mitigating harm in research and in publication. To assist implementation we introduce \textsc{HarmCheck} -- a documentation standard for handling and presenting harmful text in research.

Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security. Detecting fake images is of paramount importance to prevent illegal activities, and previous research has shown that generative models leave unique patterns in their synthetic images that can be exploited to detect them. However, the fundamental problem of generalization remains, as even state-of-the-art detectors encounter difficulty when facing generators never seen during training. To assess the generalizability and robustness of synthetic image detectors in the face of real-world impairments, this paper presents a large-scale dataset named ArtiFact, comprising diverse generators, object categories, and real-world challenges. Moreover, the proposed multi-class classification scheme, combined with a filter stride reduction strategy addresses social platform impairments and effectively detects synthetic images from both seen and unseen generators. The proposed solution significantly outperforms other top teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% on Test 3 in the IEEE VIP Cup challenge at ICIP 2022, as measured by the accuracy metric.

Internet censorship is a phenomenon of societal importance and attracts investigation from multiple disciplines. Several research groups, such as Censored Planet, have deployed large scale Internet measurement platforms to collect network reachability data. However, existing studies generally rely on manually designed rules (i.e., using censorship fingerprints) to detect network-based Internet censorship from the data. While this rule-based approach yields a high true positive detection rate, it suffers from several challenges: it requires human expertise, is laborious, and cannot detect any censorship not captured by the rules. Seeking to overcome these challenges, we design and evaluate a classification model based on latent feature representation learning and an image-based classification model to detect network-based Internet censorship. To infer latent feature representations fromnetwork reachability data, we propose a sequence-to-sequence autoencoder to capture the structure and the order of data elements in the data. To estimate the probability of censorship events from the inferred latent features, we rely on a densely connected multi-layer neural network model. Our image-based classification model encodes a network reachability data record as a gray-scale image and classifies the image as censored or not using a dense convolutional neural network. We compare and evaluate both approaches using data sets from Censored Planet via a hold-out evaluation. Both classification models are capable of detecting network-based Internet censorship as we were able to identify instances of censorship not detected by the known fingerprints. Latent feature representations likely encode more nuances in the data since the latent feature learning approach discovers a greater quantity, and a more diverse set, of new censorship instances.

The need to integrate Responsible AI (RAI) practices has become an organizational imperative. As Generative AI systems such as ChatGPT gain traction, it will quickly become easier for companies to adopt AI, thanks to lowered barriers to access. Already, as many experiment with these systems, they are unearthing serious ethical issues: scientific misinformation that looks convincing to the untrained eye, biased images and avatars, hate speech, and more. Our research has shown that investing in RAI early is essential; it minimizes failures as companies scale the development and deployment of AI systems within their organization. But we've also found that it takes three years on average for an RAI program to achieve maturity.

LitigationcategoryU.S. judge permits lawsuit claiming NBA Top Shot NFTs are securities, article with image TechnologycategoryMeta loses bid to toss $175 mln verdict in streaming patent case, article with image LitigationcategoryU.S. judge permits lawsuit claiming NBA Top Shot NFTs are securities, article with image Midjourney is an AI-based system that generates images based on text prompts entered by users. Kashtanova wrote the text of "Zarya of the Dawn," and Midjourney created the book's images based on prompts. But it said Kashtanova was not the "master mind" behind the images themselves.

Chinese regulators have reportedly clamped down on access to ChatGPT, as Chinese tech firms and universities push forward with developing domestic artificial intelligence bots. ChatGPT, the popular discussion bot created by US-based OpenAI, is not officially available in China, where the government operates a comprehensive firewall and strict internet censorship. But many had been accessing it via VPNs, and some third-party developers had produced programs that gave some access to the service. Those programs have disappeared from WeChat accounts. Multiple reports have said that major tech firms including WeChat's parent company, Tencent, and Ant Group, have been ordered to cut access to the programs.

DALL-E 2, Stable Diffusion, and others transformed the image generation space. We saw more powerful language models, PaLM, and of course ChatGPT. We saw foundation models being developed for speech, music, proteins, and many other data modalities. And, for the first time, these models are now being widely deployed and utilized by consumers to accomplish a wide breadth of useful tasks. What is clear is that while foundation models have opened up unprecedented new possibilities, they are also still raw, imperfect research artifacts that we do not entirely understand. In 2021, we founded the Center for Research on Foundation Models (CRFM), recognizing the critical role of foundation models.