The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over rigidly defined symbolic models, but also serves as a proof-point of the challenges that purely statistics-based approaches have in terms of safety and trustworthiness. As a framework for contextualizing the potential, as well as the limitations of LLMs and other foundation model-based technologies, we propose the concept of a Large Process Model (LPM) that combines the correlation power of LLMs with the analytical precision and reliability of knowledge-based systems and automated reasoning approaches. LPMs are envisioned to directly utilize the wealth of process management experience that experts have accumulated, as well as process performance data of organizations with diverse characteristics, e.g., regarding size, region, or industry. In this vision, the proposed LPM would allow organizations to receive context-specific (tailored) process and other business models, analytical deep-dives, and improvement recommendations. As such, they would allow to substantially decrease the time and effort required for business transformation, while also allowing for deeper, more impactful, and more actionable insights than previously possible. We argue that implementing an LPM is feasible, but also highlight limitations and research challenges that need to be solved to implement particular aspects of the LPM vision.

This report describes trade-offs in the design of international governance arrangements for civilian artificial intelligence (AI) and presents one approach in detail. This approach represents the extension of a standards, licensing, and liability regime to the global level. We propose that states establish an International AI Organization (IAIO) to certify state jurisdictions (not firms or AI projects) for compliance with international oversight standards. States can give force to these international standards by adopting regulations prohibiting the import of goods whose supply chains embody AI from non-IAIO-certified jurisdictions. This borrows attributes from models of existing international organizations, such as the International Civilian Aviation Organization (ICAO), the International Maritime Organization (IMO), and the Financial Action Task Force (FATF). States can also adopt multilateral controls on the export of AI product inputs, such as specialized hardware, to non-certified jurisdictions. Indeed, both the import and export standards could be required for certification. As international actors reach consensus on risks of and minimum standards for advanced AI, a jurisdictional certification regime could mitigate a broad range of potential harms, including threats to public safety.

Studying data memorization in neural language models helps us understand the risks (e.g., to privacy or copyright) associated with models regurgitating training data and aids in the development of countermeasures. Many prior works -- and some recently deployed defenses -- focus on "verbatim memorization", defined as a model generation that exactly matches a substring from the training set. We argue that verbatim memorization definitions are too restrictive and fail to capture more subtle forms of memorization. Specifically, we design and implement an efficient defense that perfectly prevents all verbatim memorization. And yet, we demonstrate that this "perfect" filter does not prevent the leakage of training data. Indeed, it is easily circumvented by plausible and minimally modified "style-transfer" prompts -- and in some cases even the non-modified original prompts -- to extract memorized information. We conclude by discussing potential alternative definitions and why defining memorization is a difficult yet crucial open question for neural language models.

A family member's hurried Google search for a last-second visa to visit New Zealand recently caused a headache--and provided a timely reminder of why Google faces a landmark US antitrust trial next week. Tapping on the first link took us off to a website that after a few swipes charged $118 for the necessary paperwork. Only later did it emerge that we'd paid a so-called "internet-based travel technology company" and not a government agency, and been fleeced for more than double the required cost. Fortunately, our panicked refund demand was fulfilled, but the miscue highlights a major frustration with Google that helped land it in court. The stacks of ads above its search results, like the visa link we clicked on, too often knock users off course from the information that they are seeking.

Accurate modeling of aircraft environmental impact is pivotal to the design of operational procedures and policies to mitigate negative aviation environmental impact. Aircraft environmental impact segmentation is a process which clusters aircraft types that have similar environmental impact characteristics based on a set of aircraft features. This practice helps model a large population of aircraft types with insufficient aircraft noise and performance models and contributes to better understanding of aviation environmental impact. Through measuring the similarity between aircraft types, distance metric is the kernel of aircraft segmentation. Traditional ways of aircraft segmentation use plain distance metrics and assign equal weight to all features in an unsupervised clustering process. In this work, we utilize weakly-supervised metric learning and partial information on aircraft fuel burn, emissions, and noise to learn weighted distance metrics for aircraft environmental impact segmentation. We show in a comprehensive case study that the tailored distance metrics can indeed make aircraft segmentation better reflect the actual environmental impact of aircraft. The metric learning approach can help refine a number of similar data-driven analytical studies in aviation.

Prior work has explicated the coloniality of artificial intelligence (AI) development and deployment. One process that that work has not engaged with much is alignment: the tuning of large language model (LLM) behavior to be in line with desired values based on fine-grained human feedback. In addition to other practices, colonialism has a history of altering the beliefs and values of colonized peoples; this history is recapitulated in current LLM alignment practices. We suggest that AI alignment be decolonialized using three proposals: (a) changing the base moral philosophy from Western philosophy to dharma, (b) permitting traditions of argument and pluralism in alignment technologies, and (c) expanding the epistemology of values beyond instructions or commandments given in natural language.

Last week, WIRED published a deep-dive investigation into Trickbot, the prolific Russian ransomware gang. This week, US and UK authorities sanctioned 11 alleged members of Trickbot and its related group, Conti, including Maksim Galochkin, aka Bentley, one of the alleged members whose real-world identity we confirmed through our investigation. In addition to the US and UK sanctions, the US Justice Department also unsealed indictments filed in three US federal courts against Galochkin and eight other alleged Trickbot members for ransomware attacks against entities in Ohio, Tennessee, and California. Because everyone charged is a Russian national, however, it is unlikely they will ever be arrested or face trial. While Russian cybercriminals typically enjoy immunity, the same may not remain true for the country's military hackers.

The US government should create a new body to regulate artificial intelligence--and restrict work on language models like OpenAI's GPT-4 to companies granted licenses to do so. That's the recommendation of a bipartisan duo of senators, Democrat Richard Blumenthal and Republican Josh Hawley, who launched a legislative framework yesterday to serve as a blueprint for future laws and influence other bills before Congress. Under the proposal, developing face recognition and other "high risk" applications of AI would also require a government license. To obtain one, companies would have to test AI models for potential harm before deployment, disclose instances when things go wrong after launch, and allow audits of AI models by an independent third party. The framework also proposes that companies should publicly disclose details of the training data used to create an AI model, and that people harmed by AI get a right to bring the company that created it to court.

But the company's CEO and co-founder, Robin Li, said in an email that "Baidu will collect massive valuable real-world human feedback. This will not only help improve Baidu's foundation model but also iterate Ernie Bot on a much faster pace, ultimately leading to a superior user experience."

The paper explores a different variation of combined regression strategy to calculate the conditional survival function. We use regression based weak learners to create the proposed ensemble technique. The proposed combined regression strategy uses proximity measure as area between two survival curves. The proposed model shows a construction which ensures that it performs better than the Random Survival Forest. The paper discusses a novel technique to select the most important variable in the combined regression setup. We perform a simulation study to show that our proposition for finding relevance of the variables works quite well. We also use three real-life datasets to illustrate the model.