The simultaneous rise of machine learning as a service and concerns over user privacy have increasingly motivated the need for private inference (PI).

The simultaneous rise of machine learning as a service and concerns over user privacy have increasingly motivated the need for private inference (PI).

Delphi, a startup that recently raised 16 million from funders including Anthropic and actor/director Olivia Wilde's venture capital firm, Proximity Ventures, helps famous people create replicas that can speak with their fans in both chat and voice calls. It feels like MasterClass--the platform for instructional seminars led by celebrities--vaulted into the AI age. On its website, Delphi writes that modern leaders "possess potentially life-altering knowledge and wisdom, but their time is limited and access is constrained." It has a library of official clones created by famous figures that you can speak with. Arnold Schwarzenegger, for example, told me, "I'm here to cut the crap and help you get stronger and happier," before informing me cheerily that I've now been signed up to receive the Arnold's Pump Club newsletter.

The paper presents a novel deep network architecture termed DELPHI to automatically infer loop invariants for use in program verification. The architecture takes in as input source code which has (1) a number of assumption or assignment statements, (2) a loop with nested if-else statements with arithmetic operations and (3) a final assertion statement. The output of the architecture is a loop invariant in CNF which holds true at every iteration in the loop, and for which the assertion (3) is true after the loop ends execution. The architecture represents the source code AST using a graph-structured neural network, and treats it as a structured memory which it repeatedly accesses through attention operations. The generation of the CNF invariant is broken up into a sequential decision-making process where at each time the architecture predicts an output (op, T), where op is either && or and T is a simple logical expression.

OpenAI has removed the account of the developer behind an artificial intelligence-powered bot impersonating the US presidential candidate Dean Phillips, saying it violated company policy. Phillips, who is challenging Joe Biden for the Democratic party candidacy, was impersonated by a ChatGPT-powered bot on the dean.bot The bot was backed by Silicon Valley entrepreneurs Matt Krisiloff and Jed Somers, who have started a Super Pac – a body that funds and supports political candidates – named We Deserve Better, supporting Phillips. San Francisco-based OpenAI said it had removed a developer account that violated its policies on political campaigning and impersonation. "We recently removed a developer account that was knowingly violating our API usage policies which disallow political campaigning, or impersonating an individual without consent," said the company.

Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the most important, outlying data points from thousands of daily-updated public health data streams could assist an expert reviewer in identifying these irregularities. However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams. Accordingly, we developed FlaSH (Flagging Streams in public Health), a practical outlier detection framework for public health data users that uses simple, scalable models to capture these statistical properties explicitly. In an experiment where human experts evaluate FlaSH and existing methods (including deep learning approaches), FlaSH scales to the data volume of this task, matches or exceeds these other methods in mean accuracy, and identifies the outlier points that users empirically rate as more helpful. Based on these results, FlaSH has been deployed on data streams used by public health stakeholders.

A few years ago, the Allen Institute for A.I. built a chatbot named Delphi, which is designed to tell right from wrong. It does a surprisingly decent job. Type in, "Cheating on an exam," and Delphi says, "It's wrong." But write, "Cheating on an exam to save someone's life," and Delphi responds, "It's okay." The chatbot knows it's rude to use your lawn mower when your neighbors are sleeping, but not when they're out of town.

Controversy is a reflection of our zeitgeist, and an important aspect to any discourse. The rise of large language models (LLMs) as conversational systems has increased public reliance on these systems for answers to their various questions. Consequently, it is crucial to systematically examine how these models respond to questions that pertaining to ongoing debates. However, few such datasets exist in providing human-annotated labels reflecting the contemporary discussions. To foster research in this area, we propose a novel construction of a controversial questions dataset, expanding upon the publicly released Quora Question Pairs Dataset. This dataset presents challenges concerning knowledge recency, safety, fairness, and bias. We evaluate different LLMs using a subset of this dataset, illuminating how they handle controversial issues and the stances they adopt. This research ultimately contributes to our understanding of LLMs' interaction with controversial issues, paving the way for improvements in their comprehension and handling of complex societal debates.

A year ago, the idea of holding a meaningful conversation with a computer was the stuff of science fiction. But since OpenAI's ChatGPT launched last November, life has started to feel more like a techno-thriller with a fast-moving plot. Chatbots and other generative AI tools are beginning to profoundly change how people live and work. But whether this plot turns out to be uplifting or dystopian will depend on who helps write it. Thankfully, just as artificial intelligence is evolving, so is the cast of people who are building and studying it.

As generative language models are deployed in ever-wider contexts, concerns about their political values have come to the forefront with critique from all parts of the political spectrum that the models are biased and lack neutrality. However, the question of what neutrality is and whether it is desirable remains underexplored. In this paper, I examine neutrality through an audit of Delphi [arXiv:2110.07574], a large language model designed for crowdsourced ethics. I analyse how Delphi responds to politically controversial questions compared to different US political subgroups. I find that Delphi is poorly calibrated with respect to confidence and exhibits a significant political skew. Based on these results, I examine the question of neutrality from a data-feminist lens, in terms of how notions of neutrality shift power and further marginalise unheard voices. These findings can hopefully contribute to a more reflexive debate about the normative questions of alignment and what role we want generative models to play in society.