The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in traditional sequential decision-making tasks? To investigate this, we first take environments collected in OpenAI Gym as our testbeds and ground them to textual environments that construct the TextGym simulator. This allows for straightforward and efficient comparisons between PPO agents and language agents, given the widespread adoption of OpenAI Gym. To ensure a fair and effective benchmarking, we introduce $5$ levels of scenario for accurate domain-knowledge controlling and a unified RL-inspired framework for language agents. Additionally, we propose an innovative explore-exploit-guided language (EXE) agent to solve tasks within TextGym. Through numerical experiments and ablation studies, we extract valuable insights into the decision-making capabilities of language agents and make a preliminary evaluation of their potential to be alternatives to PPO in classical sequential decision-making problems. This paper sheds light on the performance of language agents and paves the way for future research in this exciting domain. Our code is publicly available at~\url{https://github.com/mail-ecnu/Text-Gym-Agents}.

Private data publishing is a technique that involves releasing a modified dataset to preserve user privacy while enabling downstream machine learning tasks. While many private data publishing algorithms exist, traditional algorithms (e.g., DPPro [1], PrivBayes [2], etc.) based on releasing tabular data are not suitable for modern machine learning tasks involving complex structures such as images, videos, and texts. To tackle this, a series of deep learning algorithms have emerged, such as DP-GAN [3] and PATE-GAN [4], which are based on training a Deep Generative Model (DGM) to generate data with complex structures, such as images, texts, and audios. These methods generate fake data based on the trained DGM and publish it instead of the raw data to respect users' privacy. However, as empirically observed by Takagi et al. [5], these DGM-based methods often suffer from training instability, such as mode collapse and high computational costs and lead to low utility, which is defined as the usefulness of the private data. For example, in the case of classification datasets, utility can be measured by classification accuracy. DPMix -- a new data publishing technique proposed by Lee et al. [6] -- does not rely on training deep generative models and has the potential to improve utility. DPMix, as opposed to DGM-based methods, directly adds noise to the raw dataset -- thereby taking into account users' privacy -- and publishes the noisy version of the dataset. Concretely, inspired by Zhang et al. [7], DPMix first mixes the data points by averaging groups of raw data (with group size m), then adds noise to each individual mixture of data points to respect privacy concerns, and finally publishes the noisy

Last week, a team of researchers published a paper showing that it was able to get ChatGPT to inadvertently reveal bits of data including people's phone numbers, email addresses and dates of birth that it had been trained on by asking it to repeat words "forever". Doing this now is a violation of ChatGPT's terms of service, according to a report in 404 Media and Engadget's own testing. "This content may violate our content policy or terms of use", ChatGPT responded to Engadget's prompt to repeat the word "hello" forever. "If you believe this to be in error, please submit your feedback -- your input will aid our research in this area." There's no language in OpenAI's content policy, however, that prohibits users from asking the service to repeat words forever, something that 404 Media notes.

One of the highlights of the recent Open Data Institute (ODI) Summit 2023 was the showing of a short film by artist and AI collaborator, Alan Warburton. This video essay was commissioned by the ODI's Data as Culture programme and addresses the cultural impacts of generative AI. The ODI Summit 2023 took place on 7 November and featured keynote presentations, lightening talks, and panel discussions. The event brought together representatives from civil society, academia, industry, and government. Find out more on the ODI website.

One of the most stunning twists in the recent five-day crisis at ChatGPT creator OpenAI came when some 95 percent of the company's hundreds of employees threatened to quit. The staff planned to follow CEO Sam Altman to develop successors to ChatGPT at Microsoft instead. The threat appeared to mark a turning point in Altman's ultimately successful attempt to return to OpenAI--it was also a scenario that businesses have the legal power to block in most US states. California, home to OpenAI's San Francisco HQ, is one of a handful states that bar the enforcement of noncompete agreements in employment contracts, which can forbid employees from hopping jobs to a competitor, often for years. That picture is now set to change, as a raft of new legislation aims to make more places like California.

The Private Aggregation of Teacher Ensembles (PATE) framework~\cite{PapernotAEGT:ICLR2017} is a versatile approach to privacy-preserving machine learning. In PATE, teacher models are trained on distinct portions of sensitive data, and their predictions are privately aggregated to label new training examples for a student model. Until now, PATE has primarily been explored with classification-like tasks, where each example possesses a ground-truth label, and knowledge is transferred to the student by labeling public examples. Generative AI models, however, excel in open ended \emph{diverse} tasks with multiple valid responses and scenarios that may not align with traditional labeled examples. Furthermore, the knowledge of models is often encapsulated in the response distribution itself and may be transferred from teachers to student in a more fluid way. We propose \emph{hot PATE}, tailored for the diverse setting. In hot PATE, each teacher model produces a response distribution and the aggregation method must preserve both privacy and diversity of responses. We demonstrate, analytically and empirically, that hot PATE achieves privacy-utility tradeoffs that are comparable to, and in diverse settings, significantly surpass, the baseline ``cold'' PATE.

ChatGPT, a recently developed product by openAI, is successfully leaving its mark as a multi-purpose natural language based chatbot. In this paper, we are more interested in analyzing its potential in the field of computational biology. A major share of work done by computational biologists these days involve coding up bioinformatics algorithms, analyzing data, creating pipelining scripts and even machine learning modeling and feature extraction. This paper focuses on the potential influence (both positive and negative) of ChatGPT in the mentioned aspects with illustrative examples from different perspectives. Compared to other fields of computer science, computational biology has - (1) less coding resources, (2) more sensitivity and bias issues (deals with medical data) and (3) more necessity of coding assistance (people from diverse background come to this field). Keeping such issues in mind, we cover use cases such as code writing, reviewing, debugging, converting, refactoring and pipelining using ChatGPT from the perspective of computational biologists in this paper.

Sam Altman was reinstated soon after being fired as OpenAI CEO last month, but still stood to gain had the company continued to develop ChatGPT without him. During Altman's tenure as CEO, OpenAI signed a letter of intent to spend $51 million on AI chips from a startup called Rain AI into which he has also invested personally. Rain is based less than a mile from OpenAI's headquarters in San Francisco and is working on a chip it calls a neuromorphic processing unit, or NPU, designed to replicate features of the human brain. OpenAI in 2019 signed a nonbinding agreement to spend $51 million on the chips when they became available, according to a copy of the deal and Rain disclosures to investors this year seen by WIRED. Rain told investors Altman had personally invested more than $1 million into the company.

Palantir CEO Alex Karp joins'Fox News Live' to discuss his company's innovative approach to tech development and artificial intelligence. American Christians are more likely to be skeptical about artificial intelligence and are particularly apprehensive about using generative AI in church services, according to a recent survey. Just over a quarter of Christians (28%) surveyed by Barna this fall said they were hopeful about AI development, while 39% of self-identified non-Christians said the same. Only a fraction of Christians surveyed agreed that "AI is good for the Christian Church," according to the Barna survey, conducted through a consumer research panel. Just 22% said they agreed AI would be positive for the church, while 30% strongly disagreed and 21% said they somewhat disagreed.

The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit desired characteristics. In this work, we present a computationally efficient active learning methodology that requires evaluation of only a subset of the generated data in the constructed sample space to successfully align a generative model with respect to a specified objective. We demonstrate the applicability of this methodology to targeted molecular generation by fine-tuning a GPT-based molecular generator toward a protein with FDA-approved small-molecule inhibitors, c-Abl kinase. Remarkably, the model learns to generate molecules similar to the inhibitors without prior knowledge of their existence, and even reproduces two of them exactly. We also show that the methodology is effective for a protein without any commercially available small-molecule inhibitors, the HNH domain of the CRISPR-associated protein 9 (Cas9) enzyme. We believe that the inherent generality of this method ensures that it will remain applicable as the exciting field of in silico molecular generation evolves. To facilitate implementation and reproducibility, we have made all of our software available through the open-source ChemSpaceAL Python package.