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.

Writing radiology reports from medical images requires a high level of domain expertise. It is time-consuming even for trained radiologists and can be error-prone for inexperienced radiologists. It would be appealing to automate this task by leveraging generative AI, which has shown drastic progress in vision and language understanding. In particular, Large Language Models (LLM) have demonstrated impressive capabilities recently and continued to set new state-of-the-art performance on almost all natural language tasks. While many have proposed architectures to combine vision models with LLMs for multimodal tasks, few have explored practical fine-tuning strategies. In this work, we proposed a simple yet effective two-stage fine-tuning protocol to align visual features to LLM's text embedding space as soft visual prompts. Our framework with OpenLLaMA-7B achieved state-of-the-art level performance without domain-specific pretraining. Moreover, we provide detailed analyses of soft visual prompts and attention mechanisms, shedding light on future research directions.

OpenAI is pushing the launch of its GPT Store to early 2024, according to an email seen by The Verge. The company introduced its GPT Builder tool in early November at its first developer conference, giving subscribers an easy way to create their own custom AI bots. At the time, OpenAI also said it would soon release the GPT Store for users to list their GPTs and potentially make money from them. It was initially slated for a November launch. But, with the surprise ouster of OpenAI's since-reinstated CEO Sam Altman, the month didn't quite pan out as planned.

This article is from Big Technology, a newsletter by Alex Kantrowitz. Life got interesting for Anthropic two weeks ago, when OpenAI nearly lit itself on fire. Anthropic had been operating comfortably in OpenAI's shadow, collecting billions in investment from Amazon, Google, and others as it developed similar technology with an increased focus on safety. Then, as the chaos rolled on, companies that built their products entirely on top of OpenAI's GPT-4 model looked for a hedge. And Anthropic was there, waiting for them.

Sorry to interrupt your Saturday, but did you somehow miss that Google made a geothermal energy plant in the middle of Nevada? You know, that place with all the water for turbines? Or the incredibly dumb way security researchers were able to pull private information from ChatGPT? This week's YouTube-coated version of TMA covers that and getting far too enthusiastic (or entirely non-plussed) about all these other things from this week in tech. A female coding influencer's Instagram is apparently run by a man This week, take a look at this great profile of the growth, growth and further growth of ChatGPT, OpenAI's chatbot.