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.

Brinkmanship escalated in the US Congress this week over strategies to reauthorize the government surveillance powers known as "Section 702," as civil rights groups sounded the alarm about the consequences of the program and its potential renewal. A WIRED investigation of more than 100 restricted Telegram channels indicated that the communication app's bans on extremist discourse aren't effective or adequate bans. And the identity management platform Okta admitted this week that a security breach previously thought to impact 1 percent of its customers actually affected 100 percent. Analysis indicates that OpenAI's custom chatbots, known as GPTs, can be manipulated to leak their training data and other private information. Funding for the US Centers for Disease Control and Prevention gun violence research is at risk as Republicans quietly work to strip support.

AI-powered education technologies can support students and teachers in computer science education. However, with the recent developments in generative AI, and especially the increasingly emerging popularity of ChatGPT, the effectiveness of using large language models for solving programming tasks has been underexplored. The present study examines ChatGPT's ability to generate code solutions at different difficulty levels for introductory programming courses. We conducted an experiment where ChatGPT was tested on 127 randomly selected programming problems provided by Kattis, an automatic software grading tool for computer science programs, often used in higher education. The results showed that ChatGPT independently could solve 19 out of 127 programming tasks generated and assessed by Kattis. Further, ChatGPT was found to be able to generate accurate code solutions for simple problems but encountered difficulties with more complex programming tasks. The results contribute to the ongoing debate on the utility of AI-powered tools in programming education.

Sam Altman has appeared to lead credence to the theory he was fired from OpenAI over his company's super powerful, secret new AI system he helped build. Multiple employees reportedly warned the company's board of directors that this project, named Q* (pronounced'Q star'), was becoming so advanced it could already pass math exams and perform critical thinking tasks. And they felt Altman was not taking their warnings seriously. In an interview this week, Altman did not deny the existence of the secret program that some employees said was responsible for his firing. Instead, he called the revelation of Q* an'unfortunate leak.' Altman was fired, then hired by OpenAI investor Microsoft, and then re-hired by OpenAI - which also gave the boot to most of the board that cut Altman loose - all over the course of just five days in November.

It is surprisingly easy to remove the safety measures intended to prevent AI chatbots from giving harmful responses that could aid would-be terrorists or mass shooters. The discovery seems to be prompting companies including OpenAI to develop strategies to solve the problem – but research suggests their efforts have been met with only limited success so far.

No human celebrating a first birthday is as verbose, knowledgeable, or prone to fabrication as ChatGPT, which is blowing out its first candle as I type these words. Of course, OpenAI's game-changing large language model was precocious at birth, tumbling into civilization's ongoing conversation like an uninvited guest busting into a dinner party and instantly commanding the room. The chatbot astonished everyone who prompted it with fully realized, if not always completely factual, responses to almost any possible query. Suddenly, the world had access to a Magic 8 Ball with a PhD in every discipline. In almost no time, 100 million people became regular users, delighted and terrified to realize that humans had suddenly lost their monopoly on discourse.