Harmful content is pervasive on social media, poisoning online communities and negatively impacting participation. A common approach to address this issue is to develop detection models that rely on human annotations. However, the tasks required to build such models expose annotators to harmful and offensive content and may require significant time and cost to complete. Generative AI models have the potential to understand and detect harmful content. To investigate this potential, we used ChatGPT and compared its performance with MTurker annotations for three frequently discussed concepts related to harmful content: Hateful, Offensive, and Toxic (HOT). We designed five prompts to interact with ChatGPT and conducted four experiments eliciting HOT classifications. Our results show that ChatGPT can achieve an accuracy of approximately 80% when compared to MTurker annotations. Specifically, the model displays a more consistent classification for non-HOT comments than HOT comments compared to human annotations. Our findings also suggest that ChatGPT classifications align with provided HOT definitions, but ChatGPT classifies "hateful" and "offensive" as subsets of "toxic." Moreover, the choice of prompts used to interact with ChatGPT impacts its performance. Based on these in-sights, our study provides several meaningful implications for employing ChatGPT to detect HOT content, particularly regarding the reliability and consistency of its performance, its understand-ing and reasoning of the HOT concept, and the impact of prompts on its performance. Overall, our study provides guidance about the potential of using generative AI models to moderate large volumes of user-generated content on social media.

Check out what's clicking on Foxnews.com. Leaders of the U.S. Federal Trade Commission said on Tuesday the agency would pursue companies who misuse artificial intelligence to violate laws against discrimination or be deceptive. The sudden popularity of Microsoft-backed OpenAI's ChatGPT this year has prompted calls for regulation amid concerns around the world about the possible use of the innovation for wrongdoing even as companies are seeking ways to use it to enhance efficiency. In a congressional hearing, FTC Chair Lina Khan and Commissioners Rebecca Slaughter and Alvaro Bedoya were asked about concerns that recent innovation in artificial intelligence, which can be used to produce high quality deep fakes, could be used to make more effective scams or otherwise violate laws. FTC Chair Lina Khan testifies on Capitol Hill in Washington on April 21, 2021.

These images provide clues as to how ChatGPT will look if its creators make a physical version of their hugely popular artificial intelligence. The maker of ChatGPT, OpenAI, has invested in 1X, a company that makes humanoid robots designed to do human jobs after the success of the online chatbot. The robot, named EVE, has manipulators which can pick up objects and pack and unpack boxes - and is designed to work alongside human beings. OpenAI's Startup Fund led an investment round that raised $23.5 million for the 1X robot, which is set to hit the market this summer. The investment fuels OpenAI's rivalry with Elon Musk's Tesla bot, which has yet to begin production.

The Group of Seven digital ministers will call for accelerated research into generative artificial intelligence systems, such as ChatGPT, at a meeting later this month, a draft communique showed Wednesday, as the rapid proliferation of such tools has raised concerns about their potential impact on society. During their two-day talks from April 29 in Takasaki, Gunma Prefecture, the ministers aim to formulate an action plan on AI governance, according to the draft. With the Japanese government also considering discussing generative AI at a G7 summit in May in the city of Hiroshima, the topic is also expected to be mentioned in a joint statement by the leaders. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

OpenAI has just over a week to comply with European data protection laws following a temporary ban in Italy, and a slew of investigations in other EU countries. If it fails, it could face hefty fines, be forced to delete data, or even be banned. But experts have told MIT Technology Review that it will be next to impossible for OpenAI to comply with the rules. That's because of the way data used to train its AI models has been collected: by hoovering up content off the internet. Since the pandemic closed schools in 2020, nearly all students have been learning on school-issued laptops or tablets. But many experts suspect that the technology may be changing how they read, as reading on a screen is fundamentally different from reading on the page.

In AI development, the dominant paradigm is that the more training data, the better. OpenAI's GPT-2 model had a data set consisting of 40 gigabytes of text. GPT-3, which ChatGPT is based on, was trained on 570 GB of data. OpenAI has not shared how big the data set for its latest model, GPT-4, is. But that hunger for larger models is now coming back to bite the company. In the past few weeks, several Western data protection authorities have started investigations into how OpenAI collects and processes the data powering ChatGPT.

Companies using generative artificial intelligence tools like ChatGPT could be putting confidential customer information and trade secrets at risk, according to a report from Team8, an Israel-based venture firm. The widespread adoption of new AI chatbots and writing tools could leave companies vulnerable to data leaks and lawsuits, said the report, which was provided to Bloomberg News prior to its release. The fear is that the chatbots could be exploited by hackers to access sensitive corporate information or perform actions against the company. There are also concerns that confidential information fed into the chatbots now could be used by AI companies in the future. Major technology companies including Microsoft and Alphabet are racing to add generative AI capabilities to improve chatbots and search engines, training their models on data scraped from the Internet to give users a one-stop-shop to their queries.

The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life. Despite recent advancements in protein structure prediction, existing algorithms are so far unable to systematically predict the binding ligand structures along with their regulatory effects on protein folding. To address this discrepancy, we present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures solely using protein sequence and ligand molecular graph inputs. NeuralPLexer adopts a deep generative model to sample the 3D structures of the binding complex and their conformational changes at an atomistic resolution. The model is based on a diffusion process that incorporates essential biophysical constraints and a multi-scale geometric deep learning system to iteratively sample residue-level contact maps and all heavy-atom coordinates in a hierarchical manner. NeuralPLexer achieves state-of-the-art performance compared to all existing methods on benchmarks for both protein-ligand blind docking and flexible binding site structure recovery. Moreover, owing to its specificity in sampling both ligand-free-state and ligand-bound-state ensembles, NeuralPLexer consistently outperforms AlphaFold2 in terms of global protein structure accuracy on both representative structure pairs with large conformational changes (average TM-score=0.93) and recently determined ligand-binding proteins (average TM-score=0.89). Case studies reveal that the predicted conformational variations are consistent with structure determination experiments for important targets, including human KRAS$^\textrm{G12C}$, ketol-acid reductoisomerase, and purine GPCRs. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.

Russell Wald, director of the Stanford Institute for Human-Centered AI, sounds off on'The Story.' OpenAI CEO Sam Altman says the age of the giant artificial intelligence model is already over. "I think we're at the end of the era where it's going to be these, like, giant, giant models," he told an audience at the Massachusetts Institute of Technology over Zoom last week. "We'll make them better in other ways." During the same event, Altman also confirmed that his company is not developing Chat GPT-5. "An earlier version of the letter claimed OpenAI is training GPT-5 right now," he said, referencing a letter from billionaire Elon Musk and Apple co-founder Steve Wozniak.

Two weeks in a row, ChatGPT botched my grocery list. I thought that I had found a really solid, practical use for AI--automating one of my least favorite Sunday chores--but the bot turned out to be pretty darn bad at it. I fed it a link to a recipe for cauliflower shawarma with a spicy sauce and asked it to compile the ingredients in a list. It forgot the pita, so I forgot the pita, and then I had to use tortillas instead. The following week, I gave it a link to a taco recipe.