Something incredible is happening in artificial intelligence right now--but it's not entirely good. Everybody is talking about systems like ChatGPT, which generates text that seems remarkably human. This makes it fun to play with, but there is a dark side, too. Because they are so good at imitating human styles, there is risk that such chatbots could be used to mass-produce misinformation. To get a sense of what it does best at its best, consider this example generated by ChatGPT, sent to me over e-mail by Henry Minsky (son of Marvin Minsky, one of AI's foundational researchers).

When it first came around, students and software developers faced a steep learning curve with fitting Linux to their machines which formed an entry …

It was 2018, and the world as we knew it--or rather, how we knew it--teetered on a precipice. Against a rising drone of misinformation, The New York Times, the BBC, Good Morning America, and just about everyone else sounded the alarm over a new strain of fake but highly realistic videos. Using artificial intelligence, bad actors could manipulate someone's voice and face in recorded footage almost like a virtual puppet and pass the product off as real. In a famous example engineered by BuzzFeed, Barack Obama seemed to say, "President Trump is a total and complete dipshit." Synthetic photos, audio, and videos, collectively dubbed "deepfakes," threatened to destabilize society and push us into a full-blown "infocalypse."

Outlines how augmented analytics changes the way security, operations and risk professionals navigate or prevent potential risks before they happen. Seerist Inc., the leading augmented analytics solution for threat and security professionals, today announced the availability of its white paper, Turning Infinite Data into Insightful Threat and Risk Strategies. This white paper was written to demonstrate how leaders can better leverage global data to make more informed, strategic decisions by combining the power of machine learning, human analysis, and natural language capabilities. "Data continues to grow at an accelerated rate every year with 89 percent of big data created in the last two years. It is simply impossible for humans to adequately access and evaluate the vast quantum of information available, yet the value it provides can be life changing and should not be ignored," said Jim Brooks, Seerist's CEO.

A few weeks ago, right here on io9, readers got their first look at the cover for Christopher Paolini's newest Fractalverse novel, Fractal Noise. Almost immediately, people could see something was off about the artwork used on the cover. With some quick deduction, Twitter users realized that it was AI-generated art, which was originally posted to a stock art site by a user named Ufuk Kaya. It was also arranged by the in-house designer, who is not credited--something unusual for Tor, as the publisher often promotes the art directors and designers of each cover. After receiving widespread backlash online, Tor posted the following message on its Twitter, which explains that it licensed an image from a "reputable stock house."

There's a running joke in my household that I'm living in the future because I really never know what year I'm in at the time because I'm always focused two to three years, four years out. So even while we're sitting in the beginnings of '23, I'm focused on '25, 2026. This is CES Tech Talk. CES 2023 is January 5th through 8th in Las Vegas. We are here to get you hyped and get you smart about the world's most influential tech event. At CES, innovation is everything and everywhere, so much so that the word itself may seem to lose its meaning. But what does innovation mean, and how do you do it right? To find out, let's talk with Tom Butler, executive director of global commercial portfolio and product management at Lenovo, where he works on ThinkPad and ThinkBook laptops. What are you excited about for CES 2023? Well, James, I wish I could show you because there's some really cool things coming, but we're not quite ready to share those just yet.

Recently, online social media has become a primary source for new information and misinformation or rumours. In the absence of an automatic rumour detection system the propagation of rumours has increased manifold leading to serious societal damages. In this work, we propose a novel method for building automatic rumour detection system by focusing on oversampling to alleviating the fundamental challenges of class imbalance in rumour detection task. Our oversampling method relies on contextualised data augmentation to generate synthetic samples for underrepresented classes in the dataset. The key idea exploits selection of tweets in a thread for augmentation which can be achieved by introducing a non-random selection criteria to focus the augmentation process on relevant tweets. Furthermore, we propose two graph neural networks(GNN) to model non-linear conversations on a thread. To enhance the tweet representations in our method we employed a custom feature selection technique based on state-of-the-art BERTweet model. Experiments of three publicly available datasets confirm that 1) our GNN models outperform the the current state-of-the-art classifiers by more than 20%(F1-score); 2) our oversampling technique increases the model performance by more than 9%;(F1-score) 3) focusing on relevant tweets for data augmentation via non-random selection criteria can further improve the results; and 4) our method has superior capabilities to detect rumours at very early stage.

Large language models can perform new tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, but can also be learned with gradient-based methods from labeled data. However, it is underexplored what factors make the prompts effective, especially when the prompts are natural language. In this paper, we investigate common attributes shared by effective prompts. We first propose a human readable prompt tuning method (F LUENT P ROMPT) based on Langevin dynamics that incorporates a fluency constraint to find a diverse distribution of effective and fluent prompts. Our analysis reveals that effective prompts are topically related to the task domain and calibrate the prior probability of label words. Based on these findings, we also propose a method for generating prompts using only unlabeled data, outperforming strong baselines by an average of 7.0% accuracy across three tasks.

Pre-trained language models have been successful in natural language generation (NLG) tasks. While various decoding methods have been employed, they often produce suboptimal results. We first present an empirical analysis of three NLG tasks: summarization, machine translation, and constrained text generation. We found that selecting the best output from the results of multiple decoding methods can significantly improve performance. To further improve reranking for NLG tasks, we proposed a novel method, \textsc{PairReranker}, which uses a single encoder and a pairwise loss function to jointly encode a source input and a pair of candidates and compare them. Experiments on three NLG tasks demonstrated the effectiveness and flexibility of \textsc{PairReranker}, showing strong results, compared with previous baselines. In addition, our \textsc{PairReranker} can generalize to significantly improve GPT-3 (text-davinci-003) results (e.g., 24.55\% on CommonGen and 11.35\% on WMT18 zh-en), even though our rerankers are not trained with any GPT-3 candidates.

How do we design measures of social bias that we trust? While prior work has introduced several measures, no measure has gained widespread trust: instead, mounting evidence argues we should distrust these measures. In this work, we design bias measures that warrant trust based on the cross-disciplinary theory of measurement modeling. To combat the frequently fuzzy treatment of social bias in NLP, we explicitly define social bias, grounded in principles drawn from social science research. We operationalize our definition by proposing a general bias measurement framework DivDist, which we use to instantiate 5 concrete bias measures. To validate our measures, we propose a rigorous testing protocol with 8 testing criteria (e.g. predictive validity: do measures predict biases in US employment?). Through our testing, we demonstrate considerable evidence to trust our measures, showing they overcome conceptual, technical, and empirical deficiencies present in prior measures.