The use of machine learning (ML) models to assess and score textual data has become increasingly pervasive in an array of contexts including natural language processing, information retrieval, search and recommendation, and credibility assessment of online content. A significant disruption at the intersection of ML and text are text-generating large-language models such as generative pre-trained transformers (GPTs). We empirically assess the differences in how ML-based scoring models trained on human content assess the quality of content generated by humans versus GPTs. To do so, we propose an analysis framework that encompasses essay scoring ML-models, human and ML-generated essays, and a statistical model that parsimoniously considers the impact of type of respondent, prompt genre, and the ML model used for assessment model. A rich testbed is utilized that encompasses 18,460 human-generated and GPT-based essays. Results of our benchmark analysis reveal that transformer pretrained language models (PLMs) more accurately score human essay quality as compared to CNN/RNN and feature-based ML methods. Interestingly, we find that the transformer PLMs tend to score GPT-generated text 10-15\% higher on average, relative to human-authored documents. Conversely, traditional deep learning and feature-based ML models score human text considerably higher. Further analysis reveals that although the transformer PLMs are exclusively fine-tuned on human text, they more prominently attend to certain tokens appearing only in GPT-generated text, possibly due to familiarity/overlap in pre-training. Our framework and results have implications for text classification settings where automated scoring of text is likely to be disrupted by generative AI.

We present DECORAIT; a decentralized registry through which content creators may assert their right to opt in or out of AI training as well as receive reward for their contributions. Generative AI (GenAI) enables images to be synthesized using AI models trained on vast amounts of data scraped from public sources. Model and content creators who may wish to share their work openly without sanctioning its use for training are thus presented with a data governance challenge. Further, establishing the provenance of GenAI training data is important to creatives to ensure fair recognition and reward for their such use. We report a prototype of DECORAIT, which explores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data in order to determine training consent and reward creatives who contribute that data. DECORAIT combines distributed ledger technology (DLT) with visual fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance and Authenticity) standard to create a secure, open registry through which creatives may express consent and data ownership for GenAI.

A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect. A novel and effective strategy to obtain efficient and powerful treatment effect inferences is to incorporate predictions from generative artificial intelligence (AI) algorithms into covariate adjustment for the regression analysis of a RCT. Training a generative AI algorithm on historical control data enables one to construct a digital twin generator (DTG) for RCT participants, which utilizes a participant's baseline covariates to generate a probability distribution for their potential control outcome. Summaries of the probability distribution from the DTG are highly predictive of the trial outcome, and adjusting for these features via regression can thus improve the quality of treatment effect inferences, while satisfying regulatory guidelines on statistical analyses, for a RCT. However, a critical assumption in this strategy is homoskedasticity, or constant variance of the outcome conditional on the covariates. In the case of heteroskedasticity, existing covariate adjustment methods yield inefficient estimators and underpowered tests. We propose to address heteroskedasticity via a weighted prognostic covariate adjustment methodology (Weighted PROCOVA) that adjusts for both the mean and variance of the regression model using information obtained from the DTG. We prove that our method yields unbiased treatment effect estimators, and demonstrate via comprehensive simulation studies and case studies from Alzheimer's disease that it can reduce the variance of the treatment effect estimator, maintain the Type I error rate, and increase the power of the test for the treatment effect from 80% to 85%~90% when the variances from the DTG can explain 5%~10% of the variation in the RCT participants' outcomes.

CyberGuy breaks down how to share your WiFi password with other Android users. Many people are already using tools like OpenAI's ChatGPT generative AI chatbot and Bing, which also sources current information on the internet in its results, to help with various tasks, such as writing essays, creating images and more. Google is not far behind and has recently announced new generative AI experiences in Google Workspace that will allow you to create content with the help of AI. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK TIPS, TECH REVIEWS AND EASY HOW-TO'S TO MAKE YOU SMARTER Google Duet AI is a new feature that can assist in answering emails. Google Duet AI is a new feature to help answer emails in Gmail, create images from texts, and proofread documents in Google Docs, to name a few skills.

The generative AI bot ChatGPT has been busy helping writers, debating issues, generating code, and more--and now that developer OpenAI has opened the door to third-party plug-ins, a ton of new functionality is available. These plug-ins can look up information on the web, draw diagrams, manage travel plans, interrogate Wikipedia, and more. To access the various plug-ins, you need an active, $20-per-month subscription to ChatGPT Plus. ChatGPT and these plug-ins can help you search through, summarize, and search these files in seconds. To access plug-ins, start a new chat in the ChatGPT interface and select GPT-4, then Plug-Ins from the options at the top.

Rising attention about generative AI prompts the question: Are we witnessing the birth of a new innovation platform? The answer seems to be yes, though it remains to be seen how pervasive this new technology will become. To have an innovation platform, there must be a foundational technology, such as a widely adopted personal computer or smartphone operating system, or the Internet and cloud-computing services with application programming interfaces (APIs) (see "The Cloud as an Innovation Platform for Software Development," Communications, October 2019). Third parties are then needed to access these APIs and start creating complementary products and services. More applications attract more users, which leads to more applications and then more users, and usually improvements in the foundational technology.

Battles between human and artificial intelligence are no longer science fiction. The strikes in Hollywood led by the united guilds of actors and screenwriters have a common, intangible enemy: the algorithms and computer-generated imagery that are increasingly programmed by studios to render them redundant. In New York last week, a new front in that stand-off was opened by a group of American novelists – including John Grisham, Jodi Picoult and Jonathan Franzen – who are suing OpenAI, the creators of the ChatGPT program. The legal case may help to define and protect those increasingly porous boundaries between human creativity and the robots that mimic it. In the meantime, Amazon, these days flooded by self-published books written by AI, has taken its first half-hearted steps to curtail that practice.

This article is from Big Technology, a newsletter by Alex Kantrowitz. This week, Microsoft and Google introduced generative A.I. tools that make attending meetings, writing emails, scheduling travel, and catching up on projects vastly easier. The products channel the wonder of buzzy A.I. products like ChatGPT, DALL-E, Midjourney, and Bard into clear, applicable uses. And these obvious uses just happen to be in the workplace. About a year into the generative A.I. phenomenon, it's becoming evident that the technology is most useful in enterprise first, with broader consumer adoption perhaps to follow.

In this study, I explored the impact of Generative AI on learning efficacy in academic reading materials using experimental methods. College-educated participants engaged in three cycles of reading and writing tasks. After each cycle, they responded to comprehension questions related to the material. After adjusting for background knowledge and demographic factors, complete reliance on AI for writing tasks led to a 25.1% reduction in accuracy. In contrast, AI-assisted reading resulted in a 12% decline. Interestingly, using AI for summarization significantly improved both quality and output. Accuracy exhibited notable variance in the AI-assisted section. Further analysis revealed that individuals with a robust background in the reading topic and superior reading/writing skills benefitted the most. I conclude the research by discussing educational policy implications, emphasizing the need for educators to warn students about the dangers of over-dependence on AI and provide guidance on its optimal use in educational settings.

Background: Since the launch of various generative AI tools, scientists have been striving to evaluate their capabilities and contents, in the hope of establishing trust in their generative abilities. Regulations and guidelines are emerging to verify generated contents and identify novel uses. Objective: we aspire to demonstrate how ChatGPT claims are checked computationally using the rigor of network models. We aim to achieve fact-checking of the knowledge embedded in biological graphs that were contrived from ChatGPT contents at the aggregate level. Methods: We adopted a biological networks approach that enables the systematic interrogation of ChatGPT's linked entities. We designed an ontology-driven fact-checking algorithm that compares biological graphs constructed from approximately 200,000 PubMed abstracts with counterparts constructed from a dataset generated using the ChatGPT-3.5 Turbo model. Results: in 10-samples of 250 randomly selected records a ChatGPT dataset of 1000 "simulated" articles, the fact-checking link accuracy ranged from 70% to 86%. The computational process was followed by a manual process using IntAct Interaction database and the Gene regulatory network database (GRNdb) to confirm the validity of the links identified computationally. We also found that the proximity of the edges of ChatGPT graphs were significantly shorter (90 -- 153) while literature distances were (236 -- 765). This pattern held true in all 10-samples. Conclusion: This study demonstrated high accuracy of aggregate disease-gene links relationships found in ChatGPT-generated texts. The strikingly consistent pattern offers an illuminate new biological pathways that may open the door for new research opportunities.