April 1 (Reuters) - Last year, Kris Kashtanova typed instructions for a graphic novel into a new artificial-intelligence program and touched off a high-stakes debate over who created the artwork: a human or an algorithm. "Zendaya leaving gates of Central Park," Kashtanova entered into Midjourney, an AI program similar to ChatGPT that produces dazzling illustrations from written prompts. From these inputs and hundreds more emerged "Zarya of the Dawn," an 18-page story about a character resembling the actress Zendaya who roams a deserted Manhattan hundreds of years in the future. The images in "Zarya," the office said, were "not the product of human authorship." Now, with the help of a high-powered legal team, the artist is testing the limits of the law once again.

In a report released this week, Goldman Sachs predicted that AI advances could cause 300 million jobs, representing roughly 18% of the global workforce, to be automated in some way. OpenAI also recently released its own study with the University of Pennsylvania, which claimed that ChatGPT could affect over 80% of the jobs in the US. The numbers sound scary, but the wording of these reports can be frustratingly vague. "Affect" can mean a whole range of things, and the details are murky. People whose jobs deal with language could, unsurprisingly, be particularly affected by large language models like ChatGPT and GPT-4.

Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual hate speech analysis dataset for English, Hindi, Arabic, French, German and Spanish languages for multiple domains across hate speech - Abuse, Racism, Sexism, Religious Hate and Extremism. To the best of our knowledge, this paper is the first to address the problem of identifying various types of hate speech in these five wide domains in these six languages. In this work, we describe how we created the dataset, created annotations at high level and low level for different domains and how we use it to test the current state-of-the-art multilingual and multitask learning approaches. We evaluate our dataset in various monolingual, cross-lingual and machine translation classification settings and compare it against open source English datasets that we aggregated and merged for this task. Then we discuss how this approach can be used to create large scale hate-speech datasets and how to leverage our annotations in order to improve hate speech detection and classification in general.

Large language models have been demonstrated to be valuable in different fields. ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation by comprehending context and generating appropriate responses. It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries, with fluent and comprehensive answers surpassing prior public chatbots in both security and usefulness. However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study. Eleven categories of failures, including reasoning, factual errors, math, coding, and bias, are presented and discussed. The risks, limitations, and societal implications of ChatGPT are also highlighted. The goal of this study is to assist researchers and developers in enhancing future language models and chatbots.

Online social networks have become a fundamental component of our everyday life. Unfortunately, these platforms are also a stage for hate speech. Popular social networks have regularized rules against hate speech. Consequently, social networks like Parler and Gab advocating and claiming to be free speech platforms have evolved. These platforms have become a district for hate speech against diverse targets. We present in our paper a pipeline for detecting hate speech and its targets and use it for creating Parler hate targets' distribution. The pipeline consists of two models; one for hate speech detection and the second for target classification, both based on BERT with Back-Translation and data pre-processing for improved results. The source code used in this work, as well as other relevant sources, are available at: https://github.com/NadavSc/HateRecognition.git

Event data, or structured records of ``who did what to whom'' that are automatically extracted from text, is an important source of data for scholars of international politics. The high cost of developing new event datasets, especially using automated systems that rely on hand-built dictionaries, means that most researchers draw on large, pre-existing datasets such as ICEWS rather than developing tailor-made event datasets optimized for their specific research question. This paper describes a ``bag of tricks'' for efficient, custom event data production, drawing on recent advances in natural language processing (NLP) that allow researchers to rapidly produce customized event datasets. The paper introduces techniques for training an event category classifier with active learning, identifying actors and the recipients of actions in text using large language models and standard machine learning classifiers and pretrained ``question-answering'' models from NLP, and resolving mentions of actors to their Wikipedia article to categorize them. We describe how these techniques produced the new POLECAT global event dataset that is intended to replace ICEWS, along with examples of how scholars can quickly produce smaller, custom event datasets. We publish example code and models to implement our new techniques.

The increase in abusive content on online social media platforms is impacting the social life of online users. Use of offensive and hate speech has been making so-cial media toxic. Homophobia and transphobia constitute offensive comments against LGBT+ community. It becomes imperative to detect and handle these comments, to timely flag or issue a warning to users indulging in such behaviour. However, automated detection of such content is a challenging task, more so in Dravidian languages which are identified as low resource languages. Motivated by this, the paper attempts to explore applicability of different deep learning mod-els for classification of the social media comments in Malayalam and Tamil lan-guages as homophobic, transphobic and non-anti-LGBT+content. The popularly used deep learning models- Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) using GloVe embedding and transformer-based learning models (Multilingual BERT and IndicBERT) are applied to the classification problem. Results obtained show that IndicBERT outperforms the other imple-mented models, with obtained weighted average F1-score of 0.86 and 0.77 for Malayalam and Tamil, respectively. Therefore, the present work confirms higher performance of IndicBERT on the given task in selected Dravidian languages.

Hollywood has colourful notions about artificial intelligence (AI). The popular image is a future where robot armies spontaneously turn to malevolence, pitching humanity in a battle against extinction. In reality, the risks posed by AI today are more insidious and harder to unpick. They are often a by-product of the technology's seemingly endless application in modern society and increasing role in everyday life, perhaps best highlighted by Microsoft's latest multi-billion-dollar investment into ChatGPT-maker OpenAI. Either way, it's unsurprising that AI generates so much debate, not least in how we can build regulatory safeguards to ensure we master the technology, rather than surrender control to the machines. Right now, we tackle AI using a patchwork of laws and regulations, as well as guidance that doesn't have the force of law. Against this backdrop, it's clear that current frameworks are likely to change – perhaps significantly.

If artificial intelligence evangelists' predictions pan out, generative AI systems like ChatGPT and DALL-E are set to transform Hollywood by developing and writing scripts for the next hit TV show, "diversifying" casts with AI-generated actors and generating imagery across multiple mediums, practically instantly, for a fraction of the cost of a real, human artist. But how long will it take for the vision to meet reality, and can a select group of companies -- similar to the rise of Facebook and social media -- be trusted to herald the way? Driving much of the current conversation around AI innovation has been OpenAI, an AI research company with both non-profit and for-profit arms. Just four months after the formal launch of OpenAI's chatbot, ChatGPT, industry titans like Bill Gates were ready to hail artificial intelligence as the most revolutionary technology of our time since the advent of cell phones and the internet. Major tech companies like Google and Microsoft have invested hundreds of millions into AI companies, including OpenAI, as executives look to the technology to steward their companies into the future amid an economic downturn that has particularly hit digital native companies hard.

The Italian privacy regulator Friday ordered a ban on ChatGPT over alleged privacy violations. The national data protection authority said it will immediately block and investigate OpenAI, the U.S. company behind the popular artificial intelligence tool, from processing the data of Italian users. The order is temporary until the company respects the EU's landmark privacy law, the General Data Protection Regulation (GDPR). Calls to suspend new ChatGPT releases and investigate its maker OpenAI over a range of risks for privacy, cybersecurity and disinformation are growing on both sides of the Atlantic. Elon Musk and dozens of AI experts this week called for a pause to updates of ChatGPT.