Collaborating Authors


Why we Need Art to Cocreate the Societal Impact of AI


The societal impact of Artificial Intelligence (AI) dwarfs its technological impact. Already, we see AI everywhere in our daily lives; we see it in our grocery shopping app, our entertainment streaming lists, social media feeds, our dating lives, and the list goes on. The use of AI has become so naturally intertwined with our lives that we often forget to think about the future. We should ask ourselves the question of how we can unlock AI's full potential while keeping its risks at a minimum. And to investigate this question, we need to work together.

Why are we failing at the ethics of AI? A critical review


Anja Kaspersen and Wendell Wallach are senior fellows at Carnegie Council for Ethics in International Affairs. In November 2021, they published an article that changed the AI ethics conversation: Why Are We Failing at the Ethics of AI? Six months later, the questions the article raised are no closer to resolution. This article was a don't-hold-your-punches review on the state of AI ethics, with which I am in almost complete agreement. If we want to advance the AI conversation, this is still a good place to start. I've quoted a portion of their article, with my comments interspersed: While it is clear that AI systems offer opportunities across various areas of life, what amounts to a responsible perspective on their ethics and governance is yet to be realized.

How to write witty banter on dating apps, according to bestselling authors


It's the dream: Find a smoldering someone on a dating app, match with them, and quickly launch into a conversation filled with subtle compliments, definitive date night plans, and witty repartee. According to research conducted by Preply, -- a language learning app and platform, – more than 70 percent of dating app users surveyed said it's possible to engage in meaningful conversation, and even fall in love with someone, before ever meeting in person (having only spoken on an app). The challenge, of course, is getting there, shifting from the notification that "It's A Match!" into dialogue worthy of a Shonda Rhimes production. It's a daunting task, so we brought in the pros: rom-com authors. Mashable spoke with several -- all with books jam-packed with quippy dialogue out this spring and summer -- to get their expert takes on how to write witty banter.

InWorld introduces impressive AI-based character generation and interaction


Characters in games and other digital experiences are fairly stable and work from a series of lines and responses written long ago. But the future of the game can be more responsive, more productive, and of course AI-driven. InworldAI is trying to do this with a new beta tool that allows developers to create rich, interactive characters, as they are called other AIs. Over the past year, Inworld has claimed to be able to quickly create NPCs and similar characters with a few words of explanation and a rotating dial. Once created, it quickly becomes deeper and more interesting. Currently, these claims have obvious limitations.

Inworld shows off impressive AI-powered character generation and interaction – TechCrunch


Characters in games and other digital experiences tend to be rather static, working from a set of lines and responses written long ago. But the future of games could be more responsive, generative, and of course AI-powered -- something Inworld AI is attempting to enable with a newly available beta tool that lets developers create a rich, interactive characters as simply as they might tell another AI to draw a bird. Inworld's claims, which it has been putting about over the last year, are that it is able to quickly create NPCs and such like characters with just a few sentences of description and twiddled dials, and that once created they will instantly be deeper and more interesting to interact with than ordinary scripted characters. Now, there are obvious limitations to these claims -- you couldn't, for example, outdo the cryptic utterances of characters in Elden Ring, since those are highly crafted scripts intended to be encountered in a specific way. But what about the lady who runs the weapon shop in a fantasy world?

Can AI Perform SEO? Experimenting With OpenAI's GPT-3


AI (artificial intelligence) technology has made tremendous progress in recent years. It is now possible to assess its capacity to perform specific tasks such as generating text, images, and sound. Now, what if we go even further with more complicated tests, like evaluating a job, for example, or more particularly, evaluating an AI system on its ability to do SEO? Below, we will test Generative Pre-trained Transformer 3 (GPT-3) created by OpenAI. Let's keep in mind that an AI system will mimic the data on which it is trained. SEO has been built alongside search engine progression, and everything is well documented in blogs, books, and interviews.

Man and the machine in digital dialogue: The Conversational AI conundrum in Marketing.


Language is the mark of humanity and cognizance, and conversation or dialogue is the most fundamental and a distinctive field of language. As we use more natural interfaces with technology, like language, our relationship is shifting to one where we increasingly humanize them. The simplest kinds of dialogue systems are chatbots, systems that can carry on extended conversations with the goal of mimicking the unstructured conversations or'chats' characteristic of informal human2human (H2H) interaction. Conversational AI expands the scope of today's chatbots from stiff preset replies to one that can take astute & pliant actions. Conversational AI learns to allow humans and computers to talk and work together in a more natural way.

ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization Artificial Intelligence

We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents from two subsets (i.e., SAMSum and MediaSum) and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART-50 (a multi-lingual BART) via further pre-training. The multiple objectives used in the further pre-training stage help the pre-trained model capture the structural characteristics as well as important content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at

Monash Uni to develop AI translation app for diplomatic talks


Researchers at Monash University have been charged to lead a $5 million project, backed by the US Department of Defense's Defense Advanced Research Projects Agency (DARPA), to develop an AI-based smartphone application that could assist with real-time interpretations for diplomatic talks, international business, and tourism. The research will be led by the Monash University's Vision and Language Group at the Faculty of IT in collaboration with researchers from the David Nazarian College of Business and Economics at California State University, and King's College London. According to Monash, the project will involve developing a language processing system to be used together with smart glasses. The hope is the system can recognise and adapt to emotional, social, and cultural cues that vary between different societies and languages. For instance, the system would be able to recognise an imminent communication breakdown by analysing audio-visual cues in real time, before sending a notification to the user's smart glasses suggesting a more appropriate response to rectify the situation, such as addressing the other person more respectfully to make the other feel more comfortable, Monash said.

CASA: Conversational Aspect Sentiment Analysis for Dialogue Understanding

Journal of Artificial Intelligence Research

Dialogue understanding has always been a bottleneck for many conversational tasks, such as dialogue response generation and conversational question answering. To expedite the progress in this area, we introduce the task of conversational aspect sentiment analysis (CASA) that can provide useful fine-grained sentiment information for dialogue understanding and planning. Overall, this task extends the standard aspect-based sentiment analysis to the conversational scenario with several major adaptations. To aid the training and evaluation of data-driven methods, we annotate 3,000 chit-chat dialogues (27,198 sentences) with fine-grained sentiment information, including all sentiment expressions, their polarities and the corresponding target mentions. We also annotate an out-of-domain test set of 200 dialogues for robustness evaluation. Besides, we develop multiple baselines based on either pretrained BERT or self-attention for preliminary study. Experimental results show that our BERT-based model has strong performances for both in-domain and out-of-domain datasets, and thorough analysis indicates several potential directions for further improvements.