You can help prevent others from falling victim to the same romance scam and remember if something seems too good to be true. Online dating is the most common way for singles to get together these days. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER If you've heard of "The Tinder Swindler" on Netflix, you may already know this, yet schemes are getting extreme when it comes to online dating. Romance scams are skyrocketing, Americans were scammed out of over $500 million last year because of them. So how can you make sure you don't have a crazy person come after you when you're swiping left and right?

WASHINGTON, DC – Yesterday, the U.S. General Services Administration (GSA) launched the Applied AI Healthcare Challenge, a prize competition seeking diverse and practical solutions to help federal agencies provide the highest level of medical care. The Centers of Excellence (CoE) is working in partnership with Challenge.gov, In particular, GSA encourages large and small enterprises, women-owned, minority-owned, small disadvantaged, and service-disabled veteran-owned small businesses to participate. "Technology Transformation Services drives innovation by partnering with technologists in all sectors to identify, demonstrate, test, and prove out technology products that improve delivery of government services and benefits. The Applied AI Healthcare Challenge helps the public and private sector work together to identify promising new AI technology products that support healthcare services and initiatives, centering accessibility, privacy, and customer experience," said TTS Director and FAS Deputy Commissioner Ann Lewis.

Considering how far the tech industry has come, Microsoft Chief Executive Officer Satya Nadella feels, It is'showtime' for them. "The most exciting thing to happen in the industry, perhaps, is the coming of OpenAI's ChatGPT, an artificially intelligent chatbot," he told CNBC TV18 in an interview. Meanwhile, he also mentioned the current downturn being experienced by the tech industry is because the demand spurred by the COVID-19 pandemic has begun to cool down, and that, coupled with a recession in several parts of the world, has resulted in a "normalisation." Talking about the future of the industry, Nadella said, "I think the next phase -- if you say mobile and cloud was the last paradigm -- is going to be artificial intelligence (AI). And that's kind of going to happen in the next, I would say, two or three years. It will be more like I take it back to 2007-2008, which is when cloud and mobile became big. I think we are in that phase when it comes to AI."

The public has rapidly become fascinated with the power of a new artificial intelligence technology -- ChatGPT -- a chatbot developed by the research and deployment company OpenAI and launched late last year. Already it's demonstrated the ability to serve up detailed answers to complex questions while using the information it processes and feedback from users to improve its ability to respond. ChatGPT has proven to be versatile, with users using the technology to compose music, debug computer code, write restaurant reviews, generate advertising copy and answer test questions. It's able to deliver its responses in a conversational way, and has sparked excitement about its potential, along with some concerns with how it might be used. But what exactly is ChatGPT and what does it say about the state of AI now, and in the future?

AI systems could improve the persuasive quality of content and make those messages difficult for ordinary Internet users to recognise as part of co-ordinated disinformation campaigns, says Josh Goldstein, a co-author of the paper and a research fellow at Georgetown's Center for Security and Emerging Technology, where he works on the CyberAI Project.

In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.

Audio event detection is a widely studied audio processing task, with applications ranging from self-driving cars to healthcare. In-the-wild datasets such as Audioset have propelled research in this field. However, many efforts typically involve manual annotation and verification, which is expensive to perform at scale. Movies depict various real-life and fictional scenarios which makes them a rich resource for mining a wide-range of audio events. In this work, we present a dataset of audio events called Subtitle-Aligned Movie Sounds (SAM-S). We use publicly-available closed-caption transcripts to automatically mine over 110K audio events from 430 movies. We identify three dimensions to categorize audio events: sound, source, quality, and present the steps involved to produce a final taxonomy of 245 sounds. We discuss the choices involved in generating the taxonomy, and also highlight the human-centered nature of sounds in our dataset. We establish a baseline performance for audio-only sound classification of 34.76% mean average precision and show that incorporating visual information can further improve the performance by about 5%. Data and code are made available for research at https://github.com/usc-sail/mica-subtitle-aligned-movie-sounds

Little attention is placed on analyzing nationality bias in language models, especially when nationality is highly used as a factor in increasing the performance of social NLP models. This paper examines how a text generation model, GPT-2, accentuates pre-existing societal biases about country-based demonyms. We generate stories using GPT-2 for various nationalities and use sensitivity analysis to explore how the number of internet users and the country's economic status impacts the sentiment of the stories. To reduce the propagation of biases through large language models (LLM), we explore the debiasing method of adversarial triggering. Our results show that GPT-2 demonstrates significant bias against countries with lower internet users, and adversarial triggering effectively reduces the same.

Despite recent progress, it has been difficult to prevent semantic hallucinations in generative Large Language Models. One common solution to this is augmenting LLMs with a retrieval system and making sure that the generated output is attributable to the retrieved information. Given this new added constraint, it is plausible to expect that the overall quality of the output will be affected, for example, in terms of fluency. Can scaling language models help? Here we examine the relationship between fluency and attribution in LLMs prompted with retrieved evidence in knowledge-heavy dialog settings. Our experiments were implemented with a set of auto-metrics that are aligned with human preferences. They were used to evaluate a large set of generations, produced under varying parameters of LLMs and supplied context. We show that larger models tend to do much better in both fluency and attribution, and that (naively) using top-k retrieval versus top-1 retrieval improves attribution but hurts fluency. We next propose a recipe that could allow smaller models to both close the gap with larger models and preserve the benefits of top-k retrieval while avoiding its drawbacks.

As a result, they are often brittle and unable to adapt to new domains, can treat individuals or subgroups unfairly, and have limited ability to explain their actions or recommendations [197, 235] reducing the trust of human users [118]. Following this, a new area of research, trustworthy AI, has recently received much attention from several policymakers and other regulatory organizations. The resulting guidelines (e.g., [184, 186, 187]), introduced to increase trust in AI systems, make developing trustworthy AI not only a technical (research) and social endeavor but also an organizational and (legal) obligational requirement. In this paper, we set out to demonstrate, through an extensive survey, that causal modeling and reasoning is an emerging and very useful tool for enabling current AI systems to become trustworthy. Causality is the science of reasoning about causes and effects. Cause-and-effect relationships are central to how we make sense of the world around us, how we act upon it, and how we respond to changes in our environment. In AI, research in causality was pioneered by the Turing award winner Judea Pearl long back in his 1995 seminal paper [194]. Since then, many researchers have contributed to the development of a solid mathematical basis for causality; see, for example, the books [79, 196, 201], the survey [90] and seminal papers [197, 235].