Google's new chatbot Bard made a mistake this week, answering a question incorrectly in a product demo on Twitter. It's a measure of our high expectations of AI that this simple error wiped out $100 billion of Google's share price in a matter of hours. Investors are also underwhelmed by the company's plans to deploy the artificial intelligence in its products. You can understand why Google felt the need to accelerate the response to ChatGPT, which is backed by it's arch-rival Microsoft. Since its launch in November, ChatGPT has passed a bar exam, picked stocks in line with Warren Buffet's strategy, and produced usable computer code.

The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation -- recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, research also found that 2/3 times, these responses are rude and lack evidence. This work seeks to create a counter-misinformation response generation model to empower users to effectively correct misinformation. This objective is challenging due to the absence of datasets containing ground-truth of ideal counter-misinformation responses, and the lack of models that can generate responses backed by communication theories. In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students. We annotate the collected data to distinguish poor from ideal responses that are factual, polite, and refute misinformation. We propose MisinfoCorrect, a reinforcement learning-based framework that learns to generate counter-misinformation responses for an input misinformation post. The model rewards the generator to increase the politeness, factuality, and refutation attitude while retaining text fluency and relevancy. Quantitative and qualitative evaluation shows that our model outperforms several baselines by generating high-quality counter-responses. This work illustrates the promise of generative text models for social good -- here, to help create a safe and reliable information ecosystem. The code and data is accessible on https://github.com/claws-lab/MisinfoCorrect.

Combining 3D vision with tactile sensing could unlock a greater level of dexterity for robots and improve several manipulation tasks. However, obtaining a close-up 3D view of the location where manipulation contacts occur can be challenging, particularly in confined spaces, cluttered environments, or without installing more sensors on the end effector. In this context, this paper presents StereoTac, a novel vision-based sensor that combines tactile sensing with 3D vision. The proposed sensor relies on stereoscopic vision to capture a 3D representation of the environment before contact and uses photometric stereo to reconstruct the tactile imprint generated by an object during contact. To this end, two cameras were integrated in a single sensor, whose interface is made of a transparent elastomer coated with a thin layer of paint with a level of transparency that can be adjusted by varying the sensor's internal lighting conditions. We describe the sensor's fabrication and evaluate its performance for both tactile perception and 3D vision. Our results show that the proposed sensor can reconstruct a 3D view of a scene just before grasping and perceive the tactile imprint after grasping, allowing for monitoring of the contact during manipulation.

In recent years, hate speech has gained great relevance in social networks and other virtual media because of its intensity and its relationship with violent acts against members of protected groups. Due to the great amount of content generated by users, great effort has been made in the research and development of automatic tools to aid the analysis and moderation of this speech, at least in its most threatening forms. One of the limitations of current approaches to automatic hate speech detection is the lack of context. Most studies and resources are performed on data without context; that is, isolated messages without any type of conversational context or the topic being discussed. This restricts the available information to define if a post on a social network is hateful or not. In this work, we provide a novel corpus for contextualized hate speech detection based on user responses to news posts from media outlets on Twitter. This corpus was collected in the Rioplatense dialectal variety of Spanish and focuses on hate speech associated with the COVID-19 pandemic. Classification experiments using state-of-the-art techniques show evidence that adding contextual information improves hate speech detection performance for two proposed tasks (binary and multi-label prediction). We make our code, models, and corpus available for further research.

The biggest tech story of the year is shaping up around the seemingly sudden arrival of AI chatbots into mainstream attention: piggybacking off last year's viral reception to text-to-image generators like DALL-E 2, the launch of OpenAI's ChatGPT in November has since spurred not only widespread media coverage and netizen adoption, but also an industry-wide arms race. Whatever polite corporate doffing made to AI's thicket of ethical ramifications over the past few decades disintegrated nearly overnight in favor of Silicon Valley's primal fear of competition, and we now live in a society where Microsoft's newly AI-powered Bing ("Sydney," to her friends), Google's Bard, Meta's LLaMA, and Snapchat's My AI (which at least allows you the dignity of naming your chatbot yourself) seem poised to transform us all. The AI future feels nigh, if not terribly optimistic. In an era where major breakthroughs in tech render either inscrutable--admit it, you still don't know what a blockchain is, do you?--or We're kind of used to it already: After spending the greater part of Web 2.0 accepting the sleight of hand that invisible, algorithmic forces exert on our day-to-day, the consumer-friendly AI-powered machinations of driverless cars and actually efficient task assistants and decent predictive-text features has become a foregone conclusion.

An artificial intelligence could soon allow players to transform their video game character to look like anything or anyone they want, simply by typing a description. "If you want to be a movie star, or you want to be an animal, or you want to be anything, you just type on your keyboard and, boom, you will have a character that looks like what you described," says Zhengxia Zou at Beihang University in Beijing, China.

Last week the Center for Humane Technology summoned over 100 leaders in finance, philanthropy, industry, government, and media to the Kissinger Room at the Paley Center for Media in New York City to hear how artificial intelligence might wipe out humanity. The two speakers, Tristan Harris and Aza Raskin, began their doom-time presentation with a slide that read: "What nukes are to the physical world … AI is to everything else." We were told that this gathering was historic, one we would remember in the coming years as, presumably, the four horsemen of the apocalypse, in the guise of Bing chatbots, would descend to replace our intelligence with their own. It evoked the scene in old science fiction movies--or the more recent farce Don't Look Up--where scientists discover a menace and attempt to shake a slumbering population by its shoulders to explain that this deadly threat is headed right for us, and we will die if you don't do something NOW. At least that's what Harris and Raskin seem to have concluded after, in their account, some people working inside companies developing AI approached the Center with concerns that the products they were creating were phenomenally dangerous, saying an outside force was required to prevent catastrophe.

ChatGPT might well be the most famous, and potentially valuable, algorithm of the moment, but the artificial intelligence techniques used by OpenAI to provide its smarts are neither unique nor secret. Competing projects and open-source clones may soon make ChatGPT-style bots available for anyone to copy and reuse. Stability AI, a startup that has already developed and open-sourced advanced image-generation technology, is working on an open competitor to ChatGPT. "We are a few months from release," says Emad Mostaque, Stability's CEO. A number of competing startups, including Anthropic, Cohere, and AI21, are working on proprietary chatbots similar to OpenAI's bot.

With the continuous improvement of artificial intelligence (AI), expert research shows that AI has the power to double yearly economic growth rates by 2035 and completely revolutionize how humans work, create and communicate. However, the advent of AI engines like DALL·E 2, Midjourney and Point-E have left many creators worried that AI will leave humans with no creative outlet. The good news is that this is not the case. AI has advanced enough in its communication skills to even fill the role of a close companion or dynamic, interactive acquaintance. Programs like Journey AI and Replika have intriguing tag lines like "AI devoted to you" and "the AI companion who cares."

The latest Digital Health news industry round-up includes an award of £1.4m for Mendelian to further develop its AI solutions, a pilot for a state-of-the-art prehabilitation programme at James Paget University Hospitals and the launch of Spex Capital's €100m health tech fund. A 2023 report on the clinical alarm management market by Future Market Insights has named Tutum Medical's Bedside Equipment Alarm Monitoring System (BEAMS) as a recent development in the market to watch. The reports says: "Sheffield Children's Hospital announced a collaboration with Tutum Medical in March 2017 to create a revolutionary design for a Bedside Equipment Alarm Monitoring System (BEAMS). The system provides crucial bedside equipment monitoring to improve nursing staff response times to alarms. The solution is expected to cut alarm response time by up to 95%, considerably lowering alarm fatigue and enhancing staff availability to serve important patients effectively."