AI tech, for so long a promising vision of the future but an underwhelming experience in the present, is starting to work. And the world is going to get weird as a result. ChatGPT is the latest AI tool to go viral, sparking worry and wonder in equal measure. The system … is the latest evolution of the GPT family of text-generating AIs. Two years ago, the team's previous AI, GPT-3, was able to generate an opinion piece for the Guardian, and ChatGPT has significant further capabilities.

The coronavirus pandemic has ushered in a new era of workplace surveillance that will extend well beyond our current crisis. Companies are increasingly monitoring employees who work from home, citing worries about security concerns or the need to boost employee productivity. In Amazon warehouses and UPS delivery trucks, surveillance technologies are being built into workplace infrastructure to monitor workers' every move. In many industries, employers can easily access phone calls, texts, browser histories, emails, and even GPS locations with very little effort. These exploitative surveillance practices are rooted in a historical mistrust of workers, especially low-wage workers, that can arguably be traced back to slavery and the exploitative "scientific management" practices that emerged from it, as bosses became obsessed with tracking workers' every move to maximize productivity and profit. Earlier forms of surveillance, like in the 19th century when companies hired Pinkerton private detectives to spy on workers, required a lot of labor. But modern technological advancements mean that the cost of surveillance today is very low.

AMP Robotics uses automation and artificial intelligence (AI) to sort materials within waste streams. We asked CEO Matanya Horowitz how this works, how data can be utilised and the ways automated sorting could impact the packaging industry. AMP Robotics developed an AI platform (AMP Neuron) to distinguish recyclable materials from waste. How did you come up with the idea? Ever since I was a child I've been interested in robotics and the origins of intelligence.

Recent organizations have started to adopt AI-based decision support tools to optimize human resource development practices, while facing various challenges of using AIs in highly contextual and sensitive domains. We present our case study that aims to help professional assessors make decisions in human assessment, in which they conduct interviews with assessees and evaluate their suitability for certain job roles. Our workshop with two industrial assessors elucidated troubles they face (i.e., maintaining stable and non-subjective observation of assessees' behaviors) and derived requirements of AI systems (i.e., extracting their nonverbal cues from interview videos in an interpretable manner). In response, we employed an unsupervised anomaly detection algorithm using multimodal behavioral features such as facial keypoints, body and head pose, and gaze. The algorithm extracts outlier scenes from the video based on behavioral features as well as informing which feature contributes to the outlierness. We first evaluated how the assessors would perceive the extracted cues and discovered that the algorithm is useful in suggesting scenes to which assessors would pay attention, thanks to its interpretability. Then, we developed an interface prototype incorporating the algorithm and had six assessors use it for their actual assessment. Their comments revealed the effectiveness of introducing unsupervised anomaly detection to enhance their feeling of confidence and objectivity of the assessment along with potential use scenarios of such AI-based systems in human assessment. Our approach, which builds on top of the idea of separating observation and interpretation in human-AI collaboration, will facilitate human decision making in highly contextual domains, such as human assessment, while keeping their trust in the system.

This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language understanding evaluation (GLUE) benchmark, containing eight difficult language understanding tasks, including question answering, natural language inference, word sense disambiguation, coreference resolution, and reasoning. [Method] Instead of arbitrarily increasing the size of a pretrained language model (PLM), our aim is to 1) fully extract knowledge from the input pretraining data given a certain parameter budget, e.g., 6B, and 2) effectively transfer this knowledge to downstream tasks. To achieve goal 1), we propose self-evolution learning for PLMs to wisely predict the informative tokens that should be masked, and supervise the masked language modeling (MLM) process with rectified smooth labels. For goal 2), we leverage the prompt transfer technique to improve the low-resource tasks by transferring the knowledge from the foundation model and related downstream tasks to the target task. [Results] According to our submission record (Oct. 2022), with our optimized pretraining and fine-tuning strategies, our 6B Vega method achieved new state-of-the-art performance on 4/8 tasks, sitting atop the SuperGLUE leaderboard on Oct. 8, 2022, with an average score of 91.3.

Feeld Notes is a column about a middle-aged woman who suddenly realizes she wants to have sex again--and the beguiling app she uses to do it. Men are disappearing on me all the time on this fucking app. Within the first six weeks of my time on Feeld, I'd had some fun, but I'd also been flaked on or ghosted no fewer than six times. There was one guy, Mike, a short, brown-haired, 33-year-old writer who described himself as a "dom" and promised to show me the ropes around domination and submission. I spent a few steamy nights chatting with him on WhatsApp, then made a plan to meet up with him on a Friday night.

Machine-learning forms of artificial intelligence are going to produce a revolution in computer systems, a new kind of hardware-software union that can put AI in your toaster, according to AI pioneer Geoffrey Hinton. Learn about the leading tech trends the world will lean into over the next 12 months and how they will affect your life and your job. Hinton, offering the closing keynote Thursday at this year's Neural Information Processing Systems conference, NeurIPS, in New Orleans, said that the machine learning research community "has been slow to realize the implications of deep learning for how computers are built." He continued, "What I think is that we're going to see a completely different type of computer, not for a few years, but there's every reason for investigating this completely different type of computer." All digital computers to date have been built to be "immortal," where the hardware is engineered to be reliable so that the same software runs anywhere.

A couple of days ago, the U.K.-based YA author Melinda Salisbury wrote online: "Every time I write a character frowning now, I remember Americans think this is something you do with your mouth, and it ruins it." Soon after, I, a British person, logged into Slack to find my American Slate colleagues discussing this tweet. I read the conversation, and frowned. By which I mean I furrowed my brow, looking confusedly at the screen. Because that is what a frown is.

Can you trust what AI is telling you? It's a new AI model from OpenAI that's designed to chat in a conversational manner. Stuck for ideas on what to talk to a machine about, I decided to interview ChatGPT about the ethics of AI. Would it have the level of self-awareness to be honest about its own dangers? Would it even be willing to answer questions on how it behaves? And while ChatGPT started off by being commendably upfront about the ethics of what it does, it eventually descended into telling outright lies.

On a cloudy Christmas morning last year, a rocket carrying the most powerful space telescope ever built blasted off from a launchpad in French Guiana. After reaching its destination in space about a month later, the James Webb Space Telescope (JWST) began sending back sparkling presents to humanity--jaw-dropping images that are revealing our universe in stunning new ways. Every year since 1988, Popular Science has highlighted the innovations that make living on Earth even a tiny bit better. And this year--our 35th--has been remarkable, thanks to the successful deployment of the JWST, which earned our highest honor as the Innovation of the Year. But it's just one item out of the 100 stellar technological accomplishments our editors have selected to recognize. The list below represents months of research, testing, discussion, and debate. It celebrates exciting inventions that are improving our lives in ways both big and small. These technologies and discoveries are teaching us about the ...