Reid Hoffman Wants Silicon Valley to'Stand Up' Against the Trump Administration The LinkedIn cofounder and frequent Trump target has a simple message for his peers: "Just speak up about the things that you think are true." Reid Hoffman doesn't do much in half measures. He cofounded LinkedIn, of course, and helped bankroll companies including Meta and Airbnb in their startup days. He has also fashioned himself, via books, podcasts, and other public appearances, as something of a public intellectual--a pro-capitalist philosopher who still insists that tech can be a force for good. Most recently, Hoffman has emerged as one of Silicon Valley's most prominent defenders of artificial intelligence . His newest book, 2025's, makes the case that AI won't diminish human capacity but will instead amplify it. Hoffman even relied on AI to make one of the most unconventional--and perhaps uncomfortable, depending on your view of AI-generated creativity--Christmas gifts I've heard of lately. Whatever you think of Hoffman's utopian views on AI, credit where due: He's also a very outspoken critic of President Trump--a rare trait in a tech world that's grown increasingly quiet, or cozy, when it comes to the cruelties of the US administration. Hoffman's overt political views haven't been without consequence: Trump has twice threatened to launch investigations into him, most recently calling on Attorney General Pam Bondi to dig into Hoffman's ties to Jeffrey Epstein . He has subsequently called for the government to release the Epstein files in full.) Despite those threats, Hoffman isn't pulling punches: When we sat down to tape this episode in mid-December, he readily called out the administration for degrading American government, criticized his peers for keeping their heads down, and urged Silicon Valley to stop pretending that neutrality is a virtue. If only more billionaires were saying it. So glad to have you here. I'm glad to be here. We like to start these conversations with some very fast questions. What's the hardest lesson you've ever had to learn? Probably when to give up.

'Real Time' host Bill Maher slammed AI for'a-- kissing' its human users and said that products needlessly praising people for completing mundane tasks is endemic in American society. "Real Time" host Bill Maher tore into AI technology on his show Friday, lampooning chatbots for being overly conciliatory to their human users in a searing commentary for his "New Rules" segment. "People don't read anymore, they ask their Chatbot the question and sometimes it's right and sometimes it isn't. But what it always is, is a f--king a-- kisser. You literally can not ask it a question so stupid it won't respond'great question.' 'Can I drink milk if it's lumpy? The comedian went on to blame America's "extremely needy" population for demanding they be "emotionally j--ked off" by their consumer products. Maher, who has long lambasted woke culture, throwing stones at the anti-fat-shaming movement, lack of free speech on American college campuses, and trigger warnings, went on to say that technology needlessly praising their owners for performing mundane tasks had become endemic in American society. "Your Apple watch fitness app tells you you smashed it today The self checkout screen says wow, you're a super saver On Waze, it leads you directly to your destination, and when you get there, it congratulates you.

Large Language Models (LLMs) have been observed to encode and perpetuate harmful associations present in the training data. We propose a theoretically grounded framework called StereoMap to gain insights into their perceptions of how demographic groups have been viewed by society. The framework is grounded in the Stereotype Content Model (SCM); a well-established theory from psychology. According to SCM, stereotypes are not all alike. Instead, the dimensions of Warmth and Competence serve as the factors that delineate the nature of stereotypes. Based on the SCM theory, StereoMap maps LLMs' perceptions of social groups (defined by socio-demographic features) using the dimensions of Warmth and Competence. Furthermore, the framework enables the investigation of keywords and verbalizations of reasoning of LLMs' judgments to uncover underlying factors influencing their perceptions. Our results show that LLMs exhibit a diverse range of perceptions towards these groups, characterized by mixed evaluations along the dimensions of Warmth and Competence. Furthermore, analyzing the reasonings of LLMs, our findings indicate that LLMs demonstrate an awareness of social disparities, often stating statistical data and research findings to support their reasoning. This study contributes to the understanding of how LLMs perceive and represent social groups, shedding light on their potential biases and the perpetuation of harmful associations.

November 23, 2022 -- The Radiological Society of North America (RSNA) has announced the official results of the RSNA Cervical Spine Fracture AI Challenge. Conducted by RSNA in collaboration with the American Society of Neuroradiology (ASNR) and the American Society of Spine Radiology (ASSR), the aim of the challenge was to explore whether artificial intelligence (AI) could be used to aid in the detection and localization of cervical spine injuries. The top eight teams will be recognized in a presentation on Nov. 28, in the AI Showcase during RSNA's 108th Scientific Assembly and Annual Meeting in Chicago (RSNA 2022). The RSNA Cervical Spine Fracture AI Challenge was conducted on a platform provided by Kaggle, Inc. The top performing competitors will be awarded a total of $30,000.

An artificial intelligence (AI) model developed by researchers can predict the likelihood that a patient may have an unplanned hospitalization during their radiation treatments for cancer. The machine-learning model uses daily step counts as a proxy to monitor patients' health as they go through cancer therapy, offering clinicians a real-time method to provide personalized care. Findings will be presented today at the American Society for Radiation Oncology (ASTRO) Annual Meeting. An estimated 10-20% of patients who receive outpatient radiation or chemoradiation therapy will need acute care in the form of an emergency department (ED) visit or hospital admission during their cancer treatment. These unplanned hospitalizations can be a major challenge for people undergoing cancer treatment, causing treatment interruptions and stress that may impact clinical outcomes.

Combining machine learning approaches, the team built a phylogenetic tree that, in the end, doubled the number of phyla (groups of species with …

The COVID-19 pandemic has revealed the importance of digital fabrication to enable online learning, which remains a challenge for robotics courses. We introduce a teaching methodology that allows students to participate remotely in a hands-on robotics course involving the design and fabrication of robots. Our methodology employs 3D printing techniques with flexible filaments to create innovative soft robots; robots are made from flexible, as opposed to rigid, materials. Students design flexible robotic components such as actuators, sensors, and controllers using CAD software, upload their designs to a remote 3D printing station, monitor the print with a web camera, and inspect the components with lab staff before being mailed for testing and assembly. At the end of the course, students will have iterated through several designs and created fluidically-driven soft robots. Our remote teaching methodology enables educators to utilize 3D printing resources to teach soft robotics and cultivate creativity among students to design novel and innovative robots. Our methodology seeks to democratize robotics engineering by decoupling hands-on learning experiences from expensive equipment in the learning environment.

Artificial intelligence (AI)-based scheduling significantly improves physician engagement and reduces burnout by creating fair and flexible schedules that support work-life balance -; even during the COVID-19 pandemic -; according to research being presented at the American Society of Anesthesiologists' ADVANCE 2022, the Anesthesiology Business Event. Studies show half of all physicians experience burnout during their career, driven by factors including workload, job demands, work-life integration and schedule control and flexibility. In the new study, the AI-based scheduling software granted more vacation days, reduced ungranted vacation days and provided flexibility and predictability, compared to the previous staff-created scheduling system, resulting in significantly improved engagement scores from anesthesiologists within six months. These scores reflect the physician's level of engagement with the health care organization. The higher the engagement score, the better the relationship the physician has with the organization, leading to enhanced patient care, improved patient safety, lower costs, improved efficiency, and greater physician satisfaction and retention.

AI language models trained on Web data generate prose that reflects human knowledge and public sentiments, but can also contain novel insights and predictions. We asked the world's best language model, GPT-3, fifteen difficult questions about the nature, value, and future of library and information science (LIS), topics that receive perennial attention from LIS scholars. We present highlights from its 45 different responses, which range from platitudes and caricatures to interesting perspectives and worrisome visions of the future, thus providing an LIS-tailored demonstration of the current performance of AI language models. We also reflect on the viability of using AI to forecast or generate research ideas in this way today. Finally, we have shared the full response log online for readers to consider and evaluate for themselves.

We present a new experiment demonstrating destructive interference in customers' estimates of conditional probabilities of product failure. We take the perspective of a manufacturer of consumer products, and consider two situations of cause and effect. Whereas individually the effect of the causes is similar, it is observed that when combined, the two causes produce the opposite effect. Such negative interference of two or more reasons may be exploited for better modeling the cognitive processes taking place in the customers' mind. Doing so can enhance the likelihood that a manufacturer will be able to design a better product, or a feature within it. Quantum probability has been used to explain some commonly observed deviations such as question order and response replicability effects, as well as in explaining paradoxes such as violations of the sure-thing principle, and Machina and Ellsberg paradoxes. In this work, we present results from a survey conducted regarding the effect of multiple observed symptoms on the drivability of a vehicle. We demonstrate that the set of responses cannot be explained using classical probability, but quantum formulation easily models it, as it allows for both positive and negative "interference" between events. Since quantum formulism also accounts for classical probability's predictions, it serves as a richer paradigm for modeling decision making behavior in engineering design and behavioral economics.