Dear Prudence is Slate's advice column. For this edition, Alicia Montgomery, Slate's vice president of audio, will be filling in as Prudie. My friend Kari and I have been close since we were college roommates (we are now just about 40). Kari has been with her long-distance girlfriend Lora for the last four years, and recently Lora has been talking about moving to Kari and my town in order to better facilitate having a baby. The road for them is going to be long, given the mechanics and their ages, but they have all systems go from their doctors. The problem is that I know Kari is not 100 percent committed to Lora; she says she's not sure she's the one and has built (but not, to my knowledge, deployed) dating profiles on multiple sites and expresses jealousy to me quite often about my adventurous dating life.

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. Meet Ezra, the full-body cancer screener that just might save your life. Combining MRI imaging technology with artificial intelligence, Ezra scans for possible cancer in the human body in up to 13 organs. It also monitors for hundreds of other conditions, such as brain aneurysms or fatty liver disease.

With machine learning conferences growing ever larger, and reviewing processes becoming increasingly elaborate, more data-driven insights into their workings are required. In this report, we present the results of a survey accompanying the first "Learning on Graphs" (LoG) Conference. The survey was directed to evaluate the submission and review process from different perspectives, including authors, reviewers, and area chairs alike. The first "Learning on Graphs" (LoG) Conference (9-12 December, 2022) was remarkable in more ways than one: starting from scratch, the conference aims to be the place for graph learning research, making use of an advisory committee that consists of international experts in the field. Moreover, at is core, LoG wants to be known for its exceptional review quality.

Nanomechanical resonant sensors are used in mass spectrometry via detection of resonance frequency jumps. There is a fundamental trade-off between detection speed and accuracy. Temporal and size resolution are limited by the resonator characteristics and noise. A Kalman filtering technique, augmented with maximum-likelihood estimation, was recently proposed as a Pareto optimal solution. We present enhancements and robust realizations for this technique, including a confidence boosted thresholding approach as well as machine learning for event detection. We describe learning techniques that are based on neural networks and boosted decision trees for temporal location and event size estimation. In the pure learning based approach that discards the Kalman filter, the raw data from the sensor are used in training a model for both location and size prediction. In the alternative approach that augments a Kalman filter, the event likelihood history is used in a binary classifier for event occurrence. Locations and sizes are predicted using maximum-likelihood, followed by a Kalman filter that continually improves the size estimate. We present detailed comparisons of the learning based schemes and the confidence boosted thresholding approach, and demonstrate robust performance for a practical realization.

Sen. Josh Hawley sat down with Fox News Digital for a wide-ranging interview about his new book, "Manhood: The Masculine Virtues America Needs." Senator Josh Hawley, R-Mo., is very concerned about the power of Artificial Intelligence to "manipulate Americans and the "facts" they are given from the technology on a daily basis. "I'm worried about AI's power to manipulate our attention, to manipulate our opinions and to manipulate the information that we're given," he told Fox News Digital in a recent in-person interview. The Missouri senator, the ranking member on the Senate Judiciary Subcommittee on Privacy, Technology and the Law, continued, "Already you can see these generative AI systems – these large language models – that are trained on all the information on the internet." AI WILL BE THE POLITICAL LEFT'S'SINGLE GREATEST WEAPON' AGAINST RELIGIOUS FAITH AND TRUTH, SAYS EXPERT He added, "You can train them on your own.

"I believe that in 50 years' time it will be possible to make computers play the imitation game so well that an average interrogator will have no more than 70% chance of making the right identification after 5 minutes of questioning." Over the course of a month, the game was played by over 1.5 million users who engaged in anonymous two-minute chat sessions with either another human or an AI language model which was prompted to behave like humans. The task of the players was to correctly guess whether they spoke to a person or to an AI. This largest scale Turing-style test conducted to date revealed some interesting facts. For example, overall users guessed the identity of their partners correctly in only 68% of the games. In the subset of the games in which users faced an AI bot, users had even lower correct guess rates of 60% (that is, not much higher than chance). While this experiment calls for many extensions and refinements, these findings already begin to shed light on the inevitable near future which will commingle humans and AI. The famous Turing test, originally proposed by Alan Turing in 1950 as "the imitation game" (Turing, 1950), was proposed as an operational test of intelligence, namely, testing a machine's ability to exhibit behavior indistinguishable from that of a human. In this proposed test, a human evaluator engages in a natural language conversation with both another human and a machine, and tries to distinguish between them. If the evaluator is unable to tell which is which, the machine is said to have passed the test.

Welcome to our May 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we learn how to mitigate biases in machine learning, explore tradeoffs in school redistricting, and find out how machine learning algorithms fared in predicting the winner of this year's Eurovision Song Contest. In this blogpost, Max Springer examines the notion of fairness in hierarchical clustering. Max and colleagues demonstrate that it's possible to incorporate fairness constraints or demographic information into the optimization process to reduce biases in ML models without significantly sacrificing performance. Joar Skalse and Alessandro Abate won the AAAI 2023 outstanding paper award for their work, Misspecification in Inverse Reinforcement Learning, in which they study the question of how robust the inverse reinforcement learning problem is to misspecification of the underlying behavioural model.

I asked some of the best AI journalists in the business to share their top tips on how to talk about AI with confidence. My colleagues and I spend our days obsessing over the tech, listening to AI folks and then translating what they say into clear, relatable language with important context. I'd say we know a thing or two about what we're talking about. Here are seven things to pay attention to when talking about AI. "The tech industry is not great at explaining itself clearly, despite insisting that large language models will change the world. If you're struggling, you aren't alone," says Nitasha Tiku, the Washington Post's tech culture reporter.

During the 2020 pandemic, members of the reddit & discord r/robotics community rallied to organize an online showcase for members of our community. What was originally envisioned as a small, intimate afternoon video call turned out to be a two day event of participants from across the world. All times are recorded in Eastern Daylight Time (EDT), UTC-4. Check out the full program in our website for more details.

Context is everything, even in commonsense moral reasoning. Changing contexts can flip the moral judgment of an action; "Lying to a friend" is wrong in general, but may be morally acceptable if it is intended to protect their life. We present ClarifyDelphi, an interactive system that learns to ask clarification questions (e.g., why did you lie to your friend?) in order to elicit additional salient contexts of a social or moral situation. We posit that questions whose potential answers lead to diverging moral judgments are the most informative. Thus, we propose a reinforcement learning framework with a defeasibility reward that aims to maximize the divergence between moral judgments of hypothetical answers to a question. Human evaluation demonstrates that our system generates more relevant, informative and defeasible questions compared to competitive baselines. Our work is ultimately inspired by studies in cognitive science that have investigated the flexibility in moral cognition (i.e., the diverse contexts in which moral rules can be bent), and we hope that research in this direction can assist both cognitive and computational investigations of moral judgments.