How to Do It is Slate's sex advice column. Send it to Jessica and Rich here. I recently started casually online dating after leaving an abusive marriage, and it's been going great! There have been lots of nice guys, and we have had some sexy fun. That said, I've run into a weird situation that I'm almost certainly overthinking but am baffled by.

Reveal has been a weekly investigative podcast for nearly 10 years now, so we've produced hundreds of hours of investigative journalism over the years designed to inspire, inform, or infuriate you (and occasionally, all three at the same time). We've curated some of our favorite Reveal series and serials to take you through your holiday travel time--episodes that will resonate today and into 2025. You can find the link to each episode on your preferred podcast platform below. Mississippi Goddam (seven-part series): Billey Joe Johnson Jr. dreamed of graduating high school, going to college, and one day playing pro football. On a cold December morning in 2008, that future was shattered.

Reasoning in the real world is not divorced from situations. How to capture the present knowledge from surrounding situations and perform reasoning accordingly is crucial and challenging for machine intelligence. This paper introduces a new benchmark that evaluates the situated reasoning ability via situation abstraction and logic-grounded question answering for real-world videos, called Situated Reasoning in Real-World Videos (STAR Benchmark). This benchmark is built upon the real-world videos associated with human actions or interactions, which are naturally dynamic, compositional, and logical. The dataset includes four types of questions, including interaction, sequence, prediction, and feasibility. We represent the situations in real-world videos by hyper-graphs connecting extracted atomic entities and relations (e.g., actions, persons, objects, and relationships). Besides visual perception, situated reasoning also requires structured situation comprehension and logical reasoning. Questions and answers are procedurally generated. The answering logic of each question is represented by a functional program based on a situation hyper-graph. We compare various existing video reasoning models and find that they all struggle on this challenging situated reasoning task. We further propose a diagnostic neuro-symbolic model that can disentangle visual perception, situation abstraction, language understanding, and functional reasoning to understand the challenges of this benchmark.

The Morrissey had the right melodrama in his limbs, and his voice was strong and pained. I was at Gramercy Theatre in Manhattan to see a Smiths tribute band. I tried to get Morrissey's acid yodel in my throat, to sing along. I am human and I need to be loved / just like everybody else does. But it didn't feel right to copy a copy.

I can't stress this enough: If you haven't already taken a machine learning course, take this one. This is the first online course I ever took, and it's the reason that 746,000 other students and I are in data science today. The course was created by data scientist Kirill Eremenko. What's better than his soothing voice is the actual material. Kirill teaches you all the knowledge you need to have a basic understanding of some of the most powerful and useful machine learning models. This includes models in regression, classification, clustering, reinforcement learning, natural language processing, and deep learning.

In late November, a college senior at Brigham Young University named Nick Walton published a short fable called "My Musical Troupe of Orcs Uses Music to Advance Orc Rights." In the story, written in the second person, you are a goblin. "I am a goblin!" you say proudly. "And I'm glad to be one." "Well then, congratulations," says the orc captain. Over the course of a few hundred words, some big things happen: You ask if you can join the orc band.

We had to share this one, AI crossing boundaries for the good of the human race. How many more applications of AI will there be in medicine. I am looking forward to the advances in the coming years and hope this sort of technology will be available for the many and not just the few. "Computers were as good or better than doctors at detecting tiny lung cancers on CT scans, in a study by researchers from Google and several medical centers." "The technology is a work in progress, not ready for widespread use, but the new report, published Monday in the journal Nature Medicine, offers a glimpse of the future of artificial intelligence in medicine."

The process, known as deep learning, is already being used in many applications, like enabling computers to understand speech and identify objects so that a self-driving car will recognize a stop sign and distinguish a pedestrian from a telephone pole. In medicine, Google has already created systems to help pathologists read microscope slides to diagnose cancer, and to help ophthalmologists detect eye disease in people with diabetes. "We have some of the biggest computers in the world," said Dr. Daniel Tse, a project manager at Google and an author of the journal article. "We started wanting to push the boundaries of basic science to find interesting and cool applications to work on." In the new study, the researchers applied artificial intelligence to CT scans used to screen people for lung cancer, which caused 160,000 deaths in the United States last year, and 1.7 million worldwide.

Fortnite, a free-to-play shooter by Epic Games (Gears of War), has taken over the world. That may sound like exaggeration, but I couldn't overstate the popularity of Fortnite if I tried. It is massively played, and even more massively watched--on Twitch, 66 million hours of Fortnite have been watched in the past two weeks, with about 200,000 viewers tuning in at any given time. It is also, somehow, massively complicated. Fortnite started off as something less than a success.

Apple is touting the audio qualities of its talking speaker, but there's much more at stake.