At CES 2025, Anker has a pair of new speakers under its Soundcore brand. The first, the Soundcore Rave 3S, is a 349 party speaker with AI-powered karaoke tricks and built-in light shows. Joining it is the SoundCore Boom 2 Pro, a more powerful follow-up to its non-Pro namesake, which adds boomier bass and better protection from water and dust. The appropriately named Rave 3S is described as an "AI party speaker" with 200W output and 108dB volume. Anker says it can fill spaces of up to 1,076 square feet. Like some other Soundcore models, its RGB LEDs can create a light show that syncs with the beat of your music.

They're widely considered some of the most charismatic animals in the ocean. Now, it turns out bottlenose dolphins actually'smile' at each other while surfing, chasing and playfighting, according to a new study. Until now, little has been known about how the species - famed for their intelligence and playfulness - interact during playtime. But experts have finally revealed that these dolphins use the'open mouth' facial expression – comparable to a smile – to communicate during social play. The dolphins almost always use the facial expression when they are in their playmate's field of view, the researchers found, and playmates responded by'smiling' back a third of the time.

This week brings a preposterously generous gift for lovers of timesink role-playing games: Larian Studios's Baldur's Gate 3. Depending on your level of engagement with Dungeon and Dragons-inspired RPGs, you will know it either as the unlikely and long-awaited follow-up to 2000's Baldur's Gate 2 – one of the great computer RPGs of its era – or as the game where you can have sex with a bear. Look, technically it's not a bear – it's a shapeshifting druid in bear form. But still, the scene inevitably went viral when Larian showed it off during a livestream last month. It is the tip of the iceberg: you can romance pretty much any available character in this role-playing game, or several of them at once. You can try to steal almost anything, or throw it at an enemy as a makeshift weapon.

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.

The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings. This information could be used for developmental research and to support clinical decision-making, such as detection of developmental problems and guiding of their therapeutic interventions. MAIJU-based analyses rely fully on the classification of infant's posture and movement; it is hence essential to study ways to increase the accuracy of such classifications, aiming to increase the reliability and robustness of the automated analysis. Here, we investigated how self-supervised pre-training improves performance of the classifiers used for analyzing MAIJU recordings, and we studied whether performance of the classifier models is affected by context-selective quality-screening of pre-training data to exclude periods of little infant movement or with missing sensors. Our experiments show that i) pre-training the classifier with unlabeled data leads to a robust accuracy increase of subsequent classification models, and ii) selecting context-relevant pre-training data leads to substantial further improvements in the classifier performance.

As someone who has built multiple AI-powered businesses in the legal community, I know firsthand the exciting potential of technology to transform the way we practice law. From predictive coding in electronic discovery, to AI-based contract analysis, legal tech has the power to make our jobs easier and more efficient. But with any new technology comes risk, uncertainty and responsibility. It's easy to get caught up in the hype of the latest buzzwords and trends, but when it comes to serving a demanding audience like lawyers and their clients, you better understand that there's a difference between'playtime' and'production.' What do I mean by that?

Being a parent can feel, at times, like leading an authoritarian nation of one. You control what your subject can read, who they can speak to and what they can do; you deal with periodic revolts against your rule and occasionally engage in a simulacrum of democratic decision-making while knowing that you control the outcome. But for all that authoritarian leaders like to present themselves as a parental figure for the country at large, it's rare that they actually get involved with the day-to-day work of, well, parenting. Which is why the news that China is taking on the job of limiting gaming time caught the attention of so many parents I know. According to state news outlets, online gaming companies will be required to limit under-18s to just three hours of playtime a week, between the hours of eight and nine in the evening on Friday, Saturday and Sunday.

The game industry has long been troubled by malicious activities utilizing game bots. The game bots disturb other game players and destroy the environmental system of the games. For these reasons, the game industry put their best efforts to detect the game bots among players' characters using the learning-based detections. However, one problem with the detection methodologies is that they do not provide rational explanations about their decisions. To resolve this problem, in this work, we investigate the explainabilities of the game bot detection. We develop the XAI model using a dataset from the Korean MMORPG, AION, which includes game logs of human players and game bots. More than one classification model has been applied to the dataset to be analyzed by applying interpretable models. This provides us explanations about the game bots' behavior, and the truthfulness of the explanations has been evaluated. Besides, interpretability contributes to minimizing false detection, which imposes unfair restrictions on human players.

Sign in to report inappropriate content. Digital Maker's Martin Evans has been experimenting with TensorFlow Lite on the Raspberry Pi 4 to recognise Dr Who character shapes. This is a short video of the Pi camera recognising a Dalek & a Cyberman, with the output going to an Ada Fruit Display Screen.

For instance, players with a very rapid in-game Nowadays most video games are played online and every progression (who reach a high level after a relatively short action by every player is recorded. This generates extremely playtime, regardless of their lifetime) and low spend might rich datasets that--with the aid of machine learning be overlooked by traditional segmentation methods due to techniques--can provide deep insights on user behavior, their lack of direct economic value; however, these are the including accurate predictions of the future actions of each most skillful players, and a careful study of their traits and player. Increasingly diverse demographics are now playing behavior--allowed by our approach--could provide developers games in a highly competitive market. Furthermore, we are with a lot of useful insights.