When you purchase through links in our articles, we may earn a small commission. Making it a little easier to bring greatness everywhere you go. It's not without its faults, but the Acer Swift Edge 14 AI otherwise delivers a great all-around experience with extra points going to the gorgeous matte display. If you're more often on the move than not, the Acer Swift Edge 14 AI will make a great partner. Acer has renewed its Swift line with a new compact model in the Swift Edge 14 AI, which not only boasts the thinness the Swift line has been known for but also an exceptionally low weight at just 2.18 pounds.

The Option Keyboard (OK) was recently proposed as a method for transferring behavioral knowledge across tasks. OK transfers knowledge by adaptively combining subsets of known behaviors using Successor Features (SFs) and Generalized Policy Improvement (GPI).However, it relies on hand-designed state-features and task encodings which are cumbersome to design for every new environment.In this work, we propose the Successor Features Keyboard (SFK), which enables transfer with discovered state-features and task encodings.To enable discovery, we propose the Categorical Successor Feature Approximator (CSFA), a novel learning algorithm for estimating SFs while jointly discovering state-features and task encodings.With SFK and CSFA, we achieve the first demonstration of transfer with SFs in a challenging 3D environment where all the necessary representations are discovered.We first compare CSFA against other methods for approximating SFs and show that only CSFA discovers representations compatible with SF&GPI at this scale.We then compare SFK against transfer learning baselines and show that it transfers most quickly to long-horizon tasks.

A beautifully designed split keyboard that seems utterly determined not to work. Strange quirks in setup require extensive troubleshooting. Modules are difficult to use when tented. I really wanted to like the Naya Create. It's as if Apple tried its hand at an ergonomic keyboard.

When you purchase through links in our articles, we may earn a small commission. Logitech rejects AI gadgets: 'A solution looking for a problem that doesn't exist' Logitech's CEO says that it isn't interested in making a singular AI gadget, like the Rabbit R1 or Humane Pin. From graphics cards to mid-sized cars, you can't find any new electronics that don't claim to be "AI-powered" in some way. That includes Logitech's mice and keyboards, some of which are being loaded specifically with "AI" buttons. But the CEO of Logitech says she sees no value in infamous "AI" gadgets, such as the Rabbit A1 or the Humane pin.

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We explore surface electromyography (sEMG) as a non-invasive input modality for mapping muscle activity to keyboard inputs, targeting immersive typing in next-generation human-computer interaction (HCI). This is especially relevant for spatial computing and virtual reality (VR), where traditional keyboards are impractical. Using attention-based architectures, we significantly outperform the existing convolutional baselines, reducing online generic CER from 24.98% -> 20.34% and offline personalized CER from 10.86% -> 10.10%, while remaining fully causal. We further incorporate a lightweight decoding pipeline with language-model-based correction, demonstrating the feasibility of accurate, real-time muscle-driven text input for future wearable and spatial interfaces.

Recognition (ASR), we show strong baseline performance on predicting key-presses using sEMG signals alone.

Second, we should be able to also play chords.

Repetitive strain injury (RSI) affects roughly one in five computer users and remains largely unresolved despite decades of ergonomic mouse redesign. All such devices share a fundamental limitation: they still require fine-motor motion to operate. This work investigates whether predictive, AI-assisted input can reduce that motion by replacing physical pointing with ranked on-screen suggestions. To preserve user agency, we introduce Preview Accept Discard (PAD), a zero-click interaction paradigm that lets users preview predicted GUI targets, cycle through a small set of ranked alternatives, and accept or discard them via key-release timing. We evaluate PAD in two settings: a browser-based email client and a ISO 9241-9 keyboard-prediction task under varying top-3 accuracies. Across both studies, PAD substantially reduces hand motion relative to trackpad use while maintaining comparable task times with the trackpad only when accuracies are similar to those of the best spell-checkers.