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Ford’s $28,000 Fathom EV nears production after $2 billion factory overhaul

Ford said today that its next-generation electric vehicle - recently dubbed Fathom - will go into production at the automaker's recently overhauled Louisville Assembly Plant in the first quarter of 2027. The first Fathoms will be prototypes, with Ford's team in Louisville already in the production-level pre-tooling phase at the recently converted facility. Factory workers […]

2026-08-14 原文 →
AI 资讯

Jaguar offers a first look inside the all-electric Type 01

Jaguar's all-electric Type 01 comes with a very beige interior that divides the four seats with a "spine" that spans the length of the cabin. New photos shared by Jaguar show a low-slung driver's seat, alongside a slim dashboard with a smartphone-style display in the center. This vehicle doesn't come with a traditional rear-view mirror, […]

2026-08-12 原文 →
AI 资讯

Negative Space Is a Label

A car mask can pass review and still teach the model to keep the wrong pixels. The outline looks clean. The bumper is inside. The wheels are inside. Then the trained network holds onto the dark patch under the tires, because the label treated that patch as part of the vehicle's visual neighborhood. Training stays quiet. Production gets loud the first time a listing photo drags a strip of the old lot onto a new backdrop. AutoLensAI turns dealer photography into listing-ready vehicle media. This installment follows the earlier pieces on segmentation and image provenance, then narrows to one question: how do I teach a matting model that the shadow touching a tire is evidence against foreground rather than a faint version of it? 1. The failure arrives without an error message Vehicle matting estimates which pixels belong to the vehicle, at finer boundary resolution than segmentation gives. Tires, rocker panels, glossy showroom floors, and the halo under a lowered front lip are where a pretty binary mask does its damage. Two cases cause most of it. A cast shadow can touch rubber and still sit outside the object. A reflection can match paint color exactly and still belong to the floor. Both look like they belong to the car in a thumbnail. Neither belongs to it in geometry. A binary target has no vocabulary for that distinction. Every pixel is in or out, so the annotator's only lever is where to put the line. Push the line outward and shadow becomes vehicle. Pull it inward and the wheel arch loses its edge. Neither answer says the thing that matters, which is that some exterior pixels are ordinary background and some are adversarial background sitting one pixel from the object. The model learns the difference anyway. It learns it wrong, because nothing in the supervision ever separated the two. 2. Three states, not two The supervision contract uses three: state meaning training treatment vehicle body, glass, wheels, trim, and visible geometry foreground loss hard negative

2026-08-11 原文 →
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49ers coach says his Tesla was on Autopilot when he crashed

Four weeks ago, San Francisco 49ers coach Kyle Shanahan was involved in an accident near downtown Palo Alto. At the time Shanahan said only that the accident was his fault. But during a recent press conference he shared more details about the incident, including the fact that he had his Tesla's Autopilot engaged at the […]

2026-08-09 原文 →
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Uber CEO brushes off reports of a Waymo break-up

After Uber and Waymo ended their partnership in Phoenix earlier this year, experts and robotaxi watchers wondered whether the companies' improbable bromance was fraying. Not so, Uber CEO Dara Khosrowshahi said today. The two companies are committed to continue working together in Atlanta and Austin, and the partnership remains "very strong." "Waymo is a very […]

2026-08-06 原文 →