Google is one of the major players in AI (meaning machine learning) weather forecast model space. The models it and others generate have their strengths and weaknesses, but the main advantage is that they can have forecast performance similar to traditional models while requiring far less computing horsepower to run. That means they can be run more frequently.
Google recently released version 3 of its WeatherNext model, with the biggest change being that it now ingests some satellite weather data, shortening the lag time between current weather conditions and generating a new forecast. The update is detailed in a white paper.
Many weather models make use of what’s called a “reanalysis,” which is a sort of model of its own. Reanalyses take in all kinds of weather data and combine them into a single, consistent global snapshot of the atmosphere. That requires that they provide estimates for conditions over locations without real-world measurements, because weather forecast models need to work with a global picture.
© NASA/JPL
Google is one of the major players in AI (meaning machine learning) weather forecast model space. The models it and others generate have their strengths and weaknesses, but the main advantage is that they can have forecast performance similar to traditional models while requiring far less computing horsepower to run. That means they can be run more frequently.
Google recently released version 3 of its WeatherNext model, with the biggest change being that it now ingests some satellite weather data, shortening the lag time between current weather conditions and generating a new forecast. The update is detailed in a white paper.
Many weather models make use of what’s called a “reanalysis,” which is a sort of model of its own. Reanalyses take in all kinds of weather data and combine them into a single, consistent global snapshot of the atmosphere. That requires that they provide estimates for conditions over locations without real-world measurements, because weather forecast models need to work with a global picture.
© NASA/JPL
As Americans are being hailed as folk heroes for sawing down Flock surveillance cameras across the country, British home appliance company Dyson has come up with a form of video surveillance that we didn’t even know was possible.
Yes: the company has launched a new AI-infused electric toothbrush that features a “100k pixel macro lens camera,” allowing users — brushers? — to “see what their dentist sees.”
The appropriately named Dyson CameraJet claims to analyze 28 images per second and employ an “AI machine-learning algorithm” to identify and target “interdental gaps.” In case you’re wondering how it can take pictures in the darkness of your mouth, a “stroboscopic light” ensures “precise illumination for optimal camera visibility.”
It also hooks up to WiFi and Bluetooth, theoretically opening it up to all kinds of cybersecurity shenanigans if somebody were to identify any vulnerabilities in Dyson’s software.
“By combining a camera, machine learning, precision fluid dynamics, advanced brushing technologies, connectivity and WiFi, Dyson CameraJet introduces an entirely new way to keep your mouth healthy,” founder and chief engineer James Dyson boasted in a statement.
Of course, all of that sensitive video surveillance footage of the inside of your mouth is going to cost you dearly. The undoubtedly overengineered gizmo — which looks more like an undersized hair curling iron than a familiar electric toothbrush — goes for a whopping $499 and at least comes with two brush heads, a charging cable, and a “refill dock.”
Naturally, Dyson will happily sell you a “proprietary non-foaming toothpaste” as well, which lacks a common synthetic foaming agent called sodium lauryl sulfate (SLS) to make sure the toothbrush surveillance camera isn’t impeded by all those suds.
We’ll leave it up to you whether you deem the product, which allegedly required the input of 661 engineers, is worth the price of an iPad Air or roughly ten Oral-B Pro 1000 electric toothbrushes — a popular budget pick for those who don’t need AI to brush their teeth.
More on surveillance: Flock Is in Major Trouble
The post Dyson’s New AI Toothbrush Conducts Video Surveillance on the Inside of Your Mouth appeared first on Futurism.
Machine vision is becoming a practical part of Apple’s U.S. factory training strategy, giving smaller manufacturers access to techniques that help production systems inspect what they make. The recent opening of the Houston manufacturing training center extends that effort into a working electronics production campus, while courses developed with Michigan State University introduce businesses to image analysis, machine learning, and automated quality checks.
For a manufacturer, the attraction can be straightforward: finding a missing component before a product leaves the line, identifying a surface defect before more material is added, or recognizing an assembly error while it can still be corrected cheaply.
These are manageable problems with measurable costs. They also offer an accessible starting point for businesses interested in AI but unsure where it belongs inside their operations.
The Apple Manufacturing Academy’s current course catalog includes Machine Learning with Vision, a course introducing machine learning and deep learning through practical activities using Apple devices. It sits alongside instruction in automation, quality fundamentals, manufacturing data, maintenance, and process improvement.
That placement gives the subject useful context. A camera can produce an image, but a factory needs a decision: accept the part, flag it for review, check its position, or investigate a process that may be drifting out of tolerance.
Machine vision connects image capture to those operational decisions. Some applications use established rules to measure dimensions or identify patterns. Others use machine learning to recognize acceptable variations and defects that are difficult to describe through fixed rules.
The academy’s public description confirms introductory activities with Apple devices. It does not identify a particular iPhone, Mac configuration, software framework, or industrial camera package that every participant must use.
For smaller businesses, the educational value is learning how to define an inspection problem before purchasing equipment. A company that understands what it needs to detect can have a much more productive conversation with an automation supplier.
Apple opened its 20,000-square-foot Advanced Manufacturing Center on August 13. According to the Houston opening announcement, the free program offers small and medium-sized businesses instruction in advanced manufacturing, interactive labs, and experience with representative production equipment.
The first cohort worked with machine learning for quality control and advanced automation. Participants also explored production challenges, used a holographic table, and assembled and laser etched a product.
The location adds context: the center occupies the same Houston facility that builds advanced AI servers and is scheduled to begin Mac mini manufacturing this year. Its teaching program includes final assembly principles and design considerations for printed circuit board assembly.
Houston is the company’s second U.S. manufacturing learning site, following the Detroit academy established with Michigan State University in August 2025. By the Houston opening, Apple said the academy had helped nearly 1,000 workers, students, and entrepreneurs.
That figure measures people reached through the program. It should not be interpreted as 1,000 factories already operating AI inspection systems.
Imagine a small business assembling control panels. Each panel needs the same set of connectors, positioned correctly before the enclosure closes. A machine vision station could check for a missing connector while the assembly remains accessible.
The value comes from catching that error at the right moment. Discovering it after packaging creates extra handling and rework. Discovering it after delivery may involve a return, a service visit, and an unhappy customer.
A useful pilot would therefore begin with that single inspection point. The business could compare the existing process with the assisted one, recording missed defects, unnecessary rechecks, inspection time, and the cost of correcting mistakes.
The camera is only part of the exercise. Workers must agree on what constitutes a defect, collect representative examples, and decide what happens when the result is uncertain.
Those decisions draw heavily on experience already present on the factory floor. An operator may know that an unusual mark is harmless, while a barely visible change in alignment predicts a later assembly problem. Turning that knowledge into consistent inspection criteria is valuable work in its own right.
These are examples of how manufacturers could apply the training, rather than projects Apple has announced for individual participants.
A demonstration can look impressive when every part arrives clean, centered, and evenly lit. Production introduces variation: different surface finishes, reflections, changing positions, and batches that look slightly different while remaining within specification.
The inspection system must distinguish those acceptable differences from actual failures. Otherwise, it can reject good products or allow defective ones to continue.
Industrial vision supplier Cognex’s deployment guidance recommends validating systems across product models, defect types, and manufacturing lines. It also describes an approach in which automated inspection handles preliminary checks while experienced inspectors review borderline cases.
For a business learning machine vision, that offers a realistic way to introduce the technology. Employees can compare its decisions with their own, investigate disagreements, and determine whether the results justify extending its role.
An inspection system also needs an owner after installation. Someone must understand when a product change requires new examples, when equipment needs attention, and when performance has slipped. Training that responsibility into the workforce helps prevent an initially successful project from becoming an unreliable fixture.
The academy’s reach extends beyond companies producing consumer electronics. In its May update on participating manufacturers, Apple highlighted Block Imaging, a Michigan business that services and refurbishes medical imaging equipment.
The company hosted visitors during the academy’s Spring Forum and described applying techniques learned from Apple engineers and Michigan State experts to its operations. Apple reported that the academy had supported more than 150 American businesses at that point.
The announcement did not identify a particular machine vision deployment at Block Imaging or publish a percentage improvement attributable to visual inspection. It does provide an example of the training reaching businesses whose work differs substantially from building an iPhone.
That wider participation could benefit Apple indirectly. More capable domestic manufacturers can support equipment maintenance, tooling, components, and specialized production services. Participation itself does not establish a supplier relationship, but the skills can be used across customers and industries.
The academy’s participation information says eligible small and medium-sized U.S. manufacturers can take courses free of charge. Online learning includes on-demand materials and interactive modules, while in-person sessions add workshops and more personalized instruction.
Digital badges are available for different topics, and completed course materials remain accessible for review. That gives a business room to train an employee around existing production commitments and return to the material when an actual project begins.
Free instruction does not remove the eventual cost of cameras, lighting, integration, maintenance, or employee time. It can help a manufacturer understand those costs before committing to a system.
The first proposal brought back to the factory might be modest: inspect one recurring defect at one station and record the results for several weeks. A supervisor can then weigh the time saved against the extra reviews, maintenance, and mistakes. That is the kind of evidence a business owner can use when deciding whether to install a second station.
The post Machine Vision Joins Apple’s U.S. Factory Training Push appeared first on AppleMagazine.
Apply for just about any office job these days, and there’s a pretty good chance your first round interview won’t be with a human, but with a crummy virtual AI avatar.
Though the practice has become widespread throughout HR departments around the world, a new video released by satirical influencer Graham Zip shows just how humiliating the experience can be — and how straying from the system’s comfort zone can cause the bots to melt down completely.
On logging into the interview, Graham is welcomed by a virtual avatar of a woman identified as “Dana Whitfield,” which prompts the interviewee to “tell me about yourself.” Without skipping a beat, Graham launches into a faux-hallucinated spiel, plausibly constructed to sound like a real answer.
“Hi Dana, it’s great to meet you,” Graham begins. “I’m Chick Haversplat, or, Chick Bongo for short. And uh, Q3 pipeline, Salesforce, Kayak margin.”
The large language model’s brain immediately breaks: “It’s a pleasure to meet you, Chick Bongo,” it replies. “So it sounds like you’ve been deep in the weeds with Salesforce, hygiene, and managing those Kayak margins during the third quarter.”
Asked to elaborate more on his professional background, Graham explains that “freight, cadence, walnut, a little bit of Richard, sprucewood.”
“Six years of sprucewood — actually seven, if you count Richard,” the influencer swiftly replies.
“It sounds like you built up a significant tenure over the last seven years, including your time with Richard at sprucewood,” the bot rejoins. “I’m curious, can you tell me about a difficult situation you handled during that time?”
“Mostly palette forward, a little bit of denim,” comes Graham’s reply. “You know, every pharmacist is a hallway.”
“It sounds like you have a unique way of navigating the palette-forward and denim aspects of your work,” the AI bot replies, now completely lost in the sauce.
At one point, Graham explains that he’s looking for a job with “two plumes of gauze,” where he can follow “the raccoon protocols.”
“It sounds like you’re looking for a position where you can really lean into those raccoon protocols, and the plumes of gauze,” the virtual recruiter responds.
By the end of the “interview,” Graham has the AI bot explaining that there “was a bit of a gap in the drum counting process, after Tuesday, with the work truck and cousin Francis.”
The ease with which the influencer can immediately take control of the entire interview underscores the fact that AI recruiters aren’t there so much to actually gather information on applicants, but to screen candidates for HR departments overwhelmed by a deluge of AI-generated applications. It isn’t clear how Graham’s application progressed, or what role the interview was even for, but if the automated Dana Whitfield’s enthusiasm is any indication, things went pretty well.
“It has been a pleasure chatting today Chick Bongo,” the AI concluded, “and I will be in touch very soon about the next steps.”
More on AI hallucinations: A Scientist Was Presenting His Vibe-Coded New Work When One of His Colleagues Pointed Out Something Extremely Embarrassing
The post Man Pretends to Hallucinate in Job Interview With an AI Bot, Causing It to Go Haywire appeared first on Futurism.
Fruit flies aren't exactly famous for their brainpower; you've probably drowned more than one in a wine glass left too long on the patio table. And yet, working with roughly 140,000 neurons—a brain smaller than a poppy seed—Drosophila can sort through a huge range of smells in a fraction of a second, and then retain the memory of that scent for a long time.
In this, they do much better than current "electronic noses." Even the most advanced ones on the market tend to be expensive, painfully narrow in what they can detect, and quick to forget an odor the moment they learn a new one.
So why not just copy the fly? That's the question a growing number of researchers have been asking—including Kevin Max and Yang Shen at the Okinawa Institute of Science and Technology, whose new algorithm, Spi-Fly, is described in a paper recently published in the journal Neuromorphic Computing and Engineering.
© Joao Paulo Burini
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