Chinese Government Dismisses Tech Billionaire Calls for an AI Slowdown as “Fear-Mongering”

14 September 2026 at 22:46

In a whiplash-inducing turn away from the mythical quest to build human-level artificial intelligence, Western tech billionaires like Dario Amodei, Sam Altman, and Elon Musk are now joining hands to call for a massive slowdown of the tech’s development.

The commentariat is divided on what exactly this means — for some, it’s proof-positive of a genuine threat that AI could spin out of control and exterminate everybody. To others it’s a smokescreen, a thin cover provided by AI CEOs who are desperate for a government bailout to avoid popping the AI investment bubble.

If you ask lawmakers in Beijing, the whole thing is a bunch of nonsense.

In a routine press scrum hosted by China’s Ministry of Foreign Affairs, government spokesperson Guo Jiakun addressed a laundry list of topics, from President Xi Jinping’s recent trip to New Delhi to recovery efforts around the devastating mudslides in Nepal. One of the questions, posed by a Reuters journalist, concerned whether the ministry had any comment on calls from American tech billionaires to slow down AI development.

“AI is a consequential technology for the wellbeing of all humanity,” Guo replied. “All parties should jointly promote the open and inclusive development of AI for good and for all. Fear-mongering, confrontation and vicious competition will only hamper efforts toward sound global AI governance, which serves no one’s interest.”

Compared to the weight given to other topics, the ministry’s statement on the Western AI debacle was decidedly terse. While talk of an AI slowdown is roiling US markets and trending on social media, it seems like it’s barely a blip on Beijing’s radar.

The virtual hand-waving follows the decidedly disinterested stance Chinese policymakers have taken toward the US approach to AI development, which for years has hinged on building toward human-level artificial general intelligence.

As research fellow at the partially US-funded Brookings Institute Kyle Chan wrote in February, Beijing’s concept of “AGI” is wildly different from the one used across the pond. While US policymakers and industry leaders treat the tech as a race to AGI where one winner takes all, their Chinese counterparts aren’t in any rush. The US might get there first, the idea goes, but like electric vehicles, cancer treatments, and smartphones, Chinese industry can quickly make up the difference.

“Let’s look at the history of the internet. It started in the US, but China quickly caught up and led in consumer applications — WeChat is a world-class example,” professor emeritus at the Hong Kong University of Science and Technology Yang Qiang said during a panel at the AGI Next Summit in January. “AI is similar — it’s a tool, and Chinese ingenuity will push it to the limit.”

More on China and AI: Top Chinese Court Issues Sweeping Legal Crackdown on AI Deepfakes

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OpenCV explained: The computer vision library for AI apps

InfoWorld

14 September 2026 at 10:00

Before the rise of all-in-one frontier models, artificial intelligence and machine learning libraries tended to focus on highly specialized jobs. Even today this is still a powerful and useful approach: the more specialized the tool, the better the job it will do for its narrowly defined purpose.

One category for such tooling is computer vision: real-time analysis of video or still images. Facial recognition, OCR, motion tracking, object detection, augmented reality, and gesture detection are all common examples of computer vision jobs.

The OpenCV library is easily the most commonly used and long-lived toolkit for computer vision work. OpenCV saves you the trouble of writing from scratch the software needed for any of the above-mentioned tasks. It also gives you a handy bundle of image and video processing tools that can be used in most any project that might need them. It’s also powerful way to take existing or newly trained machine learning models for vision tasks and put them to work for you.

The basics of OpenCV

OpenCV’s original release dates back to the year 2000, when it was originally an Intel Research project. Today the project is under the stewardship of a non-profit foundation, and has gone through several major point revisions. The most recent, OpenCV 5.0, is a significant cleanup and reorganization of the toolkit, with a modernized API structure and better compatibility with the kinds of models and workflows used in machine learning today.

OpenCV is at core a collection of Apache 2-licensed C++ libraries. You can link against those libraries in your own C++ application, or work a different language and use OpenCV through an interface. Python, Java, and JavaScript are all supported, and a separate wasmCV project provides a WebAssembly interface.

OpenCV is designed to run across a broad range of architectures. It also uses a separate hardware acceleration layer, so every platform OpenCV runs on can use the acceleration techniques available for it (e.g., AVX-512). Programs written with OpenCV need not be aware of any hardware acceleration techniques; OpenCV automatically selects the best option on the current system. It’s also possible to build your own acceleration layer if you want to deploy OpenCV on custom silicon or tweak an existing deployment.

OpenCV modules and task sets

OpenCV’s modules are organized by major tasks in the general field of computer vision. Most of them are self-explanatory. For example, the objdetect module is the best place to start if you’re putting together a straightforward object-detection system.

Some high-level OpenCV modules might appear to overlap, but they are meant for different tasks. For instance, the video I/O module gives you a convenient interface for reading and writing video files or image sequences. But there’s also a video analysis module, which handles a different high-level use case: performing motion analysis or object tracking on video or image sets. If you want to generate video (with or without a large language model), use the video I/O module; if you want to analyze video (i.e., track the movement of objects), use the video analysis module.

Many of OpenCV’s functions can be hooked into and customized. For instance, object detection is a high-level module that includes barcode detection. Support for common barcodes (EAN-8, EAN-13, UPC-A, and UPC-E) is already available. But the barcode detector can optionally use an ONNX-format model for detection, so you could have it detect a bespoke variety of barcode, or attempt to decipher damaged or distorted barcodes based on a model.

OpenCV, pretrained models, and inference

OpenCV can work with external models for many other tasks, too, and without having to set up extra software to do it. The DNN module lets you load third-party, pretrained models for tasks like object detection and use them to serve predictions from still-image or video input. You can use models created with TensorFlow or PyTorch, or any other framework that can export a model in the common ONNX format.

OpenCV also curates its own collection of useful models, called the OpenCV zoo, on Hugging Face. The zoo is another good source for models and examples for common tasks, such as deblurring images, pose estimation, and object detection.

One major advantage of OpenCV for serving inferences from these models is that you don’t need anything except the model and what OpenCV itself provides. You do not have to set up a separate runtime for the model to serve inferences from it. This makes it easier to stand up a focused pipeline for a particular task. This pipeline could in turn later be attached to, or built out into, a larger and more complex pipeline if needed. But for basic inference, you can start with OpenCV.

OpenCV extras

OpenCV also comes with a large catalog of extra modules for other, highly specific tasks: direct access to encoding/decoding of video, adding text to images, video stabilization, text detection in scenes, and many more. One of these modules, datasets, provides a way to load well-known, pretrained datasets for common tasks (face recognition or gesture recognition, pedestrian detection, etc.) that can be used to make predictions or to generate performance benchmarks for those tasks.

Another convenient OpenCV feature is the high-level GUI module, which lets you create simple GUIs—windowing, buttons, images, mouse operations—with nothing but OpenCV. This is handy not only for quick prototypes, but even for creating a simple application without having to reach for an entire UI framework like GTK to create the front end. High-level GUI is no substitute for GTK or app frameworks like Electron, but it isn’t meant to be.

Fast and flexible computer vision

OpenCV’s big benefits go beyond giving you a prepackaged set of algorithms to perform computer-vision work. You can use OpenCV also to serve predictions from models for related tasks, like object recognition, without having to spin up extra infrastructure. And you can use it to build basic GUIs for those tasks with no extra libraries—quite handy to have when working with images or video. Finally, OpenCV’s decades of development and ongoing advancement make it a solid base to build on going forward.

Sam Altman Now Trying to Gain Control of Electric Grid

Futurism

By: Joe Wilkins

13 September 2026 at 12:02

Armed with sordid tales of rogue AI agents escaping containment, OpenAI CEO Sam Altman has found plenty of success using scare tactics to call for friendly regulation in the name of AI safety.

Now, it seems Altman is riding the wave of AI fearmongering to gin up some new business partners: the electric utility executives whose companies make up the US power grid.

As first reported by Politico, the CEO of the company behind ChatGPT held a series of clandestine meetings with leaders from top electric utility companies including Duke Energy, Exelon, Southern Co, and NextEra Energy, starting in July.

One of the most recent discussions took place on Wednesday in Colorado, during an annual meeting hosted by the Edison Electric Institute. Dominating the agenda, Politico notes, were discussions on electrical grid security, evidently spurred by the OpenAI-Hugging Face debacle, in which rogue AI agents allegedly broke free of their research environment, accessed the internet, and hacked into another AI company.

Though the incident spawned plenty of commentary around the pace of AI development and the advanced state of OpenAI’s models, the flow of information has been decidedly one-sided, with most of the details coming from OpenAI itself. As the New York Times reported, third-party safety researchers investigating the incident after the fact were only given a few days to access the reams of data generated by the hack, severely limiting their findings.

That said, OpenAI has an obvious incentive to play up its own AI’s supposedly terrifying capabilities, while suppressing or ignoring evidence that contradicts its own narrative: that of rogue AI agents capable of threatening the US energy grid.

To give just one example, OpenAI’s narration of the Hugging Face incident relies almost entirely on its agents’ “chains-of-thought,” where an AI model essentially generates statements explaining how it rationalized certain decisions. That sounds fine in theory, but researchers have found plenty of evidence showing that chains-of-thought aren’t reliable at narrating the actual moves made by natural language processors.

Despite the unanswered question around chain-of-thought reasoning, OpenAI is using the incident to lobby for regulatory safeguards that would cement its role as the authority around AI development.

This being the case, the timing of OpenAI’s energy security meetings is hard to overlook. As Politico notes in its reporting on these meetings, Altman has been hawking his company’s own cybersecurity services to top utility executives — conveniently riding the hype of the Hugging Face incident to secure a seat at one of the most important tables in the country.

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