Technology

OpenAI has introduced ChatGPT Atlas, a new version of its intelligent platform that marks a major step towards full web interaction. Unlike traditional versions of ChatGPT that operate as a standalone website or app, Atlas is designed to stay with the user wherever they are on the web, helping them while browsing, searching, or working on documents. Currently, the app is available for macOS and will initially be offered to Plus, Pro, and Business users.
Essentially, ChatGPT Atlas functions as a personal assistant within the browser. With a sidebar that can be opened on any page, users can ask Atlas to summarize the content of an article, compare products, or analyze data from any site they are visiting. It can remember details from previous sessions, but it also offers complete control: the user decides what ChatGPT can remember and when to clear this memory. This is an important balance between intelligent functionality and privacy.
One of the most important innovations is “Agent Mode”, which allows ChatGPT to interact with websites on behalf of the user, always under their control. This means that Atlas can help complete tasks from start to finish, for example, search for and book a trip, or find and compare offers online. Although this feature is still in the testing phase, it shows the new direction of the development of artificial intelligence as an active collaborator, not just a response tool.
As for security and privacy, OpenAI has emphasized that users will have full control over what ChatGPT sees and remembers. They can limit access to specific sites, use Incognito mode, and clear browsing history at any time. These measures come at a time when concerns about data storage and online tracking are higher than ever.
Currently, ChatGPT Atlas is only available for macOS users, but OpenAI is expected to expand support to Windows and other platforms soon. The company has not yet announced which browsers will be supported initially, but it is expected that the integration will start with Safari and then expand to Chrome or Edge.
The launch of ChatGPT Atlas signals a major change in the way users interact with artificial intelligence. From a chatbot that opens on the page, ChatGPT is transforming into an assistant that follows you every step of the way, making it part of your digital everyday life. For users who work a lot on the Internet, this approach could change the way they search for information, write, analyze or communicate online.
X/OpenAi

Apple has officially introduced iPhone Air, the lightest and thinnest iPhone to date, setting a new design standard for the company. At just 5.6 millimeters thin and weighing about 165 grams, the new device almost disappears in the hand, while offering high-end performance. Apple says this model rewrites expectations for an ultra-thin smartphone without compromising on power or features.
iPhone Air is built with polished, shiny grade 5 titanium, a material known for its strength and lightness, while being protected by new Ceramic Shield 2 glass on both sides. This improved glass offers up to three times more scratch resistance on the front and up to four times more crack resistance on the back than previous models.
On the front, the device comes with a 6.5-inch Super Retina XDR display with ProMotion technology, which supports refresh rates up to 120 Hz. The display adjusts to 1 Hz when necessary to save battery life, while outdoor visibility has been improved at maximum brightness. Apple claims that outdoor contrast is twice as high as on previous iPhones, making the display visible even in direct sunlight.
Inside, the iPhone Air is powered by the new A19 Pro chip, which Apple calls the fastest processor in a smartphone. Together with the C1X modem and redesigned architecture, the chip brings a big leap forward in efficiency and speed. According to Apple, these improvements provide consistent all-day battery life despite the thinner body, addressing concerns that the thin design could negatively affect battery life.
The iPhone Air is also equipped with an advanced camera system. On the back is a 48 MP Fusion main camera with 2× telephoto capabilities, while on the front there is an 18 MP Center Stage camera for selfies and video calls. Apple has added new software improvements such as Camera Control for instant access to key functions and smart editing tools, including the “Clean Up” option in the Photos app.
Among other features, the Action Button, first introduced on the Pro models, allows for customization of quick functions. The device supports MagSafe and Apple's ecosystem of accessories, while running iOS enriched with new artificial intelligence branded as Apple Intelligence. Like the last models, this model is eSIM-only, without a physical SIM card slot, requiring support from carriers that offer eSIM.
Pre-orders for the iPhone Air start on September 12, while official sales begin on September 19. The phone will be available in four colors: Space Black, Cloud White, Light Gold, and Sky Blue. Prices and trade-in offers vary by region and carrier, but Apple positions this model as a premium option for users looking for advanced technology in a lightweight format.
With the launch of the iPhone Air, Apple sends a clear message: performance and portability do not have to be in conflict. By combining professional-grade features with an ultra-thin body, the company believes that consumers will welcome a device that is as powerful as it is elegant.

A new research paper from OpenAI reveals why “hallucinations” occur in LLMs. The authors argue that, in fact, they are a natural consequence of the way these systems are trained and evaluated. 
Even when the training data is perfectly accurate, the objectives and measurement methods in artificial intelligence development push the model to make possible but incorrect assumptions, rather than acknowledge uncertainty.
The researchers frame hallucination as a common classification problem. Generative errors in LLMs, they explain, are very similar to classification errors in supervised learning. When the system is forced to always give an answer, it is structurally incentivized to produce confident statements even when they are incorrect.
Assessment practices further compound this problem. Most tests are based on binary accuracy metrics, where an answer is either correct or incorrect, with no reward for a “I don’t know.” 
In this setup, hesitation is not rewarded, while guessing sometimes earns points. The authors point out that this system resembles the logic of multiple-choice exams: when blank answers earn zero points, rational students choose to guess. Formal analysis shows that, under these conditions, the expected outcome of a guess is always higher than that of hesitation.
To illustrate the mechanism of this stimulation, the team proposed a simplified “Arbitrary Facts” model. In this case, each question had a correct answer chosen at random, so there was no model to train. 
The researchers showed that the number of “single facts” (which appeared only once in training) sets a lower limit on the rate of hallucinations. When facts are rare, the model faces high uncertainty and is practically forced to guess.
The study also highlights several other factors that contribute to hallucinations. A model with insufficient capacity can fail to understand patterns even when they exist, producing errors. 
The difference in distribution between training and testing questions often leads to failures. Some questions are inherently difficult, and incorrect or noisy data directly produce incorrect results.
The authors' main recommendation is to review how artificial intelligence performance is measured. 
Instead of penalizing hesitation, evaluation frameworks should reward models when they do not provide answers if they are not sure, and impose stronger penalties for incorrect answers given with high confidence. One proposal includes explicit confidence thresholds, where models should respond only when confidence exceeds a certain level, with graduated penalties for errors. Such a system would encourage models to say “I don’t know” more often when necessary. The study’s conclusions are that hallucinations are not unexplained anomalies, but predictable outcomes of the current training and testing environment.