The need for effective unlearning mechanisms in large language models (LLMs) is increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical generative AI practices. LLM unlearning is designed to reduce the impact of undesirable data influences and associated model capabilities without diminishing the utility of the model if unrelated to the information being forgotten. Despite growing interest, much of the existing research has focused on varied unlearning method designs to boost effectiveness and efficiency. However, the inherent relationship between model weights and LLM unlearning has not been extensively examined. In this paper, we systematically explore how model weights interact with unlearning processes in LLMs and we design the weight attribution-guided LLM unlearning method, WAGLE, which unveils the interconnections between'influence' of weights and'influence' of data to forget and retain in LLM generation. By strategically guiding the LLM unlearning across different types of unlearning methods and tasks, WAGLE can erase the undesired content, while maintaining the performance of the original tasks. We refer to the weight attribution-guided LLM unlearning method as WAGLE, which unveils the interconnections between'influence' of weights and'influence' of data to forget and retain in LLM generation.

We already know Apple is secretly preparing its first ever foldable iPhone. But now, keen tech fans have just got a new hint at what it may look like. According to a leak from a veteran Apple analyst, Mark Gurman of Bloomberg, the'iPhone Fold' will see some major design changes. And Mr Gurman claims that anyone who tries the new foldable device will'never want to go back'. Although it has been rumoured for years, it now looks almost certain that Apple is planning to unveil the folding iPhone in September 2026.

The need for effective unlearning mechanisms in large language models (LLMs) is increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical generative AI practices. LLM unlearning is designed to reduce the impact of undesirable data influences and associated model capabilities without diminishing the utility of the model if unrelated to the information being forgotten. Despite growing interest, much of the existing research has focused on varied unlearning method designs to boost effectiveness and efficiency. However, the inherent relationship between model weights and LLM unlearning has not been extensively examined. In this paper, we systematically explore how model weights interact with unlearning processes in LLMs and we design the weight attribution-guided LLM unlearning method, WAGLE, which unveils the interconnections between'influence' of weights and'influence' of data to forget and retain in LLM generation.

Apple unleashed some impressive gadgets last year including the iPhone 16 and the Vision Pro headset. Now, the tech giant is lining up its next set of gizmos – and the first five could arrive as soon as next month. According to reports, the company is about to unveil a new'affordable' iPhone – an alternative to the iPhone 16 which starts at 799/ 799. It's also preparing two new iPads, a new Mac and a brand new AI product, if rumours are to be believed. 'We've been off to a light start in 2025 in terms of Apple products,' says Apple news and rumours website 9to5Mac.

Additional Feedback: I think it is too strong to claim that "we also theoretically unveil the certain important mechanisms that govern the behavior of JCL." The main theoretical tool in the proposed method is an application of Jensen's inequality. There is also a section (3.3) that discusses some very basic properties of the the objective. To claim any of this as a significant "theoretical contribution" is too strong in my view. To me, the most interesting aspect of Fig2 is part (b).

Apple's Worldwide Developers Conference (WWDC) keynote is right around the corner. The festivities kick off on Monday, June 10 at 1PM ET. The keynote address is available to the public and you can watch it via Apple's event website or on the company's YouTube channel. Alternatively, you can just stay here and watch our embed. This is WWDC, so it'll be a software-focused event. Apple will most likely reveal many of the main features of iOS 18 and iPadOS 18.

Tesla is introducing a robotaxi on August 8, Elon Musk has announced on X a few hours after Reuters published a report that the automaker is scrapping its plans to produce a low-cost EV. Reuters also said that Musk's directive was to "go all in" on robotaxis built on the company's small-vehicle platform. Tesla has been promising a more affordable EV with prices expected to start at 25,000 for years, and Musk said as recently as this January that he's optimistic the model will arrive in the second half of 2025. In response to the report, the Tesla chief tweeted that "Reuters is lying (again)." He didn't clarify which part of the report was a lie, but considering he confirmed that Tesla is unveiling a robotaxi, he likely meant the news organization's claim that the company pulled the plug on a more affordable EV.

The festivities start at 12PM ET on Thursday, March 21. The best way to watch everything unfold is via the official Microsoft stream; it doesn't look like they'll have a stream on YouTube this time. This is the first Microsoft live event without former chief product officer and longtime keynote presenter Panos Panay, who departed for Amazon last year. The event is being advertised as a "new era of work", so there's going to be a major focus on the commercial space. There are plenty of rumors that Microsoft will take the opportunity to unveil a spate of new Surface devices.

AMD always brings something interesting to CES -- hopefully CES 2024 is no different. It will feature AMD's chair and CEO, Dr. Lisa Su, and the company's senior vice president and GM of computing and graphics, Jack Huynh. Like many companies, AMD says its focus for the press conference on AI -- in this case, as it pertains to personal computers. The livestream's landing page says that "AMD is powering the end-to-end infrastructure that will define the AI era, from cloud installations to enterprise clusters, AI-enabled intelligent embedded devices and PCs." If all of that sounds very vague and boring, don't fret: While we don't know exactly what AMD plans to unveil at CES 2024, it's usually the time that the company unveils the CPUs and GPUs that will be in laptops through the coming year.

Objective: Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs). Methods: ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the time-frequency representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and MASS benchmark databases. We also quantify spindle IF dynamics. Results: ConceFT-S achieves F1 scores of 0.749 in Dream and 0.786 in MASS, which is equivalent to or surpass A7 and SUMO with statistical significance. We reveal that spindle IF is generally nonlinear. Conclusion: ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.