In rebranding artificial intelligence as Apple Intelligence, Apple Inc is banking on the idea that people by and large won't buy the powerful A.I. software that its rivals are developing. Instead, they'll want really cool hardware that incorporates A.I. It's a compelling but risky strategy for a company that specializes in seamlessly integrating hardware and software into must-have products. "It's the next big step for Apple," Apple CEO Tim Cook said Monday in unveiling Apple Intelligence at the company's developers conference. Apple is diving into artificial intelligence – focused on the idea of a "virtual personal assistant" - as a potential must-have app for consumers. Since it lacks its own cutting-edge version of the predictive, sounds-like-a-human technology known as generative A.I., Apple will license that technology from other companies, starting with OpenAI.

Large language model (LLM)-based mobile agents are increasingly popular due to their capability to interact directly with mobile phone Graphic User Interfaces (GUIs) and their potential to autonomously manage daily tasks. Despite their promising prospects in both academic and industrial sectors, little research has focused on benchmarking the performance of existing mobile agents, due to the inexhaustible states of apps and the vague definition of feasible action sequences. To address this challenge, we propose an efficient and user-friendly benchmark, MobileAgentBench, designed to alleviate the burden of extensive manual testing. We initially define 100 tasks across 10 open-source apps, categorized by multiple levels of difficulty. Subsequently, we evaluate several existing mobile agents, including AppAgent and MobileAgent, to thoroughly and systematically compare their performance. All materials are accessible on our project webpage: https://MobileAgentBench.github.io,

The pitch offers an implicit contrast with the likes of Alphabet, Amazon, or Meta, which collect and store enormous amounts of personal data. Apple says any personal data passed on to the cloud will be used only for the AI task at hand and will not be retained or accessible to the company, even for debugging or quality control, after the model completes the request. Simply put, Apple is saying people can trust it to analyze incredibly sensitive data--photos, messages, and emails that contain intimate details of our lives--and deliver automated services based on what it finds there, without actually storing the data online or making any of it vulnerable. It showed a few examples of how this will work in upcoming versions of iOS. Instead of scrolling through your messages for that podcast your friend sent you, for example, you could simply ask Siri to find and play it for you.

Apple Intelligence is coming, but not to every iPhone out there. In fact, you'll need to have a device with an A17 Pro processor or M-series chip to use many of the features unveiled during the Apple Intelligence portion of WWDC 2024. That means only iPhone 15 Pro owners (and those with an M-series iPad) will get the iOS 18-related Apple Intelligence (AI?) updates like Genmoji, Image Playground, the redesigned Siri and Writing Tools. It's not evident exactly why older devices using an A16 chip (like the iPhone 14 Pro) won't work with Apple Intelligence, given its neural engine seems more than capable compared to the M1. A closer look at the specs sheets of those two processors show that the main differences appear to be in memory and GPU prowess.

In addition to revealing its initial plans for AI and annual updates to iOS, macOS and more at WWDC 2024, Apple also discussed new capabilities coming to the second-gen AirPods Pro. Siri Interactions will allow you to respond to the assistant by nodding your head yes or shaking your head no. Apple also plans to introduce improved Voice Isolation that further reduces background noise when you're on a call. Both of these items are exclusive to the most recent AirPods Pro, because they rely on the company's H2 chip like existing Adaptive Audio, Personalized Volume and Conversation Awareness features. Like those advanced audio tools that are already available on AirPods Pro, Siri Interactions and Voice Isolation use the processing abilities of the H2 chip in tandem with the power of a source device -- an iPhone or MacBook Pro, for example.

One of the bigger selling points of Apple's AirPods for some people is their unsealed design, which means they rest just outside of your ear canal instead of inserting all the way in. Open-style earbuds like these aren't good at blocking out ambient noise as a result, but they tend to be more comfortable to wear for those with sensitive ears. If this idea sounds appealing but you're on a tighter budget, the latest Amazon Echo Buds are a similar alternative that we recommend in our guide to the best budget earbuds. They normally cost 50, but a new deal at Amazon has dropped them back down to 35. That matches the lowest price we've tracked.

While Apple's new AI features might have been centre stage at WWDC, the company also introduced several major changes to the iPhone's operating system. Due to launch in autumn this year, iOS 18 is set to give Apple users even more options to customise their devices. But from the ability to lock or hide apps to new ways to send messages, some of these features have left Apple fans divided. While some tech fans have praised the'mind boggling' new tools, others say they will make cheating in relationships easier. One commenter on X, formerly Twitter, even wrote: 'The new Apple iOS is designed purely for cheating'.

First, other companies including Google, Amazon and Microsoft have bragged about similarly useful AI features that have not worked nearly as well as they promised. Second, Apple has a rocky track record in AI, including having foisted on you the dimwitted Siri for more than a decade.

Training recommendation systems (RecSys) faces several challenges as it requires the "data preprocessing" stage to preprocess an ample amount of raw data and feed them to the GPU for training in a seamless manner. To sustain high training throughput, state-of-the-art solutions reserve a large fleet of CPU servers for preprocessing which incurs substantial deployment cost and power consumption. Our characterization reveals that prior CPU-centric preprocessing is bottlenecked on feature generation and feature normalization operations as it fails to reap out the abundant inter-/intra-feature parallelism in RecSys preprocessing. PreSto is a storage-centric preprocessing system leveraging In-Storage Processing (ISP), which offloads the bottlenecked preprocessing operations to our ISP units. We show that PreSto outperforms the baseline CPU-centric system with a $9.6\times$ speedup in end-to-end preprocessing time, $4.3\times$ enhancement in cost-efficiency, and $11.3\times$ improvement in energyefficiency on average for production-scale RecSys preprocessing.

Collaborative Filtering (CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to a significant accuracy gap between popular and unpopular items. It not only hinders accurate user preference understanding but also exacerbates the Matthew effect in recommendation systems. To alleviate popularity bias, existing efforts focus on emphasizing unpopular items or separating the correlation between item representations and their popularity. Despite the effectiveness, existing works still face two persistent challenges: (1) how to extract common supervision signals from popular items to improve the unpopular item representations, and (2) how to alleviate the representation separation caused by popularity bias. In this work, we conduct an empirical analysis of popularity bias and propose Popularity-Aware Alignment and Contrast (PAAC) to address two challenges. Specifically, we use the common supervisory signals modeled in popular item representations and propose a novel popularity-aware supervised alignment module to learn unpopular item representations. Additionally, we suggest re-weighting the contrastive learning loss to mitigate the representation separation from a popularity-centric perspective. Finally, we validate the effectiveness and rationale of PAAC in mitigating popularity bias through extensive experiments on three real-world datasets. Our code is available at https://github.com/miaomiao-cai2/KDD2024-PAAC.