Breakthroughs, discoveries, and DIY tips sent every weekday. Our planet runs on pollinators. Without bees, moths, weevils, and more zooming around and spreading plants' reproductive cells, plants and important crops would not grow. Without plants we would not breathe or eat. When these crucial pollinating species visit flowers and other plants, they produce a number of characteristic sounds, such as wing flapping when hovering, landing, and taking off.

Lunar Lake is Intel's Snapdragon killer. Intel's Core Ultra Series 2 (Lunar Lake) was specifically designed to emphasize low power, but with competitive performance. In this it somewhat succeeds, though the Core Ultra 7 258V chip I tested can still run a distant second, or third, behind AMD's mobile Ryzen processors. But Lunar Lake also provides incredibly good, Snapdragon-like battery life with a powerful, embedded GPU capable of playing yesterday's top-tier games. Intel supplied us with a Lunar Lake-powered Asus ZenBook S14 laptop for review, and we've spent the last week or so testing it to answer the question: Of the AMD Ryzen AI 300, Intel's Lunar Lake, and the Qualcomm Snapdragon X Elite, which is the best laptop processor so far in 2024? And how does Lunar Lake compare to its predecessor, Meteor Lake?

Traditionally, laptop performance has been measured by CPU and GPU, but on-device AI processing is now a critical third measure. And that's especially crucial for enterprise PCs as hybrid work challenges crystalize, and the way we use our laptops has fundamentally changed over the last several years. More than ever before, cutting-edge PC technology is our most important and effective business communication and productivity tool. Without the right tools to be effective at work, Gallup found that employees often feel less connected to an organization's culture, their experience impaired collaboration and relationships, and work processes were often disrupted. That's particularly true for PCs without AI capabilities, which suffer from lagging, blurring noise and video quality, multiple distractions, unsecured privacy, unstable connectivity and short battery life.

The Boston area has long been home to innovation that leads to impactful new drugs. But manufacturing those drugs for clinical trials often involves international partners and supply chains. The vulnerabilities of that system have become all too apparent during the Covid-19 pandemic. Now Snapdragon Chemistry, co-founded by MIT Professor and Associate Provost Tim Jamison, is helping pharmaceutical companies manufacture drugs locally to shorten the time it takes for new drugs to get to patients. Snapdragon essentially starts as a chemistry lab, running experiments on behalf of pharmaceutical customers to create molecules of interest.

A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream. This paper presents a query-by-example on-device KWS system which is user-specific. The proposed system consists of two main steps: query enrollment and testing. In query enrollment step, phonetic posteriors are output by a small-footprint automatic speech recognition model based on connectionist temporal classification. Using the phonetic-level posteriorgram, hypothesis graph of finite-state transducer (FST) is built, thus can enroll any keywords thus avoiding an out-of-vocabulary problem. In testing, a log-likelihood is scored for input audio using the FST. We propose a threshold prediction method while using the user-specific keyword hypothesis only. The system generates query-specific negatives by rearranging each query utterance in waveform. The threshold is decided based on the enrollment queries and generated negatives. We tested two keywords in English, and the proposed work shows promising performance while preserving simplicity.

A group of computer vision researchers from ETH Zurich want to do their bit to enhance AI development on smartphones. To wit: They've created a benchmark system for assessing the performance of several major neural network architectures used for common AI tasks. They're hoping it will be useful to other AI researchers but also to chipmakers (by helping them get competitive insights); Android developers (to see how fast their AI models will run on different devices); and, well, to phone nerds -- such as by showing whether or not a particular device contains the necessary drivers for AI accelerators. The app, called AI Benchmark, is available for download on Google Play and can run on any device with Android 4.1 or higher -- generating a score the researchers describe as a "final verdict" of the device's AI performance. AI tasks being assessed by their benchmark system include image classification, face recognition, image deblurring, image super-resolution, photo enhancement or segmentation.

Last week, Qualcomm announced the Snapdragon 845, which sends AI tasks to the most suitable cores. There's not a lot of difference between the three company's approaches -- it ultimately boils down to the level of access each company offers to developers, and how much power each setup consumes. Before we get into that though, let's figure out if an AI chip is really all that much different from existing CPUs. A term you'll hear a lot in the industry with reference to AI lately is "heterogeneous computing." It refers to systems that use multiple types of processors, each with specialized functions, to gain performance or save energy.

Tech's biggest players have fully embraced the AI revolution. Apple, Qualcomm and Huawei have made mobile chipsets that are designed to better tackle machine learning tasks, each with a slightly different approach. Huawei launched its Kirin 970 at IFA this year, calling it the first chipset with a dedicated neural processing unit (NPU). Then, Apple unveiled the A11 Bionic chip, which powers the iPhone 8, 8 Plus and X. The A11 Bionic features a neural engine that the company says is "purpose-built for machine learning," among other things.

Qualcomm is continuing to place emphasis on the wearables segment, with senior director of Product Management for Qualcomm Atheros Pankaj Kedia telling media that the chip giant will be "doubling" its play in the market. "We have seen public announcements from some of our competitors that they are exiting the wearables space; Qualcomm is doubling our investment, because we are winning today and we intend to continue," Kedia said during the Qualcomm 4G/5G Summit in Hong Kong. Over the next two to three years, you will really see growth around all of this." Kedia said Qualcomm has a cyclical relationship in wearables market growth, increasing its investments alongside growth while in return driving the market with these investments. "Because we are investing in wearable-specific chipsets, we are able to drive market growth, and we are able to do that in a leadership fashion where a majority of wearables shipping today are based on Qualcomm," he said.