Microsoft stock has slumped 12 percent as part of a software industry sell-off, stoking fears of whether hefty investments in artificial intelligence will pay off across the sector. The Redmond, Washington-based tech giant is on track Thursday to finish at its worst day since March 2020 and has seen approximately $400bn in valuation wiped out. Capital expenditures grew by 66 percent in the second quarter compared with the same period the year before, reaching a record $37.5bn for the quarter. Meanwhile, Microsoft predicted Azure growth to stay stable in the period from January to March at 37 percent to 38 percent, after slowing in the last three months of 2025, partially due to AI chip capacity constraints. "[Wall Street] wanted to see less cap-ex spending and faster cloud/AI monetisation and coming out of the gates, it's the opposite. We have said this is a multi-year journey, and Redmond needs to focus on its data center buildout with more customers heading down the AI path. It's a balancing act with 2026 the inflection year for AI and MSFT [Microsoft]," Dan Ives, analyst at Wedbush Securities, said in a note provided to Al Jazeera.

Recent advances in Generative Artificial Intelligence have fueled numerous applications, particularly those involving Generative Adversarial Networks (GANs), which are essential for synthesizing realistic photos and videos. However, efficiently training GANs remains a critical challenge due to their computationally intensive and numerically unstable nature. Existing methods often require days or even weeks for training, posing significant resource and time constraints. In this work, we introduce ParaGAN, a scalable distributed GAN training framework that leverages asynchronous training and an asymmetric optimization policy to accelerate GAN training. ParaGAN employs a congestion-aware data pipeline and hardware-aware layout transformation to enhance accelerator utilization, resulting in over 30% improvements in throughput. With ParaGAN, we reduce the training time of BigGAN from 15 days to 14 hours while achieving 91% scaling efficiency. Additionally, ParaGAN enables unprecedented high-resolution image generation using BigGAN.

A black-and-white drone about the size of a sofa cushion took off with a gentle whir at the Hawthorne Police Department earlier this month, hovering and darting back and forth a few times before landing on a podium to a round of applause. A small audience and local TV news crews had gathered to see the unveiling of "Responder," marketed as the first drone built specifically to respond to 911 calls by quickly arriving at scenes, beaming a live video feed and, if necessary, dropping off medical supplies. The company behind the new drone, Seattle-based Brinc -- a tech startup with a 24-year-old chief executive -- has boasted it will "revolutionize the public safety landscape." But law enforcement agencies across Southern California and the country already employ drones for a variety of purposes, including 911 response, and skeptics warn about the risk of "mission creep" when the technology is weaponized or used for surveillance. Some Los Angeles activists have fought to limit police drone use, but Hawthorne's adoption of Brinc's Responder is a sign some local authorities are continuing to embrace unmanned aerial vehicles despite the pushback and price tag.

The core challenge in numerous real-world applications is to match an inquiry to the best document from a mutable and finite set of candidates. Existing industry solutions, especially latency-constrained services, often rely on similarity algorithms that sacrifice quality for speed. In this paper we introduce a generic semantic learning-to-rank framework, Self-training Semantic Cross-attention Ranking (sRank). This transformer-based framework uses linear pairwise loss with mutable training batch sizes and achieves quality gains and high efficiency, and has been applied effectively to show gains on two industry tasks at Microsoft over real-world large-scale data sets: Smart Reply (SR) and Ambient Clinical Intelligence (ACI). In Smart Reply, $sRank$ assists live customers with technical support by selecting the best reply from predefined solutions based on consumer and support agent messages. It achieves 11.7% gain in offline top-one accuracy on the SR task over the previous system, and has enabled 38.7% time reduction in composing messages in telemetry recorded since its general release in January 2021. In the ACI task, sRank selects relevant historical physician templates that serve as guidance for a text summarization model to generate higher quality medical notes. It achieves 35.5% top-one accuracy gain, along with 46% relative ROUGE-L gain in generated medical notes.

Microsoft has eliminated its entire team responsible for ensuring the ethical use of AI software at a time when the Windows giant is ramping up its use of machine learning technology. The decision to ditch the ethics and society team within its artificial intelligence organization is part of the 10,000 job cuts Microsoft announced in January, which will continue rolling through the IT titan into next year. The hit to this particular unit may remove some guardrails meant to ensure Microsoft's products that integrate machine learning features meet the mega-corp's standards for ethical use of AI. And it comes as discussion rages about the effects of controversial artificial intelligence models on society at large. Baking AI ethics into the whole business – as something for all employees to consider – seems kinda like when Bill Gates told his engineers in 2002 to make security an organization-wide priority, which obviously went really well.

Smart phones were the first step in bringing the internet, data, and AI away from the desk and allowing us to be connected and sense some of the world around us wherever we go. The next step was wearables. These started as smart watches and fitness trackers, but this is slowly expanding into smart connected clothing and other devices. In this series we learn how to build some smart wearables, leveraging the power of the cloud to make our clothes come to life along with Raspberry Pi's amazing new board, the Pi Zero W 2! Learn how to add subtitles to your speech with a smart t-shirt, light up your hoodie when people say nice things about you, or have a parrot on your shoulder that reads to you. These events are run through the Microsoft Reactor Meetup group.

Microsoft beat market expectations Tuesday with strong quarterly performance in cloud computing and software, still benefitting from the pandemic's online shifting of work, play, shopping and learning. The US tech colossus, which announced last week a blockbuster deal to buy gaming giant Activision Blizzard, said profit jumped to $18.8 billion in the final three months of last year. "Digital technology is the most malleable resource at the world's disposal to overcome constraints and reimagine everyday work and life," CEO Satya Nadella said, in announcing revenue of $51.7 billion. Microsoft investments include pouring money into the booming video game market and by extension the metaverse, the virtual reality vision for the internet's future. On an earnings call, Nadella pointed to the tens of millions of people playing games such as Forza, Halo and Minecraft, many investing in "avatar" proxies for online worlds, saying that the metaverse is a natural extension.

The company told Reuters on Thursday that a team of 14 will research "fairness" in ads over the next six months, exploring ways to spot and mitigate unintended bias, including in audiences and the messages themselves. Academic researchers and civil rights groups have found for a decade that some audiences including Black people and women can be excluded from seeing job, housing and other ads because of potentially unlawful choices made by advertisers or automated systems they use. Following complaints by U.S. anti-discrimination regulators and activists, Facebook Inc and Alphabet Inc's Google, which are the world's biggest digital ads sellers, enacted some changes. But problems remain, while greater concern about data privacy has already begun reshaping internet marketing. "The foundation of advertising is crumbling and we have to rebuild the house," IBM Senior Vice President Bob Lord said. "While we're at it, let's ensure fairness is in the blueprint."

IBM Corp is developing tools that would ensure online advertising algorithms do not unfairly show ads to only specific groups such as mostly men or wealthy people, aiming to address discrimination concerns that have drawn industrywide scrutiny. The company told Reuters on Thursday that a team of 14 will research "fairness" in ads over the next six months, exploring ways to spot and mitigate unintended bias, including in audiences and the messages themselves. Academic researchers and civil rights groups have found for a decade that some audiences including Black people and women can be excluded from seeing job, housing and other ads because of potentially unlawful choices made by advertisers or automated systems they use. Following complaints by U.S. anti-discrimination regulators and activists, Facebook Inc and Alphabet Inc's Google, which are the world's biggest digital ads sellers, enacted some changes. Also Read Google's adtech business set to face formal EU probe by year-end But problems remain, while greater concern about data privacy has already begun reshaping internet marketing.

June 24 (Reuters) - IBM Corp (IBM.N) is developing tools that would ensure online advertising algorithms do not unfairly show ads to only specific groups such as mostly men or wealthy people, aiming to address discrimination concerns that have drawn industrywide scrutiny. The company told Reuters on Thursday that a team of 14 will research "fairness" in ads over the next six months, exploring ways to spot and mitigate unintended bias, including in audiences and the messages themselves. Academic researchers and civil rights groups have found for a decade that some audiences including Black people and women can be excluded from seeing job, housing and other ads because of potentially unlawful choices made by advertisers or automated systems they use. Following complaints by U.S. anti-discrimination regulators and activists, Facebook Inc (FB.O) and Alphabet Inc's (GOOGL.O) Google, which are the world's biggest digital ads sellers, enacted some changes. But problems remain, while greater concern about data privacy has already begun reshaping internet marketing. "The foundation of advertising is crumbling and we have to rebuild the house," IBM Senior Vice President Bob Lord said.