Let's not forget that the morality police were just one very visible tool of implementing mandatory hijab. Complying with dress standards became mandatory by law four years after the 1979 Islamic Revolution. It birthed the current theocratic establishment and overthrew a monarchy backed by the United States. No senior official has seriously signalled in public that a major change in hijab laws could be implemented soon. Top authorities have emphasised over the years that they consider the issue to be a "red line".

Tehran, Iran – Iran has suspended its morality police as the country continues to deal with two months of protests, the Iranian prosecutor general has suggested. The protests erupted shortly after the death of Mahsa Amini, a 22-year-old woman who was arrested by a unit of the morality police in Tehran for allegedly not adhering to the country's mandatory dress code for women. Speaking on Saturday at an event aimed at "outlining the hybrid war during recent riots", which is how Iranian officials describe alleged foreign influence in the unrest, prosecutor general Mohammad Jafar Montazeri was quoted as saying by local media the morality police operations are over. The morality police "has no connection with the judiciary and was shut down by the same place that it had been launched from in the past", he said, reportedly answering a question on why the morality police has been shut down. There were no other confirmations that work of the patrolling units – officially tasked with ensuring "moral security" in the society – has been terminated.

What the near future of artificial intelligence could be.

Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring. Deep learning methods can facilitate the analysis of different satellite modalities, such as electro-optical (EO) and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer between the modalities to compensate for individual shortcomings. Recent progress has shown how distributional alignment of neural network embeddings can produce powerful transfer learning models by employing a sliced Wasserstein distance (SWD) loss. We analyze how this method can be applied to Sentinel-1 and -2 satellite imagery and develop several extensions toward making it effective in practice. In an application to few-shot Local Climate Zone (LCZ) prediction, we show that these networks outperform multiple common baselines on datasets with a large number of classes. Further, we provide evidence that instance normalization can significantly stabilize the training process and that explicitly shaping the embedding space using supervised contrastive learning can lead to improved performance.

Privacy protection raises great attention on both legal levels and user awareness. To protect user privacy, countries enact laws and regulations requiring software privacy policies to regulate their behavior. However, privacy policies are written in natural languages with many legal terms and software jargon that prevent users from understanding and even reading them. It is desirable to use NLP techniques to analyze privacy policies for helping users understand them. Furthermore, existing datasets ignore law requirements and are limited to English. In this paper, we construct the first Chinese privacy policy dataset, namely CA4P-483, to facilitate the sequence labeling tasks and regulation compliance identification between privacy policies and software. Our dataset includes 483 Chinese Android application privacy policies, over 11K sentences, and 52K fine-grained annotations. We evaluate families of robust and representative baseline models on our dataset. Based on baseline performance, we provide findings and potential research directions on our dataset. Finally, we investigate the potential applications of CA4P-483 combing regulation requirements and program analysis.

BY CLICKING THE "I ACCEPT" BUTTON DISPLAYED AS PART OF THE REGISTRATION PROCESS OR BY USING THE SERVICE OR ANY PORTION THEREOF, YOU ACCEPT AND AGREE TO BE BOUND BY THE TERMS AND CONDITIONS OF THIS AGREEMENT AND THE PRIVACY POLICY, INCLUDING ALL TERMS INCORPORATED HEREIN BY REFERENCE. IF YOU ARE ENTERING INTO THIS AGREEMENT ON BEHALF OF A COMPANY OR OTHER LEGAL ENTITY, YOU REPRESENT THAT YOU HAVE THE AUTHORITY TO BIND SUCH ENTITY TO THIS AGREEMENT, IN WHICH CASE THE TERMS "YOU" OR "YOUR" SHALL REFER TO SUCH ENTITY. Definitions"Service" means the Lab Service and the Lab Creation Service, collectively, along with the Qwiklab Site."Lab Service" means the educational, training, and learning services provided to you through the Qwiklabs Site, or any related website provided by Cloud vLab, in concert with your respective Lab Sponsor."Creator If you have the Creator role, all sections of this agreement apply to you including sections that reference the Lab Service and the Lab Creation Service."Lab

Weapon systems that select and engage targets without meaningful human control are unacceptable and need to be prevented. All countries have a duty to protect humanity from this dangerous development by banning fully autonomous weapons. Retaining meaningful human control over the use of force is an ethical imperative, a legal necessity, and a moral obligation. In the period since Human Rights Watch and other nongovernmental organizations launched the Campaign to Stop Killer Robots in 2013, the question of how to respond to concerns over fully autonomous weapons has steadily climbed the international agenda. The challenge of killer robots, like climate change, is widely regarded as a grave threat to humanity that deserves urgent multilateral action.

Today, billions of people use Artificial Intelligence at some level globally. This also alarms governments and humans regarding the unprecedented impacts of AI, machine learning, and big data on civilisation.

With greater scrutiny of tech practices and calls for transparency, businesses must manage the deployment of smart AI while ensuring privacy safeguards, preventing bias in algorithmic decision-making, and meeting guidelines in highly regulated industries. In this article, I look at five ways leaders can future-proof their businesses against these risks. Regulating AI is a multifaceted and difficult challenge, and as a result, the regulatory landscape is a consistently evolving environment. However, the issue of unethical and biased AI is becoming critical as organizations are increasingly relying on algorithms to support their decisions – and we will undoubtedly see the ramp-up of regulatory scrutiny in the coming years as a result. To avoid the consequences of financial and reputational damage from unethical AI, organizations will need to get ahead of the curve.

There's no reason you should have ever heard of Simon Squibb, the "chief purpose officer of the Purposeful Project". Mr Squibb, who describes himself as an "Elon Musk wanna-be" in his Twitter profile, is one of those tirelessly energetic mid-life influencers who proliferate on the petri dish of LinkedIn. Displaying the sort of enthusiasm that Matt Hancock reserves for a Bushtucker Challenge, Mr Squibb is on a mission. "I want to fix the education system", he says. This fix entails removing something that many of us consider quite an important part of the education system: the learning part.