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August 30, 2022

Experts highlight drought-tolerant landscaping and resources to help customers reduce outdoor water use

Fremont, CA State leaders and Bay Area water agencies gathered today at a drought-tolerant garden to highlight conservation efforts currently underway and discuss important outdoor water saving devices and practices that can collectively save thousands of gallons of water for Californians.

California Natural Resources Agency Secretary Wade Crowfoot joined water agency leaders at the Quarry Lakes Demonstration Garden to announce resources to assist and inspire Bay Area residents in their efforts to reduce outdoor water use as California prepares for a fourth dry year amid extreme drought.

Drought across the west is bad and getting worse. In response, were taking actions to stretch our water supplies and deliver projects that help us adjust to a hotter, drier climate, said Secretary Crowfoot. Thanks to our local partners and leaders in the Bay Area, we are making progress to conserve water and improve water efficiency. Its time to double down on these critical efforts.

For the period of January to June 2022, Bay Area residents reduced their water use to an average of 60 gallons per person per day compared to the state average of 82. More work is needed, however, to curb daily waterusage, especially outdoors where most water waste occurs. Water agency leaders serving the Bay Area expressed the need for continued partnerships and urgent action by all residents to build drought resilience both in the near- and long-term.

Regional and state partnerships like we have help save our water as we manage through drought and adapt to a changing climate, said ACWD General Manager Ed Stevenson. Residents and businesses in Alameda County Water District and throughout the Bay Area have done a very good job of conserving water during this extreme drought and we're better prepared if next year is dry. Still, we cannot take our foot off the pedal; as we face a possible fourth year of drought, every drop of water becomes more precious every day.

With California undergoing a climate transformation bringing hotter and drier conditions and more extreme weather, Bay Area water agencies along with state and local leaders are encouraging customers to make permanent water wise changes to their landscaping this fall.

EBMUD customers are phenomenal at conserving water. Our customers have conserved 46 million gallons every day compared to historic use, and by 2050 we want to increase that to 70 million gallons saved daily, said EBMUD General Manager Clifford Chan. With climate change resulting in more frequent and severe droughts, we all need to conserve to ensure our water future is reliable. Our goal may seem like a lot, but if everyone makes wise water-use decisions whether its fixing a leak, or cutting back on outdoor irrigation, or using new irrigation technology we will meet that goal. Making conservation a way of life will benefit all our communities.

We thank our customers who continue to be water wise, and we encourage those with room to save more to reach out to us for help, said SFPUC General Manager Dennis Herrera. Just as our customers are doing their part, we are doing ours to make the most of our water supplies. In San Francisco we are building a new recycled water treatment plant, initiating innovative partnerships and exploring pilot programs. As the warm months continue, now is the time for everyone to do what they can to reduce outdoor water use in particular.

Actions highlighted during todays press conference are a direct response to Governor Newsoms statewide call to action for residents to reduce their water use by 15 percent in response to the extreme drought throughout California.For more information on how Californians can take immediate action to save water, visit SaveOurWater.com. To learn more about available rebates, free water saving devices and programs provided by Bay Area water agencies, visit SFPUC.org/BayAreaAgencies.

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Bay Area Water Agencies and State Leaders Urge More Conservation Outdoors as California Prepares and Braces for Fourth Dry Year :: East Bay Municipal...

Artificial Intelligence (AI) is transforming our way of life from new forms of social organization and scientific discovery to defense and intelligence. This explosive progress is especially apparent in the subfield of machine learning (ML), where AI systems learn autonomously by identifying patterns in large volumes of data.[1] Indeed, over the last five years, the fields of AI and ML have witnessed stunning advancements in computer vision (e.g., object recognition), speech recognition, and scientific discovery.[2], [3], [4], [5] However, these advances are not without risk as transformative technologies are generally accompanied by a significant risk profile, with notable examples including the discovery of nuclear energy, the Internet, and synthetic biology. Experts are increasingly voicing concerns over AI risk from misuse by state and non-state actors, principally in the areas of cybersecurity and disinformation propagation. However, issues of control for example, how advanced AI decision-making aligns with human goals are not as prominent in the discussion of risk and could ultimately be equally or more dangerous than threats from nefarious actors. Modern ML systems are not programmed (as programming is typically understood), but rather independently developed strategies to complete objectives, which can be mis-specified, learned incorrectly, or executed in unexpected ways. This issue becomes more pronounced as AI becomes more ubiquitous and we become more reliant on AI decision-making. Thus, as AI is increasingly entwined through tightly coupled critical systems, the focus must expand beyond accidents and misuse to the autonomous decision processes themselves.

The principal mid- to long-term risks from AI systems fall into three broad categories: risks of misuse or accidents, structural risks, and misaligned objectives. The misuse or accident category includes things such as AI-enabled cyber-attacks with increased speed and effectiveness or the generation and distribution of disinformation at scale.[6] In critical infrastructures, AI accidents could manifest as system failures with potential secondary and tertiary effects across connected networks. A contemporary example of an AI accident is the New York Stock Exchange (NYSE) Flash Crash of 2010, which drove the market down 600 points in 5 minutes.[7] Such rapid and unexpected operations from algorithmic trading platforms will only increase in destructive potential as systems increase in complexity, interconnectedness, and autonomy.

The structural risks category is concerned with how AI technologies shape the social and geopolitical environment in which they are deployed. Important contemporary examples include the impact of social media content selection algorithms on political polarization or uncertainty in nuclear deterrence and the offense-to-defense balance.[8],[9] For example, the integration of AI into critical systems, including peripheral processes (e.g., command and control, targeting, supply chain, and logistics), can degrade multilateral trust in deterrence.[10] Indeed, increasing autonomy in all links of the national defense chain, from decision support to offensive weapons deployment, compounds the uncertainty already under discussion with autonomous weapons.[11]

Misaligned objectives is another important failure mode. Since ML systems develop independent strategies, a concern is that the AI systems will misinterpret the correct objectives, develop destructive subgoals, or complete them in an unpredictable way. While typically grouped together, it is important to clarify the differences between a system crash and actions executed by a misaligned AI system so that appropriate risk mitigation measures can be evaluated. Understanding the range of potential failures may help in the allocation of resources for research on system robustness, interpretability, or AI alignment.

At its most basic level, AI alignment involves teaching AI systems to accurately capture what we want and complete it in a safe and ethical manner. Misalignment of AI systems poses the highest downside risk of catastrophic failures. While system failures by themselves could be immensely damaging, alignment failures could include unexpected and surprising actions outside the systems intent or window of probability. However, ensuring the safe and accurate interpretation of human objectives is deceptively complex in AI systems. On the surface, this seems straightforward, but the problem is far from obvious with unimaginably complex subtleties that could lead to dangerous consequences.

In contrast with nuclear weapons or cyber threats, where the risks are more obvious, risks from AI misalignment can be less clear. These complexities have led to misinterpretation and confusion with some attributing the concerns to disobedient or malicious AI systems.[12] However, the concerns are not that AI will defy its programming but rather that it will follow the programming exactly and develop novel, unanticipated solutions. In effect, the AI will pursue the objective accurately but may yield an unintended, even harmful, consequence. Googles Alpha Go program, which defeated the world champion Go[13] player in 2016, provides an illustrative example of the potential for unexpected solutions. Trained on millions of games, Alpha Gos neural network learned completely unexpected actions outside of the human frame of reference.[14] As Chris Anderson explains, what took the human brain thousands of years to optimize Googles Alpha Go completed in three years, executing better, almost alien solutions that we hadnt even considered.[15] This novelty illustrates how unpredictable AI systems can be when permitted to develop their own strategies to accomplish a defined objective.

To appreciate how AI systems pose these risks, by default, it is important to understand how and why AI systems pursue objectives. As described, ML is designed not to program distinct instructions but to allow the AI to determine the most efficient means. As learning progresses, the training parameters are adjusted to minimize the difference between the pursued objective and the actual value by incentivizing positive behavior (known as reinforcement learning, or RL).[16],[17] Just as humans pursue positive reinforcement, AI agents are goal-directed entities, designed to pursue objectives, whether the goal aligns with the original intent or not.

Computer science professor Steve Omohundro illustrates a series of innate AI drives that systems will pursue unless explicitly counteracted.[18] According to Omohundro, distinct from programming, AI agents will strive to self-improve, seek to acquire resources, and be self-protective.[19] These innate drives were recently demonstrated experimentally, where AI agents tend to seek power over the environment to achieve objectives most efficiently.[20] Thus, AI agents are naturally incentivized to seek out useful resources to accomplish an objective. This power-seeking behavior was reported by Open AI, where two teams of agents, instructed to play hide-and-seek in a simulated environment, proceeded to horde objects from the competition in what Open AI described as tool use distinct from the actual objective.[21] The AI teams learned that the objects were instrumental in completing the objective.[22] Thus, a significant concern for AI researchers is the undefined instrumental sub-goals that are pursued to complete the final objective. This tendency to instantiate sub-goals is coined the instrumental convergence thesis by Oxford philosopher Nick Bostrom. Bostrom postulated that intermediate sub-goals are likely to be pursued by an intelligent agent to complete the final objective more efficiently.[23] Consider an advanced AI system optimized to ensure adequate power between several cities. The agent could develop a sub-goal of capturing and redirecting bulk power from other locations to ensure power grid stability. Another example is an autonomous weapons system designed to identify targets that develop a unique set of intermediate indicators to determine the identity and location of the enemy. Instrumental sub-goals could be as simple as locking a computer-controlled access door or breaking traffic laws in an autonomous car, or as severe as destabilizing a regional power grid or nuclear power control system. These hypothetical and novel AI decision processes raise troubling questions in the context of conflict or safety of critical systems. The range of possible AI solutions are too large to consider and can only get more consequential as systems become more capable and complex. The effect of AI misalignment could be disastrous if the AI discovers an unanticipated optimal solution to a problem that results in a critical system becoming inoperable or yielding a catastrophic result.

While the control problem is troubling by itself, the integration of multiagent systems could be far more dangerous and could lead to other (as of now unanticipated) failure modes between systems. Just like complex societies, complex agent communities could manifest new capabilities and emergent failure modes unique to the complex system. Indeed, AI failures are unlikely to happen in isolation and the roadmap for multiagent AI environments is currently underway in both the public and private sectors.

Several U.S. government initiatives for next-generation intelligent networks include adaptive learning agents for autonomous processes. The Armys Joint All-Domain Command and Control (JADC2) concept for networked operations and the Resilient and Intelligent Next-Generation Systems (RINGS) program, put forth by the National Institute of Standards and Technology (NIST), are two notable ongoing initiatives.[24], [25] Literature on cognitive Internet of Things (IoT) points to the extent of autonomy planned for self-configuring, adaptive AI communities and societies to steer networks through managing user intent, supervision of autonomy, and control.[26] A recent report from the worlds largest technical professional organization, IEEE, outlines the benefits of deep reinforcement learning (RL) agents for cyber security, proposing that, since RL agents are highly capable of solving complex, dynamic, and especially high-dimensional problems, they are optimal for cyber defense.[27] Researchers propose that RL agents be designed and released autonomously to configure the network, prevent cyber exploits, detect and counter jamming attacks, and offensively target distributed denial-of-service attacks.[28] Other researchers submitted proposals for automated penetration-testing, the ability to self-replicate the RL agents, while others propose cyber-red teaming autonomous agents for cyber-defense.[29], [30], [31]

Considering the host of problems discussed from AI alignment, unexpected side effects, and the issue of control, jumping headfirst into efforts that give AI meaningful control over critical systems (such as the examples described above) without careful consideration of the potential unexpected (or potentially catastrophic) outcomes does not appear to be the appropriate course of action. Proposing the use of one autonomous system in warfare is concerning but releasing millions into critical networks is another matter entirely. Researcher David Manheim explains that multiagent systems are vulnerable to entirely novel risks, such as over-optimization failures, where optimization pressure allows individual agents to circumvent designed limits.[32] As Manheim describes, In many-agent systems, even relatively simple systems can become complex adaptive systems due to agent behavior.[33] At the same time, research demonstrates that multiagent environments lead to greater agent generalization, thus reducing the capability gap that separates human intelligence from machine intelligence.[34] In contrast, some authors present multiagent systems as a viable solution to the control problem, with stable, bounded capabilities, and others note the broad uncertainty and potential for self-adaptation and mutation.[35] Yet, the author admits that there are risks and the multiplicative growth of RL agents could potentially lead to unexpected failures, with the potential for the manifestation of malignant agential behaviors.[36],[37] AI researcher Trent McConaughy highlights the risk from adaptive AI systems, specifically decentralized autonomous organizations (DAO) in blockchain networks. McConaughy suggests that rather than a powerful AI system taking control of resources, as is typically discussed, the situation may be far more subtle where we could simply hand over global resources to self-replicating communities of adaptive AI systems (e.g., Bitcoins increasing energy expenditures that show no sign of slowing).[38]

Advanced AI capabilities in next-generation networks that dynamically reconfigure and reorganize network operations hold undeniable risks to security and stability.[39],[40] A complex landscape of AI agents, designed to autonomously protect critical networks or conduct offensive operations, would invariably need to develop subgoals to manage the diversity of objectives. Thus, whether individual systems or autonomous collectives, the web of potential failures and subtle side-effects could unleash unpredictable dangers leading to catastrophic second- and third-order effects. As AI systems are currently designed, understanding the impact of the subgoals (or even their existence) could be extremely difficult or impossible. The AI examples above illustrate critical infrastructure and national security cases that are currently in discussion, but the reality could be far more complex, unexpected, and dangerous. While most AI researchers expect that safety will develop concurrently with system autonomy and complexity, there is no certainty in this proposition. Indeed, if there is even a minute chance of misalignment in a deployed AI system (or systems) in critical infrastructure or national defense it is important that researchers dedicate a portion of resources to evaluating the risks. Decision makers in government and industry must consider these risks and potential means to mitigate them before generalized AI systems are integrated into critical and national security infrastructure, because to do otherwise could lead to catastrophic failure modes that we may not be able to fully anticipate, endure, or overcome.

Disclaimer: The authors are responsible for the content of this article. The views expressed do not reflect the official policy or position of the National Intelligence University, the National Geospatial Intelligence Agency, the Department of Defense, the Office of the Director of National Intelligence, the U.S. Intelligence Community, or the U.S. Government.

Anderson, Chris. Life. In Possible Minds: Twenty-Five Ways of Looking at AI, by John Brockman, 150. New York: Penguin Books, 2019.

Avatrade Staff. The Flash Crash of 2010. Avatrade. August 26, 2021. https://www.avatrade.com/blog/trading-history/the-flash-crash-of-2010 (accessed August 24, 2022).

Baker, Bowen, et al. Emergent Tool Use From Multi-Agent Autocurricula. arXiv:1909.07528v2, 2020.

Berggren, Viktor, et al. Artificial intelligence in next-generation connected systems. Ericsson. September 2021. https://www.ericsson.com/en/reports-and-papers/white-papers/artificial-intelligence-in-next-generation-connected-systems (accessed May 3, 2022).

Bostrom, Nick. The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents. Minds and Machines 22, no. 2 (2012): 71-85.

Brown, Tom B., et al. Language Models are Few-Shot Learners. arXiv:2005.14165, 2020.

Buchanan, Ben, John Bansemer, Dakota Cary, Jack Lucas, and Micah Musser. Georgetown University Center for Security and Emerging Technology. Automating Cyber Attacks: Hype and Reality. November 2020. https://cset.georgetown.edu/publication/automating-cyber-attacks/.

Byford, Sam. AlphaGos battle with Lee Se-dol is something Ill never forget. The Verge. March 15, 2016. https://www.theverge.com/2016/3/15/11234816/alphago-vs-lee-sedol-go-game-recap (accessed August 19, 2022).

Drexler, K Eric. Reframing Superintelligence: Comprehensive AI Services as General Intelligence. Future of Humanity Institute. 2019. https://www.fhi.ox.ac.uk/wp-content/uploads/Reframing_Superintelligence_FHI-TR-2019-1.1-1.pdf (accessed August 19, 2022).

Duettmann, Allison. WELCOME NEW PLAYERS | Gaming the Future. Foresight Institute. February 14, 2022. https://foresightinstitute.substack.com/p/new-players?s=r (accessed August 19, 2022).

Edison, Bill. Creating an AI red team to protect critical infrastructure. MITRE Corporation. September 2019. https://www.mitre.org/publications/project-stories/creating-an-ai-red-team-to-protect-critical-infrastructure (accessed August 19, 2022).

Etzioni, Oren. No, the Experts Dont Think Superintelligent AI is a Threat to Humanity. MIT Technology Review. September 20, 2016. https://www.technologyreview.com/2016/09/20/70131/no-the-experts-dont-think-superintelligent-ai-is-a-threat-to-humanity/ (accessed August 19, 2022).

Gary, Marcus, Ernest Davis, and Scott Aaronson. A very preliminary analysis of DALL-E 2. arXiv:2204.13807, 2022.

GCN Staff. NSF, NIST, DOD team up on resilient next-gen networking. GCN. April 30, 2021. https://gcn.com/cybersecurity/2021/04/nsf-nist-dod-team-up-on-resilient-next-gen-networking/315337/ (accessed May 1, 2022).

Jumper, John, et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 (August 2021): 583589.

Kallenborn, Zachary. Swords and Shields: Autonomy, AI, and the Offense-Defense Balance. Georgetown Journal of International Affairs. November 22, 2021. https://gjia.georgetown.edu/2021/11/22/swords-and-shields-autonomy-ai-and-the-offense-defense-balance/ (accessed August 19, 2022).

Kegel, Helene. Understanding Gradient Descent in Machine Learning. Medium. November 17, 2021. https://medium.com/mlearning-ai/understanding-gradient-descent-in-machine-learning-f48c211c391a (accessed August 19, 2022).

Krakovna, Victoria. Specification gaming: the flip side of AI ingenuity. Medium. April 11, 2020. https://deepmindsafetyresearch.medium.com/specification-gaming-the-flip-side-of-ai-ingenuity-c85bdb0deeb4 (accessed August 19, 2022).

Littman, Michael L, et al. Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) Study Panel Report. Stanford University. September 2021. http://ai100.stanford.edu/2021-report (accessed August 19, 2022).

Manheim, David. Overoptimization Failures and Specification Gaming in Multi-agent Systems. Deep AI. October 16, 2018. https://deepai.org/publication/overoptimization-failures-and-specification-gaming-in-multi-agent-systems (accessed August 19, 2022).

Nguyen, Thanh Thi, and Vijay Janapa Reddi. Deep Reinforcement Learning for Cyber Security. IEEE Transactions on Neural Networks and Learning Systems. IEEE, 2021. 1-17.

Omohundro, Stephen M. The Basic AI Drives. Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference. Amsterdam: IOS Press, 2008. 483492.

Panfili, Martina, Alessandro Giuseppi, Andrea Fiaschetti, Homoud B. Al-Jibreen, Antonio Pietrabissa, and Franchisco Delli Priscoli. A Game-Theoretical Approach to Cyber-Security of Critical Infrastructures Based on Multi-Agent Reinforcement Learning. 2018 26th Mediterranean Conference on Control and Automation (MED). IEEE, 2018. 460-465.

Pico-Valencia, Pablo, and Juan A Holgado-Terriza. Agentification of the Internet of Things: A Systematic Literature Review. International Journal of Distributed Sensor Networks 14, no. 10 (2018).

Pomerleu, Mark. US Army network modernization sets the stage for JADC2. C4ISRNet. February 9, 2022. https://www.c4isrnet.com/it-networks/2022/02/09/us-army-network-modernization-sets-the-stage-for-jadc2/ (accessed August 19, 2022).

Russell, Stewart. Human Compatible: Artificial Intelligence and the Problem of Control. New York: Viking, 2019.

Shah, Rohin. Reframing Superintelligence: Comprehensive AI Services as General Intelligence. AI Alignment Forum. January 8, 2019. https://www.alignmentforum.org/posts/x3fNwSe5aWZb5yXEG/reframing-superintelligence-comprehensive-ai-services-as (accessed August 19, 2022).

Shahar, Avin, and SM Amadae. Autonomy and machine learning at the interface of nuclear weapons, computers and people. In The Impact of Artificial Intelligence on Strategic Stability and Nuclear Risk, by Vincent Boulanin, 105-118. Stockholm: Stockholm International Peace Research Institute, 2019.

Trevino, Marty. Cyber Physical Systems: The Coming Singularity. Prism 8, no. 3 (2019): 4.

Turner, Alexander Matt, Logan Smith, Rohin Shah, Andrew Critch, and Prasad Tadepalli. Optimal Policies Tend to Seek Power. arXiv:1912.01683, 2021: 8-9.

Winder, Phil. Automating Cyber-Security With Reinforcement Learning. Winder.AI. n.d. https://winder.ai/automating-cyber-security-with-reinforcement-learning/ (accessed August 19, 2022).

Zeng, Andy, et al. Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language. arXiv:2204.00598 (arXiv), April 2022.

Zewe, Adam. Does this artificial intelligence think like a human? April 6, 2022. https://news.mit.edu/2022/does-this-artificial-intelligence-think-human-0406 (accessed August 19, 2022).

Zwetsloot, Remco, and Allan Dafoe. Lawfare. Thinking About Risks From AI: Accidents, Misuse and Structure. February 11, 2019. https://www.lawfareblog.com/thinking-about-risks-ai-accidents-misuse-and-structure (accessed August 19, 2022).

[1] (Zewe 2022)

[2] (Littman, et al. 2021)

[3] (Jumper, et al. 2021)

[4] (Brown, et al. 2020)

[5] (Gary, Davis and Aaronson 2022)

[6] (Buchanan, et al. 2020)

[7] (Avatrade Staff 2021)

[8] (Russell 2019, 9-10)

[9] (Zwetsloot and Dafoe 2019)

[12] (Etzioni 2016)

[13] GO is an ancient Chinese strategy board game

[14] (Byford 2016)

[15] (Anderson 2019, 150)

[16] (Kegel 2021)

[17] (Krakovna 2020)

[18] (Omohundro 2008, 483-492)

[19] Ibid., 484.

[20] (Turner, et al. 2021, 8-9)

[21] (Baker, et al. 2020)

[22] Ibid.

[23] (Bostrom 2012, 71-85)

[24] (GCN Staff 2021)

[25] (Pomerleu 2022)

[26] (Berggren, et al. 2021)

[27] (Nguyen and Reddi 2021)

[28] Ibid.

[29] (Edison 2019)

[30] (Panfili, et al. 2018)

[31] (Winder n.d.)

[32] (Manheim 2018)

[33] Ibid.

[34] (Zeng, et al. 2022)

[35] (Drexler 2019, 18)

[36] Ibid.

[37] (Shah 2019)

[38] (Duettmann 2022)

[39] (Trevino 2019)

[40] (Pico-Valencia and Holgado-Terriza 2018)

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Artificial Intelligence, Critical Systems, and the Control Problem - HS Today - HSToday

The French government has collected nearly 10 million in additional taxes after using machine learning to spot undeclared swimming pools in aerial photos. In France, housing taxes are calculated based on a propertys rental value, so homeowners who dont declare swimming pools are potentially avoiding hundreds of euros in additional payments.

The project to spot the undeclared pools began last October, with IT firm Capgemini working with Google to analyze publicly available aerial photos taken by Frances National Institute of Geographic and Forest Information. Software was developed to identify pools, with this information then cross-referenced with national tax and property registries.

The project is somewhat limited in scope, and has so far analyzed photos covering only nine of Frances 96 metropolitan departments. But even in these areas, officials discovered 20,356 undeclared pools, according to an announcement this week from Frances tax office, the General Directorate of Public Finance (DGFiP), first reported by Le Parisien.

As of 2020, it was estimated that France had around 3.2 million private swimming pools, but constructions have reportedly surged as more people worked from home during COVID-19 lockdowns, and summer temperatures have soared across Europe.

Ownership of private pools has become somewhat contentious in France this year, as the country has suffered from a historic drought that has emptied rivers of water. An MP for the French Green party (Europe cologie les Verts) made headlines after refusing to rule out a ban on the construction of new private pools. The MP, Julien Bayou, said such a ban could be used as a last resort response. He later clarified his remarks on Twitter, saying: [T]here are ALREADY restrictions on water use, for washing cars and sometimes for filling swimming pools. The challenge is not to ban swimming pools, it is to guarantee our vital water needs.

Frances tax offices, the DGFiP (known more commonly as Le Fisc), says it now plans to expand the use of its AI-pool-spotter to the entirety of metropolitan France (excluding the countrys overseas departments), which could net an additional 40 million in taxes.

Early reports on the project suggested that the machine learning software had an unusually high error rate of 30 percent, and regularly mistook other architectural features like solar panel installations for swimming pools. Now, though, Le Fisc says its ironed out these problems, and is looking to expand the use of its software spotting pools to identifying other undeclared and taxable housing improvements, like extensions and annexes.

We are particularly targeting house extensions like verandas, but we have to be sure that the software can find buildings with a large footprint and not the dog kennel or the childrens playhouse, Antoine Magnant, the deputy director general of public finances, told Le Parisien, reports The Guardian.

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French government uses AI to spot undeclared swimming pools and tax them - The Verge

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This article is part of a Fox News Digital series examining the consequences of the U.S. military withdrawal from Afghanistan one year ago this week.

Decades of progress for Afghan women's rights rolled back in a matter of months. Widespread hunger and malnutrition exacerbated by an international freeze on aid. A draconian crackdown on any public expression that doesn't conform with a hard-line interpretation of Islam. Violent attacks that have rocked the capital of Kabul.

This is day-to-day life one year after the Taliban entered Kabul and took Afghanistan back following the withdrawal of U.S. troops last August.

Women and girls have been especially hard hit by the Taliban's rise to power. The country's new religious rulers have restricted women from working outside the home aside from a few sectors, banned girls from attending secondary school, ordered women to cover their faces in public, and implemented rules that limit a woman's ability to travel without a male chaperone.

Widespread hunger has also increased drastically amid a worsening economic crisis, with about half of Afghanistan's 38 million people experiencing acute food insecurity.

These two issues the revocation of women's rights and a cratering economy have compounded to create one of the worst humanitarian crises in the world right now, according to Phillipe Kropfe, a spokesperson who is based in Kabul for the United Nations World Food Program.

"After four decades of conflict, many households are led by widows, and they are the only breadwinner. Without the full participation of women and girls in all aspects of public life there is little chance of achieving lasting peace, stability and economic development," Kropfe told Fox News Digital.

ONE YEAR AFTER TALIBAN TAKEOVER, AFGHAN RIGHTS LEADER SIMA SAMAR STILL HEARTBROKEN

An Afghan man with a family of 12 people and lost his job at a local NGO last August said he has struggled to bring food home on a daily basis since the Taliban took over, but his more pressing concern is for the future of his daughters.

"My biggest worry right now is uncertain future of my children, especially girls," the man, who asked not to be named for fear of reprisal from the Taliban, told Fox News Digital. "My eldest daughter is in 7th class and whenever she asks me about when she will return to school it makes my heart full of pain because no one has the answer."

The Taliban's takeover last year is not the first time that women in Afghanistan have seen their hard-fought rights rolled back.

The 20th century saw steady progression of basic rights for women, but that came to an abrupt halt when the Taliban first rose to power in 1996; a rule that would continue until the United States and allies launched Operation Enduring Freedom in 2001.

"Women of Afghanistan were able to take advantage of the opportunities offered to them following the 2001 removal of the Taliban to continue their democratization efforts of the earlier decades, particularly during the 1970s and 1980s, which included extensive gender equality provisions, like womens greater access to higher education," Mona Tajali, a professor of international relations and women's studies at Agnes Scott college and an executive board member for Women Living Under Muslim Laws, told Fox News Digital.

TALIBAN CLAIMS IT WAS UNAWARE AL QAEDA CHIEF AL-ZAWAHRI WAS IN AFGHANISTAN BEFORE US DRONE STRIKE

Women inside and outside of Afghanistan voiced their concerns when the Trump administration began negotiating with the Taliban in 2019 and 2020.

"Womens warnings, however, fell on deaf ears, and the Biden administration implemented the Trump administrations timeline of troops withdrawal, while neither administration had reached any safeguards on human rights, peace, security or even girls education," Tajali said.

"Many Afghan women activists and leaders feel betrayed by the U.S government, since addressing their rights served as a justification for the occupation in 2001 only to be fully ignored in 2021."

While women and other Afghans have protested for their rights over the last year, the Taliban has cracked down on freedom of expression with extrajudicial killings and detainment of activists.

The Taliban's Government Media and Information Center issued an order in September 2021 that prohibited journalists from publishing stories "contrary to Islam" or "insulting to national figures," leading to the arrest and torture of more than 80 journalists over the past year, according to a report this month by Amnesty International.

The Afghan father who expressed fear about the future of his children also said that his ability to discuss his country's challenges has been severely curtailed by the Taliban.

"I could talk and write about situation, problems and solutions of politics, economy and country. Now I am deprived of all [those] rights and privileges," he said. "The Taliban can arrest, torture and even kill me any moment for any reason or without a reason, and there is no one [asking] them why you arrest or kill a human being."

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Despite the daily challenges that women, girls and other Afghans face one year after the Taliban's takeover, the country is anything but a lost cause, according to Zuhra Bahman, the Afghanistan director for Search for Common Ground who is based in Kabul.

"There are women who work in ministries resisting from within, there are women marching for their rights, and most importantly, there are women who are leading humanitarian efforts and making a change in their community," Bahman told Fox News Digital.

Paul Best is a reporter for Fox News Digital. Story tips can be sent to Paul.best@fox.com and on Twitter: @KincaidBest.

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Afghanistan one year later: How daily life in the war-torn country has changed since the Talibans takeover - Fox News

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This article is part of a Fox News Digital series examining the consequences of the U.S. military withdrawal from Afghanistan one year ago this week.

When the Taliban swept across Afghanistan and caused the collapse of its Western-supported government last year, it declared itself the legitimate government of the country and promised to finally bring peace and security to the Afghan people.

However, a resistance movement is seeking to challenge Taliban rule, growing its opposition to the organization in the country's Panjshir Valley.

"There's something there there's potential there," Bill Roggio, the managing editor of Long War Journal, told Fox News. "They've done this in the past. You had the Northern Alliance previously, these are fervent anti-Taliban individuals."

Roggio's comments come as Afghanistan's National Resistance Front (NRF), a group made up of local volunteers and former Afghan military and police forces, have sought to grow their movement over the last year. Members of the organization were forced to regroup and reorganize themselves after the Afghan government collapsed and U.S. military forces departed, eventually gaining a stronghold in the historically anti-Taliban Panjshir Valley.

The remote region of Afghanistan was once home to the Northern Alliance, which waged a civil war against the Taliban after it took control of the country for the first time in 1996. After the Sept. 11, 2001, terrorist attacks in the U.S., American and allied special operations forces linked up with Northern Alliance fighters to topple the Taliban government.

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National Resistance Front fighters pose for a picture. (National Resistance Front)

The leader of the new movement, Ahmad Massoud, has deep connections to the old Northern Alliance. Massoud's father, Ahmed Shah Massoud, was a revered leader of the former organization before he was assassinated by al Qaeda operatives two days before the terror attacks in the U.S.

Ali Maisam Nazary, the head of foreign relations for the NRF, told Fox News Digital that the younger Massoud has inspired the growing resistance, which he says continues to recruit new fighters who aim to one day retake Afghanistan.

"We started from two valleys," Nazary said. "Today, we are present in 12 provinces inside Afghanistan."

Nazary boasted that NRF forces have found success on the battlefield, claiming that during one battle NRF fighters captured 40 Taliban forces, while in other recent battles they killed 40 more.

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"We have been highly successful," Nazary said. "The Taliban haven't had any military victories ... they've shown their weaknesses and basically every single military option they've had they have exhausted."

A National Resistance Front fighter. (National Resistance Front)

Nazary said the Taliban has brought many forces into the region, including some of their most elite fighters, however, it has had little to no success rooting out the NRF. The NRF has had increased luck recruiting new fighters as a result of its success, Nazary said, helping grow its forces into something that they hope can one day mount an offensive capable of taking territory.

The narrative painted by Nazary stands in stark contrast to that of the Taliban, who have strongly denied that fighting has been happening in the region. Shortly after the last of the U.S. troops left Afghanistan, the Taliban fought with the remaining pockets of resistance in the Panjshir Valley, and it claims to now have full control of the security situation there.

Roggio said part of the challenge with tracking how strong the resistance truly is stems from a lack of reliable information, noting that the Taliban has been successful in keeping the fighting away from major cities and containing it to the country's most remote areas. There is also a lack of reporting from independent press, who depend on the Taliban to gain access to the area and are often only able to see what the Taliban wants them to see.

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National Resistance Front fighters in Afghanistan have launched attacks against the Taliban. (National Resistance Front)

According to a Washington Post report in June, locals in the contested valleys have cast at least some doubt on the Taliban's narrative. Reports of heavy fighting and casualties have spread from village to village, while civilian casualties have also increased as a result of the fighting.

Roggio said the truth likely rests somewhere between the competing narratives, arguing that the NRF represents a threat to the Taliban but the Taliban still maintains the upper hand in terms of areas it controls and equipment it possesses.

"They obviously are not merely a nuisance," Roggio said of the NRF.

Roggio noted that the NRF campaign is mostly dependent on guerrilla tactics currently, and a growing movement could help them take control of contested areas with strong anti-Taliban sentiment. However, for the organization to be truly successful in its long-term objectives, it would need some sort of support, most likely from countries friendly to its cause.

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Nazary spends much of his time lobbying for international support, basing himself out of Washington D.C., and Tajikistan in an attempt to sell the NRF as a legitimate challenger to Taliban rule, he told Fox News Digital. He paints the fight as a continuation of the U.S. and allied war on terror, pointing out that NRF forces have also engaged in fighting against terrorist organizations such as al Qaeda and the Islamic State.

National Resistance Front fighters scale a mountain. (National Resistance Front)

"We don't characterize the current resistance as a civil war," Nazary said. "This is the continuation of the global war on terror. However, our allies abandoned the struggle more than a year ago, and we're all alone fighting international terrorists."

However, finding support from the international community for a renewed fight against terrorism in Afghanistan has proved difficult, with Western governments showing little interest in supporting an armed uprising against the Taliban.

That reality was made more clear in July when the U.S. State Department said it does "not support organized violent opposition" to the Taliban. Instead, the U.S. is calling for the various factions in Afghanistan to settle their differences diplomatically.

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National Resistance Front rebels have been launching attacks against the Taliban in the Panjshir valley. (National Resistance Front)

The State Department's position only served to enhance the NRF's feeling of abandonment after the U.S. ended its time in Afghanistan, Nazary said. He pointed out that a little more than one year ago the U.S. government supported the Afghan military in its fight to resist a Taliban takeover of the country.

"They were funding these forces, they were supporting these forces," Nazary said. "All of a sudden, the policy has changed 180 degrees. How was it legitimate when NATO had a presence in Afghanistan, but it's illegitimate today?"

"As far as I can tell that's official U.S. policy, which I think is insane, but here we are," Roggio said of the State Department's position.

However, Roggio believes the NRF could represent a legitimate threat to Taliban rule, especially if the organization eventually does find a sympathetic ear from the international community. He noted, however, that the group has a long road ahead and will face significant difficulty in achieving its objectives.

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"It's hard to judge how successful they'll be," he said. "They look to be viable."

Nazary struck an optimistic tone, noting that the resistance to Taliban rule is only in the first phase. He argued that the NRF will continue to grow its capability, saying the leadership will be deliberate with their planning ahead of moving into an offensive phase.

"Right now, it's easy for us to take over districts especially in the north," Nazary said. "But taking a district is much different from sustaining control over it. So, we want to guarantee that once we start taking districts we'll be able to sustain control."

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Nazary said the NRF is moving slowly toward that goal, and it is not yet ready to move into a new phase of the war. He did, however, express confidence in the group's ability to reach that point.

"We are determined to continue, and we are convinced that the days of Taliban occupation in the north are numbered," he said.

Michael Lee is a writer at Fox News. Follow him on Twitter @UAMichaelLee

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How significant is resistance to the Taliban in Afghanistan? - Fox News