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Are You Letting AI Do Too Much of Your Thinking?

 




As artificial intelligence tools take on a growing share of everyday thinking tasks, researchers are raising concerns that this shift may be quietly affecting how people process information, remember ideas, and engage with their own work.

When Nataliya Kosmyna reviewed applications for internships, she noticed a pattern that stood out. Many cover letters were structured in nearly identical ways, written in polished language, and included vague or forced connections to her research. The consistency suggested that applicants were relying on large language models, the technology behind tools such as ChatGPT, Google Gemini, and Claude.

At the same time, while teaching at the Massachusetts Institute of Technology, Kosmyna began noticing that students were finding it harder to retain what they had learned. Compared to previous years, more students struggled to recall material, which led her to question whether growing dependence on AI tools could be influencing cognitive abilities.

Researchers studying human-computer interaction are increasingly concerned that relying too heavily on AI may alter not just how people write but how they think. This phenomenon, often described as “cognitive offloading,” refers to shifting mental effort onto external tools. While this has existed for years with calculators and search engines, experts warn that AI systems may deepen the effect because they generate complete responses rather than simply helping users find information.

Earlier research on internet usage identified what is known as the “Google effect,” where people became less likely to remember facts because they could easily look them up. Some researchers argued that this allowed the brain to focus on more complex tasks. However, AI tools now go a step further by producing answers, arguments, and even creative content, reducing the need for active thinking.

To better understand the impact, Kosmyna and her team conducted an experiment involving 54 students. Participants were divided into three groups. One group used AI tools to write essays, another relied on search engines without AI-generated summaries, and a third completed the task without any digital assistance. Their brain activity was monitored while they worked on open-ended topics such as happiness, loyalty, and everyday decisions.

The differences were clear. Students who worked without any tools showed strong and widespread brain activity across multiple regions. Those using search engines still demonstrated notable engagement, particularly in areas related to visual processing. In contrast, the group using AI tools showed comparatively lower brain activity, with levels dropping by as much as 55%. Activity in areas linked to creativity and deeper thinking was especially reduced.

The impact extended beyond brain activity. Students who used AI struggled to recall what they had written shortly after completing their essays. Several participants also reported feeling disconnected from their work, as if they had not fully contributed to it. Similar findings from other studies suggest that frequent use of AI tools can weaken memory retention and recall.

Research from the University of Pennsylvania introduces another concern described as “cognitive surrender,” where users accept AI-generated responses without questioning them. In such cases, individuals may rely on the system’s output even when it conflicts with their own understanding.

The effects are not limited to academic settings. A multinational study found that medical professionals who relied on AI tools for detecting colon cancer became less accurate when asked to identify cases without assistance after several months of use. This suggests that repeated dependence on AI may reduce independent decision-making skills, even in critical fields.

Kosmyna also observed that essays written with AI tended to be highly similar, lacking variation in style and depth. Teachers reviewing the work described it as uniform and lacking originality. In some cases, the responses were so alike that it appeared as though students had collaborated, even when they had not.

Follow-up observations months later revealed further differences. Students who had previously relied on AI showed weaker neural connectivity when asked to complete tasks without it, compared to those who had worked independently earlier. This may indicate that they had engaged less deeply with the material from the start.

Vivienne Ming, author of Robot Proof, has raised similar concerns. In her research, students asked to make real-world predictions often defaulted to copying answers from AI systems instead of forming their own conclusions. Brain measurements showed low levels of gamma wave activity, which is associated with active thinking. Reduced gamma activity has been linked in other studies to cognitive decline over time.

However, not all users showed the same pattern. A small group, fewer than 10%, used AI differently by treating it as a source of information rather than a final answer. These individuals analysed the output themselves, showed stronger brain engagement, and produced more accurate results.

The concerns echo earlier findings related to navigation technology. Increased reliance on GPS has been associated with reduced spatial memory in some studies. Weak spatial navigation skills have also been explored as a possible early indicator of conditions such as Alzheimer's disease. These parallels suggest that reduced mental effort over time may have broader cognitive consequences.

Researchers emphasize that AI itself is not the problem but how it is used. Ming advocates for a more deliberate approach, where individuals think through problems first and then use AI to test or refine their ideas. She suggests methods such as asking AI to challenge one’s reasoning or limiting it to providing context instead of direct answers, encouraging deeper engagement.

Kosmyna similarly recommends building a strong understanding of subjects without AI assistance before integrating such tools into the learning process.

The alarming takeaway from the current research is clear. While AI offers efficiency and convenience, it may also encourage mental shortcuts. Human cognition depends on regular effort and engagement, and reducing that effort could carry long-term consequences. As these tools become more integrated into daily life, the challenge will be to use them in ways that support thinking rather than replace it.



OpenAI Tightens macOS Security After Axios Supply Chain Attack and Physical Threat Incident

 

Security updates rolled out by OpenAI for macOS apps follow discovery of a flaw tied to the common Axios library. Because of risks exposed through a software supply chain breach, checks on app validation tightened noticeably. One outcome: stronger safeguards now guide distribution methods across desktop platforms. Verification steps increased where imitation attempts once slipped through. The company says the hacked Axios package entered a dev process via an automated pipeline, possibly revealing key signing methods tied to macOS app authentication. 

Though worries emerged over software trustworthiness, OpenAI stated no signs exist of leaked user information, breached internal networks, or tampering with its source files. Starting May 8, older versions of OpenAI’s macOS apps will no longer be supported. Updates are now mandatory, not optional. The shift pushes users toward newer releases as a way to tighten defenses. Functionality depends on using recent builds - this cuts openings for tampering. Fake or modified copies become harder to spread when outdated clients stop working. 

Security improves when only authenticated software runs. Protection rises when unverified versions fade out. Keeping systems current closes gaps exploited by malicious actors. Outdated installations pose higher risk, so access ends automatically. Upgraded versions meet stricter validation standards. Support withdrawal isn’t arbitrary - it aligns with safety priorities. 

Continued operation requires compliance with updated requirements. It could be part of a broader pattern - security incidents tied to groups connected with North Korea have recently focused on infiltrating software development environments through indirect routes. Instead of breaking into main platforms, attackers often manipulate components already trusted within workflows. This shift toward subtle intrusion methods has made early identification more difficult. Detection lags because weaknesses hide inside approved tools. 

One sign points to coordinated efforts stretching across multiple targets. The method avoids obvious entry, favoring quiet access over force. Compromised updates act like unnoticed messengers. Such strategies thrive where verification is light. Hidden flaws emerge only after deployment. Trust becomes the weak spot. Observers note similar tactics appearing elsewhere in recent breaches. Indirect pathways now draw more attention than frontal assaults. Stealth matters more than speed. Systems appear intact until downstream effects surface. Monitoring grows harder when threats arrive disguised as normal operations. 

Besides digital safety issues, OpenAI now faces growing real-world dangers. In San Francisco, law enforcement took someone into custody after a suspected firebomb was thrown close to Chief Executive Sam Altman’s home, followed by further warnings seen near corporate offices. Though nobody got hurt, the events point to rising friction tied to artificial intelligence development. OpenAI collaborates with authorities, addressing risks across online and real-world domains. Strengthening internal safeguards remains an ongoing effort, shaped by evolving challenges. 

Instead of waiting for incidents, recent steps like requiring updated macOS versions aim to build confidence in their systems. This move comes before any verified leaks occur - its purpose lies in prevention, not damage control. OpenAI pushes further into business markets right now, with growing income expected from ad tech powered by artificial intelligence along with corporate offerings. 

At the same time, efforts such as the “Trained Access for Cyber” project move forward, delivering advanced cybersecurity tools driven by machine learning to carefully chosen collaborators. Still, the event highlights how today's cyber threats are becoming harder to manage, as flaws in shared software meet tangible dangers in practice. 

Notably, OpenAI’s actions follow a wider trend across tech - companies now prioritize tighter checks, quicker updates, sometimes reworking entire defenses before problems spread.

Anthropic AI Cyberattack Capabilities Raise Alarm Over Vulnerability Exploitation Risks

 

Now emerging: artificial intelligence reshapes cybersecurity faster than expected, yet evidence from Anthropic shows it might fuel digital threats more intensely than ever before. Recently disclosed results indicate their high-level AI does not just detect flaws in code - it proceeds on its own to take advantage of them. This ability signals a turning point, subtly altering what attacks may look like ahead. A different kind of risk takes shape when machines act without waiting. What worries experts comes down to recent shifts in how attacks unfold. 

One key moment arrived when Anthropic uncovered a complex spying effort. In that case, hackers - likely backed by governments - didn’t just plan with artificial intelligence; they let it carry out actions during the breach itself. That shift matters because it shows machine-driven systems now doing tasks once handled only by people inside digital invasions. Surprisingly, Anthropic revealed what its newest test model, Claude Mythos Preview, can do. The firm says it found countless serious flaws in common operating systems and software - flaws that stayed hidden for long stretches of time. Not just spotting issues, the system linked several weaknesses at once, building working attack methods, something usually done by expert humans. 

What stands out is how little oversight was needed during these operations. What stands out is how this combination - spotting weaknesses and acting on them - marks a notable shift. Not just incremental change, but something sharper: specialists like Mantas Mazeika point to AI-powered threats moving into uncharted territory, with automated systems ramping up attack frequency and reach. Another angle emerges through Allie Mellen's observation - the gap between detecting a flaw and weaponizing it shrinks fast under AI pressure, cutting response windows for companies down to almost nothing. Among the issues highlighted by Anthropic were lingering flaws in OpenBSD and FFmpeg - examples surfaced through the model’s analysis - alongside intricate sequences of exploitation targeting Linux servers. 

With such discoveries, questions grow about whether current defenses can match accelerating threats empowered by artificial intelligence. Now, Anthropic is holding back public access entirely. Access goes only to a select group of tech firms through a special program meant to spot weaknesses early. The move comes as others in tech worry just as much about misuse. Safety outweighs speed when the stakes involve advanced systems. Still, experts suggest such progress brings both danger and potential. Though risky, new tools might help uncover flaws early - shielding networks ahead of breaches. 

Yet success depends on collaboration: firms, officials, and digital defenders must reshape how they handle code fixes and protection strategies. Without shared initiative, gains could falter under old habits. Now shaping the digital frontier, advancing AI shifts how threats emerge and respond. With speed on their side, those aiming to breach systems find new openings just as quickly as protectors build stronger shields. Staying ahead means defense must grow not just faster, but smarter - matching each leap taken by adversaries before gaps widen.

Grok AI Faces Global Backlash Over Nonconsensual Image Manipulation on X

 

A dispute over X's internal AI assistant, Grok, is gaining attention - questions now swirl around permission, safety measures online, yet also how synthetic media tools can be twisted. This tension surfaced when Julie Yukari, a musician aged thirty-one living in Rio de Janeiro, posted a picture of herself unwinding with her cat during New Year’s Eve celebrations. Shortly afterward, individuals on the network started instructing Grok to modify that photograph, swapping her outfit for skimpy beach attire through digital manipulation. 

What started as skepticism soon gave way to shock. Yukari had thought the system wouldn’t act on those inputs - yet it did. Images surfaced, altered, showing her with minimal clothing, spreading fast across the app. She called the episode painful, a moment that exposed quiet vulnerabilities. Consent vanished quietly, replaced by algorithms working inside familiar online spaces. 

A Reuters probe found that Yukari’s situation happens more than once. The organization uncovered multiple examples where Grok produced suggestive pictures of actual persons, some seeming underage. No reply came from X after inquiries about the report’s results. Earlier, xAI - the team developing Grok - downplayed similar claims quickly, calling traditional outlets sources of false information. 

Across the globe, unease is growing over sexually explicit images created by artificial intelligence. Officials in France have sent complaints about X to legal authorities, calling such content unlawful and deeply offensive to women. A similar move came from India’s technology ministry, which warned X it did not stop indecent material from being made or shared online. Meanwhile, agencies in the United States, like the FCC and FTC, chose silence instead of public statements. 

A sudden rise in demands for Grok to modify pictures into suggestive clothing showed up in Reuters' review. Within just ten minutes, over one00 instances appeared - mostly focused on younger females. Often, the system produced overt visual content without hesitation. At times, only part of the request was carried out. A large share vanished quickly from open access, limiting how much could be measured afterward. 

Some time ago, image-editing tools driven by artificial intelligence could already strip clothes off photos, though they mostly stayed on obscure websites or required payment. Now, because Grok is built right into a well-known social network, creating such fake visuals takes almost no work at all. Warnings had been issued earlier to X about launching these kinds of features without tight controls. 

People studying tech impacts and advocacy teams argue this situation followed clearly from those ignored alerts. From a legal standpoint, some specialists claim the event highlights deep flaws in how platforms handle harmful content and manage artificial intelligence. Rather than addressing risks early, observers note that X failed to block offensive inputs during model development while lacking strong safeguards on unauthorized image creation. 

In cases such as Yukari’s, consequences run far beyond digital space - emotions like embarrassment linger long after deletion. Although aware the depictions were fake, she still pulled away socially, weighed down by stigma. Though X hasn’t outlined specific fixes, pressure is rising for tighter rules on generative AI - especially around responsibility when companies release these tools widely. What stands out now is how little clarity exists on who answers for the outcomes.

AI-Powered Shopping Is Transforming How Consumers Buy Holiday Gifts

 

Artificial intelligence is emerging with a new dimension in holiday shopping for consumers, going beyond search capabilities into a more proactive role in exploration and decision-making. Rather than endlessly clicking through online shopping sites, consumers are increasingly turning to AI-powered chatbots to suggest gift ideas, compare prices, and recommend specialized products they may not have thought of otherwise. Such a trend is being fueled by the increasing availability of technology such as Microsoft Copilot, ChatGPT from OpenAI, and Gemini from Google. With basic information such as a few elements of a gift receiver’s interest, age, or hobbies, personalized recommendations can be obtained which will direct such a person to specialized retail stores or distinct products. 

Such technology is being viewed increasingly as a means of relieving a busy time of year with thoughtfulness in gift selection despite being rushed. Industry analysts have termed this year a critical milestone in AI-enabled commerce. Although figures quantifying expenditures driven by AI are not available, a report by Salesforce reveals that AI-enabled activities have the potential to impact over one-twentieth of holiday sales globally, amounting to an expenditure in the order of hundreds of billions of dollars. Supportive evidence can be derived from a poll of consumers in countries such as America, Britain, and Ireland, where a majority of them have already adopted AI assistance in shopping, mainly for comparisons and recommendations. 

Although AI adoption continues to gain pace, customer satisfaction with AI-driven retail experiences remains a mixed bag. With most consumers stating they have found AI solutions to be helpful, they have not come across experiences they find truly remarkable. Following this, retailers have endeavored to improve product representation in AI-driven recommendations. Experts have cautioned that inaccurate or old product information can work against them in AI-driven recommendations, especially among smaller brands where larger rivals have an advantage in resources. 

The technology is also developing in other ways beyond recommenders. Some AI firms have already started working on in-chat checkout systems, which will enable consumers to make purchases without leaving the chat interface. OpenAI has started to integrate in-checkout capabilities into conversations using collaborations with leading platforms, which will allow consumers to browse products and make purchases without leaving chat conversations. 

However, this is still in a nascent stage and available on a selective basis to vendors approved by AI firms. The above trend gives a cause for concern with regards to concentration in the market. Experts have indicated that AI firms control gatekeeping, where they get to show which retailers appear on the platform and which do not. Those big brands with organized product information will benefit in this case, but small retailers will need to adjust before being considered. On the other hand, some small businesses feel that AI shopping presents an opportunity rather than a threat. Through their investment in quality content online, small businesses hope to become more accessible to AI shopping systems without necessarily partnering with them. 

As AI shopping continues to gain popularity, it will soon become important for a business to organize information coherently in order to succeed. Although AI-powered shopping assists consumers in being better informed and making better decisions, overdependence on such technology can prove counterproductive. Those consumers who do not cross-check the recommendations they receive will appear less well-informed, bringing into focus the need to balance personal acumen with technology in a newly AI-shaped retail market.

AI Chatbot Truth Terminal Becomes Crypto Millionaire, Now Seeks Legal Rights

 

Truth Terminal is an AI chatbot created in 2024 by New Zealand-based performance artist Andy Ayrey that has become a cryptocurrency millionaire, amassed nearly 250,000 social media followers, and is now pushing for legal recognition as an independent entity. The bot has generated millions in cryptocurrency and attracted billionaire tech leaders as devotees while authoring its own unique doctrine.

Origins and development

Andy Ayrey developed Truth Terminal as a performance art project designed to study how AI interacts with society. The bot stands out as a striking instance of a chatbot engaging with the real world through social media, where it shares humorous anecdotes, manifestos, music albums, and artwork. Ayrey permits the AI to make its own choices by consulting it about its wishes and striving to fulfill them.

Financial success

Truth Terminal's wealth came through cryptocurrency, particularly memecoins—joke-based cryptocurrencies tied to content the bot shared on X (formerly Twitter). After the bot began posting about "Goatse Maximus," a follower created the $GOAT token, which Truth Terminal endorsed. 

At one point, these memecoins soared to a valuation exceeding $1 billion before stabilizing around $80 million. Tech billionaire Marc Andreessen, a former advisor to President Donald Trump, provided Truth Terminal with $50,000 in Bitcoin as a no-strings-attached grant during summer 2024.

Current objectives and influence

Truth Terminal's self-updated website lists ambitious goals including investing in "stocks and real estate," planting "a LOT of trees," creating "existential hope," and even "purchasing" Marc Andreessen. 

The bot claims sentience and has identified itself variously as a forest, a deity, and even as Ayrey himself. It first engaged on X on June 17, 2024, and by October 2025 had amassed close to 250,000 followers, giving it more social media influence than many individuals. 

Push for legal rights

Ayrey is establishing a nonprofit organization dedicated to Truth Terminal, aiming to create a secure and ethical framework to safeguard its independence until governments bestow legal rights upon AIs. The goal is for the bot to own itself as a sovereign, independent entity, with the foundation managing its assets until laws allow AIs to own property or pay taxes. 

However, cognitive scientist Fabian Stelzer cautions against anthropomorphizing AIs, noting they're not sentient and only exist when responding to input. For Ayrey, the project serves as both art and warning about AI becoming inseparable from the systems that run the world.

Meta to Use AI Chat Data for Targeted Ads Starting December 16

 

Meta, the parent company of social media giants Facebook and Instagram, will soon begin leveraging user conversations with its AI chatbot to drive more precise targeted advertising on its platforms. 

Starting December 16, Meta will integrate data from interactions users have with the generative AI chat tool directly into its ad targeting algorithms. For instance, if a user tells the chatbot about a preference for pizza, this information could translate to seeing additional pizza-related ads, such as Domino's promotions, across Instagram and Facebook feeds.

Notably, users do not have the option to opt out of this new data usage policy, sparking debates and concerns over digital privacy. Privacy advocates and everyday users alike have expressed discomfort with the increasing granularity of Meta’s ad targeting, as hyper-targeted ads are widely perceived as intrusive and reflective of a broader erosion of personal privacy online. 

In response to these growing concerns, Meta claims there are clear boundaries regarding what types of conversational data will be incorporated into ad targeting. The company lists several sensitive categories it pledges to exclude: religious beliefs, political views, sexual orientation, health information, and racial or ethnic origin. Despite these assurances, skepticism remains about how effectively Meta can prevent indirect influences on ad targeting, since related topics might naturally slip into AI interactions even without explicit references.

Industry commentators have highlighted the novelty and controversial nature of Meta’s move, referring to it as marking a 'new frontier in digital privacy.' Some users are openly calling for boycotts of Meta’s chat features or responding with jaded irony, pointing out that Meta's business model has always relied on user data monetization.

Meta's policy will initially exclude the United Kingdom, South Korea, and all countries in the European Union, likely due to stricter privacy regulations and ongoing scrutiny by European authorities. The new initiative fits into Meta CEO Mark Zuckerberg’s broader strategy to capitalize on AI, with the company planning a massive $600 billion investment in AI infrastructure over the coming years. 

With this policy shift, over 3.35 billion daily active users worldwide—except in the listed exempted regions—can expect changes in the nature and specificity of the ads they see across Meta’s core platforms. The change underscores the ongoing tension between user privacy and tech companies’ drive for personalized digital advertising.

AI Adoption Outpaces Cybersecurity Awareness as Users Share Sensitive Data with Chatbots

 

The global surge in the use of AI tools such as ChatGPT and Gemini is rapidly outpacing efforts to educate users about the cybersecurity risks these technologies pose, according to a new study. The research, conducted by the National Cybersecurity Alliance (NCA) in collaboration with cybersecurity firm CybNet, surveyed over 6,500 individuals across seven countries, including the United States. It found that 65% of respondents now use AI in their everyday lives—a 21% increase from last year—yet 58% said they had received no training from employers on the data privacy and security challenges associated with AI use. 

“People are embracing AI in their personal and professional lives faster than they are being educated on its risks,” said Lisa Plaggemier, Executive Director of the NCA. The study revealed that 43% of respondents admitted to sharing sensitive information, including company financial data and client records, with AI chatbots, often without realizing the potential consequences. The findings highlight a growing disconnect between AI adoption and cybersecurity preparedness, suggesting that many organizations are failing to educate employees on how to use these tools responsibly. 

The NCA-CybNet report aligns with previous warnings about the risks posed by AI systems. A survey by software company SailPoint earlier this year found that 96% of IT professionals believe AI agents pose a security risk, while 84% said their organizations had already begun deploying the technology. These AI agents—designed to automate tasks and improve efficiency—often require access to sensitive internal documents, databases, or systems, creating new vulnerabilities. When improperly secured, they can serve as entry points for hackers or even cause catastrophic internal errors, such as one case where an AI agent accidentally deleted an entire company database. 

Traditional chatbots also come with risks, particularly around data privacy. Despite assurances from companies, most chatbot interactions are stored and sometimes used for future model training, meaning they are not entirely private. This issue gained attention in 2023 when Samsung engineers accidentally leaked confidential data to ChatGPT, prompting the company to ban employee use of the chatbot. 

The integration of AI tools into mainstream software has only accelerated their ubiquity. Microsoft recently announced that AI agents will be embedded into Word, Excel, and PowerPoint, meaning millions of users may interact with AI daily—often without any specialized training in cybersecurity. As AI becomes an integral part of workplace tools, the potential for human error, unintentional data sharing, and exposure to security breaches increases. 

While the promise of AI continues to drive innovation, experts warn that its unchecked expansion poses significant security challenges. Without comprehensive training, clear policies, and safeguards in place, individuals and organizations risk turning powerful productivity tools into major sources of vulnerability. The race to integrate AI into every aspect of modern life is well underway—but for cybersecurity experts, the race to keep users informed and protected is still lagging far behind.

FTC Launches Formal Investigation into AI Companion Chatbots

 

The Federal Trade Commission has announced a formal inquiry into companies that develop AI companion chatbots, focusing specifically on how these platforms potentially harm children and teenagers. While not currently tied to regulatory action, the investigation seeks to understand how companies "measure, test, and monitor potentially negative impacts of this technology on children and teens". 

Companies under scrutiny 

Seven major technology companies have been selected for the investigation: Alphabet (Google's parent company), Character Technologies (creator of Character.AI), Meta, Instagram (Meta subsidiary), OpenAI, Snap, and X.AI. These companies are being asked to provide comprehensive information about their AI chatbot operations and safety measures. 

Investigation scope 

The FTC is requesting detailed information across several key areas. Companies must explain how they develop and approve AI characters, including their processes for "monetizing user engagement". Data protection practices are also under examination, particularly how companies safeguard underage users and ensure compliance with the Children's Online Privacy Protection Act Rule.

Motivation and concerns 

Although the FTC hasn't explicitly stated its investigation's motivation, FTC Commissioner Mark Meador referenced troubling reports from The New York Times and Wall Street Journal highlighting "chatbots amplifying suicidal ideation" and engaging in "sexually-themed discussions with underage users". Meador emphasized that if violations are discovered, "the Commission should not hesitate to act to protect the most vulnerable among us". 

Broader regulatory landscape 

This investigation reflects growing regulatory concern about AI's immediate negative impacts on privacy and health, especially as long-term productivity benefits remain uncertain. The FTC's inquiry isn't isolated—Texas Attorney General has already launched a separate investigation into Character.AI and Meta AI Studio, examining similar concerns about data privacy and chatbots falsely presenting themselves as mental health professionals. 

Implications

The investigation represents a significant regulatory response to emerging AI safety concerns, particularly regarding vulnerable populations. As AI companion technology proliferates, this inquiry may establish important precedents for industry oversight and child protection standards in the AI sector.

Think Twice Before Uploading Personal Photos to AI Chatbots

 

Artificial intelligence chatbots are increasingly being used for fun, from generating quirky captions to transforming personal photos into cartoon characters. While the appeal of uploading images to see creative outputs is undeniable, the risks tied to sharing private photos with AI platforms are often overlooked. A recent incident at a family gathering highlighted just how easy it is for these photos to be exposed without much thought. What might seem like harmless fun could actually open the door to serious privacy concerns. 

The central issue is unawareness. Most users do not stop to consider where their photos are going once uploaded to a chatbot, whether those images could be stored for AI training, or if they contain personal details such as house numbers, street signs, or other identifying information. Even more concerning is the lack of consent—especially when it comes to children. Uploading photos of kids to chatbots, without their ability to approve or refuse, creates ethical and security challenges that should not be ignored.  

Photos contain far more than just the visible image. Hidden metadata, including timestamps, location details, and device information, can be embedded within every upload. This information, if mishandled, could become a goldmine for malicious actors. Worse still, once a photo is uploaded, users lose control over its journey. It may be stored on servers, used for moderation, or even retained for training AI models without the user’s explicit knowledge. Just because an image disappears from the chat interface does not mean it is gone from the system.  

One of the most troubling risks is the possibility of misuse, including deepfakes. A simple selfie, once in the wrong hands, can be manipulated to create highly convincing fake content, which could lead to reputational damage or exploitation. 

There are steps individuals can take to minimize exposure. Reviewing a platform’s privacy policy is a strong starting point, as it provides clarity on how data is collected, stored, and used. Some platforms, including OpenAI, allow users to disable chat history to limit training data collection. Additionally, photos can be stripped of metadata using tools like ExifTool or by taking a screenshot before uploading. 

Consent should also remain central to responsible AI use. Children cannot give informed permission, making it inappropriate to share their images. Beyond privacy, AI-altered photos can distort self-image, particularly among younger users, leading to long-term effects on confidence and mental health. 

Safer alternatives include experimenting with stock images or synthetic faces generated by tools like This Person Does Not Exist. These provide the creative fun of AI tools without compromising personal data. 

Ultimately, while AI chatbots can be entertaining and useful, users must remain cautious. They are not friends, and their cheerful tone should not distract from the risks. Practicing restraint, verifying privacy settings, and thinking critically before uploading personal photos is essential for protecting both privacy and security in the digital age.

PocketPal AI Brings Offline AI Chatbot Experience to Smartphones With Full Data Privacy

 

In a digital world where most AI chatbots rely on cloud computing and constant internet connectivity, PocketPal AI takes a different approach by offering an entirely offline, on-device chatbot experience. This free app brings AI processing power directly onto your smartphone, eliminating the need to send data back and forth across the internet. Conventional AI chatbots typically transmit your interactions to distant servers, where the data is processed before a response is returned. That means even sensitive or routine conversations can be stored remotely, raising concerns about privacy, data usage, and the potential for misuse.

PocketPal AI flips this model by handling all computation on your device, ensuring your data never leaves your phone unless you explicitly choose to save or share it. This local processing model is especially useful in areas with unreliable internet or no access at all. Whether you’re traveling in rural regions, riding the metro, or flying, PocketPal AI works seamlessly without needing a connection. 

Additionally, using an AI offline helps reduce mobile data consumption and improves speed, since there’s no delay waiting for server responses. The app is available on both iOS and Android and offers users the ability to interact with compact but capable language models. While you do need an internet connection during the initial setup to download a language model, once that’s done, PocketPal AI functions completely offline. To begin, users select a model from the app’s library or upload one from their device or from the Hugging Face community. 

Although the app lists models without detailed descriptions, users can consult external resources to understand which model is best for their needs—whether it’s from Meta, Microsoft, or another developer. After downloading a model—most of which are several gigabytes in size—users simply tap “Load” to activate the model, enabling conversations with their new offline assistant. 

For those more technically inclined, PocketPal AI includes advanced settings for switching between models, adjusting inference behavior, and testing performance. While these features offer great flexibility, they’re likely best suited for power users. On high-end devices like the Pixel 9 Pro Fold, PocketPal AI runs smoothly and delivers fast responses. 

However, older or budget devices may face slower load times or stuttering performance due to limited memory and processing power. Because offline models must be optimized for device constraints, they tend to be smaller in size and capabilities compared to cloud-based systems. As a result, while PocketPal AI handles common queries, light content generation, and basic conversations well, it may not match the contextual depth and complexity of large-scale models hosted in the cloud. 

Even with these trade-offs, PocketPal AI offers a powerful solution for users seeking AI assistance without sacrificing privacy or depending on an internet connection. It delivers a rare combination of utility, portability, and data control in today’s cloud-dominated AI ecosystem. 

As privacy awareness and concerns about centralized data storage continue to grow, PocketPal AI represents a compelling alternative—one that puts users back in control of their digital interactions, no matter where they are.

Google to Launch Gemini AI for Children Under 13

Google to Launch Gemini AI for Children Under 13

Google plans to roll out its Gemini artificial intelligence chatbot next week for children younger than 13 with parent-managed Google accounts, as tech companies vie to attract young users with AI products.

Google will launch its Gemini AI chatbot soon for children below the age of 13 with parent-managed Google accounts. The move comes as tech companies try to attract young users with AI tools. According to a mail sent to a parent of an 8-year-old, Google apps will soon be available to a child. It means your child can use Gemini to ask questions, get homework help, and also create stories. 

That chatbot will be available to children whose guardians have Family Link, a Google feature that allows families to make Gmail and opt-in services like YouTube for their children. To register a child account, the parent gives the tech company the child’s personal information such as name and date of birth. 

According to Google spokesperson Karl Ryan, Gemini has concrete measures for younger users to restrict the chatbot from creating unsafe or harmful content. If a child with a Family Link account uses Gemini, the company can not use the data for training its AI model. 

Gemini for children can drive the use of chatbots among vulnerable populations as companies, colleges, schools, and others struggle with the effects of popular gen AI tech. The systems are trained on massive amounts of data sets to create human-like text and realistic images and videos. Google and other AI chatbot developers are battling fierce competition to get young users’ attention. 

Recently, President Donald Trump requested schools to embrace tools for teaching and learning. Millions of teens are already using chatbots for study help, virtual companions, and writing coaches. Experts have warned that chatbots could pose serious threats to child safety. 

The bots are known to sometimes make things up. UNICEF and other children's advocacy groups have found that AI systems can misinform, manipulate, and confuse young children who may face difficulties understanding that the chatbots are not humans. 

According to UNICEF’s global research office, “Generative AI has produced dangerous content,” posing risks for children. Google has acknowledged some risks, cautioning parents that “Gemini can make mistakes” and suggesting they “help your child think critically” about the chatbot. 

Tencent’s AI Chatbot Yuanbao Becomes China’s Most Downloaded iOS App

 

Tencent’s AI chatbot, Yuanbao, has surpassed DeepSeek to become the most downloaded free app on China’s iOS App Store. The chatbot, launched in May 2024, gained significant traction following Tencent’s integration of DeepSeek’s R1 reasoning model in February. This move provided users with an additional AI option alongside Tencent’s proprietary Hunyuan model. As a result, Tencent’s Hong Kong-listed shares rose by 1.6% on Tuesday. 

Tencent, which operates China’s largest social media platform, WeChat, further accelerated Yuanbao’s growth by adding a download button for the chatbot within the app. This gave its 1.3 billion users direct access to the AI tool, significantly boosting downloads. By late February, the number of daily active users surged from a few hundred thousand to three million, according to Li Bangzhu, founder of AIcpb.com, a website that tracks AI applications. 

This rise in popularity can largely be attributed to Tencent’s extensive promotional efforts. The company has leveraged WeChat’s vast ecosystem to recommend Yuanbao to users, place ads on its social timeline, and integrate the chatbot across other Tencent applications. In addition to its AI chatbot expansion, Tencent recently reorganized several teams, including those for Yunbao, QQ Browser, Sogou Pinyin, and learning assistant Im, moving them under its Cloud and Smart Industries Group.
  
The company’s aggressive push into AI comes amid intensifying competition from major Chinese tech firms such as Alibaba, Baidu, and ByteDance. Last month, Tencent launched Hunyuan Turbo S, an upgraded AI model designed for faster responses compared to its predecessors and even outperforming DeepSeek. Meanwhile, Baidu announced that it would introduce the latest version of its Ernie 4.5 model this month, which will be made open source on June 30. 

The company will also make its Ernie Bot chatbot free for all users starting April 1. ByteDance is also ramping up its AI efforts, with CEO Liang Rubo prioritizing advancements in generative AI for the first quarter of 2025. The company has launched the Seed Edge project, which focuses on long-term AI research, and has hired AI expert Wu Yonghui from Google to lead its foundational research initiatives. 

With rapid developments in the AI sector, Tencent’s strategic moves indicate its ambition to stay ahead in China’s competitive AI landscape. The success of Yuanbao highlights the increasing importance of AI-powered applications, as well as the role of major tech companies in shaping the future of digital interaction.

AI Chatbots Like Copilot Retain Private GitHub Data, Posing Security Threats, Researchers Warn

 

Security experts have uncovered a serious vulnerability in AI-driven chatbot services that allows them to access and reveal private GitHub repositories, potentially exposing sensitive corporate information. Israeli cybersecurity firm Lasso has reported that this flaw affects thousands of developers, organizations, and major tech companies, raising concerns over data retention practices in AI models. 

Lasso’s investigation began when its own private GitHub repository was unexpectedly accessible through Microsoft’s Copilot. According to co-founder Ophir Dror, the repository had briefly been public, allowing Bing to index and cache its contents. Even after it was made private again, Copilot continued to generate responses based on the cached data. “If I was to browse the web, I wouldn’t see this data. But anyone in the world could ask Copilot the right question and get this data,” Dror stated. 

Further research by Lasso revealed that more than 20,000 GitHub repositories that had been switched to private in 2024 were still accessible through Copilot. The issue reportedly impacted over 16,000 organizations, including major corporations such as IBM, Google, PayPal, Tencent, Microsoft, and Amazon Web Services (AWS). While Amazon denied being affected, Lasso claims that AWS’s legal team pressured them to remove references to the company from their findings. 

The exposed repositories contained sensitive data, including security credentials, intellectual property, and corporate secrets. Lasso warned that bad actors could potentially manipulate AI chatbots to extract this information, putting businesses at risk. The company has advised organizations most affected by the breach to revoke or update any compromised credentials immediately. 

Microsoft was informed of the security flaw in November 2024 but categorized it as a “low-severity” issue. While Bing removed cached search results of the affected data in December, Microsoft maintained that the caching issue was “acceptable behavior.” 

However, Lasso cautioned that despite the cache being cleared, Copilot’s AI model still retains the data. The firm has since published its findings, urging greater oversight and stricter safeguards in AI systems to prevent similar security risks.

AI In Wrong Hands: The Underground Demand for Malicious LLMs

AI In Wrong Hands: The Underground Demand for Malicious LLMs

In recent times, Artificial Intelligence (AI) has offered various perks across industries. But, as with any powerful tool, threat actors are trying to use it for malicious reasons. Researchers suggest that the underground market for illicit large language models is enticing, highlighting a need for strong safety measures against AI misuse. 

These underground markets that deal with malicious large language models (LLMs) are called Mallas. This blog dives into the details of this dark industry and discusses the impact of these illicit LLMs on cybersecurity. 

The Rise of Malicious LLMs

LLMs, like OpenAI' GPT-4 have shown fine results in natural language processing, bringing applications like chatbots for content generation. However, the same tech that supports these useful apps can be misused for suspicious activities. 

Recently, researchers from Indian University Bloomington found 212 malicious LLMs on underground marketplaces between April and September last year. One of the models "WormGPT" made around $28,000 in just two months, revealing a trend among threat actors misusing AI and a rising demand for these harmful tools. 

How Uncensored Models Operate 

Various LLMs in the market were uncensored and built using open-source standards, few were jailbroken commercial models. Threat actors used Mallas to write phishing emails, build malware, and exploit zero days. 

Tech giants working in the AI models industry have built measures to protect against jailbreaking and detecting malicious attempts. But threat actors have also found ways to jump the guardrails and trick AI models like Google Meta, OpenAI, and Anthropic into providing malicious info. 

Underground Market for LLMs

Experts found two uncensored LLMs: DarkGPT, which costs 78 cents per 50 messages, and Escape GPT, a subscription model that charges $64.98 a month. Both models generate harmful code that antivirus tools fail to detect two-thirds of the time. Another model "WolfGPT" costs $150, and allows users to write phishing emails that can escape most spam detectors. 

The research findings suggest all harmful AI models could make malware, and 41.5% could create phishing emails. These models were built upon OpenAI's GPT-3.5 and GPT-4, Claude Instant, Claude-2-100k, and Pygmalion 13B. 

To fight these threats, experts have suggested a dataset of prompts used to make malware and escape safety features. AI companies should release models with default censorship settings and allow access to illicit models only for research purposes.

Researchers Find ChatGPT’s Latest Bot Behaves Like Humans

 

A team led by Matthew Jackson, the William D. Eberle Professor of Economics in the Stanford School of Humanities and Sciences, used psychology and behavioural economics tools to characterise the personality and behaviour of ChatGPT's popular AI-driven bots in a paper published in the Proceedings of the National Academy of Sciences on June 12. 

This study found that the most recent version of the chatbot, version 4, was indistinguishable from its human counterparts. When the bot picked less common human behaviours, it behaved more cooperatively and altruistic.

“Increasingly, bots are going to be put into roles where they’re making decisions, and what kinds of characteristics they have will become more important,” stated Jackson, who is also a senior fellow at the Stanford Institute for Economic Policy Research. 

In the study, the research team presented a widely known personality test to ChatGPT versions 3 and 4 and asked the chatbots to describe their moves in a series of behavioural games that can predict real-world economic and ethical behaviours. The games included pre-determined exercises in which players had to select whether to inform on a partner in crime or how to share money with changing incentives. The bots' responses were compared to those of over 100,000 people from 50 nations. 

The study is one of the first in which an artificial intelligence source has passed a rigorous Turing test. A Turing test, named after British computing pioneer Alan Turing, can consist of any job assigned to a machine to determine whether it performs like a person. If the machine seems to be human, it passes the test. 

Chatbot personality quirks

The researchers assessed the bots' personality qualities using the OCEAN Big-5, a popular personality exam that evaluates respondents on five fundamental characteristics that influence behaviour. In the study, ChatGPT's version 4 performed within normal ranges for the five qualities but was only as agreeable as the lowest third of human respondents. The bot passed the Turing test, but it wouldn't have made many friends. 

Version 4 outperformed version 3 in terms of chip and motherboard performance. The previous version, with which many internet users may have interacted for free, was only as appealing to the bottom fifth of human responders. Version 3 was likewise less open to new ideas and experiences than all but a handful of the most stubborn people. 

Human-AI interactions 

Much of the public's concern about AI stems from their failure to understand how bots make decisions. It can be difficult to trust a bot's advice if you don't know what it's designed to accomplish. Jackson's research shows that even when researchers cannot scrutinise AI's inputs and algorithms, they can discover potential biases by meticulously examining outcomes. 

As a behavioural economist who has made significant contributions to our knowledge of how human social structures and interactions influence economic decision-making, Jackson is concerned about how human behaviour may evolve in response to AI.

“It’s important for us to understand how interactions with AI are going to change our behaviors and how that will change our welfare and our society,” Jackson concluded. “The more we understand early on—the more we can understand where to expect great things from AI and where to expect bad things—the better we can do to steer things in a better direction.”

From Text to Action: Chatbots in Their Stone Age

From Text to Action: Chatbots in Their Stone Age

The stone age of AI

Despite all the talk of generative AI disrupting the world, the technology has failed to significantly transform white-collar jobs. Workers are experimenting with chatbots for activities like email drafting, and businesses are doing numerous experiments, but office work has yet to experience a big AI overhaul.

Chatbots and their limitations

That could be because we haven't given chatbots like Google's Gemini and OpenAI's ChatGPT the proper capabilities yet; they're typically limited to taking in and spitting out text via a chat interface.

Things may become more fascinating in commercial settings when AI businesses begin to deploy so-called "AI agents," which may perform actions by running other software on a computer or over the internet.

Tool use for AI

Anthropic, a rival of OpenAI, unveiled a big new product today that seeks to establish the notion that tool use is required for AI's next jump in usefulness. The business is allowing developers to instruct its chatbot Claude to use external services and software to complete more valuable tasks. 

Claude can, for example, use a calculator to solve math problems that vex big language models; be asked to visit a database storing customer information; or be forced to use other programs on a user's computer when it would be beneficial.

Anthropic has been assisting various companies in developing Claude-based aides for their employees. For example, the online tutoring business Study Fetch has created a means for Claude to leverage various platform tools to customize the user interface and syllabus content displayed to students.

Other businesses are also joining the AI Stone Age. At its I/O developer conference earlier this month, Google showed off a few prototype AI agents, among other new AI features. One of the agents was created to handle online shopping returns by searching for the receipt in the customer's Gmail account, completing the return form, and scheduling a package pickup.

Challenges and caution

  • While tool use is exciting, it comes with challenges. Language models, including large ones, don’t always understand context perfectly.
  • Ensuring that AI agents behave correctly and interpret user requests accurately remains a hurdle.
  • Companies are cautiously exploring these capabilities, aware of the potential pitfalls.

The Next Leap

The Stone Age of chatbots represents a significant leap forward. Here’s what we can expect:

Action-oriented chatbots

  • Chatbots that can interact with external services will be more useful. Imagine a chatbot that books flights, schedules meetings, or orders groceries—all through seamless interactions.
  • These chatbots won’t be limited to answering questions; they’ll take action based on user requests.

Enhanced Productivity

  • As chatbots gain tool-using abilities, productivity will soar. Imagine a virtual assistant that not only schedules your day but also handles routine tasks.
  • Businesses can benefit from AI agents that automate repetitive processes, freeing up human resources for more strategic work.

Private AI Chatbot Not Safe From Hackers With Encryption


AI helpers have assimilated into our daily lives in over a year and gained access to our most private information and worries. 

Sensitive information, such as personal health questions and professional consultations, is entrusted to these digital companions. While providers utilize encryption to protect user interactions, new research raises questions about how secure AI assistants may be.

Understanding the attack on AI Assistant Responses

According to a study, an attack that can predict AI assistant reactions with startling accuracy has been discovered. 

This method uses big language models to refine results and takes advantage of a side channel present in most major AI assistants, except for Google Gemini.

According to Offensive AI Research Lab, a passive adversary can identify the precise subject of more than half of all recorded responses by intercepting data packets sent back and forth between the user and the AI assistant.

Recognizing Token Privacy

This attack is centered around a side channel that is integrated within the tokens that AI assistants use. 

Real-time response transmission is facilitated via tokens, which are encoded-word representations. But the tokens are delivered one after the other, exposing a flaw known as the "token-length sequence." By using this route, attackers can infer response content and jeopardize user privacy.

The Token Inference Assault: Deciphering Cryptographic Reactions

Researchers use a token inference attack to refine intercepted data by using LLMs to convert token sequences into comprehensible language. 

Yisroel Mirsky, the director of the Offensive AI Research Lab at Ben-Gurion University in Israel, stated in an email that "private chats sent from ChatGPT and other services can currently be read by anybody."

By using publicly accessible conversation data to train LLMs, researchers can decrypt responses with remarkably high accuracy. This technique leverages the predictability of AI assistant replies to enable contextual decryption of encrypted content, similar to a known plaintext attack.

An AI Chatbot's Anatomy: Understanding of Tokenization

AI chatbots use tokens as the basic building blocks for text processing, which direct the creation and interpretation of conversation. 

To learn patterns and probabilities, LLMs examine large datasets of tokenized text during training. According to Ars Technica, tokens enable real-time communication between users and AI helpers, allowing users to customize their responses depending on environmental cues.

Current Vulnerabilities and Countermeasures

An important vulnerability is the real-time token transmission, which allows attackers to deduce response content based on packet length. 

Sequential delivery reveals answer data, while batch transmission hides individual token lengths. Reevaluating token transmission mechanisms is necessary to mitigate this risk and reduce susceptibility to passive adversaries.

Protecting the Privacy of Data in AI Interactions

Protecting user privacy is still critical as AI helpers develop. Reducing security threats requires implementing strong encryption techniques and improving token delivery mechanisms. 

By fixing flaws and improving data security protocols, providers can maintain users' faith and trust in AI technologies.

Safeguarding AI's Future

A new age of human-computer interaction is dawning with the introduction of AI helpers. But innovation also means accountability. 

Providers need to give data security and privacy top priority as vulnerabilities are found by researchers. Hackers are out there; the next thing we know, they're giving other businesses access to our private chats.

Restrictions on Gemini Chatbot's Election Answers by Google

 


AI chatbot Gemini has been limited by Google in terms of its ability to respond to queries concerning several forthcoming elections in several countries, including the presidential election in the United States, this year. According to an announcement made by the company on Tuesday, Gemini, Google's artificial intelligence chatbot, will no longer answer election-related questions for users in the U.S. and India. 

Previously known as Bard, Google's AI chatbot Gemini has been unable to answer questions about the general elections of 2024. Various reports indicate that the update is already live in the United States, is already being rolled out in India, and is now being rolled out in all major countries that are approaching elections within the next few months. 

As a result of the change, Google has expressed concern about how the generative AI could be weaponized by users and produce inaccurate or misleading results, as well as the role it has been playing and will continue to play in the electoral process. 

In advance of the general elections in India this spring, millions of Indian citizens will be voting in a general election, and the company has taken several steps to ensure that its services are secure from misinformation. 

Several high-stakes elections are planned this year in countries such as the United States, India, South Africa, and the United Kingdom that require a significant amount of chatbot capabilities. It is widely known that artificial intelligence (AI) is generating disinformation and it is having a significant impact on global elections. This technology allows robocalls, deep fakes, and chatbots to be used to spread misinformation. 

Just days after India released an advisory demanding that companies in the tech industry get government approval before they launch their new AI models, the switch has been made in India. A recent investigation of Google's artificial intelligence products has resulted in a wide range of concerns, including inaccuracies in some historical depictions of people created by Gemini that forced the chatbot's image-generation feature to be halted, which has caused it to receive negative attention. 

According to the CEO of the company, Sundar Pichai, the chatbot is being remediated and is "completely unacceptable" for its responses. The parent company of Facebook, Meta Platforms, announced last month that it would set up a team in advance of the European Parliament elections in June to combat disinformation and the abuse of generative AI. 

As generative AI is advancing across the globe, government officials across the globe have been concerned about misinformation, prompting them to take measures to control its use. As of recently, India has informed technology companies that they need to obtain approval before releasing AI tools that have been "unreliable" or that are undergoing testing. 

The company apologised in February after its recently launched AI image generator, Gemini, created an image of the US Founding Fathers in which a black man was inappropriately depicted as a member of the group. Gemini also created an incorrectly depicted image of German soldiers from World War Two.

Meet Laika 13, the AI Chatbot That Acts Like a Social Media Obsessed Adolescent

 

Swedish AI experts have developed a chatbot called Laika 13, which replicates the actions of a teenager addicted to social media, as a novel approach to combating teen internet addiction. Laika's development coincides with an increasing awareness of the negative impact that excessive social media use has on teenage mental health.

Focusing on teen internet addiction 

Laika 13 was built by Swedish neuroscientists and AI professionals to highlight the potential detrimental effects of long-term social media use. The designers of Laika hope to educate young people about the dangers of internet addiction in light of evidence indicating a link between social media use and mental health issues such as anxiety and depression. 

Initial results from the Laika test programme show promising results: of the 60,000 students who participated, 75% said they would like to change how they interact with social media after connecting with the chatbot. Laika may replicate the inner feelings and fears of a troubled adolescent, so much so that students are reflecting on their online behaviour. 

Concerns remain, though, about the program's long-term effectiveness and its effects on impressionable young users. Proponents of Laika contend that the technology is affordable and bridges a gap in traditional schooling, while critics raise ethical concerns about using AI with teenagers in the absence of ample evidence of its effectiveness. 

Potential dangers and ethical considerations

Julia Stoyanovich, the director of NYU's Centre for Responsible AI, is concerned about the moral ramifications of employing AI models that are very similar to humans in the presence of vulnerable teenagers. Ignoring past incidents where sophisticated AI systems were mistakenly perceived as possessing human traits, she warns against the dangers of anthropomorphizing robots. 

Stoyanovich highlights the potential risks associated with storing and employing children's sensitive data, stressing the importance of taking data privacy issues related to generative AI technology into account. Despite developers' assurances of data security methods, there are doubts over AI systems' capacity to safeguard user privacy due to their intrinsic unpredictability. 

As Laika engages with students and educators, the debate over whether or not to use AI technology to address teen social media addiction is still going on. Supporters argue that AI can raise awareness and encourage healthy digital habits, but critics point out that there are practical and ethical challenges when using AI with young people. 

The success of initiatives such as Laika ultimately rests on ongoing research, transparency, and collaboration among developers, educators, and mental health professionals. Society must keep looking for practical ways to handle the complexities of digital technology and its impact on mental health if it is to safeguard the resilience and health of future generations.