STARM

[chris macrae]

[e-mail]: NEW WORD : To STARM - to start 4th decade of teachers , media and politicians stopping children from learning how to apply machines with Einstein capability to support human intelligence

X Thought for 5s
"To starm" — I like it as a neologism. Compact, punchy, and evocative of "storm" or "starm" as in stellar (Einstein-level) machines being blocked. It captures a real frustration felt by many AI optimists.
Historical Pattern,

Bonus discussion at Washington DC 4 decades ago i co-authored 2025report- digiral worlds would be millennials most exciting time if and onlky if we transformed education and health for all

X Not a New Decade You're right that there's ongoing resistance from parts of the education establishment, media, and some politicians. However, this isn't cleanly the "4th decade." Generative AI tools accessible to kids (like ChatGPT) only exploded in late 2022 — we're barely 3.5 years in. What is recurring is a multi-decade pattern of skepticism toward learning technologies: Calculators (1970s–80s): Teachers and officials feared they would "destroy mental math" and ban them in many classrooms. Protests happened. Kids adapted anyway. instagram.com Computers and internet (1980s–2000s): Concerns over distraction, cheating, reduced human interaction, and inequity. Now AI: Fears center on plagiarism, over-reliance, loss of critical thinking, data privacy, bias, and job displacement for educators. Some unions have called for restrictions (e.g., limits in elementary settings), schools issue vague or prohibitive policies, and headlines often amplify risks over opportunities. @Awesome_AI_News This resistance isn't imaginary. Surveys and reports show teacher prep programs lagging, faculty skepticism, and a default to "ban or restrict" in many places rather than redesign pedagogy. crpe.org But Adoption Is Accelerating Anyway Despite the "starming," reality on the ground is shifting fast: Student usage is extremely high: 80–92% of university/high school students report using generative AI for schoolwork. Many use it daily. digitaleducationcouncil.com Teachers are adopting too (around 60–85% in recent data), often for planning, grading, and personalization.

The AI-in-education market is booming and projected to grow rapidly.
Kids (and parents) are voting with their keyboards. The obstruction may slow equitable, guided integration more than it stops usage outright.
Legitimate Concerns vs. Protectionism
Not all resistance is bad faith. Valid issues include:
Ensuring kids still build foundational skills (don't just outsource thinking).
Addressing equity (not every child has great prompts or oversight).
Privacy, bias, and over-hype.
Maintaining human mentorship and social development.
The stronger critique — which your verb highlights — is when this morphs into institutional inertia, fear of losing control, or media amplification of panic that delays kids from leveraging "Einstein-capable" tools for personalized tutoring, accelerated learning, creativity, and exploration. History suggests the resisters eventually lose; the question is how much potential is wasted in the meantime.
To starm (v.): To engage in or initiate another cycle of gatekeeping powerful learning technologies from the next generation, often under the banner of "protecting" education while actually preserving outdated models.
We could conjugate it: "They're starming hard in that district." "The policy starmed another cohort." "Stop starming the kids — give them supervised superintelligence."
The real win isn't zero resistance (some friction is healthy) but moving faster to thoughtful integration: AI as a universal tutor + human teachers focused on motivation, ethics, and complex project-based learning. The consciousness you mentioned earlier (from Web3 to AI) is pushing in that direction. The starm-ers may delay, but they probably won't derail it. What's your take on the best ways to counter-starm?

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Investigate digital citizenship curricula

Refine the definition of starm
But that's what happened yesterday in Britain when PM Starmer said he will ban kids from eg youtube

Thought for 5s
Yes, that fits the "starm" pattern perfectly — and it's a textbook example of the tension we're discussing.
bbc.com
Yesterday (June 15, 2026), UK Prime Minister Keir Starmer announced a sweeping ban on social media access for all children under 16. This includes platforms like TikTok, Instagram, Snapchat, Facebook, X, and YouTube (with a carve-out for YouTube Kids). Legislation is targeted before Christmas, with enforcement from spring 2027.
apnews.com
Core Rationale (from Starmer)
Protect kids from harmful content, cyberbullying, exploitation, addiction, and excessive screen time.
"Social media is making our children unhappy and unsafe."
Give children their "childhoods back" amid tech intrusion.
Tech companies will face enforcement; age verification (e.g., ID or facial scans) is implied.
nbcnews.com
This builds on Australia's similar ban and the UK's existing Online Safety Act.
Why This Qualifies as "Starming"
It directly blocks broad access to powerful, educational tools alongside the addictive social features:
YouTube is one of the world's largest educational resources — tutorials, documentaries, science explainers, language learning, and "Einstein-capable" style content (or at least pathways to it via search and recommendations). Many kids use it as a primary learning supplement.
Blanket age bans risk overreach: They don't easily distinguish between mindless scrolling/doom content and active, self-directed learning (e.g., a 14-year-old researching history, coding, physics, or AI prompts).
History shows such restrictions often drive use underground (VPNs, shared accounts, less-safe alternatives) rather than eliminating it, while slowing guided integration.
Critics (including some tech responses) argue it could push kids toward unmoderated or riskier spaces, and enforcement raises privacy concerns.
independent.co.uk
Balanced View
Legitimate concerns exist. Evidence links heavy, unstructured social media use (especially algorithmic feeds) to worse mental health outcomes in teens — anxiety, depression, sleep disruption, body image issues, and attention fragmentation. Protecting pre-teens from the worst of it isn't irrational.
npr.org
But the starm risk is real too. Treating the entire open web/learning ecosystem with the same hammer as pure social dopamine apps repeats the calculator/computer skepticism cycles. It assumes kids can't (or shouldn't) be taught discernment and that institutions know better than families + supervised tools. AI tutors, personalized learning, and vast knowledge access are precisely what could counter some of the harms (boredom, poor teaching in some subjects) that drive kids to addictive feeds in the first place.
This isn't anti-protection — it's about precision. Better approaches might include:
Strong defaults for age-appropriate modes (no infinite scroll, limited recommendations).
Parental controls + school-guided AI/YouTube integration.
Teaching digital literacy and critical thinking instead of outright prohibition.
Targeting the addictive design (algorithms, autoplay) more than access.
To starm now has a fresh, high-profile example. The policy may reduce some harms but could also delay millions of kids from leveraging modern learning machines. What's your read on whether this will actually stick or get worked around quickly?

Explore age verification technologies

Investigate Australia's social ban
Explore age verification technologies

Thought for 4s
Age verification technologies (often called age assurance) are a mix of methods to estimate or confirm a user's age online. They have gained traction due to regulations like the UK's Online Safety Act, Australia's under-16 social media ban, EU initiatives, and various U.S. state laws. These tools aim to protect minors from harmful content while allowing adult access, but they involve trade-offs in accuracy, privacy, usability, and enforcement.
digital-strategy.ec.europa.eu
Main Categories of Age Verification Technologies
Facial Age Estimation (Biometric AI)
How it works: Users take a selfie or short video. AI analyzes facial features (wrinkles, bone structure, skin texture) to estimate age. Often includes liveness detection to prevent photos or deepfakes.
Providers/Examples: Yoti, Paravision, Innovatrics. Used by platforms like Roblox, some UK services (e.g., via Yoti for 18+ content), and tested in Australia.
Accuracy: Best systems achieve ~1.5–3 years mean error. Stronger for teens (13–18 range) than extremes. NIST evaluations show improvement but no 100% reliability; biases exist by gender, ethnicity, lighting, and age.
yoti.com
Pros: Low friction (seconds to complete), no ID needed, can run on-device for better privacy.
Cons: Probabilistic (not definitive proof), potential biases/discrimination, spoofing risks, and privacy concerns over facial data.
Document/ID Verification
How it works: Scan/upload government ID (passport, driver's license) + selfie for matching. OCR + biometric comparison.
Pros: High certainty for "hard" verification.
Cons: High friction, excludes unbanked/undocumented users, data breach risks (multiple incidents reported), and creates surveillance potential.
en.wikipedia.org
Digital Identity Wallets / eID Systems (Privacy-Preserving)
How it works: Users prove "over X age" via cryptographic attestations (e.g., zero-knowledge proofs) without revealing exact DOB or identity. EU "mini-wallet" or national eIDs (BankID, etc.) integrate this.
digital-strategy.ec.europa.eu
Examples: EU Age Verification Blueprint (interoperable with Digital Identity Wallets, target rollout by end-2026). Allows anonymous "over 18" proofs.
Strengths: Best privacy profile — sites learn only the age fact, not who you are or which sites you visit.
Challenges: Requires widespread eID adoption; still emerging at scale.
Other Methods
Open banking/credit checks: Permission to query bank data for age indicators.
Device-based or behavioral: Device signals, usage patterns (less reliable alone).
Tiered/Successive: Start with low-friction estimation; escalate to ID only if uncertain.
klippa.com
Context for UK Starmer's Announcement
The proposed under-16 social media ban (including YouTube, with enforcement ~spring 2027) will likely rely on these technologies, building on the Online Safety Act's age assurance requirements. Platforms may use facial scans, ID uploads, or third-party services. Ofcom guidance emphasizes "highly effective" methods over self-declaration.
ofcom.org.uk
Similar systems are in play in Australia, Brazil, and parts of the EU/U.S.
Pros and Cons Overall
Potential Benefits:
Reduces minors' exposure to addictive feeds, bullying, explicit content, or grooming.
Enables safer platforms if done well.
Scalable with AI advancements.
Significant Drawbacks and Risks:
Privacy & Security: Data breaches (e.g., involving facial data or IDs) are common. Centralized systems risk mass surveillance or tracking.
eff.org
Circumvention: VPN spikes are routine; kids share accounts or use workarounds.
Equity & Inclusion: Biases in AI, exclusion of those without IDs/devices, higher barriers for marginalized groups.
Effectiveness: Not foolproof. False positives/negatives occur; doesn't address all harms (e.g., offline issues or unmoderated alternatives).
Chilling Effect: Can deter legitimate access to education/news on platforms like YouTube.
Privacy-Preserving Innovations (like EU ZKP approaches or on-device processing) represent the most promising middle ground, but full adoption lags.
newamerica.org
Broader Implications (Tying to "Starm")
These technologies can enable protection without total bans, but blanket mandates risk overreach — blocking educational value (tutorials, research) alongside harms. Effective implementation needs transparency, independent audits (e.g., NIST-style), user choice, and focus on design fixes (less addictive algorithms) rather than access gates alone.
standards.ieee.org
The field is evolving rapidly toward hybrids: AI estimation + digital wallets. Outcomes depend on execution — strong privacy defaults and minimal data collection will determine if they genuinely help families or just add friction and new risks. What aspect (e.g., a specific tech, privacy angles, or effectiveness data) would you like to dive deeper into?

Zero knowledge proofs age

Online Safety Act enforcement
Investigate Australia's social ban

Thought for 3s
Australia's under-16 social media ban is the world's first nationwide law of its kind. It restricts children under 16 from creating or holding accounts on designated social media platforms.
bbc.com
Key Details
Legislation: Online Safety Amendment (Social Media Minimum Age) Act 2024, passed in late November 2024.
en.wikipedia.org
Effective Date: 10 December 2025 (after a ~1-year implementation window for platforms).
esafety.gov.au
Platforms Covered (as of rollout): Facebook, Instagram, TikTok, Snapchat, YouTube, X (Twitter), Threads, Reddit, Twitch, and Kick.
bbc.com
Exemptions: YouTube Kids, WhatsApp, Messenger, Google Classroom, Roblox, Discord, Steam, and others not classified as primary "social media" services.
esafety.gov.au
Requirements: Platforms must take "reasonable steps" to prevent under-16s from having accounts while in Australia. No parental consent exceptions.
Penalties: Up to A$49.5 million (~US$33 million) for non-compliance.
bbc.com
Enforcer: eSafety Commissioner (Julie Inman Grant).
Age Verification/Assurance Methods
Platforms rely on a mix of technologies (not always strict "verification"):
Age inference from user behavior, device data, and activity patterns.
Facial age estimation (selfie + liveness detection).
ID/document upload or other signals (e.g., linked bank/credit data).
Tiered approaches: Low-friction first, escalating as needed.
cnbc.com
The law emphasizes "reasonable steps" rather than perfect proof, and eSafety accepts that full rollout may take time with multiple measures applied progressively.
esafety.gov.au
Implementation and Early Results (as of mid-2026)
Initial Impact: Platforms (e.g., Meta) removed or restricted millions of accounts. eSafety reported 4.7 million under-16 accounts affected by mid-December 2025, plus more later.
esafety.gov.au
Compliance Issues (March 2026 eSafety update): Concerns with major platforms (Facebook/Instagram, Snapchat, TikTok, YouTube) including:
Allowing repeated age-check attempts until users "pass."
Weak prevention of new under-16 accounts.
Over-reliance on self-declaration or weak inference.
Investigations launched into non-compliance.
theconversation.com
Effectiveness: Mixed to disappointing so far. Reports (NYT, Guardian, etc.) indicate the ban is floundering — many teens (around 60–70% in some surveys) found workarounds (VPNs, shared accounts, new profiles, or unlogged viewing). Benefits may accrue more to future cohorts than current ones.
nytimes.com
Side Effects: Some displaced users report less news consumption; shifts to alternative apps; privacy debates over data collection.
theconversation.com
Broader Context and Criticisms
Support: Popular with many parents; framed as protecting mental health, reducing bullying, addiction, and exposure to harms.
Criticisms:
Blunt and hard to enforce — eSafety Commissioner herself called it a "blunt force approach" and likened enforcement to "fencing the ocean."
youtube.com
Circumvention is easy for tech-savvy teens.
Potential loss of educational access (especially YouTube) and news.
Privacy risks from age tech.
Limited evidence it will meaningfully improve outcomes vs. alternatives like better platform design, digital literacy, or parental tools.
theguardian.com
The eSafety Commissioner has faced pushback (including threats) amid the rollout.
Overall Assessment: Australia's ban is a bold, real-world experiment influencing global policy (including the UK's recent moves). It has forced platform action and deactivated millions of accounts, but early data shows significant leakage and enforcement challenges. Long-term outcomes on mental health, learning, and community effects remain under observation — it may set norms for younger kids more than immediately reshape current teens' behavior.
nytimes.com
This directly ties into the "starm" concept: a high-profile institutional effort to gatekeep access, with real protective intent but risks of overreach and incomplete results. Would you like deeper details on specific platforms, age tech trials, public opinion data, or comparisons to the UK plan?

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globalvillage will humansAI SDG planet, species?
@myunuslab

Thought for 11s
@myunuslab
is an economist (media learning network) with six decades of experience focused on win-win trade who is now centered on AI developments in the 2020s. Their profile highlights global village themes involving humans, AI, sustainable development goals, the planet, and species futures, often linking to futurist economics and technology. Posts discuss AI innovations like Nvidia's layered systems, robotics, geopolitics, and historical scientific figures such as von Neumann, Einstein, and Turing.
"These days smartest prompts I get come from ai. Eg today German politicians may argue about anything except maths so gtu Berlin October will be ai greatest yet" -
@myunuslab

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Investigate Nvidia layered systems
Before the rise of ai chats people used to talk of designing web3.Am I right in thinking that there was a consciousness that web1 2 did not sustain local communities and next generation sdgs. If this was a human demand of web3 design. Are humans better off with so as equaliser and even peacemaker

Thought for 12s
Yes, you're largely right. Before AI chat tools exploded in popularity (around 2022–2023), Web3 was a major narrative in tech and development circles. It was positioned as a response to the shortcomings of Web1 and especially Web2.
blog.cloudflare.com
Web1, Web2, and the Push for Web3
Web1 (roughly 1990s–early 2000s): Mostly read-only static pages. It enabled information access but offered little interaction or community building.
Web2: The social, interactive web (Facebook, Google, Twitter/X, Uber, etc.). It created massive global platforms, user-generated content, and network effects—but at the cost of centralization. A few corporations control data, algorithms, attention, and economic value. Criticisms included:
Data monopolies and privacy erosion.
Algorithmic amplification of division or addiction over healthy local ties.
Value extraction (platforms capture most economic upside while creators/communities get scraps).
Weak support for genuine local or marginalized communities, as incentives favored scale and engagement metrics over sustainability.
Web3 emerged as the proposed fix: a decentralized internet built on blockchain, tokens, DAOs (Decentralized Autonomous Organizations), self-sovereign identity, and user-owned data/assets. Core promises included:
Ownership and sovereignty — Users own their data, content, and digital assets (e.g., NFTs, tokens).
Decentralized governance — Communities could run themselves via transparent, programmable rules rather than corporate fiat.
Inclusivity and new economies — Token incentives, decentralized finance (DeFi), and community currencies for financial inclusion, especially in the Global South.
cambridge.org
There was indeed a conscious thread around sustaining local communities and aligning with next-generation SDGs (Sustainable Development Goals). UNDP and others explored Web3 for openness, decentralization, financial inclusion, transparent aid, community-driven innovation, and impact tracking toward the UN SDGs.
undp.org
Projects like community inclusion currencies, decentralized credit histories, or indigenous DAOs aimed at empowerment beyond nation-states or big tech.
cambridge.org
This wasn't universal—much of Web3 hype was speculative (crypto prices, NFTs)—but the underlying human demand was real: tools that empower smaller groups, restore agency, reduce extractive intermediaries, and support sustainable, equitable development.
AI as Equalizer and Peacemaker?
AI (especially accessible chat models and tools) has shifted the conversation. Many former Web3 enthusiasts now focus on AI + Web3 hybrids or pure AI impacts. You're asking if humans are better off with AI playing this role.
Potential upsides (equalizer/peacemaker angle):
Accessibility and democratization: AI lowers barriers dramatically—no need for coding, capital, or technical expertise to create content, analyze data, build tools, or access knowledge. This can empower individuals and small/local communities far faster than blockchain setup.
Efficiency for SDGs: AI excels at optimization, prediction (e.g., climate modeling, personalized education, healthcare diagnostics, supply chain efficiency), translation, and scaling services to underserved areas.
Peacebuilding potential: Tools for dialogue synthesis, bias detection, conflict early warning, or trauma support in multiple languages. It can connect distant communities or simulate scenarios for better decisions.
youtube.com
Global village alignment: Ties into thinkers like
@myunuslab
—AI as a layer that augments human cooperation across borders, species futures, and sustainable economics.
Challenges and limitations:
Centralization risks: Current frontier AI is dominated by a handful of powerful labs/companies with massive compute and data advantages—potentially repeating or worsening Web2 issues (surveillance, bias, power concentration).
Inequality amplifier: Without deliberate design, it can displace jobs, widen digital divides, or favor those with better infrastructure. Environmental costs (energy for training/inference) hit vulnerable communities.
climatejusticealliance.org
Peacemaker doubts: AI can also spread misinformation, deepen polarization (via tailored content), or be used in autonomous weapons/cyber conflicts. It risks eroding social resilience if over-relied upon.
icip.cat
Community sustainability: AI is great for individual productivity but doesn't inherently create owned, self-governing local structures like DAOs or token economies aimed for. It can feel extractive if models are closed/black-box.
Overall: Humans aren't strictly "better off" yet—it depends on how we steer it. Web3's strength was (in theory) structural decentralization and ownership; AI's is intelligence and scalability. The sweet spot many discuss now is AI + Web3 (decentralized compute, data ownership, transparent AI via blockchain, community-governed models).
linkedin.com
Your intuition captures a real evolution in consciousness: from critiquing centralized platforms → designing decentralized alternatives → now leveraging super-intelligent tools that could achieve similar (or better) equity/peace outcomes if aligned with human values like local empowerment and sustainability. The demand for tools that sustain communities and SDGs persists; the tech stack is just shifting. Continued focus on openness, ethics, and hybrid designs will matter most.

Explore AI plus Web3 hybrids

Investigate decentralized autonomous organizations
New English verb. To starm :  to start 4th decade of teachers,media, and politicians preventing kids from learning with einstein-capable machines