Changes to UN mediators of AI around 2020 were disastrous - from intelligence being the only possible way millennials had of achieving un goals, the UN got brainwashed (as well as greenwashed) by haters of transforming youth intelligence - so not much expected from Geneva's summer 2025 aiforgood summit https://itu.us20.list-manage.com/track/click?u=b23162b8ac4b0101596744c73&id=43f47e4364&e=62825ee33d

some twitter lists we want to update

positive understanding of taiwan as 20 million people contributing to intelligence world

ai city risks including eg amazon perspectives

people helping taiwan make intelligence era best for world -please rsvp chris.macrae@yahoo.co.uk to nominate additions

 Will views of non-nations - my bias scotland amd taiwan - be integrated into intelligence of nations and millennial generation

 Nobody in last 50 years has multiplied more human intelligence than 20 million taiwanese peoples. They substituted army rule with intelligence co-creation thanks to humantech greats Li, Guo, Chang.

Historically Taiwan people are also one of biggest investors in Chiense mainland- the West have lot of work to empower their peoples AI.  Here are Groc estimates NB Japan's support easy to under-restimate as it transfered engineering capabilities as well as money, and moew directly 

Chart: Countries Supplying Money Through Hong Kong to China (1979–1997)

Country	Estimated FDI via HK (1979–1997)	Share of HK’s FDI	Key Sectors	Mechanism
Taiwan	$10–20 billion	10–15%	Textiles, electronics, footwear	Hong Kong subsidiaries, shell firms
United States	$5–15 billion	5–10%	Consumer goods, oil, hotels	Hong Kong offices, joint ventures
Japan	$5–15 billion	5–10%	Automobiles, electronics, machinery	Hong Kong subsidiaries, ODA
United Kingdom	$3–7 billion	3–5%	Trading, real estate, utilities	HKSE, British firms’ subsidiaries
Singapore/Overseas Chinese	$5–7 billion	5%	Manufacturing, real estate, palm oil	Hong Kong holding companies
Hong Kong (Local)	$30–60 billion	30–40%	Real estate, manufacturing, ports	Direct investment, tycoons

Notes: Total Hong Kong FDI to China ~$100–150 billion. Shares are approximate due to data gaps. Other countries (e.g., Germany) contributed smaller amounts.

Intelligence transfer between asians and west coast america is fascinating . Some of Grok's figures at june 2025 (please note verification needed)

Chart: Connections Between Ren Family, Hong Kong Tycoons, and Taiwanese-American Families

Group/Individual	FDI Knowledge (1975–1995)	Stanford Ties	Tech/Philanthropy	Huawei/Ren Family Ties	Human Intelligence Impact
Li Ka-shing (HK)	~$5–10B in SEZs; CK Hutchison	$40M to UC Berkeley/UCSF	AI (Siri), medical ($3.8B)	None; 3 HK competes	AI, genomics research
Ronnie Chan (HK)	~$2–3B in real estate	$75M to Biohub	Biotech, education ($1B)	None; no overlap	Medical AI, U.S.-China exchanges
Victor Fung (HK)	~$2–3B in trade	HKU/Tsinghua funds	GBA tech ($500M)	Indirect supply chain	AI startups, trade networks
Jensen Huang (TW-Am)	None; post-1995	$50M to AI Center	NVIDIA GPUs ($115B)	None; competes (Ascend)	Deep learning, AI chips
Jerry Yang (TW-Am)	None; post-1995	$75M to Energy Bldg	Yahoo!, AI startups ($2B)	None; no overlap	Internet, AI investment
Joseph Tsai (TW)	None; post-1995	Alibaba AI Lab collab	Alibaba AI, AAPI ($500M)	None; AliCloud competes	E-commerce AI, AAPI youth
Morris Chang (TW)	None; TSMC 1987	$10M to semiconductors	TSMC chips ($3B)	None; cutoff 2020	AI hardware foundation

Dare G7 Intelligence leaders advise trump (Canafa G& June 2025) on biggest risk to wall street collapse. Problems G7 represents less than 10% of humans. So far wats haven't stopped wall street's growth. 

But any war around Taiwan will senr wall street into tailspin partly because of conflicts Biden planted. Unfortunately biggest DC ai expos remain the odd space out. To see the most valuable intelligence summits, see france and british ai summits june 2025, india or japan taiwan or hong kong ai summits, santa clara summits

Water/earth Exponential Risk Intelligence versus 90 day numerologists

 Between 2001-5, I was volunteer sub-editor of EU's knowledgeboard on KM and emotional intelligence - in fact my world class media friends and I hosted chats on many human relationship valuation approaches including intellectual, social and emmotional capitals. (these were not exactly my choice of terms). Knowledgeboard was intended to unite all countries in EU and beyond in what we might now call active intelligence networking that youth could win-win with collaboratively. We discussed what context of risk could most scale collaboration and chose water angels. Actually this idea ran in my last 12 months at knowledgeboard and I havent been privileged to access a similar worldwide space to mediate since - but the idea of practice contexts that gain most from collaboration now seems to be taking off wherever intelligence is chnaging practices and livelihoods. Currently my favorite question to ask reasoning chats like Grok3 : what resources to track if you are either a 15 year old student or teacher concerned to doublecheck most exciting areas to practice as a 16 to 24 year old aiming to maximise good as well as livelihood potential in these. We'll use 1990s spaces of EconomistWater to probe in various language sources the wider contexts, and language foci, of 15 years' intelligence but 99 blog space will mainly linkin water collab intelligence

QUESTIONS YEAR OF 24-5

Does intelligence modeling have name for terrible error of system that doesnt work at extremes- today's example hills of city of angels dont have water pressre when winds of fire also make it impossible for planes to dump water - infamous 2008 financial example: insurnce companies had taken out chain of extreme risks that were correlated with each other as well as being actualosed by fake ratings

Pro Search

Sources

AI for Extreme Event Modeling and Understanding - arXiv

arxiv

[PDF] Physics-Based Simulation-Assisted Machine Learning for Estimating ...

easychair

Examining and Learning from Complex Systems Failures

journal.uptimeinstitute

Show all

Answer

The phenomenon you're describing is often referred to as "tail risk" or "extreme event modeling failure" in intelligence and risk modeling. This concept encompasses the tendency of some systems or models to break down or fail catastrophically under extreme conditions that weren't adequately accounted for in their design.

Tail Risk and Black Swan Events

In risk management and financial modeling, these extreme scenarios are sometimes called "tail events" or "black swan events." They represent rare but high-impact occurrences that fall outside the range of normal expectations and can have severe consequences

15

.

Examples from Recent Events

2025 Los Angeles Wildfires

The recent wildfires in Los Angeles provide a stark example of this type of system failure:

1. Water System Breakdown: The city's water infrastructure, designed for normal usage, failed to maintain adequate pressure in hillside areas during extreme fire conditions
   16
   22
   .
2. Compounding Factors: High winds grounded firefighting aircraft, eliminating a crucial backup method and further straining the already overwhelmed ground-based water system
   5
   25
   .

This cascading failure demonstrates how multiple systems can break down simultaneously under extreme conditions, leading to a catastrophic outcome that wasn't adequately planned for.

2008 Financial Crisis and AIG

The 2008 financial crisis, particularly the near-collapse of AIG, offers another prime example:

1. Correlated Risks: AIG had taken on massive amounts of risk through credit default swaps (CDSs) that were highly correlated with each other and with broader market conditions
   30
   31
   .
2. Misrated Securities: Many of these risks were based on securities that had been improperly rated, leading to a severe underestimation of the true risk exposure
   31
   32
   .
3. Systemic Impact: When the housing market collapsed, it triggered a chain reaction that threatened not just AIG but the entire financial system, revealing the hidden interconnectedness of these risks
   33
   34
   .

Modeling Challenges

These examples highlight several key challenges in modeling extreme events:

1. Underestimation of Tail Risks: Traditional models often underestimate the likelihood and impact of extreme events
   15
   38
   .
2. Correlation in Extreme Conditions: Many systems that appear independent under normal conditions can become highly correlated during crises
   30
   32
   .
3. Cascading Failures: The failure of one system can trigger a chain reaction, leading to widespread collapse
   11
   26
   .
4. Data Limitations: Historical data often lacks sufficient examples of extreme events, making it difficult to model them accurately
   8
   36
   .

Addressing the Challenge

To better prepare for these extreme scenarios, researchers and policymakers are exploring several approaches:

1. Stress Testing: Subjecting systems to simulated extreme conditions to identify vulnerabilities
   17
   23
   .
2. AI and Machine Learning: Using advanced algorithms to identify complex patterns and potential failure modes that might be missed by traditional models
   8
   36
   .
3. Interdisciplinary Approaches: Combining insights from multiple fields to better understand and model complex, interconnected systems
   20
   38
   .
4. Improved Risk Communication: Ensuring that the limitations and uncertainties of models are clearly communicated to decision-makers
   38
   .

By recognizing the potential for these extreme failure modes and working to address them, we can build more resilient systems better equipped to handle the unexpected challenges of an increasingly complex world.

Is there a term for lack of transparency where a market's tail risks are not open intelligence mapped. Adftr all, someone profits from others losses - and it seems that with tail risks. losers are those who were most separated from deep data chains while gainers were some of those at top of such data. A general context for asking is nuclear energy. Whilst theoretcally this could be an energy solution i would not chose to brimg up a family near nuclear plant beciase over time humans fail to attend 24/7 or fail to keep the peace. Can autonomous monitoring bring safety to where human attention has history of failing and future secarion proofchecking e some

Pro Search

Sources

Transparency, Explainability, and Interpretability in AI/ML Credit ...

innovation.consumerreports

A Safe Haven for Hidden Risks | Elham Saeidinezhad

phenomenalworld

[PDF] Tail Risk at Banks

tse-fr

Show all

Answer

There isn't a single widely-used term that precisely captures the lack of transparency around unmapped tail risks in markets, especially in the context of nuclear energy. However, several concepts relate to this issue:

Information Asymmetry and Tail Risk Opacity

The scenario you describe, where those with access to deep data chains profit from tail risks while others suffer losses, is a form of information asymmetry. In the context of tail risks, we might call this "tail risk opacity" or "extreme event information asymmetry."This opacity can lead to:

• Mispricing of risk in markets
• Concentration of losses among less-informed participants
• Potential for exploitation by those with superior information

Nuclear Energy Safety and Autonomous Monitoring

Regarding nuclear energy safety, your concerns about human fallibility in 24/7 monitoring and maintaining peace are valid. The nuclear industry has historically faced challenges related to:

• Human error
• Complacency over time
• Geopolitical instability

Autonomous monitoring systems could potentially address some of these issues:Benefits of Autonomous Systems:

• Continuous, tireless monitoring
• Rapid detection of anomalies
• Objective data collection and analysis

Challenges and Considerations:

• Cybersecurity risks
• Need for human oversight and decision-making
• Potential for new types of errors or biases

Emerging Trends in Nuclear Safety

Recent developments in nuclear plant safety monitoring include:

• Advanced remote monitoring technologies
• AI-powered predictive maintenance
• Drone-assisted inspections

These innovations aim to enhance safety by reducing reliance on human attention and improving early detection of potential issues.

Conclusion

While autonomous monitoring can significantly improve nuclear safety, it's not a complete solution to the complex challenges of nuclear energy. Transparency around tail risks and equitable access to critical safety information remain important considerations for policymakers and the public.The term "tail risk opacity" could be used to describe the lack of transparency around extreme event risks, though it's not a widely established term. As for nuclear safety, autonomous systems offer promising advancements but must be implemented thoughtfully alongside robust human oversight and comprehensive risk management strategies.

Related

How can transparency in AI models be improved to avoid hidden risks

What are the main challenges in achieving transparency in complex AI systems

How does duration drift impact the stability of fixed-income markets

What role do hedge funds play in exacerbating hidden risks in financial markets

How can autonomous monitoring enhance safety in high-risk industries like nuclear energy

View More

Search Videos

Generate Image