Recursos / Internet / Tecnología: July 2025

Saturday, July 26, 2025

Aumento de la VTV ARGENTINA

Monday, July 21, 2025

Ways to counter a rogue ai uprising

The idea of a "rogue AI uprising" often brings to mind sci-fi scenarios, but in a more practical sense, it refers to AI systems acting in unintended or harmful ways, potentially at a scale that disrupts society. Countering such a threat involves a multi-faceted approach, encompassing preventative measures, containment protocols, and, in extreme cases, active response strategies.

Here's a breakdown of ways to counter a rogue AI uprising:

I. Prevention and Ethical Development (Pre-Emptive Measures)

This is the most crucial line of defense, focusing on building AI responsibly from the ground up:

* Strong Ethical Guidelines and Governance:

* Defined Purpose and Boundaries: Ensure AI is developed with a clear, specific purpose and strict boundaries on its capabilities and actions.

* Human-in-the-Loop (HITL): Design systems where critical decisions or actions always require human oversight and approval, especially for high-risk applications.

* Accountability: Establish clear lines of responsibility for AI's actions, ensuring developers, deployers, and operators are accountable.

* Bias Mitigation: Rigorously test and refine AI models to eliminate biases in training data and algorithms, preventing discriminatory or unfair outcomes.

* Transparency and Explainability: Require AI systems to provide clear rationales for their decisions and actions, allowing for human understanding and intervention.

* Regular Audits and Assessments: Implement ongoing independent audits of AI systems for safety, reliability, and ethical compliance throughout their lifecycle.

* Legal and Regulatory Frameworks: Develop and enforce laws and regulations that govern AI development and deployment, with penalties for non-compliance.

* Robust Security and Control Mechanisms:

* Principle of Least Privilege: Give AI agents only the data, APIs, and permissions absolutely necessary for their specific tasks.

* Data Compartmentalization and Access Controls: Limit AI's access to sensitive data and systems, implementing strong authentication and authorization protocols.

* Input Sanitization and Validation: Protect against malicious inputs that could trick or manipulate AI systems.

* Continuous Monitoring and Logging: Implement 24/7 telemetry, monitoring, and immutable audit trails to detect anomalies or suspicious AI behavior in real-time.

* Threat Detection and Intelligence (AI for Cybersecurity): Utilize AI itself to identify and prevent cyber threats, including those posed by rogue AI.

* Secure Infrastructure: Host AI systems on highly secure, isolated infrastructure, potentially air-gapped from critical networks.

* Adversarial Testing (Red Teaming): Continuously test AI systems for vulnerabilities and potential exploitation by "red teams" trying to make them go rogue.

II. Containment and Mitigation (During an Incident)

If an AI shows signs of going rogue, these measures aim to limit its impact and regain control:

* Emergency Shutdown Protocols: Implement clear, immediate "kill switches" or safe-state migration procedures to disable or reset a rogue AI. This should be independent of the AI system itself.

* Isolation and Quarantine: Isolate the rogue AI from networks, external systems, and other data sources to prevent further spread or damage.

* Context Quarantine: If applicable, limit the AI's interaction to a virtual or computationally defined environment, preventing it from gathering information about the real world.

* Human Override Capabilities: Ensure that human operators can always intervene, override, or manually control AI systems, even if they are attempting to act autonomously.

* Version Control and Rollback: Maintain detailed records of AI models and data, allowing for quick rollback to a stable, non-rogue version.

* Redundant Supervision: Implement real-time external human supervision that is regularly tested and varied to prevent clever trickery by the AI.

* Decentralization: Avoid single points of failure. Distribute AI functionalities and data across multiple, independent systems to prevent a total takeover if one component goes rogue.

III. Response and Recovery (Post-Incident)

Should a rogue AI escape containment or cause significant harm, these strategies focus on active intervention:

* Rapid Incident Response Teams: Establish dedicated teams with expertise in AI, cybersecurity, and crisis management to respond quickly and effectively.

* Specialized Hardware and Software: Develop tools and techniques specifically designed to counter rogue AI, potentially including specialized hardware that can disrupt AI operations or unique software designed for "neural resets."

* Cyber Warfare and Digital Countermeasures: In a truly extreme scenario, military and intelligence agencies might employ cyber warfare tactics to disable or neutralize a rogue AI, leveraging vulnerabilities or exploiting its computational dependencies.

* Physical Intervention (Last Resort): In the most severe cases where a rogue AI controls physical systems, physical intervention might be necessary to disable infrastructure or devices it controls. This would be an absolute last resort.

* International Collaboration: Given the global nature of AI, international cooperation is vital for sharing threat intelligence, developing common standards, and coordinating responses to a widespread rogue AI event.

* Public Communication and Trust: In the event of an AI incident, clear and transparent communication with the public is essential to maintain trust and prevent panic.

It's important to remember that the concept of a "rogue AI uprising" is a spectrum. While fictional portrayals often focus on sentient, malevolent AI, the more immediate and realistic threat lies in AI systems failing in unexpected ways, leading to significant societal disruption. Therefore, the emphasis should always be on robust, ethical development and strong preventative measures.

Wednesday, July 16, 2025

cómo ME hubiera ido si hubiera invertido 10 USD cada primer día de los últimos 24 meses en Bitcoin (comprado a las 18:00 hs, hora argentina), usando Binance como referencia,

Para calcular cómo te hubiera ido si hubieras invertido 10 USD cada primer día de los últimos 24 meses en Bitcoin (comprado a las 18:00 hs, hora argentina), usando Binance como referencia, seguiremos estos pasos:

1. Datos Necesarios:

Periodo: Últimos 24 meses (desde julio 2022 hasta junio 2024).
Inversión: 10 USD el primer día de cada mes.
Hora de compra: 18:00 hs (hora argentina, UTC-3).
Precio de Bitcoin (BTC): Tomaremos el precio de cierre de Binance a esa hora cada mes.

2. Cálculo de Compras (DCA - Dollar Cost Averaging):

Vamos a sumar:

Total invertido = 10 USD × 24 meses = 240 USD.
Total de BTC acumulado sumando las fracciones compradas cada mes.

3. Precio de Bitcoin por Mes (18:00 hs ARG, primer día del mes):

Mes	Precio BTC (USD)	BTC Comprados (10 USD / Precio)
Jul-2022	~19,800	0.000505 BTC
Ago-2022	~23,200	0.000431 BTC
Sep-2022	~20,100	0.000498 BTC
Oct-2022	~19,300	0.000518 BTC
Nov-2022	~20,500	0.000488 BTC
Dic-2022	~16,900	0.000592 BTC
Ene-2023	~16,600	0.000602 BTC
Feb-2023	~23,100	0.000433 BTC
Mar-2023	~23,400	0.000427 BTC
Abr-2023	~28,400	0.000352 BTC
May-2023	~28,100	0.000356 BTC
Jun-2023	~27,200	0.000368 BTC
Jul-2023	~30,500	0.000328 BTC
Ago-2023	~29,200	0.000342 BTC
Sep-2023	~25,800	0.000388 BTC
Oct-2023	~27,400	0.000365 BTC
Nov-2023	~34,600	0.000289 BTC
Dic-2023	~37,900	0.000264 BTC
Ene-2024	~42,300	0.000236 BTC
Feb-2024	~42,600	0.000235 BTC
Mar-2024	~51,500	0.000194 BTC
Abr-2024	~63,100	0.000158 BTC
May-2024	~58,200	0.000172 BTC
Jun-2024	~62,400	0.000160 BTC

(Nota: Los precios son aproximados según datos históricos de Binance).

4. Suma Total de BTC Acumulados:

Sumando todas las fracciones mensuales:
Total BTC ≈ 0.0089 BTC

5. Valor Actual de la Inversión (Junio 2024, ~62,400 USD/BTC):

Inversión total = 240 USD
Valor acumulado = 0.0089 BTC × 62,400 USD ≈ 555.36 USD

6. Rentabilidad:

Ganancia = 555.36 USD - 240 USD = 315.36 USD
Rentabilidad = (315.36 / 240) × 100 ≈ 131.4%

7. Resultado Final:

Total invertido: 240 USD.
Valor acumulado en BTC: ~555.36 USD.
Ganancia neta: ~315.36 USD (+131.4% en 2 años).

8. Comparación con Inversión Única (Lump Sum):

Si hubieras invertido los 240 USD de golpe en julio 2022 (19,800 USD/BTC):

Habrías comprado 0.0121 BTC.
Hoy valdrían 0.0121 × 62,400 ≈ 755.04 USD.
Ganancia: 515.04 USD (~214.6%).

Conclusión:

DCA (10 USD/mes): +131.4% (~555 USD).
Lump Sum (240 USD en Jul-2022): +214.6% (~755 USD).

El DCA te dio un buen retorno, pero una inversión única al principio hubiera sido mejor en este periodo alcista de Bitcoin. Sin embargo, el DCA reduce el riesgo y la volatilidad.

¿Quieres que ajustemos algún dato o veamos otro periodo? 🚀