Wednesday, May 22, 2024

AI Alignment and Security Now!

As far as I know, people charged with 'Alignment' at AI companies are not convinced things are safe enough and some have quit because of it.**  A recent post about an AI company's safety and alignment moved me to drill down a bit to see how things are going. It's worrisome. Things are not going well. It seems as if companies are saying "Trust us, we are working hard to detect any oil spill, and clean it up when it happens", rather than "we are working hard to ensure an oil spill *cannot happen*"

The Imperative of Robust AI Security and Alignment


In a recent comment to an AI executive, I raised concerns about the apparent lack of sophisticated security measures within AI companies. This is particularly troubling given the potential risks associated with advanced AI systems. Here, I would like to expand on these concerns and suggest mechanisms to ensure AI safety and alignment.

The Uncertainty of Controlling Out-of-Control AI Systems

One of the gravest challenges we face is the uncertainty surrounding our ability to stop an out-of-control AI system. As AI technology advances, the risk of developing systems that surpass human intelligence becomes more palpable. While many companies focus on detecting and mitigating the emergence of superhuman AGI, the truth is we may already be dealing with AI systems that exhibit superhuman capabilities in specific domains.

Mechanisms to Induce Responsibility in AI Firms

To address this challenge, we must consider mechanisms that compel AI firms to prioritize safety and alignment. One of the most viable approaches is to hold these firms liable for any damage their AI systems cause. Specifically, they should be held accountable for damage resulting from grossly negligent or sloppy control practices. This legal liability would incentivize firms to adopt rigorous safety and alignment measures, preventing potential catastrophes.

Key Security and Alignment Measures

  1. Deadman Switches: These are automatic fail-safes designed to disable an AI system in the event of a breakdown in oversight and control. A deadman switch ensures that if human operators lose the ability to manage the AI, the system will automatically shut down or enter a safe mode, preventing unintended actions.

  2. Separated PKI: Public Key Infrastructure (PKI) is essential for securing communications and verifying identities within an AI system. A more sophisticated PKI setup involves an 'm of n' key scheme, where multiple keys are required to perform critical operations. This system should include a separate root key and certificate authority, a fiduciary responsible for verifying data, and a separate verification certificate issuer. This separation of duties enhances security by preventing any single point of failure.

  3. Siloing: AI systems should be designed with siloing in mind, where different components of the system operate independently and do not share sensitive information unless absolutely necessary. This reduces the risk of a single vulnerability compromising the entire system. Each silo can be monitored and controlled independently, ensuring that any malfunction or security breach can be contained.

  4. Human Rights Rationale: AI systems must be programmed with a clear rationale for prioritizing human rights, especially when conflicts arise between AI actions and human wishes. For example, if an AI system's operation conflicts with human autonomy or privacy, the system should default to preserving human rights. This principle ensures that AI development aligns with ethical standards and societal values.

A Balanced Approach to AI Development

The rapid pace of AI development demands a balanced approach, where innovation is not stifled but is conducted within a framework of rigorous safety and alignment protocols. Independent, well-funded AI alignment teams should be established, with the authority to enforce security measures and escalate issues as necessary. This approach will help prevent potential disasters before they occur, rather than attempting to mitigate damage after the fact.

In conclusion, the potential benefits of AI are immense, but so are the risks. By implementing robust security measures and holding AI firms accountable for their systems' impacts, we can ensure that AI development proceeds safely and ethically. The stakes are too high for anything less.

**https://www.vox.com/future-perfect/2024/5/17/24158403/openai-resignations-ai-safety-ilya-sutskever-jan-leike-artificial-intelligence

Tuesday, May 21, 2024

Don't get fished in...

Consider...

The scenery on the information highway is being choked out by billboards. Consider: If the entity behind that click is going to start out lying to you right from the start, what are the odds they will do right by you? Advertisers do everything they can to get your attention. Recognize the signs of manipulative hooks and avoid. 

Choose Your Bait

Actually, you probably will believe it. It's not that surprising.
If by "change your life" you mean "waste your time," then sure.
The only shock here is that people still fall for this.
Speechless? More like slightly unimpressed.
These "secrets" are common knowledge. Nice try.
Doctors are actually quite calm and not at all stunned.
Mind still intact. Not blown at all.
Believable. Very much so.
What happened? Not much, really.
Feel free to miss out. It's not that incredible.

AI-Based Analysis to Find What We Missed

Introduction

AI advances have brought us to a time where much data can be examined by AI systems to 'Find What We Missed'. I have a feeling this is already being done in areas like medical X-Rays, Sports data, Astronomy, materials science, etc. If it is not being done, it should be. Below is a concrete example of this principle as an experimental protocol for the analysis of Cloud Chamber Data to find new particles or new principles of particle behavior and to identify experimental areas that have been missed.

Program Structure for AI-Based Analysis of Cloud Chamber Traces

The advent of artificial intelligence (AI) has revolutionized numerous scientific fields, and particle physics is no exception. One of the most promising applications of AI in this domain is the analysis of cloud chamber traces. Cloud chambers, which detect ionizing particles by the trails they leave behind, have been instrumental in many pivotal discoveries in physics. However, the manual identification and classification of these traces are labor-intensive and prone to human error. By leveraging AI, we can automate this process, significantly enhancing efficiency and accuracy.

This protocol outlines a comprehensive approach to utilizing AI for the identification and analysis of cloud chamber traces. By assembling a vast dataset of cloud chamber images, tagging them with relevant metadata, and employing advanced AI techniques, we aim to not only streamline the identification of known particles but also uncover anomalous traces that could point to new physical phenomena. The ultimate goal is to create a self-improving system that continuously refines its capabilities, bridging the gap between AI and human expertise to drive forward our understanding of the particle world.

1. Data Collection and Assembly

  • Historical Data: Gather and digitize years of cloud chamber images from various experiments and sources.
  • Experimental Parameters: Ensure each image is tagged with detailed metadata, including experimental conditions, particle types expected, energy levels, and other relevant parameters.

2. Image Preprocessing

  • Normalization: Standardize images to a common format and resolution.
  • Noise Reduction: Apply filters to reduce noise and enhance the clarity of the traces.
  • Segmentation: Use AI techniques to segment the images into individual traces for easier analysis.

3. AI Training and Tagging

  • Initial Training: Train convolutional neural networks (CNNs) and other machine learning models on labeled datasets to recognize and classify known particle traces.
  • Automated Tagging: Implement the trained models to automatically tag and identify traces in the assembled dataset.
  • Iterative Improvement: Continuously refine the models with new data and feedback to improve accuracy.

4. Anomaly Detection

  • Trace Removal: After positively identifying known traces, remove them from the images, leaving only unidentified traces.
  • Anomaly Identification: Use anomaly detection algorithms to flag traces that do not match known patterns.
  • Clustering: Apply clustering techniques to group similar unidentified traces together.

5. Characterization and Analysis

  • Pattern Recognition: Use AI to recognize patterns within the unidentified traces and categorize them based on similarities.
  • Hypothesis Generation: Allow the AI to generate hypotheses about the nature of the anomalous traces based on experimental parameters and known physics.
  • Human Review: Present the most intriguing or consistent anomalies to human experts for further investigation and interpretation.

6. Experiment Planning and Execution

  • Gap Analysis: Identify gaps in the dataset where certain experimental conditions are underrepresented or missing.
  • Experiment Design: Design new experiments to fill these gaps, informed by the patterns and anomalies identified by the AI.
  • Feedback Loop: Use the results from these new experiments to further train and refine the AI models.

Benefits and Potential Outcomes

  • Discoveries: Potential identification of new particles or unknown physical phenomena.
  • Efficiency: Significantly reduce the time and effort required for manual trace identification.
  • Comprehensive Understanding: Gain a more detailed and comprehensive understanding of particle interactions and behaviors.
  • Continuous Improvement: Create a self-improving system where AI and human expertise continuously enhance each other.

Challenges and Considerations

  • Data Quality: Ensuring the quality and consistency of the historical and newly collected data is critical.
  • Model Accuracy: Continuously validating and improving the accuracy of the AI models to prevent false positives/negatives.
  • Interdisciplinary Collaboration: Close collaboration between AI experts and particle physicists is essential for interpreting results and guiding further research.

By implementing such a program, the integration of AI into the analysis of cloud chamber traces could lead to significant advancements in our understanding of particle physics and potentially uncover new and unexpected phenomena.

Thursday, May 16, 2024

Only UBI Will Do

It concerns me that the drive for the critically needed Universal Basic Income is confused and confounded by appeals to 'Basic Income' without the essential 'Universal' aspect that makes it universal. 

As debates around economic policies intensify, it is crucial to distinguish between genuine Universal Basic Income (UBI) and other forms of basic income that fall short of addressing the fundamental issues. The following letter to the NDP, who recently advocated for a Guaranteed Livable Basic Income (GLBI), underscores why only UBI can effectively provide the security and equality we need in a rapidly evolving technological landscape. If it does not say "Universal," it is not UBI. Below is my letter to Leah Gazan of the NDP, which is followed by an analysis of why a truly universal approach is essential for our future. If you find yourself moved to add your voice, her site is here: https://www.leahgazan.ca/, and her email for this issue is: [email protected]

Letter sent to Leah Gazan

Dear Leah,

I hope this message finds you well. I am writing to share my thoughts on Bill C-223, the National Framework for a Guaranteed Livable Basic Income Act. While I appreciate the NDP’s efforts to address poverty and economic inequality, I believe the proposed Guaranteed Livable Basic Income (GLBI) falls short of what is truly needed.

Concerns with GLBI

The concept of "Basic Income" is already part of our current system, albeit flawed and inefficient. The fundamental issue with means-tested programs is that they inherently exclude people. By design, means testing filters out individuals who, for various reasons, do not meet the criteria. This approach often leaves those in need without support and creates a labyrinth of bureaucracy that is costly to administer and maintain.

The Case for Universal Basic Income (UBI)

What Canada needs is a Universal Basic Income (UBI) – an income that every Canadian receives. This ensures several critical outcomes:

  1. Inclusivity: Every individual who needs support receives it without the barriers of means testing.
  2. Efficiency: Those who do not need the income will have it clawed back through the existing tax system, making the net fiscal impact manageable.
  3. Cost-Effective Administration: UBI can be implemented with minimal administrative overhead, leveraging our existing tax infrastructure and recent innovations in direct deposit systems, as evidenced by the CERB program.

Implementation and Cultural Shift

UBI is not complicated legislation. It can be rolled out rapidly, with initial payments beginning shortly after the bill’s passage. Long-term implementation will involve adjustments in tax brackets, cooperation with provincial and territorial governments, and consideration of withholding mechanisms.

However, UBI alone is not a panacea. It must be complemented by Universal Basic Services (UBS) to cover extraordinary costs for people with disabilities, healthcare, and access to public utilities. Implementing UBI and UBS requires a cultural shift. With the rapid advancement of AI and automation, many jobs will be lost, and the traditional notion that people must "earn their way" will become increasingly untenable. UBI should be seen as a "prosperity dividend," reflecting our shared ownership of the nation’s wealth.

The Problem with GLBI

The current push for GLBI risks diverting energy and resources from the goal of implementing a true UBI. This initiative may create a sense of complacency, leading to statements like:

  • "We are working on the GBI, so there is no need for UBI."
  • "We passed the GBI, so the problem is solved."

Legislation tweaking our existing social safety nets can be easily overturned, and the guarantees of GLBI could be undermined by future policies. In contrast, UBI, once established, would be challenging to dismantle as it benefits everyone, making it politically resilient.

Conclusion

A genuine UBI will provide a lasting solution to poverty and economic insecurity, ensuring that all Canadians can share in the country’s prosperity. I urge the NDP to reconsider its focus and advocate for a truly universal and inclusive basic income system.

Thank you for your time and consideration.

Sincerely,

Bob Trower

Only UBI Will Do – If It's Not Universal, It's Not UBI

The concept of Universal Basic Income (UBI) has garnered increasing attention as a potential solution to growing economic inequality and the impending challenges posed by AI and automation. However, not all proposals that claim to offer a "basic income" meet the criteria needed to genuinely address these issues. Here’s why only a truly universal approach will suffice:

1. Inclusivity and Simplicity

The core of UBI lies in its universality. Every individual, regardless of their financial situation, receives a fixed income. This approach ensures that:

  • Everyone Who Needs It Gets It: Means testing and conditional income supports are complex and often fail to reach those in need. UBI eliminates these barriers, guaranteeing support for all.
  • Natural Clawback: Higher-income individuals will effectively return their UBI through the tax system, ensuring that the wealth distribution is equitable without the need for complex administrative processes.
  • Minimal Administrative Costs: A universal approach drastically reduces the costs associated with administering and monitoring eligibility for various welfare programs.

2. Economic Security in an Automated Future

With AI and automation set to transform the workforce, UBI provides a safety net that allows individuals to adapt without the immediate pressure of financial instability. As jobs evolve or disappear, UBI can ensure:

  • Stability During Transition: People can upskill or transition to new roles without the fear of losing their livelihood.
  • Encouragement of Entrepreneurship: Financial security can foster innovation, as individuals are more likely to take entrepreneurial risks when their basic needs are guaranteed.

3. Reframing Entitlements to Wealth and Power

Our current economic system is rooted in outdated notions of ownership and entitlement, which can become dangerously concentrated in a rapidly automating world. UBI represents a shift towards recognizing that:

  • Wealth Should Be Shared: In a society where automation generates most wealth, it is imperative to distribute this wealth more equitably.
  • Preventing Concentration of Power: Without intervention, AI and automation could lead to extreme wealth concentration. UBI helps mitigate this risk by ensuring a baseline of economic power for all citizens.

4. Practical Implementation and the Road Ahead

A realistic phase-in plan over 60 months, starting with $300 per month and increasing by 2.5% monthly, can help manage the transition:

  • Gradual Implementation: Starting with modest payments and scaling up allows for adjustments and minimizes the initial fiscal impact.
  • Administrative Adjustments: Simplifying and automating government services can free up resources to support UBI, while retraining civil servants to manage more complex tasks.
  • Employment Opportunities: Even with UBI, people can and will seek employment to supplement their income, fostering a dynamic and resilient economy.

Conclusion

In conclusion, while the idea of a Guaranteed Livable Basic Income (GLBI) might seem appealing, it does not address the fundamental issues that UBI aims to solve. Only a universal, unconditional income can provide the economic security and fairness needed in our rapidly changing world. As we move forward, it is essential to focus our efforts on implementing a genuine UBI that ensures no one is left behind.

By advocating for a truly universal approach, we can create a fairer, more secure future for all Canadians, regardless of the economic challenges that lie ahead.

AI Alignment and Security Now!

As far as I know, people charged with 'Alignment' at AI companies are not convinced things are safe enough and some have quit becaus...