🧠 Is Predicting Existential Risks From Advanced AI Models Pointless?

Subscribe | AI Toolkit | Meet The Team

Welcome back AI prodigies!

In today’s Sunday Special:

• 🧨An AI-Powered Catastrophic Event, Described.

• 📊How Do Policymakers Predict the Odds?

• 🔍Challenges of Inductive Estimation Methods

• 📖Shortcomings of Deductive Estimation Methods

• 🥽Limitations of Subjective Estimation Methods

• 🔑Key Takeaway

Read Time: 7 minutes

🎓Key Terms

• Cyberattacks: An intentional effort to steal, expose, or disable data or gain unauthorized access to computers and networks.

• Base Rate: The underlying probability of an event occurring without considering any other factors.

• Reference Class: A method for predicting the future by looking at similar past situations and their outcomes.

• Generative AI (GenAI): Uses AI models pre-trained on text, image, audio, video, or code data to generate new content.

• Artificial General Intelligence (AGI): A theoretical concept where AI systems achieve human-level learning, perception, and cognitive ability.

🩺 PULSE CHECK

🧨AN AI-POWERED CATASTROPHIC EVENT, DESCRIBED.

The possibility of existential risks from advanced AI models has garnered significant attention in recent years. Tech billionaire Elon Musk explained: “We’re building advanced AI models much smarter than we can comprehend. It’s like having a child that’s a super genius you know will be smarter than you.”

One frequently discussed threat is AI-powered Cyberattacks, which automate and enhance traditional Cyberattacks by making them more targeted, sophisticated, and challenging to detect.

What Is the Use Case?

For example, OpenAI’s ChatGPT has given bad actors the ability to craft, test, and send highly personalized Spear Phishing emails that exploit a person’s “routines, interests, or characteristics” to access their sensitive information.

That’s just the tip of the iceberg. In the future, AI-powered Cyberattacks will autonomously perform:

• Obfuscation: Employ techniques such as rerouting commands through multiple compromised computers to avoid being traced.

• Malware Creation: Deploy AI-powered algorithms to design malware tailored to exploit the weaknesses of specific computers and networks.

• Execution: Shut down critical elements of computers and networks to impact essential infrastructure like power grids or transportation systems (e.g., air traffic control).

What Are the Actual Chances?

Earlier last year, the U.S. Department of State collaborated with Gladstone AI to develop an action plan outlining catastrophic national security risks posed by advanced AI models. It detailed several AI-powered threats, including AI-powered Cyberattacks on America’s power grids. But what are the actual chances of this happening?

📊HOW DO POLICYMAKERS PREDICT THE ODDS?

Policymakers often rely on estimation methods provided by statisticians. However, the reliability of these estimates is questionable, raising concerns about their utility in informing policy decisions. Assigning precise probabilities to complex, unprecedented events like AI-powered Cyberattacks on America’s power grids can create a false sense of certainty. Estimation Methods often rest on speculative assumptions about advanced AI models and how bad actors may deploy them irresponsibly. Such predictions can obscure the issue’s complexity, causing overconfidence and misallocating resources to combat the wrong threats.

Nevertheless, Pseudo-Quantification, or generating error-prone percentages, is a dominant approach to estimating the probability of catastrophic events. For example, predicting a 5% chance of global conflict within the next 5 years.

Policymakers and statisticians use inductive, deductive, and subjective estimation methods to determine the possibility of catastrophic events occurring in the future. Today, we’ll focus on how each estimation method prevents statisticians from generating accurate and actionable assessments of existential risks from advanced AI models.

🔍CHALLENGES OF INDUCTIVE ESTIMATION METHODS

Inductive estimation methods rely on historical data to predict future catastrophic events. For example, meteorologists predict the likelihood of a hurricane hitting a specific area in weather forecasts by analyzing past hurricane patterns and historical weather data. However, existential risks from advanced AI models lack a relevant Reference Class.

Unlike hurricane prediction, which builds on decades of empirical data, there’s no precedent for catastrophic events perpetrated by advanced AI models because the Base Rate for human extinction is extremely low. In the early 2000s, some feared that the Large Hadron Collider (LHC), a machine that accelerates and collides tiny particles for scientific research, could create miniature Black Holes: areas of gravity from which nothing can escape. So, some feared that a small Black Hole created by LHC could destroy the Earth. These fears were based on speculative predictions without historical precedent, and no such black holes appeared. Despite capturing the public imagination, the LHC incident demonstrated the importance of a Reference Class and a Base Rate when estimating the probability of a catastrophic event.

In other inductive estimation efforts, statisticians compare advanced AI models to nuclear weapon development, as both present existential risks. However, these comparisons break down when considering the decentralized nature of AI-powered ecosystems. During the Cold War, nuclear technology was controlled by two state actors: the United States (U.S.) and the Soviet Union (U.S.S.R.). In contrast, cutting-edge AI research occurs globally, often in open-source environments. Thus, many AI firms and governments control critical AI resources, making predictability more difficult.

📖SHORTCOMINGS OF DEDUCTIVE ESTIMATION METHODS

Deductive estimation methods involve deriving probabilities from logical reasoning based on existing knowledge. When it comes to existential risks from advanced AI models, this approach requires detailing how AI developments will unfold, their interactions with society, and their unintended consequences. Creating such comprehensive guesses is nearly impossible due to the unpredictability of technical advances and societal responses.

Consider Tesla’s Full Self-Driving (FSD) program. Despite extensive frameworks of traffic rules, urban environments, and driver behavior, the program faces frequent challenges from Edge Cases: unexpected situations not covered by training data, such as unusual road markings, debris on highways, or emergency vehicles parked irregularly. These novel driving conditions have led to FSD malfunctions and crashes, highlighting the difficulty in anticipating every possible scenario in real-world driving environments. Similarly, deductive estimation methods for predicting existential risk from advanced AI models are vulnerable to such unpredictable variables. Thus, they’re inherently incomplete forecasts of what might happen.

🥽LIMITATIONS OF SUBJECTIVE ESTIMATION METHODS

Subjective estimation methods lean on personal judgment to assign probabilities to outcomes. However, these forecasts usually reflect the biases, assumptions, and perspectives of the person making them.

For example, AI researchers sometimes forecast high probabilities for scenarios where an unaligned AI system optimizes for harmful goals, such as maximizing resource consumption at the expense of human life. These forecasts depend heavily on unscientific assumptions, such as the inevitability of a single AGI system gaining uncontested dominance. Critics argue these predictions often overestimate the probability of such outcomes as they don’t fully consider human intervention or regulatory constraints.

Without empirical data to validate such claims, subjective estimation methods become more reflective of personal beliefs than objective truths. Policymakers relying on these forecasts may inadvertently prioritize fringe scenarios over more pressing concerns like GenAI misuse through disinformation campaigns on social media platforms.

🔑KEY TAKEAWAY

Assessing existential risks from advanced AI models is filled with challenges that undermine the reliability of estimation methods.

Inductive estimation methods fail due to a lack of relevant historical data, such as trying to predict the risk of a black hole consuming Earth. Deductive estimation methods struggle to account for Edge Cases, like in Tesla’s FSD program. Subjective estimation methods are often shaped by personal biases rather than objective realities.

These limitations highlight the need for humility when assessing the probability of existential risks from advanced AI models. Policymakers must prioritize adaptability rather than fixating on uncertain estimation methods when making policy decisions.

📒FINAL NOTE

FEEDBACK

❤️TAIP Review of The Week

REFER & EARN

🎉Your Friends Learn, You Earn!

You currently have 0 referrals, only 1 away from receiving ⚙️Ultimate Prompt Engineering Guide.

Refer 3 friends to learn how to 👷‍♀️Build Custom Versions of OpenAI’s ChatGPT.

Copy and paste this link to friends: https://theaipulse.beehiiv.com/subscribe?ref=PLACEHOLDER