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A Bird's Eye View of the ML Field [Pragmatic AI Safety #2]

This is the second post in a sequence of posts that describe our models for Pragmatic AI Safety. The internal dynamics of the ML field are not immediately obvious to the casual observer. This post will present some important high-level points that are critical to beginning to understand the field, and is meant as background for our later posts. Driving dynamics of the ML field How is progress made in ML? While the exact dynamics of progress are not always predictable, we will present three basic properties of ML research that are important to understand. The importance of defining the problem A problem well-defined is a problem half solved. —John Dewey (apocryphal) The mere formulation of a problem is often more essential than its solution, which [...] requires creative imagination and marks real advances in science. —Albert Einstein I have been struck by how important measurement is... This may seem basic, but it is amazing how often it is not done and how hard it is to get right. —Bill Gates If you cannot measure it, you cannot improve it. —Lord Kelvin (paraphrase) For better or worse, benchmarks shape a field. —David Patterson, Turing award winner Progress in AI arises from objective evaluation metrics. —David McAllester Science requires that we clarify the question and then refine the answer: it is impossible to solve a problem until we know what it is. Empirical ML research, which is the majority of the field, progresses through well-defined metrics for progress towards well-defined goals. Once a goal is defined empirically, is tractable, and is incentivized properly, the ML field is well-equipped to make progress towards it. A variation on this model is that artists (writers, directors, etc.) come first. They help give ideas, and philosophers add more logical constraints to those ideas to come up with goals or questions, and finally scientists can help make iterative progress towards those goals. To give an example: golems, animate being

165May 9, 2022

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Risks from AI Overview: Summary

We’ve recently published on our website a summary of our paper on catastrophic risks from AI, which we are cross-posting here. We hope that this summary helps to make our research more accessible and to share our policy recommendations in a more convenient format. (Previously we had a smaller summary...

Aug 18, 202325

Catastrophic Risks from AI #6: Discussion and FAQ

This is the final post in a sequence of posts giving an overview of catastrophic AI risks. 6 Discussion of Connections Between Risks So far, we have considered four sources of AI risk separately, but they also interact with each other in complex ways. We give some examples to illustrate...

Jun 27, 202325

Catastrophic Risks from AI #5: Rogue AIs

This is the fifth post in a sequence of posts giving an overview of catastrophic AI risks. Rogue AIs So far, we have discussed three hazards of AI development: environmental competitive pressures driving us to a state of heightened risk, malicious actors leveraging the power of AIs to pursue negative...

Jun 27, 202315

Catastrophic Risks from AI #4: Organizational Risks

This is the fourth post in a sequence of posts giving an overview of catastrophic AI risks. 4 Organizational Risks In January 1986, tens of millions of people tuned in to watch the launch of the Challenger Space Shuttle. Approximately 73 seconds after liftoff, the shuttle exploded, resulting in the...

Jun 26, 202323

Catastrophic Risks from AI #3: AI Race

This is the third post in a sequence of posts giving an overview of catastrophic AI risks. 3 AI Race The immense potential of AIs has created competitive pressures among global players contending for power and influence. This “AI race” is driven by nations and corporations who feel they must...

Jun 23, 202318

Catastrophic Risks from AI #2: Malicious Use

This is the second post in a sequence of posts giving an overview of catastrophic AI risks. 2 Malicious Use On the morning of March 20, 1995, five men entered the Tokyo subway system. After boarding separate subway lines, they continued for several stops before dropping the bags they were...

Jun 22, 202338

Catastrophic Risks from AI #1: Introduction

This is the first post in a sequence of posts giving an overview of catastrophic AI risks. Abstract Rapid advancements in artificial intelligence (AI) have sparked growing concerns among experts, policymakers, and world leaders regarding the potential for increasingly advanced AI systems to pose catastrophic risks. Although numerous risks have...

Jun 22, 202340

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A Bird's Eye View of the ML Field [Pragmatic AI Safety #2]

A Year of AI Increasing AI Progress

Did ChatGPT just gaslight me?

Examples of AI Increasing AI Progress

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Risks from AI Overview: Summary

Catastrophic Risks from AI #6: Discussion and FAQ

Catastrophic Risks from AI #5: Rogue AIs

Catastrophic Risks from AI #4: Organizational Risks

Catastrophic Risks from AI #3: AI Race

Catastrophic Risks from AI #2: Malicious Use

Catastrophic Risks from AI #1: Introduction

A Bird's Eye View of the ML Field [Pragmatic AI Safety #2]

A Year of AI Increasing AI Progress

Did ChatGPT just gaslight me?

Examples of AI Increasing AI Progress

Risks from AI Overview: Summary

Catastrophic Risks from AI #6: Discussion and FAQ

Catastrophic Risks from AI #5: Rogue AIs

Catastrophic Risks from AI #4: Organizational Risks

Catastrophic Risks from AI #3: AI Race

Catastrophic Risks from AI #2: Malicious Use

Catastrophic Risks from AI #1: Introduction