AI Safety Fundamentals

Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.The stereotyped image of AI catastrophe is a powerful, malicious AI system that takes its creators by surprise and quickly achieves a decisive advantage over the rest of humanity.I think this is probably not what failure will look like, and I want to try to paint a more realistic picture. I’ll tell the story in two parts:Part I: machine learning will increase our ability to “get what we can measure,” which could cause a slow-rolling catastrophe. ("Going out with a whimper.")Part II: ML training, like competitive economies or natural ecosystems, can give rise to “greedy” patterns that try to expand their own influence. Such patterns can ultimately dominate the behavior of a system and cause sudden breakdowns. ("Going out with a bang," an instance of optimization daemons.) I think these are the most important problems if we fail to solve intent alignment.In practice these problems will interact with each other, and with other disruptions/instability caused by rapid progress. These problems are worse in worlds where progress is relatively fast, and fast takeoff can be a key risk factor, but I’m scared even if we have several years.Crossposted from the LessWrong Curated Podcast by TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

Our goal here is to popularize obscure and hard-to-understand areas of AI alignment.So let’s try to understand the incomprehensible meme! Our main source will be Hubinger et al 2019, Risks From Learned Optimization In Advanced Machine Learning Systems.Mesa- is a Greek prefix which means the opposite of meta-. To “go meta” is to go one level up; to “go mesa” is to go one level down (nobody has ever actually used this expression, sorry). So a mesa-optimizer is an optimizer one level down from you.Consider evolution, optimizing the fitness of animals. For a long time, it did so very mechanically, inserting behaviors like “use this cell to detect light, then grow toward the light” or “if something has a red dot on its back, it might be a female of your species, you should mate with it”. As animals became more complicated, they started to do some of the work themselves. Evolution gave them drives, like hunger and lust, and the animals figured out ways to achieve those drives in their current situation. Evolution didn’t mechanically instill the behavior of opening my fridge and eating a Swiss Cheese slice. It instilled the hunger drive, and I figured out that the best way to satisfy it was to open my fridge and eat cheese.Source:https://astralcodexten.substack.com/p/deceptively-aligned-mesa-optimizersCrossposted from the Astral Codex Ten podcast.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.Original article:https://arxiv.org/abs/2108.07258Authors:Bommasani et al.A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

The two tasks of supervised learning: regression and classification. Linear regression, loss functions, and gradient descent.How much money will we make by spending more dollars on digital advertising? Will this loan applicant pay back the loan or not? What’s going to happen to the stock market tomorrow?Original article:https://medium.com/machine-learning-for-humans/supervised-learning-740383a2feabAuthor:Vishal MainiA podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

As we build increasingly advanced AI systems, we want to make sure they don’t pursue undesired goals. This is the primary concern of the AI alignment community. Undesired behaviour in an AI agent is often the result of specification gaming —when the AI exploits an incorrectly specified reward. However, if we take on the perspective of the agent we’re training, we see other reasons it might pursue undesired goals, even when trained with a correct specification. Imagine that you are the agent (the blue blob) being trained with reinforcement learning (RL) in the following 3D environment: The environment also contains another blob like yourself, but coloured red instead of blue, that also moves around. The environment also appears to have some tower obstacles, some coloured spheres, and a square on the right that sometimes flashes. You don’t know what all of this means, but you can figure it out during training! You start exploring the environment to see how everything works and to see what you do and don’t get rewarded for.For more details, check out our paper. By Rohin Shah, Vikrant Varma, Ramana Kumar, Mary Phuong, Victoria Krakovna, Jonathan Uesato, and Zac Kenton.Original text:https://deepmindsafetyresearch.medium.com/goal-misgeneralisation-why-correct-specifications-arent-enough-for-correct-goals-cf96ebc60924Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

According to the orthogonality thesis, intelligent agents may have an enormous range of possible final goals. Nevertheless, according to what we may term the “instrumental convergence” thesis, there are some instrumental goals likely to be pursued by almost any intelligent agent, because there are some objectives that are useful intermediaries to the achievement of almost any final goal. We can formulate this thesis as follows:The instrumental convergence thesis:"Several instrumental values can be identified which are convergent in the sense that their attainment would increase the chances of the agent’s goal being realized for a wide range of final goals and a wide range of situations, implying that these instrumental values are likely to be pursued by a broad spectrum of situated intelligent agents."Original article:https://drive.google.com/file/d/1KewDov1taegTzrqJ4uurmJ2CJ0Y72EU3/viewAuthor:Nick BostromA podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

In this post, we’ll present ARC’s approach to an open problem we think is central to aligning powerful machine learning (ML) systems: Suppose we train a model to predict what the future will look like according to cameras and other sensors. We then use planning algorithms to find a sequence of actions that lead to predicted futures that look good to us.But some action sequences could tamper with the cameras so they show happy humans regardless of what’s really happening. More generally, some futures look great on camera but are actually catastrophically bad.In these cases, the prediction model “knows” facts (like “the camera was tampered with”) that are not visible on camera but would change our evaluation of the predicted future if we learned them. How can we train this model to report its latent knowledge of off-screen events?We’ll call this problem eliciting latent knowledge (ELK). In this report we’ll focus on detecting sensor tampering as a motivating example, but we believe ELK is central to many aspects of alignment. Source:https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit#Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

This post is about language model scaling laws, specifically the laws derived in the DeepMind paper that introduced Chinchilla. The paper came out a few months ago, and has been discussed a lot, but some of its implications deserve more explicit notice in my opinion. In particular: Data, not size, is the currently active constraint on language modeling performance. Current returns to additional data are immense, and current returns to additional model size are miniscule; indeed, most recent landmark models are wastefully big. If we can leverage enough data, there is no reason to train ~500B param models, much less 1T or larger models. If we have to train models at these large sizes, it will mean we have encountered a barrier to exploitation of data scaling, which would be a great loss relative to what would otherwise be possible. The literature is extremely unclear on how much text data is actually available for training. We may be "running out" of general-domain data, but the literature is too vague to know one way or the other. The entire available quantity of data in highly specialized domains like code is woefully tiny, compared to the gains that would be possible if much more such data were available. Some things to note at the outset: This post assumes you have some familiarity with LM scaling laws. As in the paper, I'll assume here that models never see repeated data in training.Original text:https://www.alignmentforum.org/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implicationsNarrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

This more technical article explains the motivations for a system like RLHF, and adds additional concrete details as to how the RLHF approach is applied to neural networks.While reading, consider which parts of the technical implementation correspond to the 'values coach' and 'coherence coach' from the previous video.A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

Most conversations around the societal impacts of artificial intelligence (AI) come down to discussing some quality of an AI system, such as its truthfulness, fairness, potential for misuse, and so on. We are able to talk about these characteristics because we can technically evaluate models for their performance in these areas. But what many people working inside and outside of AI don’t fully appreciate is how difficult it is to build robust and reliable model evaluations. Many of today’s existing evaluation suites are limited in their ability to serve as accurate indicators of model capabilities or safety.At Anthropic, we spend a lot of time building evaluations to better understand our AI systems. We also use evaluations to improve our safety as an organization, as illustrated by our Responsible Scaling Policy. In doing so, we have grown to appreciate some of the ways in which developing and running evaluations can be challenging.Here, we outline challenges that we have encountered while evaluating our own models to give readers a sense of what developing, implementing, and interpreting model evaluations looks like in practice.Source:https://www.anthropic.com/news/evaluating-ai-systemsNarrated for AI Safety Fundamentals by Perrin WalkerA podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.