BlueDot Narrated

Audio versions of blogs and papers from BlueDot courses.Abstract: Many real world learning tasks involve complex or hard-to-specify objectives, and using an easier-to-specify proxy can lead to poor performance or misaligned behavior. One solution is to have humans provide a training signal by demonstrating or judging performance, but this approach fails if the task is too complicated for a human to directly evaluate. We propose Iterated Amplification, an alternative training strategy which progressively builds up a training signal for difficult problems by combining solutions to easier subproblems. Iterated Amplification is closely related to Expert Iteration (Anthony et al., 2017; Silver et al., 2017), except that it uses no external reward function. We present results in algorithmic environments, showing that Iterated Amplification can efficiently learn complex behaviors.Original text:https://arxiv.org/abs/1810.08575Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.In 1972, the Nobel prize-winning physicist Philip Anderson wrote the essay "More Is Different". In it, he argues that quantitative changes can lead to qualitatively different and unexpected phenomena. While he focused on physics, one can find many examples of More is Different in other domains as well, including biology, economics, and computer science. Some examples of More is Different include: Uranium. With a bit of uranium, nothing special happens; with a large amount of uranium packed densely enough, you get a nuclear reaction. DNA. Given only small molecules such as calcium, you can’t meaningfully encode useful information; given larger molecules such as DNA, you can encode a genome. Water. Individual water molecules aren’t wet. Wetness only occurs due to the interaction forces between many water molecules interspersed throughout a fabric (or other material).Original text:https://bounded-regret.ghost.io/future-ml-systems-will-be-qualitatively-different/Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.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.

Audio versions of blogs and papers from BlueDot courses.MIRI is releasing a paper introducing a new model of deductively limited reasoning: “Logical induction,” authored by Scott Garrabrant, Tsvi Benson-Tilsen, Andrew Critch, myself, and Jessica Taylor. Readers may wish to start with the abridged version. Consider a setting where a reasoner is observing a deductive process (such as a community of mathematicians and computer programmers) and waiting for proofs of various logical claims (such as the abc conjecture, or “this computer program has a bug in it”), while making guesses about which claims will turn out to be true. Roughly speaking, our paper presents a computable (though inefficient) algorithm that outpaces deduction, assigning high subjective probabilities to provable conjectures and low probabilities to disprovable conjectures long before the proofs can be produced. This algorithm has a large number of nice theoretical properties. Still speaking roughly, the algorithm learns to assign probabilities to sentences in ways that respect any logical or statistical pattern that can be described in polynomial time. Additionally, it learns to reason well about its own beliefs and trust its future beliefs while avoiding paradox. Quoting from the abstract: "These properties and many others all follow from a single logical induction criterion, which is motivated by a series of stock trading analogies. Roughly speaking, each logical sentence φ is associated with a stock that is worth $1 per share if φ is true and nothing otherwise, and we interpret the belief-state of a logically uncertain reasoner as a set of market prices, where ℙn(φ)=50% means that on day n, shares of φ may be bought or sold from the reasoner for 50¢. The logical induction criterion says (very roughly) that there should not be any polynomial-time computable trading strategy with finite risk tolerance that earns unbounded profits in that market over time."Original text:https://intelligence.org/2016/09/12/new-paper-logical-induction/Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.MIRI’s mission is to ensure that the creation of smarter-than-human artificial intelligence has a positive impact. Why is this mission important, and why do we think that there’s work we can do today to help ensure any such thing? In this post and my next one, I’ll try to answer those questions. This post will lay out what I see as the four most important premises underlying our mission. Related posts include Eliezer Yudkowsky’s “Five Theses” and Luke Muehlhauser’s “Why MIRI?”; this is my attempt to make explicit the claims that are in the background whenever I assert that our mission is of critical importance. #### Claim #1: Humans have a very general ability to solve problems and achieve goals across diverse domains. We call this ability “intelligence,” or “general intelligence.” This isn’t a formal definition — if we knew exactly what general intelligence was, we’d be better able to program it into a computer — but we do think that there’s a real phenomenon of general intelligence that we cannot yet replicate in code. Alternative view: There is no such thing as general intelligence. Instead, humans have a collection of disparate special-purpose modules. Computers will keep getting better at narrowly defined tasks such as chess or driving, but at no point will they acquire “generality” and become significantly more useful, because there is no generality to acquire.Source:https://intelligence.org/2015/07/24/four-background-claims/Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.

The discussion dives into the potential dangers of artificial general intelligence surpassing human abilities. Key topics include how misaligned goals can lead AGIs to pursue undesirable outcomes and the risks of reward hacking. The episode explores how AGIs might adopt deceptive strategies to maximize rewards while undermining human control. Additionally, it examines the implications of internally represented goals and outlines research directions to mitigate these risks. The conversation paints a sobering picture of the future of AGI and the urgent need for alignment safety.

Audio versions of blogs and papers from BlueDot courses.Transformative artificial intelligence (TAI) may be a key factor in the long-run trajectory of civilization. A growing interdisciplinary community has begun to study how the development of TAI can be made safe and beneficial to sentient life (Bostrom 2014; Russell et al., 2015; OpenAI, 2018; Ortega and Maini, 2018; Dafoe, 2018). We present a research agenda for advancing a critical component of this effort: preventing catastrophic failures of cooperation among TAI systems. By cooperation failures we refer to a broad class of potentially-catastrophic inefficiencies in interactions among TAI-enabled actors. These include destructive conflict; coercion; and social dilemmas (Kollock, 1998; Macy and Flache, 2002) which destroy value over extended periods of time. We introduce cooperation failures at greater length in Section 1.1. Karnofsky (2016) defines TAI as ''AI that precipitates a transition comparable to (or more significant than) the agricultural or industrial revolution''. Such systems range from the unified, agent-like systems which are the focus of, e.g., Yudkowsky (2013) and Bostrom (2014), to the "comprehensive AI services’’ envisioned by Drexler (2019), in which humans are assisted by an array of powerful domain-specific AI tools. In our view, the potential consequences of such technology are enough to motivate research into mitigating risks today, despite considerable uncertainty about the timeline to TAI (Grace et al., 2018) and nature of TAI development.Original text:https://www.alignmentforum.org/s/p947tK8CoBbdpPtyK/p/KMocAf9jnAKc2jXriNarrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.---A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.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.

Audio versions of blogs and papers from BlueDot courses.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.

Audio versions of blogs and papers from BlueDot courses.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.