We made it— 300 episodes of This Is Woman’s Work ! And we’re marking this milestone by giving you something that could seriously change the game in your business or career: the skill of pitching yourself effectively. Whether you’re dreaming of being a podcast guest, landing a speaking gig, signing a client, or just asking for what you want with confidence—you’re already pitching yourself, every day. But are you doing it well? In this milestone episode, Nicole breaks down exactly how to pitch yourself to be a podcast guest … and actually hear “yes.” With hundreds of pitches landing in her inbox each month, she shares what makes a guest stand out (or get deleted), the biggest mistakes people make, and why podcast guesting is still one of the most powerful ways to grow your reach, authority, and influence. In This Episode, We Cover: ✅ Why we all need to pitch ourselves—and how to do it without feeling gross ✅ The step-by-step process for landing guest spots on podcasts (and more) ✅ A breakdown of the 3 podcast levels: Practice, Peer, and A-List—and how to approach each ✅ The must-haves of a successful podcast pitch (including real examples) ✅ How to craft a pitch that gets read, gets remembered, and gets results Whether you’re new to pitching or want to level up your game, this episode gives you the exact strategy Nicole and her team use to land guest spots on dozens of podcasts every year. Because your voice deserves to be heard. And the world needs what only you can bring. 🎁 Get the FREE Podcast Pitch Checklist + Additional Information on your Practice Group, Peer Group, and A-List Group Strategies: https://nicolekalil.com/podcast 📥 Download The Podcast Pitch Checklist Here Related Podcast Episodes: Shameless and Strategic: How to Brag About Yourself with Tiffany Houser | 298 How To Write & Publish A Book with Michelle Savage | 279 How To Land Your TED Talk and Skyrocket Your Personal Brand with Ashley Stahl | 250 Share the Love: If you found this episode insightful, please share it with a friend, tag us on social media, and leave a review on your favorite podcast platform! 🔗 Subscribe & Review: Apple Podcasts | Spotify | Amazon Music…
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เนื้อหาจัดทำโดย BlueDot Impact เนื้อหาพอดแคสต์ทั้งหมด รวมถึงตอน กราฟิก และคำอธิบายพอดแคสต์ได้รับการอัปโหลดและจัดหาให้โดยตรงจาก BlueDot Impact หรือพันธมิตรแพลตฟอร์มพอดแคสต์ของพวกเขา หากคุณเชื่อว่ามีบุคคลอื่นใช้งานที่มีลิขสิทธิ์ของคุณโดยไม่ได้รับอนุญาต คุณสามารถปฏิบัติตามขั้นตอนที่แสดงไว้ที่นี่ https://th.player.fm/legal
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Introduction to Mechanistic Interpretability
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เนื้อหาจัดทำโดย BlueDot Impact เนื้อหาพอดแคสต์ทั้งหมด รวมถึงตอน กราฟิก และคำอธิบายพอดแคสต์ได้รับการอัปโหลดและจัดหาให้โดยตรงจาก BlueDot Impact หรือพันธมิตรแพลตฟอร์มพอดแคสต์ของพวกเขา หากคุณเชื่อว่ามีบุคคลอื่นใช้งานที่มีลิขสิทธิ์ของคุณโดยไม่ได้รับอนุญาต คุณสามารถปฏิบัติตามขั้นตอนที่แสดงไว้ที่นี่ https://th.player.fm/legal
Our introduction introduces common mech interp concepts, to prepare you for the rest of this session's resources.
Original text: https://aisafetyfundamentals.com/blog/introduction-to-mechanistic-interpretability/
Author(s): Sarah Hastings-Woodhouse
A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.
บท
1. Introduction to Mechanistic Interpretability (00:00:00)
2. Why might mechanistic interpretability be useful? (00:01:16)
3. Looking inside neural networks (00:03:34)
4. What makes mechanistic interpretability hard? (00:06:33)
5. Addressing polysemanticity (00:08:34)
85 ตอน
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Manage episode 458945499 series 3498845
เนื้อหาจัดทำโดย BlueDot Impact เนื้อหาพอดแคสต์ทั้งหมด รวมถึงตอน กราฟิก และคำอธิบายพอดแคสต์ได้รับการอัปโหลดและจัดหาให้โดยตรงจาก BlueDot Impact หรือพันธมิตรแพลตฟอร์มพอดแคสต์ของพวกเขา หากคุณเชื่อว่ามีบุคคลอื่นใช้งานที่มีลิขสิทธิ์ของคุณโดยไม่ได้รับอนุญาต คุณสามารถปฏิบัติตามขั้นตอนที่แสดงไว้ที่นี่ https://th.player.fm/legal
Our introduction introduces common mech interp concepts, to prepare you for the rest of this session's resources.
Original text: https://aisafetyfundamentals.com/blog/introduction-to-mechanistic-interpretability/
Author(s): Sarah Hastings-Woodhouse
A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.
บท
1. Introduction to Mechanistic Interpretability (00:00:00)
2. Why might mechanistic interpretability be useful? (00:01:16)
3. Looking inside neural networks (00:03:34)
4. What makes mechanistic interpretability hard? (00:06:33)
5. Addressing polysemanticity (00:08:34)
85 ตอน
All episodes
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Our introduction introduces common mech interp concepts, to prepare you for the rest of this session's resources. Original text: https://aisafetyfundamentals.com/blog/introduction-to-mechanistic-interpretability/ Author(s): Sarah Hastings-Woodhouse A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
This lays out a number of open questions, in what the author calls a 'Science of Evals'. Original text: https://www.apolloresearch.ai/blog/we-need-a-science-of-evals Author(s): Apollo Research blog 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.…
This paper explains Anthropic’s constitutional AI approach, which is largely an extension on RLHF but with AIs replacing human demonstrators and human evaluators. Everything in this paper is relevant to this week's learning objectives, and we recommend you read it in its entirety. It summarises limitations with conventional RLHF, explains the constitutional AI approach, shows how it performs, and where future research might be directed. If you are in a rush, focus on sections 1.2, 3.1, 3.4, 4.1, 6.1, 6.2. A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
1 Constitutional AI Harmlessness from AI Feedback 1:01:49
1:01:49 
ลิสต์เล่นในภายหลัง 
ลิสต์เล่นในภายหลัง 
ลิสต์ 
ถูกใจ 
ที่ถูกใจแล้ว1:01:49
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วThis paper explains Anthropic’s constitutional AI approach, which is largely an extension on RLHF but with AIs replacing human demonstrators and human evaluators. Everything in this paper is relevant to this week's learning objectives, and we recommend you read it in its entirety. It summarises limitations with conventional RLHF, explains the constitutional AI approach, shows how it performs, and where future research might be directed. If you are in a rush, focus on sections 1.2, 3.1, 3.4, 4.1, 6.1, 6.2. A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
1 Intro to Brain-Like-AGI Safety 1:02:10
1:02:10 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:02:10
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว(Sections 3.1-3.4 , 6.1-6.2 , and 7.1-7.5 ) Suppose we someday build an Artificial General Intelligence algorithm using similar principles of learning and cognition as the human brain. How would we use such an algorithm safely? I will argue that this is an open technical problem, and my goal in this post series is to bring readers with no prior knowledge all the way up to the front-line of unsolved problems as I see them. If this whole thing seems weird or stupid, you should start right in on Post #1 , which contains definitions, background, and motivation. Then Posts #2 – #7 are mainly neuroscience, and Posts #8 – #15 are more directly about AGI safety, ending with a list of open questions and advice for getting involved in the field. Source: https://www.lesswrong.com/s/HzcM2dkCq7fwXBej8 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-implications 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.…
We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don’t yet fully understand why it happens, and view further study of this phenomenon as an important research direction. Source: https://openai.com/research/deep-double-descent 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.…
1 Eliciting Latent Knowledge 1:00:27
1:00:27 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:00:27
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วIn 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.…
Previously, I argued that emergent phenomena in machine learning mean that we can’t rely on current trends to predict what the future of ML will be like. In this post, I will argue that despite this, empirical findings often do generalize very far, including across “phase transitions” caused by emergent behavior. This might seem like a contradiction, but actually I think divergence from current trends and empirical generalization are consistent. Findings do often generalize, but you need to think to determine the right generalization, and also about what might stop any given generalization from holding. I don’t think many people would contest the claim that empirical investigation can uncover deep and generalizable truths. This is one of the big lessons of physics, and while some might attribute physics’ success to math instead of empiricism, I think it’s clear that you need empirical data to point to the right mathematics. However, just invoking physics isn’t a good argument, because physical laws have fundamental symmetries that we shouldn’t expect in machine learning. Moreover, we care specifically about findings that continue to hold up after some sort of emergent behavior (such as few-shot learning in the case of ML). So, to make my case, I’ll start by considering examples in deep learning that have held up in this way. Since “modern” deep learning hasn’t been around that long, I’ll also look at examples from biology, a field that has been around for a relatively long time and where More Is Different is ubiquitous (see Appendix: More Is Different In Other Domains). Source: https://bounded-regret.ghost.io/empirical-findings-generalize-surprisingly-far/ 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.…
Right now I’m working on finding a good objective to optimize with ML, rather than trying to make sure our models are robustly optimizing that objective. (This is roughly “outer alignment.”) That’s pretty vague, and it’s not obvious whether “find a good objective” is a meaningful goal rather than being inherently confused or sweeping key distinctions under the rug. So I like to focus on a more precise special case of alignment: solve alignment when decisions are “low stakes.” I think this case effectively isolates the problem of “find a good objective” from the problem of ensuring robustness and is precise enough to focus on productively. In this post I’ll describe what I mean by the low-stakes setting, why I think it isolates this subproblem, why I want to isolate this subproblem, and why I think that it’s valuable to work on crisp subproblems. Source: https://www.alignmentforum.org/posts/TPan9sQFuPP6jgEJo/low-stakes-alignment Narrated for AI Safety Fundamentals by TYPE III AUDIO . --- A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
Using hard multiple-choice reading comprehension questions as a testbed, we assess whether presenting humans with arguments for two competing answer options, where one is correct and the other is incorrect, allows human judges to perform more accurately, even when one of the arguments is unreliable and deceptive. If this is helpful, we may be able to increase our justified trust in language-model-based systems by asking them to produce these arguments where needed. Previous research has shown that just a single turn of arguments in this format is not helpful to humans. However, as debate settings are characterized by a back-and-forth dialogue, we follow up on previous results to test whether adding a second round of counter-arguments is helpful to humans. We find that, regardless of whether they have access to arguments or not, humans perform similarly on our task. These findings suggest that, in the case of answering reading comprehension questions, debate is not a helpful format. Source: https://arxiv.org/abs/2210.10860 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.…
Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving problems harder than the exemplars shown in the prompts. To overcome this challenge of easy-to-hard generalization, we propose a novel prompting strategy, least-to-most prompting. The key idea in this strategy is to break down a complex problem into a series of simpler subproblems and then solve them in sequence. Solving each subproblem is facilitated by the answers to previously solved subproblems. Our experimental results on tasks related to symbolic manipulation, compositional generalization, and math reasoning reveal that least-to-most prompting is capable of generalizing to more difficult problems than those seen in the prompts. A notable finding is that when the GPT-3 code-davinci-002 model is used with least-to-most prompting, it can solve the compositional generalization benchmark SCAN in any split (including length split) with an accuracy of at least 99% using just 14 exemplars, compared to only 16% accuracy with chain-of-thought prompting. This is particularly noteworthy because neural-symbolic models in the literature that specialize in solving SCAN are trained on the entire training set containing over 15,000 examples. We have included prompts for all the tasks in the Appendix. Source: https://arxiv.org/abs/2205.10625 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 paper presents a technique to scan neural network based AI models to determine if they are trojaned. Pre-trained AI models may contain back-doors that are injected through training or by transforming inner neuron weights. These trojaned models operate normally when regular inputs are provided, and mis-classify to a specific output label when the input is stamped with some special pattern called trojan trigger. We develop a novel technique that analyzes inner neuron behaviors by determining how output acti- vations change when we introduce different levels of stimulation to a neuron. The neurons that substantially elevate the activation of a particular output label regardless of the provided input is considered potentially compromised. Trojan trigger is then reverse-engineered through an optimization procedure using the stimulation analysis results, to confirm that a neuron is truly compromised. We evaluate our system ABS on 177 trojaned models that are trojaned with vari-ous attack methods that target both the input space and the feature space, and have various trojan trigger sizes and shapes, together with 144 benign models that are trained with different data and initial weight values. These models belong to 7 different model structures and 6 different datasets, including some complex ones such as ImageNet, VGG-Face and ResNet110. Our results show that ABS is highly effective, can achieve over 90% detection rate for most cases (and many 100%), when only one input sample is provided for each output label. It substantially out-performs the state-of-the-art technique Neural Cleanse that requires a lot of input samples and small trojan triggers to achieve good performance. Source: https://www.cs.purdue.edu/homes/taog/docs/CCS19.pdf Narrated for AI Safety Fundamentals the Effective Altruism Forum Joseph Carlsmith LessWrong 80,000 Hours by Perrin Walker of TYPE III AUDIO . --- A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
This post tries to explain a simplified version of Paul Christiano’s mechanism introduced here , (referred to there as ‘Learning the Prior’) and explain why a mechanism like this potentially addresses some of the safety problems with naïve approaches. First we’ll go through a simple example in a familiar domain, then explain the problems with the example. Then I’ll discuss the open questions for making Imitative Generalization actually work, and the connection with the Microscope AI idea. A more detailed explanation of exactly what the training objective is (with diagrams), and the correspondence with Bayesian inference, are in the appendix. Source: https://www.alignmentforum.org/posts/JKj5Krff5oKMb8TjT/imitative-generalisation-aka-learning-the-prior-1 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.…
It would be very convenient if the individual neurons of artificial neural networks corresponded to cleanly interpretable features of the input. For example, in an “ideal” ImageNet classifier, each neuron would fire only in the presence of a specific visual feature, such as the color red, a left-facing curve, or a dog snout. Empirically, in models we have studied, some of the neurons do cleanly map to features. But it isn't always the case that features correspond so cleanly to neurons, especially in large language models where it actually seems rare for neurons to correspond to clean features. This brings up many questions. Why is it that neurons sometimes align with features and sometimes don't? Why do some models and tasks have many of these clean neurons, while they're vanishingly rare in others? In this paper, we use toy models — small ReLU networks trained on synthetic data with sparse input features — to investigate how and when models represent more features than they have dimensions. We call this phenomenon superposition . When features are sparse, superposition allows compression beyond what a linear model would do, at the cost of "interference" that requires nonlinear filtering. 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.…
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AI Safety Fundamentals: Alignment
Abstract: Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect. We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a method for accurately answering yes-no questions given only unlabeled model activations. It works by finding a direction in activation space that satisfies logical consistency properties, such as that a statement and its negation have opposite truth values. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it outperforms zero-shot accuracy by 4\\% on average. We also find that it cuts prompt sensitivity in half and continues to maintain high accuracy even when models are prompted to generate incorrect answers. Our results provide an initial step toward discovering what language models know, distinct from what they say, even when we don't have access to explicit ground truth labels. Original text: https://arxiv.org/abs/2212.03827 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.…
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity. For an RL agent to address these challenges, it is essential that it can plan effectively. Prior work has typically utilized an explicit model of the environment, combined with a specific planning algorithm (such as tree search). More recently, a new family of methods have been proposed that learn how to plan, by providing the structure for planning via an inductive bias in the function approximator (such as a tree structured neural network), trained end-to-end by a model-free RL algorithm. In this paper, we go even further, and demonstrate empirically that an entirely model-free approach, without special structure beyond standard neural network components such as convolutional networks and LSTMs, can learn to exhibit many of the characteristics typically associated with a model-based planner. We measure our agent’s effectiveness at planning in terms of its ability to generalize across a combinatorial and irreversible state space, its data efficiency, and its ability to utilize additional thinking time. We find that our agent has many of the characteristics that one might expect to find in a planning algorithm. Furthermore, it exceeds the state-of-the-art in challenging combinatorial domains such as Sokoban and outperforms other model-free approaches that utilize strong inductive biases toward planning. Source: https://arxiv.org/abs/1901.03559 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.…
Gradient hacking is a hypothesized phenomenon where: A model has knowledge about possible training trajectories which isn’t being used by its training algorithms when choosing updates (such as knowledge about non-local features of its loss landscape which aren’t taken into account by local optimization algorithms). The model uses that knowledge to influence its medium-term training trajectory, even if the effects wash out in the long term. Below I give some potential examples of gradient hacking, divided into those which exploit RL credit assignment and those which exploit gradient descent itself. My concern is that models might use techniques like these either to influence which goals they develop, or to fool our interpretability techniques. Even if those effects don’t last in the long term, they might last until the model is smart enough to misbehave in other ways (e.g. specification gaming , or reward tampering ), or until it’s deployed in the real world—especially in the RL examples, since convergence to a global optimum seems unrealistic (and ill-defined) for RL policies trained on real-world data. However, since gradient hacking isn’t very well-understood right now, both the definition above and the examples below should only be considered preliminary. Source: https://www.alignmentforum.org/posts/EeAgytDZbDjRznPMA/gradient-hacking-definitions-and-examples 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 article explains key drivers of AI progress, explains how compute is calculated, as well as looks at how the amount of compute used to train AI models has increased significantly in recent years. Original text: https://epochai.org/blog/compute-trends Author(s): Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos. A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
Alternative title: “When should you assume that what could go wrong, will go wrong?” Thanks to Mary Phuong and Ryan Greenblatt for helpful suggestions and discussion, and Akash Wasil for some edits. In discussions of AI safety, people often propose the assumption that something goes as badly as possible. Eliezer Yudkowsky in particular has argued for the importance of security mindset when thinking about AI alignment. I think there are several distinct reasons that this might be the right assumption to make in a particular situation. But I think people often conflate these reasons, and I think that this causes confusion and mistaken thinking. So I want to spell out some distinctions. Throughout this post, I give a bunch of specific arguments about AI alignment, including one argument that I think I was personally getting wrong until I noticed my mistake yesterday (which was my impetus for thinking about this topic more and then writing this post). I think I’m probably still thinking about some of my object level examples wrong, and hope that if so, commenters will point out my mistakes. Original text: https://www.lesswrong.com/posts/yTvBSFrXhZfL8vr5a/worst-case-thinking-in-ai-alignment 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.…
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AI Safety Fundamentals: Alignment
I’ve been obsessed with managing information, and communications in a remote team since Get on Board started growing. Reducing the bus factor is a primary motivation — but another just as important is diminishing reliance on synchronicity . When what I know is documented and accessible to others, I’m less likely to be a bottleneck for anyone else in the team . So if I’m busy, minding family matters, on vacation, or sick, I won’t be blocking anyone. This, in turn, gives everyone in the team the freedom to build their own work schedules according to their needs , work from any time zone, or enjoy more distraction-free moments. As I write these lines, most of the world is under quarantine, relying on non-stop video calls to continue working. Needless to say, that is not a sustainable long-term work schedule. Original text: https://www.getonbrd.com/blog/public-by-default-how-we-manage-information-visibility-at-get-on-board Author: Sergio Nouvel A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
Feedback is essential for learning. Whether you’re studying for a test, trying to improve in your work or want to master a difficult skill, you need feedback. The challenge is that feedback can often be hard to get. Worse, if you get bad feedback, you may end up worse than before. Original text: https://www.scotthyoung.com/blog/2019/01/24/how-to-get-feedback/ Author: Scott Young A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
(In the process of answering an email, I accidentally wrote a tiny essay about writing. I usually spend weeks on an essay. This one took 67 minutes—23 of writing, and 44 of rewriting.) Original text: https://paulgraham.com/writing44.html Author: Paul Graham A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
This introduces the concept of Pareto frontiers. The top comment by Rob Miles also ties it to comparative advantage. While reading, consider what Pareto frontiers your project could place you on. Original text: https://www.lesswrong.com/posts/XvN2QQpKTuEzgkZHY/being-the-pareto-best-in-the-world Author: John Wentworth A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
I am approaching the end of my AI governance PhD, and I’ve spent about 2.5 years as a researcher at FHI. During that time, I’ve learnt a lot about the formula for successful early-career research. This post summarises my advice for people in the first couple of years. Research is really hard, and I want people to avoid the mistakes I’ve made. Original text: https://forum.effectivealtruism.org/posts/jfHPBbYFzCrbdEXXd/how-to-succeed-as-an-early-stage-researcher-the-lean-startup#Conclusion Author: Toby Shevlane A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
The next four weeks of the course are an opportunity for you to actually build a thing that moves you closer to contributing to AI Alignment, and we're really excited to see what you do! A common failure mode is to think "Oh, I can't actually do X" or to say "Someone else is probably doing Y." You probably can do X, and it's unlikely anyone is doing Y! It could be you! Original text: https://www.neelnanda.io/blog/become-a-person-who-actually-does-things Author: Neel Nanda A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
We took 10 years of research and what we’ve learned from advising 1,000+ people on how to build high-impact careers, compressed that into an eight-week course to create your career plan , and then compressed that into this three-page summary of the main points. (It’s especially aimed at people who want a career that’s both satisfying and has a significant positive impact, but much of the advice applies to all career decisions.) Original article: https://80000hours.org/career-planning/summary/ Author: Benjamin Todd A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
1 Working in AI Alignment 1:08:44
1:08:44 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:08:44
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วThis guide is written for people who are considering direct work on technical AI alignment. I expect it to be most useful for people who are not yet working on alignment, and for people who are already familiar with the arguments for working on AI alignment. If you aren’t familiar with the arguments for the importance of AI alignment, you can get an overview of them by doing the AI Alignment Course . by Charlie Rogers-Smith, with minor updates by Adam Jones Source: https://aisafetyfundamentals.com/blog/alignment-careers-guide Narrated for AI Safety Fundamentals by Perrin Walker A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
This post summarises a new report, “Computing Power and the Governance of Artificial Intelligence.” The full report is a collaboration between nineteen researchers from academia, civil society, and industry. It can be read here . GovAI research blog posts represent the views of their authors, rather than the views of the organisation. Source: https://www.governance.ai/post/computing-power-and-the-governance-of-ai Narrated for AI Safety Fundamentals by Perrin Walker A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
Generative AI allows people to produce piles upon piles of images and words very quickly. It would be nice if there were some way to reliably distinguish AI-generated content from human-generated content. It would help people avoid endlessly arguing with bots online, or believing what a fake image purports to show. One common proposal is that big companies should incorporate watermarks into the outputs of their AIs. For instance, this could involve taking an image and subtly changing many pixels in a way that’s undetectable to the eye but detectable to a computer program. Or it could involve swapping words for synonyms in a predictable way so that the meaning is unchanged, but a program could readily determine the text was generated by an AI. Unfortunately, watermarking schemes are unlikely to work . So far most have proven easy to remove, and it’s likely that future schemes will have similar problems. Source: https://transformer-circuits.pub/2023/monosemantic-features/index.html Narrated for AI Safety Fundamentals by Perrin Walker A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
The UK recognises the enormous opportunities that AI can unlock across our economy and our society. However, without appropriate guardrails, such technologies can pose significant risks. The AI Safety Summit will focus on how best to manage the risks from frontier AI such as misuse, loss of control and societal harms. Frontier AI organisations play an important role in addressing these risks and promoting the safety of the development and deployment of frontier AI. The UK has therefore encouraged frontier AI organisations to publish details on their frontier AI safety policies ahead of the AI Safety Summit hosted by the UK on 1 to 2 November 2023. This will provide transparency regarding how they are putting into practice voluntary AI safety commitments and enable the sharing of safety practices within the AI ecosystem. Transparency of AI systems can increase public trust, which can be a significant driver of AI adoption. This document complements these publications by providing a potential list of frontier AI organisations’ safety policies. Source: https://www.gov.uk/government/publications/emerging-processes-for-frontier-ai-safety/emerging-processes-for-frontier-ai-safety Narrated for AI Safety Fundamentals by Perrin Walker 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-systems Narrated for AI Safety Fundamentals by Perrin Walker A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
We’ve released a paper, AI Control: Improving Safety Despite Intentional Subversion . This paper explores techniques that prevent AI catastrophes even if AI instances are colluding to subvert the safety techniques. In this post: We summarize the paper; We compare our methodology to the methodology of other safety papers. Source: https://www.alignmentforum.org/posts/d9FJHawgkiMSPjagR/ai-control-improving-safety-despite-intentional-subversion Narrated for AI Safety Fundamentals by Perrin Walker A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
Research in mechanistic interpretability seeks to explain behaviors of machine learning (ML) models in terms of their internal components. However, most previous work either focuses on simple behaviors in small models or describes complicated behaviors in larger models with broad strokes. In this work, we bridge this gap by presenting an explanation for how GPT-2 small performs a natural language task called indirect object identification (IOI). Our explanation encompasses 26 attention heads grouped into 7 main classes, which we discovered using a combination of interpretability approaches relying on causal interventions. To our knowledge, this investigation is the largest end-to-end attempt at reverse-engineering a natural behavior "in the wild" in a language model. We evaluate the reliability of our explanation using three quantitative criteria–faithfulness, completeness, and minimality. Though these criteria support our explanation, they also point to remaining gaps in our understanding. Our work provides evidence that a mechanistic understanding of large ML models is feasible, pointing toward opportunities to scale our understanding to both larger models and more complex tasks. Code for all experiments is available at https://github.com/redwoodresearch/Easy-Transformer. Source: https://arxiv.org/pdf/2211.00593.pdf Narrated for AI Safety Fundamentals by Perrin Walker A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks. Many important transition points in the history of science have been moments when science “zoomed in.” At these points, we develop a visualization or tool that allows us to see the world in a new level of detail, and a new field of science develops to study the world through this lens. For example, microscopes let us see cells, leading to cellular biology. Science zoomed in. Several techniques including x-ray crystallography let us see DNA, leading to the molecular revolution. Science zoomed in. Atomic theory. Subatomic particles. Neuroscience. Science zoomed in. These transitions weren’t just a change in precision: they were qualitative changes in what the objects of scientific inquiry are. For example, cellular biology isn’t just more careful zoology. It’s a new kind of inquiry that dramatically shifts what we can understand. The famous examples of this phenomenon happened at a very large scale, but it can also be the more modest shift of a small research community realizing they can now study their topic in a finer grained level of detail. Source: https://distill.pub/2020/circuits/zoom-in/ Narrated for AI Safety Fundamentals by Perrin Walker A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
Using a sparse autoencoder, we extract a large number of interpretable features from a one-layer transformer. Mechanistic interpretability seeks to understand neural networks by breaking them into components that are more easily understood than the whole. By understanding the function of each component, and how they interact, we hope to be able to reason about the behavior of the entire network. The first step in that program is to identify the correct components to analyze. Unfortunately, the most natural computational unit of the neural network – the neuron itself – turns out not to be a natural unit for human understanding. This is because many neurons are polysemantic : they respond to mixtures of seemingly unrelated inputs. In the vision model Inception v1 , a single neuron responds to faces of cats and fronts of cars . In a small language model we discuss in this paper, a single neuron responds to a mixture of academic citations, English dialogue, HTTP requests, and Korean text. Polysemanticity makes it difficult to reason about the behavior of the network in terms of the activity of individual neurons. Source: https://transformer-circuits.pub/2023/monosemantic-features/index.html Narrated for AI Safety Fundamentals by Perrin Walker A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
Widely used alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on the ability of humans to supervise model behavior—for example, to evaluate whether a model faithfully followed instructions or generated safe outputs. However, future superhuman models will behave in complex ways too difficult for humans to reliably evaluate; humans will only be able to weakly supervise superhuman models. We study an analogy to this problem: can weak model supervision elicit the full capabilities of a much stronger model? We test this using a range of pretrained language models in the GPT-4 family on natural language processing (NLP), chess, and reward modeling tasks. We find that when we naively fine-tune strong pretrained models on labels generated by a weak model, they consistently perform better than their weak supervisors, a phenomenon we call weak-to-strong generalization. However, we are still far from recovering the full capabilities of strong models with naive fine-tuning alone, suggesting that techniques like RLHF may scale poorly to superhuman models without further work. We find that simple methods can often significantly improve weak-to-strong generalization: for example, when fine-tuning GPT-4 with a GPT-2-level supervisor and an auxiliary confidence loss, we can recover close to GPT-3.5-level performance on NLP tasks. Our results suggest that it is feasible to make empirical progress today on a fundamental challenge of aligning superhuman models. Source: https://arxiv.org/pdf/2312.09390.pdf Narrated for AI Safety Fundamentals by Perrin Walker A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique for steering large language models (LLMs) toward desired behaviours. However, relying on simple human feedback doesn’t work for tasks that are too complex for humans to accurately judge at the scale needed to train AI models. Scalable oversight techniques attempt to address this by increasing the abilities of humans to give feedback on complex tasks. This article briefly recaps some of the challenges faced with human feedback, and introduces the approaches to scalable oversight covered in session 4 of our AI Alignment course . Source: https://aisafetyfundamentals.com/blog/scalable-oversight-intro/ Narrated for AI Safety Fundamentals by Perrin Walker 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-740383a2feab Author: Vishal Maini A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
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 . Learn more on the AI Safety Fundamentals website.…
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AI Safety Fundamentals: Alignment
1 Biological Anchors: A Trick That Might Or Might Not Work 1:10:46
1:10:46 ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้ว1:10:46
ลิสต์เล่นในภายหลัง ลิสต์เล่นในภายหลัง ลิสต์ ถูกใจ ที่ถูกใจแล้วI've been trying to review and summarize Eliezer Yudkowksy's recent dialogues on AI safety. Previously in sequence: Yudkowsky Contra Ngo On Agents . Now we’re up to Yudkowsky contra Cotra on biological anchors, but before we get there we need to figure out what Cotra's talking about and what's going on. The Open Philanthropy Project ("Open Phil") is a big effective altruist foundation interested in funding AI safety. It's got $20 billion, probably the majority of money in the field, so its decisions matter a lot and it’s very invested in getting things right. In 2020, it asked senior researcher Ajeya Cotra to produce a report on when human-level AI would arrive. It says the resulting document is "informal" - but it’s 169 pages long and likely to affect millions of dollars in funding, which some might describe as making it kind of formal. The report finds a 10% chance of “transformative AI” by 2031, a 50% chance by 2052, and an almost 80% chance by 2100. Eliezer rejects their methodology and expects AI earlier (he doesn’t offer many numbers, but here he gives Bryan Caplan 50-50 odds on 2030, albeit not totally seriously ). He made the case in his own very long essay, Biology-Inspired AGI Timelines: The Trick That Never Works , sparking a bunch of arguments and counterarguments and even more long essays. Source: https://astralcodexten.substack.com/p/biological-anchors-a-trick-that-might Crossposted from the Astral Codex Ten podcast. --- A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…
Despite the current popularity of machine learning, I haven’t found any short introductions to it which quite match the way I prefer to introduce people to the field. So here’s my own. Compared with other introductions, I’ve focused less on explaining each concept in detail, and more on explaining how they relate to other important concepts in AI, especially in diagram form. If you're new to machine learning, you shouldn't expect to fully understand most of the concepts explained here just after reading this post - the goal is instead to provide a broad framework which will contextualise more detailed explanations you'll receive from elsewhere. I'm aware that high-level taxonomies can be controversial, and also that it's easy to fall into the illusion of transparency when trying to introduce a field; so suggestions for improvements are very welcome! The key ideas are contained in this summary diagram: First, some quick clarifications: None of the boxes are meant to be comprehensive; we could add more items to any of them. So you should picture each list ending with “and others”. The distinction between tasks and techniques is not a firm or standard categorisation; it’s just the best way I’ve found so far to lay things out. The summary is explicitly from an AI-centric perspective. For example, statistical modeling and optimization are fields in their own right; but for our current purposes we can think of them as machine learning techniques. Original text: https://www.alignmentforum.org/posts/qE73pqxAZmeACsAdF/a-short-introduction-to-machine-learning 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.…
Machine learning is touching increasingly many aspects of our society, and its effect will only continue to grow. Given this, I and many others care about risks from future ML systems and how to mitigate them. When thinking about safety risks from ML, there are two common approaches, which I'll call the Engineering approach and the Philosophy approach: The Engineering approach tends to be empirically-driven, drawing experience from existing or past ML systems and looking at issues that either: (1) are already major problems, or (2) are minor problems, but can be expected to get worse in the future. Engineering tends to be bottom-up and tends to be both in touch with and anchored on current state-of-the-art systems. The Philosophy approach tends to think more about the limit of very advanced systems. It is willing to entertain thought experiments that would be implausible with current state-of-the-art systems (such as Nick Bostrom's paperclip maximizer) and is open to considering abstractions without knowing many details. It often sounds more "sci-fi like" and more like philosophy than like computer science. It draws some inspiration from current ML systems, but often only in broad strokes. I'll discuss these approaches mainly in the context of ML safety, but the same distinction applies in other areas. For instance, an Engineering approach to AI + Law might focus on how to regulate self-driving cars, while Philosophy might ask whether using AI in judicial decision-making could undermine liberal democracy. Original text: https://bounded-regret.ghost.io/more-is-different-for-ai/ 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.…
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 . Learn more on the AI Safety Fundamentals website.…
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