diff --git a/web/api/data/dataset.json b/web/api/data/dataset.json new file mode 100644 index 0000000..fd12bfd --- /dev/null +++ b/web/api/data/dataset.json @@ -0,0 +1 @@ +{"metadata": [["Bad AI Alignment Takes Bingo", "Rob Bensinger", "2022-05-04", null, null], ["AISC5: Research Summaries", "Remmelt Ellen", "Wed, 23 Jun 2021", "https://aisafety.camp/2021/06/23/aisc5-research-summaries/", "Uncategorized"], ["AISC4: Research Summaries", "Sebastian Kosch", "Sat, 30 May 2020", "https://aisafety.camp/2020/05/30/aisc4-research-summaries/", "Uncategorized"], ["AISC3: Research Summaries", "Kristina N\u011bmcov\u00e1", "Thu, 07 Nov 2019", "https://aisafety.camp/2019/11/07/aisc3-research-summaries/", "Uncategorized"], ["Photos from the second AI Safety Camp", "Kristina N\u011bmcov\u00e1", "Sat, 08 Dec 2018", "https://aisafety.camp/2018/12/08/aisc2-photos/", "Uncategorized"], ["Photos from the first AI Safety Camp", "Kristina N\u011bmcov\u00e1", "Sat, 08 Dec 2018", "https://aisafety.camp/2018/12/08/aisc1_photos/", "Uncategorized"], ["AISC2: Research Summaries", "Johannes", "Fri, 07 Dec 2018", "https://aisafety.camp/2018/12/07/aisc2-research-summaries/", "Uncategorized"], ["The first AI Safety Camp & onwards", "Johannes", "Wed, 06 Jun 2018", "https://aisafety.camp/2018/06/06/the-first-ai-safety-camp-onwards/", "Uncategorized"], ["AISC 1: Research Summaries", "Johannes", "Tue, 05 Jun 2018", "https://aisafety.camp/2018/06/05/aisc-1-research-summaries/", "Uncategorized"]], "embedding_strings": ["\" # Rob Bensinger\u2019s Bad AI Alignment Take Bingo Answers\n\n Bad AI alignment take: It sounds like scifi, so it's not possible.\n\n It's wrong to infer \"X will happen\" from \"X shows up in fiction\". But it's also wrong to infer \"X won't happen\" from \"X shows up in fiction\". Fiction just isn't relevant.\n\n Bad AI alignment take: Smarter Al will also be more moral.\n\n Smarter Al would have more ability to be moral. It wouldn't thereby want to do moral things, unless we train it to want that. Safely training goals like that is a major unsolved technical problem.\n\n Bad AI alignment take: AI wouldn't want to kill us.\n\n If we solve alignment, yes. But optimizing a random objective hard enough tends to imply killing potential threats; and humans are made of atoms an Al can use as raw materials.\"\n- Title: Bad AI Alignment Takes Bingo, Author: Rob Bensinger", "\" Bad AI alignment take: Al killing us is actually a good thing.\n\n The boring and obvious \"no, killing people is actually bad\" response seems to just be correct here.\n\n Bad AI alignment take: We shouldn't obstruct the evolution of intelligence.\n\n We shouldn't give up on ever building AGI, sure. But there isn't a god, Evolution, that wants Al to maximize some random goal like paperclips. Al alignment isn't bioconservatism.\n\n Bad AI alignment take: Smart Al would never pursue dumb goals.\n\n Paperclips are only \"dumb\" by our lights. Intelligence doesn't entail changing your goals; and if you do change your goals, you'll be using your old, \"dumb\" goal to decide what change to make.\n\n Bad AI alignment take: AGI is too far away to worry about right now.\n\n Timing technologies is hard, and there aren't obvious future developments we'll I see iff AGI is 10 years away, or 20, etc. Short timelines are likely enough to deserve major attention!\"\n- Title: Bad AI Alignment Takes Bingo, Author: Rob Bensinger", "\" Bad AI alignment take: Just give the Al sympathy for humans.\n\n Human value is complex and fragile; and you often won't get an advance warning if the goal you instill catastrophically diverges from human values in a new distribution.\n\n Bad AI alignment take: Al will never be smarter than humans.\n\n Computers have many known advantages, and are already superhuman at various narrow tasks; it seems bizarre to insist that this can hold for Go but never for STEM work.\n\n Bad AI alignment take: We'll just solve alignment when we get there.\n\n Alignment looks difficult, and will likely need to be solved very quickly once AGI is here. Early work is likely necessary, e.g., to ensure AGI is sufficiently interpretable.\n\n Bad AI alignment take: Maybe AGI will keep us around like pets.\n\n The entire idea of a \"pet\" is a value-laden human concept. Is this really the best use of resources for maximizing paperclips?\"\n- Title: Bad AI Alignment Takes Bingo, Author: Rob Bensinger", "\" Bad AI alignment take: Just use Asimov's three laws.\n\n Maybe AGI will keep us robustly load laws like those into an Al system; and even if we did, Asimov's laws would have terrible outcomes in practice. (Which, indeed, was the point of the laws in Asimov's stories.)\n\n Bad AI alignment take: Just keep the Al in a box.\n\n This is much harder than it sounds, and doesn't obviously help. E.g., how would you use a boxed superintelligence to safely design a complex machine for you?\n\n Bad AI alignment take: Just turn it off if it turns against us.\n\n Computers have an \"off switch\"; humans have an \"off switch\" too. Off switches don't eliminate the risks or obviate the need for alignment.\n\n Bad AI alignment take: Just don\u2019t give the AI access to the real world.\"\n- Title: Bad AI Alignment Takes Bingo, Author: Rob Bensinger", "\" Keeping AGIs in a simulated environment may help reduce risk. But if the idea is just to not give it physical actuators, you have to worry about it building or seizing control of actuators.\n\n Bad AI alignment take: Just merge with the AIs.\n\n An unaligned AI is an unaligned AI, whether it\u2019s outside your body or inside of it. Human values aren\u2019t transmitted by magical proximity. Whether or not we \u201cmerge\u201d, we need to solve the alignment problem.\n\n Bad AI alignment take: Just raise the AI like you would a child.\n\n This works for human children because we\u2019ve evolved to have exactly the right complex conditional response to being raised that way. It\u2019s not clear how to instill any of that into an AGI.\n\n Bad AI alignment take: We can\u2019t solve alignment without understanding consciousness.\n\n It\u2019s not clear why \u201cconsciousness\u201d is needed, e.g. to robustly give an AGI system a goal like \u201cbuild a nanofactory\u201d.\"\n- Title: Bad AI Alignment Takes Bingo, Author: Rob Bensinger", "\" Bad AI alignment take: The real danger is actually modern AI, not superintelligence.\n\n The risk from superintelligence seems drastically more serious. But if there is dangerous pre-AGI tech, this doesn\u2019t thereby make AGI less dangerous.\n\n Bad AI alignment take: Just legally mandate that AIs must be aligned.\n\n We don\u2019t know how to align AGI systems, so no one could follow such a law if it existed. And if your unaligned AI destroys the world, legality doesn\u2019t matter.\n\n Bad AI alignment take: AI can\u2019t do x yet, therefore AGI is far away.\n\n This could be a valid argument, if there\u2019s good reason to think that either x is far away, or AGI won\u2019t come until a long time after x. But what specific x meets those conditions?\n\n Bad AI alignment take: Just penalize the AI for killing people.\n\n We don\u2019t know how to penalize AGI systems for an action in training, in a way that robustly ensures it won\u2019t carry out the action. And, again, human values are complex and fragile.\"\n- Title: Bad AI Alignment Takes Bingo, Author: Rob Bensinger", "\" thereby make AGI less dangerous.\n\n Bad AI alignment take: Just legally mandate that AIs must be aligned.\n\n We don\u2019t know how to align AGI systems, so no one could follow such a law if it existed. And if your unaligned AI destroys the world, legality doesn\u2019t matter.\n\n Bad AI alignment take: AI can\u2019t do x yet, therefore AGI is far away.\n\n This could be a valid argument, if there\u2019s good reason to think that either x is far away, or AGI won\u2019t come until a long time after x. But what specific x meets those conditions?\n\n Bad AI alignment take: Just penalize the AI for killing people.\n\n We don\u2019t know how to penalize AGI systems for an action in training, in a way that robustly ensures it won\u2019t carry out the action. And, again, human values are complex and fragile. Bad AI alignment take: Just train multiple AGIs and have them fight it out.\n\n As stated, this doesn\u2019t seem to solve the alignment problem, or obviate the need for a solution.\n\n Bad AI alignment take: It might be hard but we\u2019ll rise to the occasion like always.\n\n Hopefully! But we should be wary of the bystander effect. If we rise to the occasion, it will be because we didn\u2019t sit around waiting for someone else to rise to the occasion.\"\n- Title: Bad AI Alignment Takes Bingo, Author: Rob Bensinger", "\" AISC5: Research Summaries\n\n Modularity Loss Function Team members:\u00a0 Logan Smith, Viktor Rehnberg, Vlado Baca, Philip Blagoveschensky, Viktor PetukhovExternal collaborators:\u00a0 Gurkenglas Making neural networks (NNs) more modular may improve their interpretability. If we cluster neurons or weights together according to their different functions, we can analyze each cluster individually. Once we better understand the clusters that make up a NN, we can better understand the whole. To that end, we experimented with pairwise distances according to the neuron\u2019s jacobian correlation, coactivations, and estimated mutual information. These metrics can be plugged into spectral clustering algorithms to optimize for modules in the network; however, having a modular NN does not equate to a more interpretable one. We investigated task-based masking methods to test for modularity as well as neuron group activation (via Google Dream) in order to test for these modules being more interpretable than an equivalent amount of neurons. We ran out of time before fitting all the pieces together, but are intending on working on it more over the summer.Presentation on final weekend (slides)\"\n- Title: AISC5: Research Summaries, Author: Remmelt Ellen", "\" Cooperativity & Common Pool Resources\u00a0 Team members:\u00a0 Quinn Doughtery, Ben Greenberg, Ariel Kwiatkowski In environments with common pool resources, a typical failure mode is the tragedy of the commons, wherein agents exploit the scarce public resource as much as possible. An every-man-for-himself dynamic emerges, further increasing scarcity. This behavior results in conflict, inefficient allocation, and resource depletion. Even if an individual would prefer to harvest the resource sustainably, they are punished for doing so unilaterally. What\u2019s missing is an institution that will incentivize the group to \u201ccooperate\u201d. In this project, we study such interventions for avoiding tragedies of the commons in environments with multiple selfish agents. In particular, a reputation system can incentivize agents to harvest resources more sustainably. Our goal in this project was to see if a transparent reputation system would allow agents to trust each other enough to cooperate, such that their combined rewards would be higher over time. This problem is relevant for existential safety as it relates to climate change and sustainability, as well as conflict over finite resources. Presentation on final weekend (slides) GitHub repository Post: retrospective\"\n- Title: AISC5: Research Summaries, Author: Remmelt Ellen", "\" Showing Objective Robustness Failures Team members:\u00a0 Jack Koch, James Le, Jacob PfauExternal collaborators:\u00a0 Lauro Langosco We study objective robustness failures, in the context of reinforcement learning (RL). Objective robustness failures occur when an RL agent retains its capabilities out-of-distribution yet pursues the wrong objective (this definition is broader than misaligned mesa-optimizers: a model can fail at objective robustness without being a mesa-optimizer). This kind of failure is particularly bad, since it involves agents that leverage their capabilities to pursue the wrong objective rather than simply failing to do anything useful. The main goal of our project is to provide explicit empirical demonstrations of objective robustness failures. To do this, we modify environments from the Procgen benchmark to create test environments that induce OR failure. For example, in the CoinRun environment, an agent\u2019s goal is to collect the coin at the end of the level. When we deploy the agent on a test environment in which coin position is randomized, the agent ignores the coin and instead pursues a simple proxy objective: it navigates to the end of the level, where the coin is usually located. Presentation on final weekend (slides)\"\n- Title: AISC5: Research Summaries, Author: Remmelt Ellen", "\" Published paper Post explaining the experiments Post discussing two paradigmatic approaches \u00a0 Multi-Objective Decision-Making Team members:\u00a0 Robert Klassert, Roland Pihlakas (message), Ben Smith (message)External collaborators:\u00a0 Peter Vamplew (research mentor) Balancing multiple competing and conflicting objectives is an essential task for any artificial intelligence tasked with satisfying human values or preferences while avoiding Goodhart\u2019s law. Objective conflict arises both from misalignment between individuals with competing values, but also between conflicting value systems held by a single human. We were guided by two key principles: loss aversion and balanced outcomes. Loss aversion, conservatism, or soft maximin is aimed at emulating aspects of human cognition and will heavily penalize proposed actions that score more negatively on any particular objective. We also aim to balance outcomes across objectives. This embodies conservatism in the case where each objective represents a different moral system by ensuring that any action taken does not grossly violate any particular principle. Where each objective represents another subject\u2019s principles this embodies an inter-subject fairness principle.\"\n- Title: AISC5: Research Summaries, Author: Remmelt Ellen", "\"We tested these on previously-tested environments, and found that one new approach in particular, \u2018split-function exp-log loss aversion\u2019, performs better across a range of reward penalties in the \u201cBreakableBottles\u201d environment relative to the thresholded alignment objective (more generally lexicographic) method, the state of the art described in Vamplew et al. 2021. We explore approaches to further improve multi-objective decision-making using soft maximin approaches. Our soft maximin covers a middle ground between the linear approach and the lexicographic approaches with the aim of enabling an agent to respond well in a wider variety of circumstances. In future we would like to implement more complex scenarios with more numerous competing objectives to explore how our models perform with them. More complex scenarios were already sorted out from various lists of AI failure scenarios, analysed, and improvements were proposed. Other future directions to explore might be \u201cdecision-paralysis as a feature, not a bug\u201d, where the agent responds to objective conflict by stopping and asking the human for additional input or for additional clarification on their preferences. We plan to present our work at the Multi-Objective Decision Making Workshop 2021 and subsequently submit the work to a special issue of the Journal of Autonomous Agents and Multi-Agent Systems. Presentation on final weekend (slides) Post reviewing the case for multi-objective RL\"\n- Title: AISC5: Research Summaries, Author: Remmelt Ellen", "\" Pessimistic Ask-For-Help Agents for Safe Exploration Team members:\u00a0 Jamie Bernardi, David Reber, Magdalena Wache, Peter Barnett, Max ClarkeExternal collaborators:\u00a0\u00a0Michael Cohen (research mentor)In reinforcement learning (RL), an agent explores its environment in order to learn a task. However, in safety-critical situations this can have catastrophic consequences. We demonstrate that if the agent has access to a safe mentor, and if it is pessimistic about unknown situations, a safe learning process can be achieved. We trained an RL agent to exceed the mentor\u2019s capability while avoiding unsafe consequences with high probability. We demonstrate that the agent acts more autonomously the more it learns, and eventually stops asking for help. Cohen/Hutter 2020 propose a model-based pessimistic agent, with the desired safety and performance properties. However, this agent is intractable except for in very simple environments. To overcome this intractability we devise a model-free approach to pessimism that is based on keeping a distribution over Q-values. It is a variation of Q-Learning, which does not decide its policy based on an approximation of the Q-value, but rather based on an approximation of the i-quantile Q\u1d62 with i<0.5. We call this approach Distributional Q-Learning (DistQL).\"\n- Title: AISC5: Research Summaries, Author: Remmelt Ellen", "\"We demonstrate that in a finite environment, DistQL successfully avoids taking risky actions. A subset of the team is excited to be continuing beyond the AISC deadline and to apply DistQL to more complex\u00a0 environments. For example in the cartpole environment the goal is to learn balancing the pole without ever dropping it. To apply DistQL to a continuous environment, we use gated linear networks for approximating Q\u1d62\n\n We demonstrate the properties of a pessimistic agent in a finite, discrete gridworld environment with stochastic rewards and transitions, and bordering \u2018cliffs\u2019 around the edge, where stepping across leads to 0 reward forever. From top to bottom: the agent stops querying the mentor, exceeds the performance of a random, safe mentor; and never falls off the cliff. Presentation on final weekend (slides)\n\n GitHub repository\n\n Understanding RL agents using generative visualisation Team members:\u00a0 Lee Sharkey, Daniel Braun, Joe Kwon, Max Chiswick Feature visualisation methods can generate visualisations of inputs that maximally or minimally activate certain neurons. Feature visualisation can be used to understand how a network computes its input-output function by building up an understanding of how neural activations in lower layers cause specific patterns of activation in later layers. These methods have produced some of the deepest understanding of feedforward convolutional neural networks.\"\n- Title: AISC5: Research Summaries, Author: Remmelt Ellen", "\"Feature visualisation methods work because, within a neural network, the causal chain of activations between input and output is differentiable. This enables backpropagation through the causal chain to see what inputs cause certain outputs (or certain intermediate activations). But RL agents are situated in an environment, which means causality flows both through its networks and through the environment. Typically the environment is not differentiable. This means that, in the RL setting, gradient-based feature visualisation techniques can\u2019t build up a complete picture of how certain inputs cause particular neural activations at later timesteps. We get around this difficulty by training a differentiable simulation of the agent\u2019s environment. Specifically, we train a variational autoencoder (VAE) to produce realistic agent environment sequences. The decoder consists of both a recurrent environment simulator (an LSTM) and the agent that we wish to interpret. Crucially, this enables us to optimize the latent space of the VAE to produce realistic agent-environment rollouts that maximise specific neurons at specific timesteps in the same way that feature visualisation methods maximise specific neurons in specific layers. This has yielded promising early results, though the resolution of the generative model needs improvement.\"\n- Title: AISC5: Research Summaries, Author: Remmelt Ellen", "\"In an agent trained on procedurally generated levels of CoinRun, we find that we can optimise the latent space of the generative model to produce agent-environment rollouts that maximise or minimise the agent\u2019s value or action neurons for whole or partial sequences. We also find that individual neurons in the agent\u2019s hidden state exhibit mixed selectivity i.e. they encode multiple features in different contexts and do not encode easily interpretable features. Such mixed selectivity is consistent with prior neuroscientific findings of task representations. Instead of optimizing single neurons, ongoing work optimizes for particular directions in the agent\u2019s hidden state activation-space; we expect this to yield more semantically meaningful categories than single neurons. In future work, we plan to improve the realism of the generated samples; to identify discrete agent behaviours; to build up a timestep-by-timestep and layer-by-layer understanding of how the agent computes specific behaviours; and to safely un-train an agent using the learned simulator such that it no longer performs an arbitrarily chosen behaviour. Presentation on final weekend (slides)\"\n- Title: AISC5: Research Summaries, Author: Remmelt Ellen", "\" AISC4: Research Summaries\n\n The fourth AI Safety Camp took place in May 2020 in Toronto. Due to COVID-19, the camp was held virtually. Six teams participated and worked on the following topics:\n\n Survey on AI risk scenarios Options to defend a vulnerable world Extraction of human preferences Transferring reward functions across environments to encourage safety for agents in the real world Formalization of goal-directedness Generalization in reward-learning\n\n\n\n Survey on AI risk scenarios Alexis Carlier, Sam Clarke, Jonas Schuett\n\n It has been argued that artificial intelligence could pose existential risks for humanity. However, the original arguments made by Bostrom (2014) and Yudkowsky (2008) have been criticised (Shah, 2018; Christiano, 2018; Drexler, 2019), and a number of others have been proposed (Christiano, 2019; Zwetsloot & Dafoe, 2019; Dafoe, 2018; Dai, 2018; Dai, 2019; Brundage et al., 2018; Garfinkel, 2018). The result of this dynamic is that we no longer know which of these arguments motivate researchers to work on reducing existential risks from AI.\"\n- Title: AISC4: Research Summaries, Author: Sebastian Kosch", "\"To make matters worse, none of the alternative arguments have been examined in sufficient detail. Most are only presented as blog posts with informal discussion, with neither the detail of a book, nor the rigour of a peer-reviewed publication. Therefore, as a first step in clarifying the strength of the longtermist case for AI safety, we prepared an online survey, aimed at researchers at top AI safety research organisations (e.g. DeepMind, OpenAI, FHI and CHAI), to find out which arguments are motivating those researchers. We hope this information will allow future work evaluating the plausibility of AI existential risk to focus on the scenarios deemed most important by the experts. See AI Risk Survey project overview. See abbreviated summary of survey results.\n\n Options to defend a vulnerable world Samuel Curtis, Otto Barten, Chris Cooper, Rob Anue\n\n We have made steps in getting an overview of ways to mitigate the risks we face if we live in a Vulnerable World, as hypothesized by Nick Bostrom. We were especially concerned with Type-1 risks \u2013 the \u201ceasy nukes\u201d scenario, where it becomes easy for individuals or small groups to cause mass destruction, but in the context of AI. One idea we looked into was a publishing system with restricted access, and we consider this a promising option.\"\n- Title: AISC4: Research Summaries, Author: Sebastian Kosch", "\"A related option, which also seemed to be original, was to apply limitations to software libraries. In just one week, we seem to have done some original work \u2013 and learned a lot \u2013 so this field certainly seems promising to work on.\n\n Extraction of human preferences Mislav Juric, Taylor Kulp-McDowall, Arun Raja, Riccardo Volpato, Nevan Wichers\n\n Developing safe and beneficial AI systems requires making them aware and aligned with human preferences. Since humans have significant control over the environment they operate in, we conjecture that RL agents implicitly learn human preferences. Our research aims to first show that these preferences exist in an agent and then extract these preferences. To start, we tackle this problem in a toy grid-like environment where a reinforcement learning (RL) agent is rewarded for collecting apples. After showing in previous work that these implicit preferences exist and can be extracted, our first approach involved applying a variety of modern interpretability techniques to the RL agent trained in this environment to find meaningful portions of its network. We are currently pursuing methods to isolate a subnetwork within the trained RL agent which predicts human preferences.\"\n- Title: AISC4: Research Summaries, Author: Sebastian Kosch", "\" Transferring reward functions across environments to encourage safety for agents in the real world Nevan Wichers, Victor Tao, Ti Guo, Abhishek Ahuja\n\n Github Link: https://github.com/platers/meta-transfer-learning A lot of times, it is hard to encourage safety and altruism for the agent in the real world. We want to test to see if transferring the reward function could be a solution to this problem. Our approach is building a reward function that encourages safety in the simulation and transfers that to the real world to train agents for safe actions. Due to the constraint of the research, the testing environment is also in simulation but has a different structure than the training environments. In the first experiment, we hoped to test if it is possible to transfer a reward function that promotes the same action in an environment slightly different than the testing environment. We first trained a reward function using a supervised convolutional neural network to estimate the score based on recognizing an agent\u2019s position in a 2D grid world environment. Then we test the accuracy in a different environment with slightly different coloring. The result was positive. The reward function in the testing environment can achieve 90% of the performance in the training environment.\"\n- Title: AISC4: Research Summaries, Author: Sebastian Kosch", "\"In the second experiment, we hope to test if we can evolve a reward function that can successfully train agents for safety or altruism related action in a different environment. We design a collection game where each agent can collect apple or banana for itself or for other agents. In order to encourage safety, the agent is given more score for collecting food for others than for itself. There are 3 environments, including one testing environment where both types of food counts for the score, and two training environments where one counts apples for the score, and another counts bananas for the score. Reward functions are created using evolution. At each round of evolution, the best reward functions are selected based on the performance of agents trained through Reinforcement Learning using those reward functions. In the end, this result is very close to proving our hypothesis and still requires more analysis. After analyzing the weights in our best-performing reward functions, we find that most of the time, it can reward the right action in each environment correctly. The agents trained in the testing environment can consistently achieve above 50% safety evaluated by our best reward function. At the same time, here are some good practices we have learned that helps with training for the reward functions that encourage safety for training agents in another environment.\"\n- Title: AISC4: Research Summaries, Author: Sebastian Kosch", "\" Training reward functions in various environments with different structure will boost the performance of the reward function in the testing environment. Training reward functions in environments that are more different than the testing environment will make the reward function perform better in the testing environment.\n\n In conclusion, the result gave us some level of confidence to say that it is possible to build a reward function that encourages safety in the simulation and transfers that to the real world to train agents for safe actions.\n\n We tried to evaluate if transferring the reward function is a feasible alternative to transferring the model itself in the context of altruism We implemented several simple environments to train and test reward functions We used evolution to find reward functions which lead to altruistic behavior The reward function are evaluated by training multiple reinforcement learning agents to optimize them and measuring the average performance We encountered many technical roadblocks, such as computation time and reinforcement learning instability In conclusion, we are not convinced either way if this idea has potential.\"\n- Title: AISC4: Research Summaries, Author: Sebastian Kosch", "\" Formalization of goal-directedness Adam Shimi, Michele Campolo, Sabrina Tang, Joe Collman\n\n A common argument for the long-term risks of AI and AGI is the difficulty of specifying our wants without missing important details implicit in our values and preferences. However, Rohin Shah among others argued in a series of posts that this issue need not arise for every design of AGI \u2014 only for ones that are goal-directed. He then hypothesizes that some goal-directedness property is not strictly required for building useful and powerful AI. However, Shah admits \u201c\u2026it\u2019s not clear exactly what we mean by goal-directed behavior.\u201d Consequently, we propose clarifying the definition of goal directedness for both formal and operational cases. Then the definition will be assessed based on risks and alternatives for goal-directedness. See five blogposts published after the camp\n\n Generalization in reward-learning Liang Zhou, Anton Makiievskyi, Max Chiswick, Sam Clarke\"\n- Title: AISC4: Research Summaries, Author: Sebastian Kosch", "\" hypothesizes that some goal-directedness property is not strictly required for building useful and powerful AI. However, Shah admits \u201c\u2026it\u2019s not clear exactly what we mean by goal-directed behavior.\u201d Consequently, we propose clarifying the definition of goal directedness for both formal and operational cases. Then the definition will be assessed based on risks and alternatives for goal-directedness. See five blogposts published after the camp\n\n Generalization in reward-learning Liang Zhou, Anton Makiievskyi, Max Chiswick, Sam Clarke One of the primary goals in machine learning is to create algorithms and architectures that demonstrate good generalization ability to samples outside of the training set. In reinforcement learning, however, the same environments are often used for both training and testing, which may lead to significant overfitting. We build on previous work in reward learning and model generalization to evaluate reward learning on random, procedurally generated environments. We implement algorithms such as T-REX (Brown et al 2019) and apply them to procedurally generated environments from the Procgen benchmark (Cobbe et al 2019). Given this diverse set of environments, our experiments involve training reward models on a set number of levels and then evaluating them, as well as policies trained on them, on separate sets of test levels. See two blog posts published after the camp. See GitHub.\"\n- Title: AISC4: Research Summaries, Author: Sebastian Kosch", "\" AISC3: Research Summaries\n\n The third AI Safety Camp took place in April 2019 in Madrid. Our teams worked on the projects summarized below: Categorizing Wireheading in Partially Embedded Agents: Team:\u00a0Embedded agents\u00a0\u2013 Arushi, Davide, Sayan They presented their work at the AI Safety Workshop in IJCAI 2019. Read their paper here. AI Safety Debate and Its Applications: Team: Debate\u00a0\u2013 Vojta Kovarik, Anna Gajdova, David Lindner, Lukas Finnveden, Rajashree Agrawal Read their blog post here. See their GitHub here. Regularization and visualization of attention in reinforcement learning agents Team:\u00a0 RL Attention \u2013 Dmitry Nikulin, Sebastian Kosch, Fabian Steuer, Hoagy Cunningham Read their research report here. Modelling Cooperation See visualisation of their mathematical model here.\"\n- Title: AISC3: Research Summaries, Author: Kristina N\u011bmcov\u00e1", "\" They presented their work at the AI Safety Workshop in IJCAI 2019. Read their paper here. AI Safety Debate and Its Applications: Team: Debate\u00a0\u2013 Vojta Kovarik, Anna Gajdova, David Lindner, Lukas Finnveden, Rajashree Agrawal Read their blog post here. See their GitHub here. Regularization and visualization of attention in reinforcement learning agents Team:\u00a0 RL Attention \u2013 Dmitry Nikulin, Sebastian Kosch, Fabian Steuer, Hoagy Cunningham Read their research report here. Modelling Cooperation See visualisation of their mathematical model here.Robustness of Multi-Armed Bandits Team: Bandits \u2013 Dominik Fay, Misha Yagudin, Ronak Mehta Learning Models of Mistakes Team Mistakes \u2013 Lewis Hammond,\u00a0Nikolas Bernaola,\u00a0Saasha Nair Cooperative Environments with Terminal Consequences Team CIRL Environment: Jason Hepburn, Nix Goldowsky-Dill, Pablo Antonio Moreno Casares, Ross Gruetzemacher, Vasilios Mavroudis Responsible Disclosure in AI Research Team AI Governance: Cynthia Yoon, Jordi Bieger, Laszlo Treszkai, Ronja Lutz Psychological Distance and Group Blindspots Team \u2013 Psychological Distance: Remmelt Ellen\"\n- Title: AISC3: Research Summaries, Author: Kristina N\u011bmcov\u00e1", "\" Photos from the second AI Safety Camp\n\n [See image gallery at aisafety.camp]\"\n- Title: Photos from the second AI Safety Camp, Author: Kristina N\u011bmcov\u00e1", "\" Photos from the first AI Safety Camp\n\n [See image gallery at aisafety.camp]\"\n- Title: Photos from the first AI Safety Camp, Author: Kristina N\u011bmcov\u00e1", "\" AISC2: Research Summaries\n\n The second AI Safety Camp took place this October in Prague. Our teams have worked on exciting projects which are summarized below: \u00a0 AI Governance and the Policymaking Process: Key Considerations for Reducing AI Risk: Team:\u00a0Policymaking for AI Strategy \u2013\u00a0Brandon Perry, Risto Uuk Our project was an attempt to introduce literature from theories on the public policymaking cycle to AI strategy to develop a new set of crucial considerations and open up research questions for the field. We began by defining our terms and laying out a big picture approach to how the policymaking cycle interacts with the rest of the AI strategy field. We then went through the different steps and theories in the policymaking cycle to develop a list of crucial considerations that we believe to be valuable for future AI policy practitioners and researchers to consider. For example, policies only get passed once there\u2019s significant momentum and support for that policy, which creates implications to consider such as how many chances we get to implement certain policies. In the end, we believe that we have opened up a new area of research in AI policymaking strategy, where the way that solutions are implemented have strategic considerations for the entire AI risk field itself. Read our paper here.\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\" Detecting Spiky Corruption in Markov Decision Processes: Team: Corrupt Reward MDPs\u00a0\u2013\u00a0Jason Mancuso, Tomasz Kisielewski, David Lindner, Alok Singh We Presented our work at AI Safety Workshop in IJCAI 2019 Read our paper here. Corrupt Reward MDPs: Team:\u00a0Tomasz Kisielewski, David Lindner, Jason Mancuso, Alok Singh\n\n We worked on solving Markov Decision Processes with corrupt reward functions (CRMDPs), in which the observed and true rewards are not necessarily the same. The general class of CRMDPs is not solvable, so we focused on finding useful subclasses that are. We developed a set of assumptions that define what we call Spiky CRMDPs and an algorithm that solves them by identifying corrupt states, i.e. states that have corrupted reward. We worked out regret bounds for our algorithm in the class of Spiky CRMDPs, and found a specific subclass under which our algorithm is provably optimal. Even for Spiky CRMDPs in which our algorithm is suboptimal, we can use the regret bound in combination with semi-supervised RL to reduce supervisor queries.\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\" We are currently working on implementing the algorithm in safe-grid-agents to be able to test it on official and custom AI Safety Gridworlds. We also plan to make our code OpenAI Gym-compatible for easier interfacing of the AI Safety Gridworlds and our agents with the rest of the RL community. Our current code is available on GitHub. Human Preference Types Team:\u00a0Sabrina Kavanagh, Erin M. Linebarger, Nandi Schoots\n\n We analyzed the usefulness of the framework of preference types to value learning. We zoomed in on the preference types liking, wanting and approving. We described the framework of preference types and how these can be inferred. We considered how an AI could aggregate our preferences and came up with suggestions for how to choose an aggregation method. Our initial approach to establishing a method for aggregation of preference types was to find desiderata any potential aggregation function should comply with. As a source of desiderata, we examined the following existing bodies of research that dealt with aggregating preferences, either across individuals or between different types: Economics & Social Welfare Theory; Social Choice Theory; Constitutional Law; and Moral Philosophy. We concluded that the aggregation method should be chosen on a case-by-case basis.\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\"For example by asking people for their meta-preferences; considering the importance of desiderata to the end-user; letting the accuracy of measurement decide its weight; implementing a sensible aggregation function and adjusting it on the go; or identifying a more complete preference type.\n\n This is a blogpost we wrote during the camp. Feature Visualization for Deep Reinforcement Learning Team:\u00a0Zera Alexander,\u00a0Andrew Schreiber,\u00a0Fabian Steuer\n\n Completed a literature review of visualization in Deep Reinforcement Learning. Built a prototype of Agent, a Tensorboard plugin for interpretability of RL/IRL models focused on the time-step level. Open-sourced the Agent prototype on GitHub. Reproduced and integrated a paper\u00a0on perturbation-based saliency map in Deep RL. Applied for an EA Grant to continue our work. (Currently at the 3rd and final stage in the process.)\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\" Ongoing work:\n\n Developing the prototype into a functional tool. Collecting and integrating feedback from AI Safety researchers in Deep RL/IRL. Writing an introductory blog post to Agent.\n\n Corrigibility Team:\u00a0Vegard Blindheim, Anton Osika,\u00a0Roland Pihlakas\n\n The initial project topic was: Corrigibility and interruptibility via the principles of diminishing returns and conjunctive goals (originally titled: \u201cCorrigibility and interruptibility of homeostasis based agents\u201d) Vegard focused on finding and reading various corrigibility related materials and proposed an idea of building a public reading list of various corrigibility related materials, since currently these texts are scattered over the internet. Anton contributed to the discussions of the initial project topic in the form of various very helpful questions, but considered the idea of diminishing returns too obvious and simple, and very unlikely to be successful. Therefore, he soon switched over to other projects in another team. The initial project of diminishing returns and conjunctive goals evolved into a blog post by Roland, proposing a solution to the problem of the lack of common sense in paper-clippers and other Goodhart\u2019s law-ridden utility maximising agents, possibly enabling them to even surpass the relative safety of humans:\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\" Future plans:\n\n Vegard works on preparing the website offering a reading list of corrigibility related materials. Roland continuously updates his blog post with additional information, additionally contacting Stuart Armstrong, and continuing correspondence with Alexander Turner and Victoria Krakovna. Additionally, Roland will design a set of gridworlds-based gamified simulation environments (at www.gridworlds.net) for various corrigibility and interruptibility related toy problems, where the efficiency of applying the principles of diminishing returns and conjunctive goals can be compared to other approaches in the form of a challenge \u2014 the participants would be able to provide their own agent code in order to measure, which principles are best or most convenient as a solution for the most challenge scenarios. Anton is looking forward to participating in these challenges with his coding skills.\n\n IRL Benchmark Team:\u00a0Adria Garriga-Alonso, Anton Osika, Johannes Heidecke, Max Daniel, Sayan Sarkar\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\" Our objective is to create a unified platform to compare existing and new algorithms for inverse reinforcement learning. We made an extensive review of existing inverse reinforcement learning algorithms with respect to different criteria such as: types of reward functions, necessity of known transition dynamics, metrics used for evaluation, used RL algorithms. We set up our framework in a modular way that is easy to extend for new IRL algorithms, test environments, and metrics. We released a basic version of the benchmark with 2 environments and 3 algorithms and are continuously extending it.\n\n See our GitHub here. Value Learning in Games Team: Stanislav B\u00f6hm, Tom\u00e1\u0161 Gaven\u010diak, Torben Swoboda, Mikhail Yagudin Learning rewards of a task by observing expert demonstrations is a very active research area, mostly in the context of Inverse reinforcement learning (IRL) with some spectacular results. While the reinforcement learning framework assumes non-adversarial environments (and is known to fail in general games), our project focuses on value learning in general games, introduced in Inverse Game Theory (2015). We proposed a sparse stochastic gradient descent algorithm for learning values from equilibria and experiment with learning the values of the game of Goofspiel. We are developing a game-theoretic library GameGym to collect games, algorithms and reproducible experiments.\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\"We also studied value learning under bounded rationality models and we hope to develop this direction further in the future. A longer report can be found here. AI Safety for Kids\n\n We arrived at camp with the intention of developing storyboards targeted at AI Policymakers, inspired by the \u2018Killbots YouTube video\u2019 and the Malicious Compliance Report. The goal of these storyboards was to advance policies that prevent the weaponization of AI, while disrupting popular images of what an AI actually is or could become. We would achieve this by lowering the barriers of entry for non-experts to understanding core concepts and challenges in AI Safety. In considering our target audience, we quickly decided that the most relevant stakeholders for these storyboards are a minimum of 20 years away from assuming their responsibilities (based on informal surveys of camp participants on the ETA of AGI). In other words, we consider our audience for these storyboards to be children. We realized that by targeting our message to a younger audience, we could prime them to think differently and perhaps more creatively about addressing these complex technical and social challenges. Although we consider children\u2019s books to be broadly appealing to all ages and helpful for spreading a message in a simple yet profound manner, to our knowledge no children\u2019s books have been specifically published on the topic of AI Safety.\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\"During camp we wrote drafts for three main children\u2019s book ideas focused on AI Safety. We presented one of these concepts to the group and gathered feedback about our approach. In summary, we decided to move forward with writing a children\u2019s book on AI Safety while remaining cognizant of the challenges of effective communication so as to avoid the pitfalls of disinformation and sensationalism. We developed a series of milestones for the book such that we could meet our goal of launching the book by the one year anniversary of the camp in Fall 2019. After camp, we applied to the Effective Altruism Foundation for a $5,000 grant to engage animators for preliminary graphic support to bring the book into a working draft phase to aid in pitching the idea to publishers in order to secure additional funding and complete the project. After this request was declined, we continued to compile lists of potential animators to reach out to once funding is secured. We adjusted our plan to focus more on getting to know our potential audience. To this end, Chris has been in contact with a local high school teacher for advanced students specializing in maths and physics. Chris has arranged to give a talk to the students on problems of AI alignment in January 2019. Chris plans to prepare the presentation and Noah will provide feedback.\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\"After the presentation, Noah and Chris will reconvene to discuss the student reactions and interest in AI Alignment and Safety in Jan/Feb 2019.\n\n Assumptions of Human Values Team:\u00a0Jan Kulveit,\u00a0Linda Linsefors,\u00a0Alexey Turchin There are many theories about the nature of human values, originating from diverse fields ranging from psychology to AI alignment research. Most of them rely on making various assumptions, which are sometimes given explicitly, often hidden (for example: humans having introspective access to their values; preferences being defined for arbitrary alternatives; some specific part of mind having normative power). We started with mapping the space \u2013 reading the papers, noting which assumptions are made, and trying to figure out what are the principal dimensions on which to project the space of value theories. Later, we tried to attack the problem directly, and find solutions which would be simple and make just explicit assumptions. While we did not converge on a solution, we become less confused, and the understanding created will likely lead to several posts from different team members. Jan has written a blog post about his\u00a0best-guess model of how human values and motivations work.\"\n- Title: AISC2: Research Summaries, Author: Johannes", "\" The first AI Safety Camp & onwards\n\n by Remmelt Ellen and\u00a0Linda Linsefors Summary Last month, 5 teams of up-and-coming researchers gathered to solve concrete problems in AI-alignment at our 10-day AI safety research camp in Gran Canaria. This post describes\n\n the event format we came up with \u00a0\u00a0\u00a0\u00a0\u00a0our experience & lessons learned in running it in Gran Canaria \u00a0\u00a0\u00a0\u00a0\u00a0how you can contribute to the next camp in Prague on 4-14 October & future editions\n\n The event format In February, we proposed our plans for the AI Safety Camp: Goals: Efficiently launch aspiring AI safety and strategy researchers into concrete productivity by creating an \u2018on-ramp\u2019 for future researchers. Specifically:\n\n Get people started on and immersed into concrete research work intended to lead to published papers. Address the bottleneck in AI safety/strategy of few experts being available to train or organize aspiring researchers by efficiently using expert time. Create a clear path from \u2018interested/concerned\u2019 to \u2018active researcher\u2019. Test a new method for bootstrapping talent-constrained research fields.\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\" Note: this does not involve reaching out to or convincing outside researchers \u2013 those who are not yet doing work in the field of AI safety \u2013 on the imperative of solving alignment problems. Method: Run an online study group culminating in an intensive in-person research camp. Participants work in teams on tightly-defined research projects on the following topics:\n\n Agent foundations \u00a0\u00a0\u00a0\u00a0\u00a0Machine learning safety \u00a0\u00a0\u00a0\u00a0\u00a0Policy & strategy \u00a0\u00a0\u00a0\u00a0\u00a0Human values\n\n Project ideas are proposed by participants prior to the start of the program. After that, participants split into teams around the most popular research ideas (each participant joins one team, and each team focuses on one research topic). What the camp isn\u2019t about: The AI Safety Camp is not about convincing anyone about the importance of AI-alignment research. The camp is for people who are already on board with the general ideas, and who want to develop their research skills and/or find like-minded people to collaborate with. Trying to convert people from adjacent research fields is a very different project, which we do not think mixes well with this event. The AI Safety Camp is not a summer school (unlike the one coming up this August in Prague). There are no teachers although teams can correspond with experienced advisors. Participants are expected to have the knowledge needed to do research together.\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\"However, it is not required for everyone to have research experience, or to know every relevant fact. That is what the team is for \u2013 to help each other, and lift each other up. The first camp How it came to be: The project got started when Linda Linsefors tried to figure out how to find AI safety researchers to cowork with in a supportive environment. Effective Altruism Global London (November 2017) was coming up so she decided to network there to look for a \u201cmultiplayer solution\u201d to the problem \u2013 one that would also also help others in a similar situation. After bouncing ideas off various people in the conference corridors, Linda had formed a vague plan of starting a research retreat \u2013 renting a venue somewhere and inviting others to try to do research together. While joining an Open Philanthropy Project open office hour, Tom McGrath (who became our team preparation leader) overheard Linda talking about her idea and wanted to explore it further. Later, while couchsurfing at Sam Hilton\u2019s place, she met Remmelt Ellen (who became our meetings & logistics leader) and together they created and drew attention to a Facebook group and form where people could indicate their interest.\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\"Nandi Schoots (who became our interviews & programme leader) and David Kristoffersson (who became our international connector) quickly found the Facebook group and joined our first organisers\u2019 call. Our core organising team formed within a week, after which we scheduled regular video calls to sort out the format, what to call the event, where to organise it, and so on. We hit the ground running and coordinated well through Facebook chats and Zoom calls considering we were a bunch of international volunteers. Perhaps our team members were unusually dedicated because each of us had taken the initiative to reach out and join the group. We also deliberately made fast decisions on next actions and who would carry them out \u2013 thus avoiding the kind of dragged-out discussions where half of the team has to sit idly by to wait for conclusions that no one acts upon. Initially, we decided to run the first camp in July 2018 in either Berlin or the UK. Then, Las Palmas, Gran Canaria was suggested as an alternative in our Facebook group by Maia Pasek from Crow\u2019s Nest (sadly Maia passed away before the camp started).\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\"We decided to run a small pilot camp there in April to test how well the format worked \u2013 thinking that Gran Canaria was a cheap, attractive sub-tropical island with on-the-ground collaborators to sort out the venue (this ended up being mostly Karol Kubicki). However, in February a surprising number of researchers (32) submitted applications of mostly high quality \u2013 too many for our 12-person AirBnB apartment (a cancellable booking made by Greg Colbourn). Instead, we booked an entire hostel to run the full edition that we had originally envisaged for July, effectively shortening our planning time by 3 months. This forced us to be effective and focus on what was most important to make the camp happen. But we were also basically chasing the clock at every step of the organising process, which led to costly mistakes such as rushing out documents and spending insufficient time comparing available venues (we reviewed many more lessons learned in a 14-page internal document). Most of the original organisers were exhausted after the event finished and were not going to lead a second edition any time soon. Fortunately, some of the Gran Canaria camp participants are taking up the mantle to organise the second camp together with EA Czech Republic in Prague this October (for more on this see \u201cNext Camp\u201d below).\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\"Team formation: Each applicant was invited for an interview call (with the help of Markus Salmela), of which we accepted 25 for the camp (of these, 4 people were unable to join the event). From there, we invited participants to jot down their preferences for topics to work on and planned a series of calls to form research teams around the most popular topics. After forming 5 teams, we had an online preparation period of roughly 6 weeks to get up to speed on our chosen research topics (through Slack channels, calls and in-person chats). This minimised the need to study papers at the camp itself. However, it was up to each team to decide how to best spend this time \u2013 e.g. some divided up reading materials, or wrote research proposals and got feedback from senior researchers (including Victoria Krakovna, Stuart Armstrong and Owain Evans). Event structure: The camp consisted of coworking punctuated by team support sessions and participant-organised activities.\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\"Programme summary: Day 1: Arrival, starting ceremony Day 2: Team research Day 3: Team research Day 4: Research idea presentations, half day off Day 5: Team debugging, research ducking in pairs, team research Day 6: Inter-team hamming circles, team research, research ducking in pairs Day 7: Day off Day 8: Team research Day 9: Team research, AlphaZero presentation (participant initiative), career circle Day 10: Team research, research presentations, closing ceremony Day 11: Feedback form, departure The programme was split into three arcs (day 1-4, day 4-7, day 7-11) where the workload gradually intensified until it turned down again \u2013 hopefully enabling teams to do intensive work sprints while not burning out. The support sessions on day 5 and 6 were aimed at helping teams resolve bottlenecks and become more productive. Although a few participants mentioned they were surprisingly useful, doing them during daylight hours hindered teams from getting on with research. For future camps, we suggest having only optional Hamming circles and research ducking sessions in the evening. Participants also shared their own initiatives on the dining room blackboard such as morning yoga, beach walks, mountain hiking, going out for dinner, a clicker game and an AlphaZero presentation.\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\"We wholeheartedly recommend fostering unconference-style initiatives at research events \u2013 they give participants the freedom to make up for what you have missed. Two experienced Centre for Applied Rationality workshop mentors, Ben Sancetta and Anne Wissemann, had the job of supporting participants in sorting out any issues they or their team encountered, and helping ensure that everyone was happy (Anne also oversaw supplies). Luckily, everyone got along so well that Anne and Ben only had a handful of one-on-ones. Nevertheless, having them around was a treat for some participants, as it allowed them to drop in and vent whatever was on their mind, knowing that it would not unduly bother either of them. Budget:\n\n The total cost of organising the camp was \u20ac11,572 (excluding some items paid for by the organisers themselves). The funds were managed through the bank account of Effective Altruism Netherlands. Unspent money was transferred to the Czech Association for Effective Altruism for the next camp (they are open to donations if their EA Grant application for the camp gets delayed or rejected). Results: Each team has written a brief summary of the work they did during the camp (as well as future plans). Other outcomes include:\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\" The bounded rationality team has received funding from Paul Christiano to continue their work. The Gridworld team has written a blogpost and are making a GitHub pull request for their work to be added to the Safety Gridworlds repository. The Safe AF team is writing a paper on their results. At least 2 participants have changed their career plans towards working on AI safety (many participants were already junior researchers or had already made up their minds prior to the camp). 8 more participants reported an increase in their motivation and/or confidence in doing research work.\n\n As our Gran Canaria \u201cpilot camp\u201d grew in ambition we implicitly worked towards the outcomes we expected to see for the \u201cmain camp\u201d:\n\n Three or more draft papers have been written that are considered to be promising by the research community. Three or more researchers who participated in the project would obtain funding or a research role in AI safety/strategy in the year following the camp.\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\" It is too soon to say about whether the first goal will be met, although with one paper in preparation and one team having already obtained funding it is looking plausible. The second goal was already met less than a month after the camp. Improving the format The format of the AI Safety Camp is still under development. Here are two major points we would like to improve. Suggestions are welcome.\n\n Managing team onboarding: After the interviews, we accepted applicants on the condition that they would find a research team, which created uncertainty for them. Furthermore, we did not even enforce this rule (though perhaps we should have).\n\n Forming research teams that consist of people with a good fit for promising topics lies at the foundation of a productive camp. But it is also a complex problem with many variables and moving parts (e.g. Do we accept people first and form teams around these people, or do we form teams first and accept people based on their fit with a team? Should we choose research topics first and then decide who joins which team, or should we form teams first and then let them choose topics?). We handled this at the first camp by trying to do everything at the same time. Although this worked out okay, the onboarding process can be made easier to follow and smoother at future camps.\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\"Note: The irrationality team of 5 people ended up splitting into two sub-groups since one of the topics seemed too small in scope for 5 people. We suggest limiting group sizes to 4 people at future camps. Integrating outside advisors: Many senior AI Safety researchers replied slowly to our email requests to advise our teams, presumably because of busy schedules. This led to a dilemma:A. If we waited until we knew what the research topics would be, then we might not have gotten an answer from potential advisors in time. B. If we acted before topics had been selected, we would end up contacting many senior researchers who were not specialised in the final topics. At the first camp, we lacked time for working out a clear strategy, so teams ended up having to reach out to advisors we found. For future camps, it should be easier to connect advisors with teams given that the next organisers are already on the move. Hopefully, experienced researchers reading this post will also be inclined to offer a few spare hours to review research proposals and draft papers (please send us a short email).\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\" Next camps The next camps will happen in: 4-14 Oct 2018: Prague, Czechia in collaboration with the Czechia Association for Effective Altruism (they will also organize the Human-aligned AI Summer School in August)\n\n ~ March 2019: Blackpool, United Kingdom at the EA Hotel (offers free accommodation for researchers) If you\u2019ve gotten this far, we can use your contribution:\n\n Apply to join the Prague camp Email contact@aisafety.camp if you are considering\n\n advising research teams on their projects contributing your skills to organising camps funding future camps running your own edition next year criteria: experienced organisers who will run our general format & uphold a high quality standard that reflects well on the wider research community\n\n Join our Facebook group to stay informed\n\n Acknowledgement Centre for Effective Altruism (\u20ac2,961) Machine Intelligence Research Institute (\u20ac3,223) Greg Colbourn (\u20ac3,430) Lotta and Claes Linsefors (\u20ac4,000)\"\n- Title: The first AI Safety Camp & onwards, Author: Johannes", "\" AISC 1: Research Summaries\n\n The 2018 Gran Canaria AI safety camp teams have worked hard in the preparation of the camp and in the 10 day sprint. Each team has written a brief summary of the work they did during the camp: Irrationality Team: Christopher Galias, Johannes Heidecke, Dmitrii Krasheninnikov, Jan Kulveit, Nandi Schoots\n\n Our team worked on how to model human (ir)rationality in the context of value learning when trying to learn a human\u2019s reward function based on expert demonstrations with inverse reinforcement learning (IRL). We focussed on two different sub-topics: bounded rationality and time-correlated irrationality.\n\n Bounded rationality topic:\n\n We analyzed the difference between perfectly rational and boundedly rational agents and why the latter might provide a better model for human behavior, explaining many biases observed in human thinking. We looked at existing formalizations of bounded rationality, especially an information theoretic perspective introduced by Ortega and Braun. We started investigating how to model bounded rational agents for reinforcement learning problems. We began formalizing how to model the inverse step of IRL for bounded rational agents, based both on Maximum Causal Entropy IRL and Guided Cost Learning.\"\n- Title: AISC 1: Research Summaries, Author: Johannes", "\"We set up a small test environment with many satisficing solutions and an optimal solution which is hard to find. We collected human expert demonstrations for this environment and compared it to the performance of a fully rational computer agent. The observed differences support the claim that bounded rationality models are needed in IRL to extract adequate reward functions. We received funding from Paul Christiano to continue our work.\n\n\n\n Time-correlated irrationality topic:\n\n The project consists of 2 parts: introducing a Laplace prior on the softmax temperatures of the transitions of the Boltzmann-rational agent, and enforcing a correlation between the temperatures at nearby timesteps. During the camp we worked out the math & the algorithm for the first part, and have started working on the implementation. The second part of the project and the writeup will be done in the following months. We plan to both work remotely and meet up in person.\"\n- Title: AISC 1: Research Summaries, Author: Johannes", "\" Wisdom Team: Karl Koch, David Kristoffersson, Markus Salmela, Justin Shovelain We further developed tools for determining the harm versus benefit of projects on the long-term future:\n\n (Context: We have earlier work here, notably a decision tree for analyzing projects.) Heuristics: Worked extensively on developing practical heuristics for determining whether a technological development is net beneficial or harmful in the long run Scaffolding: Defined a wider context for the decision tree, to tell you when to use the decision tree and how to improve interventions/projects to be more good for the world. Race/competitive dynamics: Modeled some conditions of generating competitive races. Information concealment: Incorporated information from man-made disasters and information concealment\n\n Developed a potential existential risk reduction funding delegation strategy for rich donors:\n\n Analyzed how to maximize a funder\u2019s ability to update on data and use the knowledge of others, and yet mostly avoid the principal agent problem and Goodhart\u2019s law Developed a funding organization design with expert delegates, collaborative investment decisions, and strong self-improving elements\"\n- Title: AISC 1: Research Summaries, Author: Johannes", "\" Zero Safety Team: Vojta Kovarik, Igor Sieradzki, Michael \u015awi\u0119tek\n\n Goal: Better understand the strategy learned by Alpha Zero algorithm Implemented Alpha Zero in Gomoku, trained Alpha Zero in (a) 6*6 board, 4-in-a-row and (b) 8*8, 5-in-a-row Training the neural net in (a) took ~40M samples. We managed to train a new neural net using only 350 unique samples in such a way that the resulting strategy is very similar to the original Alpha Zero player. This led us to discover a weakness in the strategy learned by both the new Alpha Zero and the original one. Future plans: Test on more complex games, experiment with more robust ways of finding representative subsets of the training data, visualize these representative subsets in an automated way.\n\n Safe AF Team: James Bell, Linda Linsefors, Caspar Oesterheld, Joar Skalse\"\n- Title: AISC 1: Research Summaries, Author: Johannes", "\" Investigated the behaviour of common very simple machine learning algorithms in Newcomb like contexts, with the idea of trying to figure out what decision theory they are implicitly implementing. Specifically we looked at the epsilon-greedy and softmax algorithms for bandit problems. At each step these algorithms compute a probability distribution over actions and then draw their next action from that distribution. The reward for each action depended on the probability distribution that the algorithms had found as an intermediate step but they were trained in the standard way i.e. assuming that there was no such dependence. Formulated a selection of decision theory problems as bandit problems. Such bandit problems provide a general enough framework to include variants of playing a prisoners dilemma against a copy, evidential blackmail and death in Damascus. We found that the algorithms did not coherently follow any established decision theory, however they did show a preference for ratifiable choices of probability distribution and we were able to find some results on their convergence properties. We are writing a paper with our results.\"\n- Title: AISC 1: Research Summaries, Author: Johannes", "\" Side effects in Gridworlds Team: Jessica Cooper, Karol Kubicki, Gavin Leech, Tom McGrath\n\n Implemented a baseline Q-learning agent for gridworld environments. Implemented inverse reinforcement learning in the Sokoban gridworld from Deepmind\u2019s original paper. Created new gridworlds to cover a wider variety of side effects and expose more nuances, for instance the difficulty in defining \u201cleaving the environment unchanged\u201d when the environment is dynamic or stochastic. Code is available on our Github repository and Gavin Leech has written a blog post that goes into more detail.\n\n Future plans:\n\n Generalise the tools that we created to work with arbitrary pycolab environments. Add maximum entropy deep IRL. Submit a pull request with the above to the Safety Gridworlds repository in order to make it easier for others to get started doing machine learning safety research.\n\n\n\n Last but not least\u2026 We would like to thank those who have funded the camp: MIRI, CEA, Greg Colbourne, Lotta and Claes Linsefors.\"\n- Title: AISC 1: Research Summaries, Author: Johannes"], "embeddings_metadata_index": [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8], "articles_count": {"manual": 1, "https://aisafety.camp": 8}, "total_articles_count": 9, "total_char_count": 60781, "total_word_count": 9487, "total_sentence_count": 417, "total_block_count": 56, "sources_so_far": [], "info_types": {}, "embeddings": [[0.00027985445922240615, -0.03458615019917488, 0.00390674127265811, 0.021573254838585854, -0.03001871146261692, 0.028970209881663322, -0.017853233963251114, -0.017580335959792137, 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-0.004168979823589325, -0.004326867405325174, 0.004977213218808174, -0.02721678465604782, -0.03130682185292244, -0.01796909235417843, -0.00011371653818059713, -0.031366970390081406, -0.03623892366886139, -0.02216438762843609]]} \ No newline at end of file diff --git a/web/api/informed_assistant.py b/web/api/informed_assistant.py deleted file mode 100644 index 7435086..0000000 --- a/web/api/informed_assistant.py +++ /dev/null @@ -1,300 +0,0 @@ -# # ---------------------------------- web code ---------------------------------- - -# import json - -# from http.server import BaseHTTPRequestHandler - -# class handler(BaseHTTPRequestHandler): - -# # post request = calculate factorial of passed number -# def do_POST(self): -# self.send_response(200) -# self.send_header('Content-type', 'application/json') -# self.end_headers() -# content_length = int(self.headers['Content-Length']) -# post_data = self.rfile.read(content_length) -# data = json.loads(post_data) - -# results = {} - -# for i, link in enumerate(informed_assistant(data['query'])): -# results[i] = json.dumps(link.__dict__) - -# self.wfile.write(json.dumps(results).encode('utf-8')) - - -# # -------------------------------- non-web-code -------------------------------- -# import time -# import os -# import openai - -# import requests -# from typing import List, Dict -# import openai -# import tiktoken -# import asyncio - -# import config -# from semantic_search import get_top_k_blocks - - -# # OpenAI API key -# try: -# import config -# OPENAI_API_KEY = config.OPENAI_API_KEY -# except ImportError: -# OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY') -# openai.api_key = OPENAI_API_KEY - -# # OpenAI models -# EMBEDDING_MODEL = "text-embedding-ada-002" -# COMPLETIONS_MODEL = "gpt-3.5-turbo" - -# # OpenAI parameters -# LEN_EMBEDDINGS = 1536 -# MAX_LEN_PROMPT = 4095 # This may be 8191, unsure. - -# # Paths -# from pathlib import Path -# project_path = Path(__file__).parent.parent.parent -# PATH_TO_DATA = project_path / "web" / "api" / "data" / "alignment_texts.jsonl" # Path to the dataset .jsonl file. -# PATH_TO_EMBEDDINGS = project_path / "web" / "api" / "data" / "embeddings.npy" # Path to the saved embeddings (.npy) file. -# PATH_TO_DATASET = project_path / "web" / "api" / "data" / "dataset.pkl" # Path to the saved dataset (.pkl) file, containing the dataset class object. - - -# class Dataset: -# pass - -# class Block: -# def __init__(self, title: str, author: str, date: str, url: str, tags: str, text: str): -# self.title = title -# self.author = author -# self.date = date -# self.url = url -# self.tags = tags -# self.text = text - - -# MODERATION_ENDPOINT = "https://api.openai.com/v1/moderations" -# def moderate_query(query: str) -> List[str]: -# """This function uses the OpenAI Moderation API to check if a query contains any offensive language. - -# Args: -# query (str): The query to be checked. - -# Raises: -# Exception: If the API call fails. - -# Returns: -# List[str]: A list of categories that the query was flagged for. -# """ - -# headers = {"Content-Type": "application/json","Authorization": f"Bearer {OPENAI_API_KEY}"} - -# data = {"input": query} - -# response = requests.post(MODERATION_ENDPOINT, headers=headers, data=json.dumps(data)) -# flagged_categories = [] - -# if response.status_code == 200: -# moderation_results = response.json() -# flagged = moderation_results['results'][0]['flagged'] -# categories = moderation_results['results'][0]['categories'] - -# if flagged: -# for category, is_flagged in categories.items(): -# if is_flagged: -# flagged_categories.append(category) -# else: -# raise Exception(f"Error: {response.status_code} {response.reason}") - -# return flagged_categories - -# def limit_tokens(text: str, max_tokens: int, encoding_name: str = "cl100k_base") -> str: -# encoding = tiktoken.get_encoding(encoding_name) -# tokens = encoding.encode(text)[:max_tokens] -# return encoding.decode(tokens) - -# def generate_prompt(user_query: str, previous_dialogue: List[Dict[str, str]] = [], blocks: List[Block] = [], mode: str = "standard") -> List[Dict[str, str]]: -# """ -# This function generates a prompt in messages format for the OpenAI ChatCompletions API. -# First, it picks a system description using the mode. -# Second, it adds the previous dialogue to the prompt. -# Third, it adds an instruction to the prompt based on the mode. -# Fourth, it adds the context from the top-k most relevant blocks from the Alignment Research Dataset to the prompt. -# Fifth, it adds the user query to the prompt. - -# Messages take the following format: -# messages=[ -# {"role": "system", "content": "You are a helpful assistant."}, -# {"role": "user", "content": "Who won the world series in 2020?"}, -# {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."}, -# {"role": "user", "content": "Where was it played?"} -# ] - -# Args: -# user_query (str): The user query. -# previous_dialogue (List[Dict[str, str]]): The previous dialogue. Defaults to []. -# blocks (List[Block]): The top-k most relevant blocks from the Alignment Research Dataset. Defaults to []. -# mode (str): The mode of the assistant. Can be "standard", etc. Defaults to "standard". - -# Returns: -# List[Dict[str, str]]: The prompt in messages format. -# """ -# # Initialize prompt -# prompt = [] - -# # Generate system description -# if mode == "standard": -# prompt.append({"role": "system", "content": "You are a helpful assistant knowledgeable about AI Alignment and Safety."}) -# # elif mode == "other": -# else: -# raise Exception(f"Invalid mode: {mode}") - -# # Add previous dialogue -# for message in previous_dialogue: -# prompt.append(message) - -# # Add instruction -# if mode == "standard": -# instruction_prompt = "Please answer my question (after the Q:) using the provided context." -# prompt.append({"role": "assistant", "content": instruction_prompt}) -# # elif mode == "other": -# else: -# raise Exception(f"Invalid mode: {mode}") - -# # Add context from top-k blocks -# if blocks is None: -# return "Context missing." -# context_prompt = "Context:\n\n" -# for i, block in enumerate(blocks): -# context_prompt += f"[{i}] {block.text}\n\n" -# context_prompt = context_prompt[:-2] -# context_prompt = limit_tokens(context_prompt, 2000) -# prompt.append({"role": "user", "content": f"{context_prompt}"}) - -# # Add user query -# prompt.append({"role": "user", "content": f"Q: {user_query}"}) - -# return prompt - -# def normal_completion(prompt: List[Dict[str, str]]) -> str: -# """ -# This function uses the OpenAI ChatCompletions API to answer a user query. - -# Args: -# messages (Dict[str, str]): A dictionary containing the system prompt and user prompt, in addition to any previous dialogue. - -# Returns: -# str: The answer generated by the API. - -# Raises: -# Exception: If the API call fails. -# """ -# try: -# return openai.ChatCompletion.create( -# model=COMPLETIONS_MODEL, -# messages=prompt -# )["choices"][0]["message"]["content"] -# except Exception as e: -# print(e) -# return "I'm sorry, I failed to process your query. Please try again. If the problem persists, please contact the administrator." - -# async def stream_completion(prompt: List[Dict[str, str]], stream_delay: float = 0.1) -> str: -# """ -# This function uses the OpenAI ChatCompletions API to answer a user query, streaming the response. - -# Args: -# messages (Dict[str, str]): A dictionary containing the system prompt and user prompt, in addition to any previous dialogue. - -# Returns: -# str: The answer generated by the API. - -# Raises: -# Exception: If the API call fails. -# """ -# try: -# async for part in await openai.ChatCompletion.acreate( -# model=COMPLETIONS_MODEL, -# messages=prompt, -# stream=True -# ): -# finish_reason = part["choices"][0]["finish_reason"] -# if "content" in part["choices"][0]["delta"]: -# content = part["choices"][0]["delta"]["content"] -# yield content -# elif finish_reason: -# print(f"Stream finished: {finish_reason}") -# break -# except Exception as e: -# print(e) -# response = "I'm sorry, I failed to process your query. Please try again. If the problem persists, please contact the administrator." -# for word in response.split(): -# time.sleep(stream_delay) -# yield f"{word} " - -# def informed_assistant(user_query: str, previous_dialogue: List[Dict[str, str]] = [], k: str = 10, mode: str = "standard", HyDE: bool = False, stream: bool = True, stream_delay: float = 0.1) -> str: -# """ -# This function uses the OpenAI ChatCompletions API to answer a user query. -# It first checks if the query is offensive, and if so, raises an exception. -# Then, it finds the top-k most relevant blocks from the Alignment Research Dataset and uses them as context for the ChatCompletions API. -# It uses the blocks to generate a prompt for the ChatCompletions API. -# Finally, it uses the ChatCompletions API to generate an answer to the user query. - -# Args: -# user_query (str): The user query. -# previous_dialogue (List[Dict[str, str]]): The previous dialogue. Defaults to []. -# k (str): The number of blocks to use as context. -# mode (str): The mode to use for the ChatCompletions API. Defaults to "standard". -# HyDE (bool): Whether to use the HyDE technique for semantic search. This makes search slower, but better. Defaults to False. -# stream (bool): Whether to stream the results from the ChatCompletions API. Defaults to True. -# stream_delay (float): The delay between each word in the streamed response when streaming a hard-coded response. Defaults to 0.1. - -# Returns: -# str: The answer to the user query. - -# Raises: -# Exception: If the query is offensive. -# """ -# # 1. Check if the query is offensive -# flagged_categories: List[str] = moderate_query(user_query) -# if len(flagged_categories) > 0: -# response = f"Your query contains offensive language. Please try again." -# if stream: -# for word in response.split(): -# time.sleep(stream_delay) -# yield f"{word} " -# else: -# return response - -# # 2. Find the top-k most relevant blocks from the Alignment Research Dataset -# top_k_blocks: List[Block] = get_top_k_blocks(user_query, k, HyDE) - -# # 3. Generate a prompt for the ChatCompletions API -# prompt: List[Dict[str, str]] = generate_prompt(user_query, previous_dialogue, top_k_blocks, mode) - -# # 4. Use the top-k most relevant blocks as context for the ChatCompletions API, and generate an answer to the user query -# if stream: -# return stream_completion(prompt) -# else: -# return normal_completion(prompt) - - -# if __name__ == "__main__": -# # Test the question answering function -# user_query = "Within the area of mitigating AI risk, there are several broad classes of action being taken. What does Technical safety research focus on?" -# previous_dialogue = [ -# {"role": "assistant", "content": "Hi! I know all about AI Alignment. Ask me a question!"}, -# ] -# k = 10 -# mode = "standard" -# HyDE = True -# stream = False # Doesn't quite work yet - -# print(asyncio.run(informed_assistant(user_query, previous_dialogue, k, mode, HyDE, stream))) - -# # if stream: -# # for part in informed_assistant(user_query, previous_dialogue, k, mode, HyDE, stream): -# # print(part, end="") -# # else: -# # print(informed_assistant(user_query, previous_dialogue, k, mode, HyDE, stream)) \ No newline at end of file diff --git a/web/api/semantic_search.py b/web/api/semantic_search.py index aaac965..f589f40 100644 --- a/web/api/semantic_search.py +++ b/web/api/semantic_search.py @@ -1,3 +1,37 @@ +# ---------------------------------- web code ---------------------------------- + +import json +import dataclasses +from http.server import BaseHTTPRequestHandler + +@dataclasses.dataclass +class Block: + title: str + author: str + date: str + url: str + tags: str + text: str + +class Encoder(json.JSONEncoder): + def default(self, o): + return dataclasses.asdict(o) if dataclasses.is_dataclass(o) else super().default(o) + +class handler(BaseHTTPRequestHandler): + + def do_POST(self): + + self.send_response(200) + self.send_header('Content-type', 'application/json') + self.end_headers() + + content_length = int(self.headers['Content-Length']) + post_data = self.rfile.read(content_length) + data = json.loads(post_data) + + self.wfile.write(json.dumps(get_top_k_blocks(data['query']), cls = Encoder).encode('utf-8')) + + # -------------------------------- non-web-code -------------------------------- import time import os @@ -48,15 +82,6 @@ class Dataset: self.info_types = dataset_dict['info_types'] self.embeddings = np.array(dataset_dict['embeddings']) -class Block: - def __init__(self, title: str, author: str, date: str, url: str, tags: str, text: str): - self.title = title - self.author = author - self.date = date - self.url = url - self.tags = tags - self.text = text - def get_embedding(text: str) -> np.ndarray: """Get the embedding for a given text. The function will retry with exponential backoff if the API rate limit is reached, up to 4 times. @@ -129,34 +154,3 @@ def get_top_k_blocks(user_query: str, k: int = 10, HyDE: bool = False) -> List[B return blocks -# ---------------------------------- web code ---------------------------------- -from http.server import BaseHTTPRequestHandler - - -class handler(BaseHTTPRequestHandler): - - def do_POST(self): - self.send_response(200) - self.send_header('Content-type', 'application/json') - self.end_headers() - content_length = int(self.headers['Content-Length']) - post_data = self.rfile.read(content_length) - data = json.loads(post_data) - - results = {} - - query = data['query'] - if 'k' in data: - k = data['k'] - else: - k=10 - if 'HyDE' in data: - HyDE = data['HyDE'] - else: - HyDE = False - - for i, block in enumerate(get_top_k_blocks(query, k=k, HyDE=HyDE)): - results[i] = json.dumps(block.__dict__) - - self.wfile.write(json.dumps(results).encode('utf-8')) - \ No newline at end of file diff --git a/web/src/pages/index.tsx b/web/src/pages/index.tsx index cab9877..61acc78 100644 --- a/web/src/pages/index.tsx +++ b/web/src/pages/index.tsx @@ -39,10 +39,19 @@ const Home: NextPage = () => { // them back from OpenAI - I think we can just do this with a websocket, which // shouldn't be too much harder. +type SemanticEntry = { + title: string; + author: string; + date: string; + url: string; + tags: string; + text: string; +}; + const SearchBox: React.FC = () => { const [query, setQuery] = useState(""); - const [results, setResults] = useState<{title: string, author: string, date: string, url: string, tags: string, text: string}[]>([]); + const [results, setResults] = useState([]); const [loading, setLoading] = useState(false); const semantic_search = async (query: String) => { @@ -55,19 +64,18 @@ const SearchBox: React.FC = () => { body: JSON.stringify({query: query}), }) + if (!res.ok) { + setLoading(false); + return "load failure: " + res.status; + } + const data = await res.json(); - setLoading(false); - - // data looks like - // { 0: "{'title': 'First Title', 'author': 'Bob Miles', 'date': 'March 1st, 2023', 'url': 'https://example.com', 'tags': ['tag1', 'tag2'], 'text': 'This is the content of the article'}", - // 1: "{'title': 'Second Title', 'author': 'Frank Ocean', 'date': 'March 6th, 2023', 'url': 'https://ai.com', 'tags': ['tag3', 'tag4'], 'text': 'This is the content of the article'}" - // } - // so we need to convert it to a list of objects - - return Object.keys(data).map((key) => JSON.parse(data[key])) || [{title: "error", author: "error", date: "error", url: "error", tags: ["error"], text: "error"}]; + return data; }; + + return ( <>
{ // store in a form so that submits @@ -86,15 +94,17 @@ const SearchBox: React.FC = () => { - {loading ?

loading...

: ( // display results in list + { + loading ?

loading...

: + typeof results === "string" ?

{results}

:
    - {results.map((result) => ( -
  • + {results.map((result, i) => ( +
  • {result.text}
  • ))}
- )} + } ); };