{
  localUrl: '../page/2nd.html',
  arbitalUrl: 'https://arbital.com/p/2nd',
  rawJsonUrl: '../raw/2nd.json',
  likeableId: '1581',
  likeableType: 'page',
  myLikeValue: '0',
  likeCount: '0',
  dislikeCount: '0',
  likeScore: '0',
  individualLikes: [],
  pageId: '2nd',
  edit: '1',
  editSummary: '',
  prevEdit: '0',
  currentEdit: '1',
  wasPublished: 'true',
  type: 'comment',
  title: '"Presumably the advantage of..."',
  clickbait: '',
  textLength: '787',
  alias: '2nd',
  externalUrl: '',
  sortChildrenBy: 'recentFirst',
  hasVote: 'false',
  voteType: '',
  votesAnonymous: 'false',
  editCreatorId: 'PaulChristiano',
  editCreatedAt: '2016-03-16 16:39:08',
  pageCreatorId: 'PaulChristiano',
  pageCreatedAt: '2016-03-16 16:39:08',
  seeDomainId: '0',
  editDomainId: 'EliezerYudkowsky',
  submitToDomainId: '0',
  isAutosave: 'false',
  isSnapshot: 'false',
  isLiveEdit: 'true',
  isMinorEdit: 'false',
  indirectTeacher: 'false',
  todoCount: '0',
  isEditorComment: 'false',
  isApprovedComment: 'true',
  isResolved: 'false',
  snapshotText: '',
  anchorContext: 'The problem of conservatism is an extension of the supervised learning problem in which, given labeled examples, we try to generate further cases that are almost certainly positive examples of a concept, rather than demanding that we label all possible further examples correctly\\.  Another way of looking at it is that, given labeled training data, we don't just want to learn a simple concept that fits the labeled data, we want to learn a simple small concept that fits the data \\- one that, subject to the constraint of labeling the training data correctly, predicts as few other positive examples as possible\\.',
  anchorText: 'we want to learn a simple small concept that fits the data \\- one that, subject to the constraint of labeling the training data correctly, predicts as few other positive examples as possible\\.',
  anchorOffset: '423',
  mergedInto: '',
  isDeleted: 'false',
  viewCount: '479',
  text: 'Presumably the advantage of this approach---rather than simply learning to imitate the human burrito-making process or even human burritos, is that it might be easier to do. Is that right?\n\nI think that's a valid goal, but I'm not sure how well "conservative generalizations" actually address the problem. Certainly it still leaves you at a significant disadvantage relative to a non-conservative agent, and it seems more natural to first consider direct approaches to making imitation effective (like bootstrapping + [meeting halfway](https://medium.com/ai-control/mimicry-maximization-and-meeting-halfway-c149dd23fc17)).\n\nOf course all of these approaches still involve a lot of extra work, so maybe the difference is are expectations about how different research angles will work out.',
  metaText: '',
  isTextLoaded: 'true',
  isSubscribedToDiscussion: 'false',
  isSubscribedToUser: 'false',
  isSubscribedAsMaintainer: 'false',
  discussionSubscriberCount: '1',
  maintainerCount: '1',
  userSubscriberCount: '0',
  lastVisit: '',
  hasDraft: 'false',
  votes: [],
  voteSummary: 'null',
  muVoteSummary: '0',
  voteScaling: '0',
  currentUserVote: '-2',
  voteCount: '0',
  lockedVoteType: '',
  maxEditEver: '0',
  redLinkCount: '0',
  lockedBy: '',
  lockedUntil: '',
  nextPageId: '',
  prevPageId: '',
  usedAsMastery: 'false',
  proposalEditNum: '0',
  permissions: {
    edit: {
      has: 'false',
      reason: 'You don't have domain permission to edit this page'
    },
    proposeEdit: {
      has: 'true',
      reason: ''
    },
    delete: {
      has: 'false',
      reason: 'You don't have domain permission to delete this page'
    },
    comment: {
      has: 'false',
      reason: 'You can't comment in this domain because you are not a member'
    },
    proposeComment: {
      has: 'true',
      reason: ''
    }
  },
  summaries: {},
  creatorIds: [
    'PaulChristiano'
  ],
  childIds: [],
  parentIds: [
    'taskagi_open_problems'
  ],
  commentIds: [],
  questionIds: [],
  tagIds: [],
  relatedIds: [],
  markIds: [],
  explanations: [],
  learnMore: [],
  requirements: [],
  subjects: [],
  lenses: [],
  lensParentId: '',
  pathPages: [],
  learnMoreTaughtMap: {},
  learnMoreCoveredMap: {},
  learnMoreRequiredMap: {},
  editHistory: {},
  domainSubmissions: {},
  answers: [],
  answerCount: '0',
  commentCount: '0',
  newCommentCount: '0',
  linkedMarkCount: '0',
  changeLogs: [
    {
      likeableId: '0',
      likeableType: 'changeLog',
      myLikeValue: '0',
      likeCount: '0',
      dislikeCount: '0',
      likeScore: '0',
      individualLikes: [],
      id: '8630',
      pageId: '2nd',
      userId: 'PaulChristiano',
      edit: '1',
      type: 'newEdit',
      createdAt: '2016-03-16 16:39:08',
      auxPageId: '',
      oldSettingsValue: '',
      newSettingsValue: ''
    },
    {
      likeableId: '0',
      likeableType: 'changeLog',
      myLikeValue: '0',
      likeCount: '0',
      dislikeCount: '0',
      likeScore: '0',
      individualLikes: [],
      id: '8629',
      pageId: '2nd',
      userId: 'PaulChristiano',
      edit: '0',
      type: 'newParent',
      createdAt: '2016-03-16 16:33:24',
      auxPageId: 'taskagi_open_problems',
      oldSettingsValue: '',
      newSettingsValue: ''
    }
  ],
  feedSubmissions: [],
  searchStrings: {},
  hasChildren: 'false',
  hasParents: 'true',
  redAliases: {},
  improvementTagIds: [],
  nonMetaTagIds: [],
  todos: [],
  slowDownMap: 'null',
  speedUpMap: 'null',
  arcPageIds: 'null',
  contentRequests: {}
}