7:00 – ASRC TL

• Fast editor for very large files: EmEditor
• Topic Modeling Systems and Interfaces
  • The 4Humanities “WhatEvery1Says” project conducted a comparative analysis in 2016 of the following topic modeling systems/interfaces. As a result, it chose to implement Andrew Goldstone’s DFR-browser for its own work.
• Deep Learning for Video Game Playing
  • In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards.
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• Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
  • A longstanding goal in deep learning research has been to precisely characterize training and generalization. However, the often complex loss landscapes of neural networks have made a theory of learning dynamics elusive. In this work, we show that for wide neural networks the learning dynamics simplify considerably and that, in the infinite width limit, they are governed by a linear model obtained from the first-order Taylor expansion of the network around its initial parameters. Furthermore, mirroring the correspondence between wide Bayesian neural networks and Gaussian processes, gradient-based training of wide neural networks with a squared loss produces test set predictions drawn from a Gaussian process with a particular compositional kernel. While these theoretical results are only exact in the infinite width limit, we nevertheless find excellent empirical agreement between the predictions of the original network and those of the linearized version even for finite practically-sized networks. This agreement is robust across different architectures, optimization methods, and loss functions.
• AI Safety Needs Social Scientists
  • We believe the AI safety community needs to invest research effort in the human side of AI alignment. Many of the uncertainties involved are empirical, and can only be answered by experiment. They relate to the psychology of human rationality, emotion, and biases. Critically, we believe investigations into how people interact with AI alignment algorithms should not be held back by the limitations of existing machine learning. Current AI safety research is often limited to simple tasks in video games, robotics, or gridworlds, but problems on the human side may only appear in more realistic scenarios such as natural language discussion of value-laden questions. This is particularly important since many aspects of AI alignment change as ML systems increase in capability.
• Started on slides for Thursday
• Working on white paper. Adding in the paper above on Deep Learning for Video Game Playing.
• From Command Dysfunction
  • There are three perceptual biases that affect the accuracy of one’s view of the environment: the conditioning of expectations, the resistance to change and the impact of ambiguity. (Page 14)
  • There are three primary areas in which cognitive biases degrade the accuracy of judgment within a decision process: (Page 16)
    • the attribution of causality,
    • the evaluation of probability and
      • availability bias is a rule of thumb that works on the ease with which one can remember or recall other similar instances
      • anchoring bias is a phenomenon in which decision makers adjust too little from their initial judgments as additional evidence becomes available.
      • overconfidence bias is a tendency for individual decision makers to be subjectively overconfident about the extent and accuracy of their knowledge
      • Other typical problems in estimating probabilities derive from the misunderstanding of statistics.
    • the evaluation of evidence.
      • Decision makers tend to value consistent information from a small data set over more variable information from a larger sample.
      • Absence of Evidence bias is when decision makers to miss data in complicated problems. Analysts often do not recognize that data is missing and adjust the certainty of their inferences accordingly.
      • The Persistence of Impressions bias follows a natural tendency to maintain first impressions concerning causality. It appears that the initial association of evidence to an outcome forms a strong cognitive linkage.
  • AI systems can help with these errors of judgement though, since that can be explicitly programmed or placed in the training set.
  • These are all contributors to Normal Accidents
  • What about incorporating doctrine, rules of engagement and standard operating procedures? These can change dynamically and at different scales. (Allison Model II – Organizational Processes)
  • Also, it should be possible to infer the adversaries’ rules and then find areas in the latent space that they do not cover. They will be doing the same to us. How to guard against this? Diversity?
  • While the division of labor and SOP specialization is intended to make the organization efficient, the same division generates requirements to coordinate the intelligent collection and analysis of data.41 The failure to coordinate the varied perceptions and interests within the organization can lead to a number of uncoordinated rational decisions at the lower echelons, which in tum lead to an overall irrational outcome. (Page 20)
  • There are two common cultural biases that deserve mention for their role in forming erroneous perceptions: arrogance and projection. Arrogance is the attitude of superiority over others or the opposing side. It can manifest as a national or individual perception. In the extreme case, it forgoes any serious search of alternatives or decision analysis beyond what the decision maker has already decided. It can become highly irrational. The projection bias sees the rest of the world through one’s own values and beliefs, thus tending to estimate the opposition’s intentions, motivations and capabilities as one’s own. (Page 21)
    • Again, a good case for well-designed AI/ML. That being said, a commander’s misaligned biases may discount the AI system
  • The overconfidence or hubris bias tends toward an overreaching inflation of one’s abilities and strengths. In the extreme it promotes a prideful self-confidence that is self-intoxicating and oblivious to rational limits. A decision maker affected with hubris will in his utter aggressiveness invariably be led to surprise and eventual downfall, The Hubris-Nemesis Complex is dangerous mindset that combines hubris (self intoxicating “pretension to godliness”) and nemesis (“vengeful desire” to wreak havoc and destroy). Leaders possessing this bias combination are not easily deterred or compelled by normal or rational solutions (Page 22)
  • Three major decision stress areas include the consequential weight of the decision, uncertainty and the pressure of time (Page 23)
    • Crisis settings complicate the use of rational and analytical decision processes in two ways. First, they add numerous unknowns, which in tum create many possible alternatives to the decision problem. Second, they reduce the time available to process and evaluate data, choose a course of action, and execute it.
    • As uncertainty becomes severe, decision makers begin resorting to maladaptive search and evaluation methods to reach conclusions. Part of this may stem from a desire to avoid the anxiety of being unsure, an intolerance of ambiguity. It may also be that analytical approaches are difficult when the link between the data and the outcomes is not predictable 
      • Still true for ML systems, even without stress. Being forced to make shorter searches of the solution space (not letting the results converge, etc. could be an issue)
  • The logic of dealing with the time pressure normally follows a somewhat standard pattern. Increasing time pressure first leads to an acceleration of information processing. Decision makers and their organizations will pick up the pace by expending additional resources to maintain existing decision strategies. As the pace begins to outrun in-place processing capabilities, decision makers reduce their data search and processing. In some cases this translates to increased selectivity, which the decision maker biases or weights toward details considered more important. In other cases, it does not change data collection but leads to a shallower data analysis. As the pace continues to increase, decision strategies begin to change. At this point major problems can creep into the process. The problems result from maladaptive strategies (satisficing, analogies, etc.) that save time but misrepresent data to produce inappropriate solutions. The lack of time also prevents critical introspection for perceptual and cognitive biases. In severe time pressure cases, the process may deteriorate to avoidance, denial or panic. (Page 26)
    • The goal is to create this in the adversary, but not us. Which makes this in many respects a algorithm efficiency / processing power arms race
  • In some decision situations, a timely, relatively correct response is better than an absolutely correct response that is made too late. In other words, the situation generates a tension between analysis and speed. (Page 30)
  • The Recognition-Primed Decision (RPD) process works in the following manner. First, an experienced decision maker recognizes a problem situation as familiar or prototypical. The recognition brings with it a solution. The recognition also evokes an appreciation for what additional information to monitor: plausible outcomes, typical reactions, timing cues and causal dynamics. Second, given time, the decision maker evaluates his solution for suitability by testing it through mental simulation for pitfalls and needed adjustments. Normally, the decision maker implements the first solution “on the run” and makes adjustments as required. The decision maker will not be discard a solution unless it becomes plain that it is unworkable. If so, he will attempt a second option, if available. The RPD process is one of satisficing. It assumes that experienced decision makers identify a first solution that is “reasonably good” and are capable of mentally projecting its implementation. The RPD process also assumes that experienced decision makers are able to implement their one solution at any time during the process. (Page 31)
    • It should be possible to train systems to approximate this, possibly at different levels of abstraction
  • The RPD is a descriptive model that explains how experienced decision makers work problems in high stress decision situations (Page 31)
    • It is reflexive, and as such well suited to ML techniques, assuming there is enough data…
  • Situations that require the careful deployment of resources and analysis of abstract data, such as anticipating an enemy’s course of action, require an analytical approach. If there is time for analysis, a rational process normally provides a better solution for these kinds of problems (page 31)
    • This is not what AI/ML is good at. As reaction requirements become tighter, these actions will have to be run in “slow motion” offline and used to train the system.
  • The RPD model provides some insight as to how operational commanders survive in high-load, ambiguous and time pressured situations. The key seems to be experience. The experience serves as the base for what may be seen as an intuitive way to overcome stress. (Page 32)
    • This is why training with attribution may be the best way. “Ms. XXX trained this system and I trust her” may be the best option. We may want to build a “stable” of machine trainers.
  • Decision makers with more experience will tend to employ intuitive methods more often than analytical processes. This reliance on pattern recognition among experienced commanders may provide an opportunity for an adversary to manipulate the patterns to his advantage in deception operations. (Page 32)

7:00 – 6:00 ASRC TL IRAD

• Something to listen to tomorrow morning? Tracing the Spread of Fake News
  • Two years after a presidential election that shocked so many, we are still trying to understand the role that fake news sources played, and how a swarm of propaganda clouded social media. Now a comprehensive study has looked carefully at the impact of untrustworthy online sources in the election, with some surprising results, and some suggestions for how to avoid problems in the future. In the studio for this episode is David Lazer, Professor of Political Science and Computer and Information Science at Northeastern University. He is one of the authors of Fake news on Twitter during the 2016 U.S. presidential election, which was just published in Science Magazine. 
• Finished my writeup on Clockwork Muse. Now I need to make slides by Thursday.
• Visual analytics for collaborative human-machine confidence in human-centric active learning tasks
  • Active machine learning is a human-centric paradigm that leverages a small labelled dataset to build an initial weak classifier, that can then be improved over time through human-machine collaboration. As new unlabelled samples are observed, the machine can either provide a prediction, or query a human ‘oracle’ when the machine is not confident in its prediction. Of course, just as the machine may lack confidence, the same can also be true of a human ‘oracle’: humans are not all-knowing, untiring oracles. A human’s ability to provide an accurate and confident response will often vary between queries, according to the duration of the current interaction, their level of engagement with the system, and the difficulty of the labelling task. This poses an important question of how uncertainty can be expressed and accounted for in a human-machine collaboration. In short, how can we facilitate a mutually-transparent collaboration between two uncertain actors—a person and a machine—that leads to an improved outcome? In this work, we demonstrate the benefit of human-machine collaboration within the process of active learning, where limited data samples are available or where labelling costs are high. To achieve this, we developed a visual analytics tool for active learning that promotes transparency, inspection, understanding and trust, of the learning process through human-machine collaboration. Fundamental to the notion of confidence, both parties can report their level of confidence during active learning tasks using the tool, such that this can be used to inform learning. Human confidence of labels can be accounted for by the machine, the machine can query for samples based on confidence measures, and the machine can report confidence of current predictions to the human, to further the trust and transparency between the collaborative parties. In particular, we find that this can improve the robustness of the classifier when incorrect sample labels are provided, due to unconfidence or fatigue. Reported confidences can also better inform human-machine sample selection in collaborative sampling. Our experimentation compares the impact of different selection strategies for acquiring samples: machine-driven, human-driven, and collaborative selection. We demonstrate how a collaborative approach can improve trust in the model robustness, achieving high accuracy and low user correction, with only limited data sample selections.
• Look into principle of least effort and game theory. See if there is anything
  • Human Behaviour and the Principle of Least Effort. An Introduction to Human Ecology
    • Subtitled “An introduction to human ecology,” this work attempts systematically to treat “least effort” (and its derivatives) as the principle underlying a multiplicity of individual and collective behaviors, variously but regularly distributed. The general orientation is quantitative, and the principle is widely interpreted and applied. After a brief elaboration of principles and a brief summary of pertinent studies (mostly in psychology), Part One (Language and the structure of the personality) develops 8 chapters on its theme, ranging from regularities within language per se to material on individual psychology. Part Two (Human relations: a case of intraspecies balance) contains chapters on “The economy of geography,” “Intranational and international cooperation and conflict,” “The distribution of economic power and social status,” and “Prestige values and cultural vogues”—all developed in terms of the central theme. 20 pages of references with some annotation, keyed to the index. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
  • Decision Making and the Avoidance of Cognitive Demand
    • Behavioral and economic theories have long maintained that actions are chosen so as to minimize demands for exertion or work, a principle sometimes referred to as the “law of less work.” The data supporting this idea pertain almost entirely to demands for physical effort. However, the same minimization principle has often been assumed also to apply to cognitive demand. We set out to evaluate the validity of this assumption. In six behavioral experiments, participants chose freely between courses of action associated with different levels of demand for controlled information processing. Together, the results of these experiments revealed a bias in favor of the less demanding course of action. The bias was obtained across a range of choice settings and demand manipulations, and was not wholly attributable to strategic avoidance of errors, minimization of time on task, or maximization of the rate of goal achievement. Remarkably, the effect also did not depend on awareness of the demand manipulation. Consistent with a motivational account, avoidance of demand displayed sensitivity to task incentives and co-varied with individual differences in the efficacy of executive control. The findings reported, together with convergent neuroscientific evidence, lend support to the idea that anticipated cognitive demand plays a significant role in behavioral decision-making.
  • Intuition, deliberation, and the evolution of cooperation
    • Humans often cooperate with strangers, despite the costs involved. A long tradition of theoretical modeling has sought ultimate evolutionary explanations for this seemingly altruistic behavior. More recently, an entirely separate body of experimental work has begun to investigate cooperation’s proximate cognitive underpinnings using a dual process framework: Is deliberative self-control necessary to reign in selfish impulses, or does self-interested deliberation restrain an intuitive desire to cooperate? Integrating these ultimate and proximate approaches, we introduce dual-process cognition into a formal game theoretic model of the evolution of cooperation. Agents play prisoner’s dilemma games, some of which are one-shot and others of which involve reciprocity. They can either respond by using a generalized intuition, which is not sensitive to whether the game is oneshot or reciprocal, or pay a (stochastically varying) cost to deliberate and tailor their strategy to the type of game they are facing. We find that, depending on the level of reciprocity and assortment, selection favors one of two strategies: intuitive defectors who never deliberate, or dual-process agents who intuitively cooperate but sometimes use deliberation to defect in one-shot games. Critically, selection never favors agents who use deliberation to override selfish impulses: Deliberation only serves to undermine cooperation with strangers. Thus, by introducing a formal theoretical framework for exploring cooperation through a dual-process lens, we provide a clear answer regarding the role of deliberation in cooperation based on evolutionary modeling, help to organize a growing body of sometimes conflicting empirical results, and shed light on the nature of human cognition and social decision making.
  • Complexity Aversion: Influences of Cognitive Abilities, Culture and System of Thought
    • Complexity aversion describes the preference of decision makers for less complex options that cannot be explained by expected utility theory. While a number of research articles investigate the effects of complexity on choices, up to this point there exist only theoretical approaches aiming to explain the reasons behind complexity aversion. This paper presents two experimental studies that aim to fill this gap. The first study considers subjects’ cognitive abilities as a potential driver of complexity aversion. Cognitive skills are measured in a cognitive reflection test and, in addition, are approximated by subjects’ consistency of choices. In opposition to our hypothesis, subjects with higher cognitive skills display stronger complexity aversion compared to their peers. The second study deals with cultural background. The experiment was therefore conducted in Germany and in Japan. German subjects prefer less complex lotteries while Japanese are indifferent regarding choice complexity.
  • Space Time Dynamics of Insurgent Activity in Iraq
    • This paper describes analyses to determine whether there is a space-time dependency for insurgent activity. The data used for the research were 3 months of terrorist incidents attributed to the insurgency in Iraq during U.S. occupation and the methods used are based on a body of work well established using police recorded crime data. It was found that events clustered in space and time more than would be expected if the events were unrelated, suggesting communication of risk in space and time and potentially informing next event prediction. The analysis represents a first but important step and suggestions for further analysis addressing prevention or suppression of future incidents are briefly discussed.
• Large teams develop and small teams disrupt science and technology
  • One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence^1,2,3. Increases in team size have been attributed to the specialization of scientific activities³, improvements in communication technology^4,5, or the complexity of modern problems that require interdisciplinary solutions^6,7,8. This shift in team size raises the question of whether and how the character of the science and technology produced by large teams differs from that of small teams. Here we analyse more than 65 million papers, patents and software products that span the period 1954–2014, and demonstrate that across this period smaller teams have tended to disrupt science and technology with new ideas and opportunities, whereas larger teams have tended to develop existing ones. Work from larger teams builds on more-recent and popular developments, and attention to their work comes immediately. By contrast, contributions by smaller teams search more deeply into the past, are viewed as disruptive to science and technology and succeed further into the future—if at all. Observed differences between small and large teams are magnified for higher-impact work, with small teams known for disruptive work and large teams for developing work. Differences in topic and research design account for a small part of the relationship between team size and disruption; most of the effect occurs at the level of the individual, as people move between smaller and larger teams. These results demonstrate that both small and large teams are essential to a flourishing ecology of science and technology, and suggest that, to achieve this, science policies should aim to support a diversity of team sizes.
• Meeting with Panos about JuryRoom. Interesting! Tony Smith looks like someone to ping. Need to ask Panos