# Phil 1.5.20

• Roger pointed me at ‘Most advanced, yet acceptable’: Typicality and novelty as joint predictors of aesthetic preference in industrial design
• Typicality and novelty have often been shown to be related to aesthetic preference of human artefacts. Since a typical product is rarely new and, conversely, a novel product will not often be designated as typical, the positive effects of both features seem incompatible. In three studies it was shown that typicality (operationalized as ‘goodness of example’) and novelty are jointly and equally effective in explaining the aesthetic preference of consumer products, but that they suppress each other’s effect. Direct correlations between both variables and aesthetic preference were not significant, but each relationship became highly significant when the influence of the other variable was partialed out. In Study 2, it was furthermore demonstrated that the expertise level of observers did not affect the relative contribution of novelty and typicality. It was finally shown (Study 3) that a more ‘objective’ measure of typicality, central tendency — operationalized as an exemplar’s average similarity to all other members of the category — yielded the same effect of typicality on aesthetic preference. In sum, all three studies showed that people prefer novel designs as long as the novelty does not affect typicality, or, phrased differently, they prefer typicality given that this is not to the detriment of novelty. Preferred are products with an optimal combination of both aspects.
• Trust is earned in the smallest of moments. It is earned not through heroic deeds, or even highly visible actions, but through paying attention, listening, and gestures of genuine care and connection. Brené Brown
• If we share group membership with other across a range of social settings it becomes more likely that the actors will face future exchanges with reversed roles (Resnick, 2002). Repeated interactions with stable identities also allow the trustor to accumulate knowledge about the trustee and to make better predictions about his behavior. Thus, by extrapolating from past behavior trust in future encounters can grow. The mechanics of trust: A framework for research and design
• Dissertation
• Simulation – done
• Maps
• Human Study
• Discussion
• Conclusions

# Phil 1.3.20

7:00 – 5:00 ASRC PhD

• Diversity promotes collective intelligence in large groups but harms small ones
• Diverse groups are often said to be less susceptible to decision errors resulting from herding and polarization. Thus, the fact that many modern interactions happen in a digital world, where filter bubbles and homophily bring people together, is an alarming yet poorly understood phenomenon. But online interactions are also characterized by unprecedented scale, where thousands of individuals can exchange ideas simultaneously. Evidence in collective intelligence however suggests that small (rather than large) groups tend to do better in complex information environments. Here, we adopt the well-established framework of social learning theory (from the fields of ecology and cultural evolution) to explore the causal link between diversity and performance as a function of group size. In this pre-registered study, we experimentally manipulate both group diversity and group size, and measure individual and group performance in realistic geo-political judgements. We find that diversity hinders the performance of individuals in small groups, but improves it in large groups. Furthermore, aggregating opinions of modular crowds composed of small independent but homogeneous groups achieves better results than using non-modular diverse ones. The results are explained by greater conflict of opinion in diverse groups, which negatively impacts small (but not large) groups. The present work sheds light on the causal mechanisms underlying the success (or lack thereof) of diverse groups in digital environments, and suggests that diversity research can benefit from adopting a wider social learning perspective.
• “I Just Google It”: Folk Theories of Distributed Discovery
• A significant minority of people do not follow news regularly, and a growing number rely on distributed discovery (especially social media and search engines) to stay informed. Here, we analyze folk theories of news consumption. On the basis of an inductive analysis of 43 in-depth interviews with infrequent users of conventional news, we identify three complementary folk theories (“news finds me,” “the information is out there,” and “I don’t know what to believe”) that consumers draw on when making sense of their information environment. We show that the notion of folk theories help unpack the different, complementary, sometimes contradictory cultural resources people rely on as they navigate digital media and public affairs, and we argue that studying those who rarely engage directly with news media but do access information via social media and search provides a critical case study of the dynamics of an environment increasingly defined by platforms.
• Dissertation
• Working on Lit Review overview
• Fixed the margins for blockquotes by creating a more flexible changemargin command
\def\changemargin#1#2{\list{}{\rightmargin#2\leftmargin#1}\item[]}
\let\endchangemargin=\endlist
• Which is used like this
\begin{changemargin}{1.5cm}{1.5cm}
They were one man, not thirty. For as the one ship that held them all; though it was put together of all contrasting things-oak, and maple, and pine wood; iron, and pitch, and hemp-yet all these ran into each other in the one concrete hull, which shot on its way, both balanced and directed by the long central keel; even so, all the individualities of the crew, this man’s valor, that man’s fear; guilt and guiltiness, all varieties were welded into oneness, and were all directed to that fatal goal which Ahab their one lord and keel did point to.
\end{changemargin}
• Fixed a bunch of things, including blockquotes
• Biological Basis – done
• Human Belief Spaces – done
• Dimension Reduction – done
• Orientation – done
• Velocity – done
• Social Influence Horizon – done
• Bones in a hut – started
• 1:00 Dentist

# Phil 12.24.19

ASRC PhD 6:30 – 9:30

• The Worldwide Web of Chinese and Russian Information Controls
• The global diffusion of Chinese and Russian information control technology and techniques has featured prominently in the headlines of major international newspapers.1 Few stories, however, have provided a systematic analysis of both the drivers and outcomes of such diffusion. This paper does so – and finds that these information controls are spreading more efficiently to countries with hybrid or authoritarian regimes, particularly those that have ties to China or Russia. Chinese information controls spread more easily to countries along the Belt and Road Initiative; Russian controls spread to countries within the Commonwealth of Independent States. In arriving at these findings, this working paper first defines the Russian and Chinese models of information control and then traces their diffusion to the 110 countries within the countries’ respective technological spheres, which are geographical areas and spheres of influence to which Russian and Chinese information control technology, techniques of handling information, and law have diffused.
• Wrote up some preliminary thoughts on Antonio’s Autonomous Shuttles concept. Need to share the doc
• Listening to World Affairs Council, and the idea of B-Corporations came up, which are a kind of contractual mechanism for diversity injection?
• Certified B Corporations are a new kind of business that balances purpose and profit. They are legally required to consider the impact of their decisions on their workers, customers, suppliers, community, and the environment. This is a community of leaders, driving a global movement of people using business as a force for good.
• Deciding to leave this out of the dissertation, since I’m more focussed on individual interfaces with global effects as opposed to corporate legal structures. It’s just too tangential.
• Dissertation
• H3 conclusions – done!

# Phil 11.20.19

7:00 – 5:00 ASRC

• Reading User Experience as a Legitimacy Trap, by Paul Dourish. Solid stuff.
• Why are HCI researchers and practitioners now on the wrong side of many of the problematic developments in the contemporary technology landscape? Why is it so challenging for us to reformulate the objectives of our discipline and the central values of our educational programs? It is because those were not the basis upon which we argued for the legitimacy of our practice. By legitimizing HCI and its role in technology production in terms of user experience, user delight, and user acceptance—which were only ever means toward other ends—we have ceded the space from which we could argue for the considerations that were actually at the center of the discipline’s ambitions (to nurture and sustain human dignity and flourishing.).
• I think I can cite this in the conclusions section, where I think I need to address the issue that some might not consider this appropriate research for an HCI PhD
•  Dissertation
• More discussion. Send a note out to folks to workshop on Friday?
• Mostly spent my time cleaning up the beginning. Didn’t write much new, but clarified and tightened up.
• Found the original Bellman cite for the curse of dimensionality
• Evolver
• Need to change chromosomes so that they point to the history index in the genome. The args Dict for the user function can be created from that, and the value/parameter spreadsheet can be too.
• That reconstruction will need to ripple through the arguments axis to the function as well. That might be the problem that I was having yesterday.
• AIMS Telemetry meeting
• Need to start an MS-Project chart for nextGen efforts. ASRC doesn’t seem to have Project in its stack?

# Phil 11.15.19

7:00 – 4:00 ASRC GOES

• Morning Meeting with Wayne
• Quotes need page numbers
• Found out more about why Victor’s defense was postponed. Became nervous as a result
• Dissertation – starting the discussion section
• I’m thinking about objective functions and how individual and group objectives work together, particularly in extreme conditions.
• In extreme situations, the number of options available to an agent or group is diminished. There may be only one move apparently available in a chess game. A race car at the limits of adhesion has only one path through a turn. A boxer has a tiny window to land a blow. As the floodwaters rise, the range of options diminish. In a tsunami, there is only one option – run.
• Here’s a section from article 2 of the US Military Code of Conduct (from here):
• Surrender is the willful act of members of the Armed Forces turning themselves over to enemy forces when not required by utmost necessity or extremity. Surrender is always dishonorable and never allowed. When there is no chance for meaningful resistance, evasion is impossible, and further fighting would lead to their death with no significant loss to the enemy, members of Armed Forces should view themselves as “captured” against their will versus a circumstance that is seen as voluntarily “surrendering.”
• If a machine is trained for combat, will it have learned the concept of surrender? According to the USCoC, no, surrender is never allowed. A machine trained to “win”, like Google’s Alpha Go, do not learn to resign. That part has to be explicitly coded in (from Wired):
• According to David Silver, another DeepMind researcher who led the creation of AlphaGo, the machine will resign not when it has zero chance of winning, but when its chance of winning dips below 20 percent. “We feel that this is more respectful to the way humans play the game,” Silver told me earlier in the week. “It would be disrespectful to continue playing in a position which is clearly so close to loss that it’s almost over.”
• Human organizations, like armys and companies are a kind of superhuman intelligence, made up of human parts with their own objective functions. In the case of a company, that objective is often to maximise shareholder value (NYTimes by Milton Friedman):
• But the doctrine of “social responsibility” taken seriously would extend the scope of the political mechanism to every human activity. It does not differ in philosophy from the most explicitly collectivist doctrine. It differs only by professing to believe that collectivist ends can be attained without collectivist means. That is why, in my book “Capitalism and Freedom,” I have called it a “fundamentally subversive doctrine” in a free society, and have said that in such a society, “there is one and only one social responsibility of business – to use its resources and engage in activities designed to increase its profits so long as it stays within the rules of the game, which is to say, engages in open and free competition without deception fraud.”
• When any kind of population focuses singly on a particular goal, it creates shared social reality. The group aligns with the goal and pursues it. In the absence of the awareness of the environmental effects of this orientation, it is possible to stampede off a cliff, or shape the environment so that others deal with the consequences of this goal.
• It is doubtful that many people deliberately choose to be obese. However, markets and the profit motive have resulted in a series of innovations, ranging from agriculture to aisles of high-fructose corn syrup-based drinks at the local supermarket. The logistics chain that can create and sell a 12oz can of brand-name soda for about 35 cents is a modern miracle, optimized to maximize income for every link in the chain. But in this case, the costs of competition have created an infinite supply of heavily marketed empty calories. Even though we are aware at some level that we should rarely – if ever – have one of these beverages, they are consumed by the billions
• The supply chain for soda is a form of superintelligence, driven by a simple objective function. It is resilient and adaptive, capable of dealing with droughts, wars, and changing fashion. It is also contributing to the deaths of approximately 300,000 Americans annually.
• How is this like combat? Reflexive vs. reflective. Low-diversity thinking are a short-term benefit for many organizations, they enable first-mover advantage, which can serve to crowd out more diverse (more expensive) thinking. More here…

# Phil 11.13.19

7:00 – 3:00 ASRC

3rd Annual DoD AI Industry Day

From Stewart Russell, via BBC Business Daily and the AI Alignment podcast:

Although people have argued that this creates a filter bubble or a little echo chamber where you only see stuff that you like and you don’t see anything outside of your comfort zone. That’s true. It might tend to cause your interests to become narrower, but actually that isn’t really what happened and that’s not what the algorithms are doing. The algorithms are not trying to show you the stuff you like. They’re trying to turn you into predictable clickers. They seem to have figured out that they can do that by gradually modifying your preferences and they can do that by feeding you material. That’s basically, if you think of a spectrum of preferences, it’s to one side or the other because they want to drive you to an extreme. At the extremes of the political spectrum or the ecological spectrum or whatever image you want to look at. You’re apparently a more predictable clicker and so they can monetize you more effectively.

So this is just a consequence of reinforcement learning algorithms that optimize click-through. And in retrospect, we now understand that optimizing click-through was a mistake. That was the wrong objective. But you know, it’s kind of too late and in fact it’s still going on and we can’t undo it. We can’t switch off these systems because there’s so tied in to our everyday lives and there’s so much economic incentive to keep them going.

So I want people in general to kind of understand what is the effect of operating these narrow optimizing systems that pursue these fixed and incorrect objectives. The effect of those on our world is already pretty big. Some people argue that operation’s pursuing the maximization of profit have the same property. They’re kind of like AI systems. They’re kind of super intelligent because they think over long time scales, they have massive information, resources and so on. They happen to have human components, but when you put a couple of hundred thousand humans together into one of these corporations, they kind of have this super intelligent understanding, manipulation capabilities and so on.

• Predicting human decisions with behavioral theories and machine learning
• Behavioral decision theories aim to explain human behavior. Can they help predict it? An open tournament for prediction of human choices in fundamental economic decision tasks is presented. The results suggest that integration of certain behavioral theories as features in machine learning systems provides the best predictions. Surprisingly, the most useful theories for prediction build on basic properties of human and animal learning and are very different from mainstream decision theories that focus on deviations from rational choice. Moreover, we find that theoretical features should be based not only on qualitative behavioral insights (e.g. loss aversion), but also on quantitative behavioral foresights generated by functional descriptive models (e.g. Prospect Theory). Our analysis prescribes a recipe for derivation of explainable, useful predictions of human decisions.
• Adversarial Policies: Attacking Deep Reinforcement Learning
• Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent’s observations. This might lead one to wonder: is it possible to attack an RL agent simply by choosing an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial? We demonstrate the existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents. The adversarial policies reliably win against the victims but generate seemingly random and uncoordinated behavior. We find that these policies are more successful in high-dimensional environments, and induce substantially different activations in the victim policy network than when the victim plays against a normal opponent. Videos are available at this http URL.

# Phil 10.28.19

Capacity, Bandwidth, and Compositionality in Emergent Language Learning

• Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compositionality. We note that most of those works have focused on communicative bandwidth as being of primary importance. While important, it is not the only contributing factor. In this paper, we investigate the learning biases that affect the efficacy and compositionality of emergent languages. Our foremost contribution is to explore how capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.

• In the early 60s, Robert Axelrod was a math major messing around with refrigerator-sized computers. Then a dramatic global crisis made him wonder about the space between a rock and a hard place, and whether being good may be a good strategy. With help from Andrew Zolli and Steve Strogatz, we tackle the prisoner’s dilemma, a classic thought experiment, and learn about a simple strategy to navigate the waters of cooperation and betrayal. Then Axelrod, along with Stanley Weintraub, takes us back to the trenches of World War I, to the winter of 1914, and an unlikely Christmas party along the Western Front.
• Need to send a note for them to look into Axelrod’s “bully” saddle point

7:00 – ASRC GOES

• Dissertation – Nearly done with the agent cartography section?
• CTO Rehearsal – 10:30 – 12:00 done
• ML Dinner – 4:30 fun!
• Meeting With Aaron M
• More thinking about what to do with the paper. We decided to try for the CHI4EVIL workshop, and then try something like IEEE Spectrum. I think I’d like to reframe it around the concept of Expensive Information and Automation. Try to tie together AI weapons, spam filters, and deepfakes
• Automation makes negotiation more difficult, locks in trajectories
• Handing off responsibility to automation amplifies opportunities and destructive potential
• OODA loop could be generalized if you look at it from the perspective of attention.

# Phil 10.26.19

The dynamics of norm change in the cultural evolution of language

• What happens when a new social convention replaces an old one? While the possible forces favoring norm change—such as institutions or committed activists—have been identified for a long time, little is known about how a population adopts a new convention, due to the difficulties of finding representative data. Here, we address this issue by looking at changes that occurred to 2,541 orthographic and lexical norms in English and Spanish through the analysis of a large corpora of books published between the years 1800 and 2008. We detect three markedly distinct patterns in the data, depending on whether the behavioral change results from the action of a formal institution, an informal authority, or a spontaneous process of unregulated evolution. We propose a simple evolutionary model able to capture all of the observed behaviors, and we show that it reproduces quantitatively the empirical data. This work identifies general mechanisms of norm change, and we anticipate that it will be of interest to researchers investigating the cultural evolution of language and, more broadly, human collective behavior.

When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization

• It has been shown in the past that a multistart hillclimbing strategy compares favourably to a standard genetic algorithm with respect to solving instances of the multimodal problem generator. We extend that work and verify if the utilization of diversity preservation techniques in the genetic algorithm changes the outcome of the comparison. We do so under two scenarios: (1) when the goal is to find the global optimum, (2) when the goal is to find all optima.
A mathematical analysis is performed for the multistart hillclimbing algorithm and a through empirical study is conducted for solving instances of the multimodal problem generator with increasing number of optima, both with the hillclimbing strategy as well as with genetic algorithms with niching. Although niching improves the performance of the genetic algorithm, it is still inferior to the multistart hillclimbing strategy on this class of problems.
An idealized niching strategy is also presented and it is argued that its performance should be close to a lower bound of what any evolutionary algorithm can do on this class of problems.

# Phil 10.10.19

7:00 – 4:00 ASRC GOES

• The Daily has an episode on how to detach from environmental reality and create a social reality stampede
• Dissertation, working on finishing up the “unexpected findings” piece of the research plan
• Tie together explore/exploit, the Three Patterns, and M&R three behaviors.
• Also, set up the notion that it was initially explore OR exploit, with no thought given to the middle ground. M&R foreshadowed that there would be, though
• Registered for Navy AI conference Oct 22
• Get together with Vadim to see how the physics are going on Tuesday?
• More evolver
• installed the new timeseriesML2
• The test run blew up with a tensorflow/core/framework/op_kernel.cc:1622] OP_REQUIRES failed at cwise_ops_common.cc:82 error. Can’t find any direct help, though maybe try this?
• Reduce your Batchsize of datagen.flow (by default set 32 so you have to set 8/16/24 )
• Figured it out – I’m saving models in memory. Need to write them out instead.
• Swing by campus and check on Will

# Phil 7.19.19

7:00 – 4:30 ASRC GEOS

• Still looking at what’s wrong with my NK model. I found Random Boolean Networks, when looking for “random binary networks kauffman example“. It also has a bibliography that looks helpful as well
• Introduction to Random Boolean Networks
• The goal of this tutorial is to promote interest in the study of random Boolean networks (RBNs). These can be very interesting models, since one does not have to assume any functionality or particular connectivity of the networks to study their generic properties. Like this, RBNs have been used for exploring the configurations where life could emerge. The fact that RBNs are a generalization of cellular automata makes their research a very important topic. The tutorial, intended for a broad audience, presents the state of the art in RBNs, spanning over several lines of research carried out by different groups. We focus on research done within artificial life, as we cannot exhaust the abundant research done over the decades related to RBNs.
• I can add a display that shows this:
• Got that working
• Rewrote so that there is an evolve without a fitness test. Trying to set up transition patterns like this:
• The thing is, I don’t see how the K part works here…
• I think I got it working!
• Complex and Adaptive Dynamical Systems: A Primer
• An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems.
It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.

# Phil 7.16.19

7:00 – 6:30ASRC GEOS

• Working more on NK Models. I have the original paper – Towards a general theory of adaptive walks on rugged landscapes, and I’ve pulled out my copy of The Origins of Order
• Determine if I have the evaluation function right
• Draw the networks
• Draw an N/K/Fitness landscape?
• As an aside, I think that an NK model can be modified to use backpropagation rather than mutation. That could be interesting.
• Ok, here’s everything working the way I think it should work, but I’m not sure it’s right….
• Need to get back to Antonio about authorship and roles. I think that it makes sense if he can get a sense of what – done
• Discovered the trumptwitterarchive, which is downloadable. Would like to build a network of the retweets and tagging by sentiment, gender and race.
• Code review with Chris. Unfortunately, it was more like an interrogation than a tour. My sense is that he was expecting us to ask questions and we were expecting a presentation.
• It went ok, but the audio connection was terrible

# Phil 5.28.19

Phil 7:00 – 5:00 ASRC NASA GEOS

• Factors Motivating Customization and Echo Chamber Creation Within Digital News Environments
• With the influx of content being shared through social media, mobile apps, and other digital sources – including fake news and misinformation – most news consumers experience some degree of information overload. To combat these feelings of unease associated with the sheer volume of news content, some consumers tailor their news ecosystems and purposefully include or exclude content from specific sources or individuals. This study explores customization on social media and news platforms through a survey (N = 317) of adults regarding their digital news habits. Findings suggest that consumers who diversify their online news streams report lower levels of anxiety related to current events and highlight differences in reported anxiety levels and customization practices across the political spectrum. This study provides important insights into how perceived information overload, anxiety around current events, political affiliations and partisanship, and demographic characteristics may contribute to tailoring practices related to news consumption in social media environments. We discuss these findings in terms of their implications for industry, policy, and theory
• More JASSS paper
• Installing new IntelliJ and re-indexing
• Discovered a few bugs with the JsonUtils.find. Fixed and submitted a version to StackOverflow. Eeeep!

# Phil 5.13.19

7:00 – 3:00 ASRC NASA GEOS-R

• Really good Ted Radio Hour on Jumpstarting Creativity
• More dissertation
• Integrating Artificial Intelligence into Weapon Systems is up!
• Played around with the speed of agents, hoping to make some super-slow agents, but the way that the speed is updated is an accumulation of speed influences set by the neighbors, based on influence distance. To do this, I need to set the minmax separate from the initial variance
• Help Aaron with MatrixScalar – Nope
• Slides for talk -Yep, all day

# Phil 5.6.19

7:00 – 5:00 ASRC GOES-R

• Finished the AI/ML paper with Aaron M over the weekend. I need to have him ping me when it goes in. I think it turned out pretty well, even when cut down to 7 pages (with references!! Why, IEEE, why?)
• Sent a copy to Wayne, and distributed around work. Need to put in on ArXiv on Thursday
• Starting to pull parts from phifel.com to make the lit review for the dissertation. Those reviews may have had a reason after all!
• And oddly (though satisfying), I wound up adding a section on Moby-Dick as a way of setting up the rest of the lit review
• More Matrix scalar class. Basically a satisfying day of just writing code.
• Need to fix IEEE letter and take a self-portrait. Need to charge up the good camera

# Phil 5.1.19

7:00 – 7:00 ASRC NASA AIMS

• Added lit review section to the dissertation, and put the seven steps of sectarianism in.
• Spent most of yesterday helping Aaron with TimeSeriesML. Currently working on a JSON util that will get a value on a provided path
• Had to set up python at the module and not project level, which was odd. Here’s how: www.jetbrains.com/help/idea/2016.1/configuring-global-project-and-module-sdks.html#module_sdk
• Done!
    def lfind(self, query_list:List, target_list:List, targ_str:str = "???"):
for tval in target_list:
if isinstance(tval, dict):
return self.dfind(query_list[0], tval, targ_str)
elif tval == query_list[0]:
return tval

def dfind(self, query_dict:Dict, target_dict:Dict, targ_str:str = "???"):
for key, qval in query_dict.items():
# print("key = {}, qval = {}".format(key, qval))
tval = target_dict[key]
if isinstance(qval, dict):
return self.dfind(qval, tval, targ_str)
elif isinstance(qval, list):
return self.lfind(qval, tval, targ_str)
else:
if qval == targ_str:
return tval
if qval != tval:
return None

def find(self, query_dict:Dict):
# pprint.pprint(query_dict)
result = self.dfind(query_dict, self.json_dict)
return result


• It’s called like this:
ju = JsonUtils("../../data/output_data/lstm_structure.json")
# ju.pprint()
print("result 1 = {}".format(result))
print("result 2 = {}".format(result))
• Here’s the results:
result 1 = [None, 12, 1]
result 2 = 666.0
• Got Aaron’s code running!
• Meeting with Joel
• A quicker demo that I was expecting, though I was able to walk through how to create and use Corpus Manager and LMN. Also, we got a bug where the column index for the eigenvector didn’t exist. Fixed that in JavaUtils.math.Labeled2DMatrix.java
• Meeting with Wayne
• Walked through the JASSS paper. Need to make sure that the lit review is connected and in the proper order
• Changed the title of the dissertation to
• Stampede Theory: Mapping Dangerous Misinformation at Scale
• Solidifying defense over the winter break, with diploma in the Spring
• Mentioned the “aikido with drones” concept. Need to make an image. Actually, I wonder if there is a way for that model to be used for actually getting a grant to explore weaponized AI in a way that isn’t directly mappable to weapons systems, but is close enough to reality that people will get the point.
• Also discussed the concept of managing runaway AI with the Sanhedrin-17a concept, where unanimous agreement to convict means acquittal.  Cities had Sanhedrin of 23 Judges and the Great Sanhedrin had 71 Judges en.wikipedia.org/wiki/Sanhedrin
• Rav Kahana says: In a Sanhedrin where all the judges saw fit to convict the defendant in a case of capital law, they acquit him. The Gemara asks: What is the reasoning for this halakha? It is since it is learned as a tradition that suspension of the trial overnight is necessary in order to create a possibility of acquittal. The halakha is that they may not issue the guilty verdict on the same day the evidence was heard, as perhaps over the course of the night one of the judges will think of a reason to acquit the defendant. And as those judges all saw fit to convict him they will not see any further possibility to acquit him, because there will not be anyone arguing for such a verdict. Consequently, he cannot be convicted.