# Phil 6.11.18

7:00 – 6:00 ASRC MKT

• More Bit by Bit. Reading the section on ethics. It strikes me that simulation could be a way to cut the PII Gordion Knot in some conditions. If a simulation can be developed that generates statistically similar data to the desired population, then the simulated data and the simulation code can be released to the research community. The dataset becomes infinite and adjustable, while the PII data can be held back. Machine learning systems trained on the simulated data can then be evaluated on the confidential data. The differences in the classification by the ML systems between real data and simulated data can also provide insight into the gaps in fidelity of the simulated data, which would provide an ongoing improvement to the simulation, which could in turn be released to the community.
• Continuing with the cleanup of the SASO paper. Mostly done but some trimming of redundent bits and the “Ose Simple Trick” paragraph.
• Monday prices:
• Fika
• Come up with 3-5 options for a finished state for the dissertation. It probably ranges from “pure theory” through “instance based on theory” to “a map generated by the system that matches the theory”
• Once the SASO paper is in, set up a “wine and cheese” get together for the committee to go over the current work and discuss changes to the next phase
• Start on a new IRB. Emphasize how everyone will have the same system to interact with, though their interactions will be different. Emphasize that the system has to allow open interaction to provide the best chance to realize theoretical results.
• Will and I are on the hook for a Fika about LaTex

# Phil 6.7.18

7:00 – 4:30 ASRC MKT

• Che Dorval
• The SLT meeting went well, apparently. Need to determine next steps
• Back to Bit by Bit. Reading about mass collaboration. eBird looks very interesting. All kinds of social systems involved here.
• Research
• Deep Multi-Species Embedding
• Understanding how species are distributed across landscapes over time is a fundamental question in biodiversity research. Unfortunately, most species distribution models only target a single species at a time, despite strong ecological evidence that species are not independently distributed. We propose Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding to multiple species as well as vectors representing environmental covariates into a common high-dimensional feature space via a deep neural network. Applied to bird observational data from the citizen science project \textit{eBird}, we demonstrate how the DMSE model discovers inter-species relationships to outperform single-species distribution models (random forests and SVMs) as well as competing multi-label models. Additionally, we demonstrate the benefit of using a deep neural network to extract features within the embedding and show how they improve the predictive performance of species distribution modelling. An important domain contribution of the DMSE model is the ability to discover and describe species interactions while simultaneously learning the shared habitat preferences among species. As an additional contribution, we provide a graphical embedding of hundreds of bird species in the Northeast US.
• Start fixing This one Simple Trick
• Highlighted all the specified changes. There are a lot of them!
• Started working on figure 2, and realized (after about an hour of Illustrator work) that the figure is correct. I need to verify each comment before fixing it!
• Researched NN anomaly detection. That work seems to have had its heyday in the ’90s, with more conventional (but computationally intensive) methods being preferred these days.
• I also thought that Dr. Li’s model had a time-orthogonal component for prediction, but I don’t think that’s true. THe NN is finding the frequency and bounds on its own.
• Wrote up a paragraph expressing my concerns and sent to Aaron.

# Phil 6.1.18

7:00 – 6:00 ASRC MKT

• Bot stampede reaction to “evolution” in a thread about UNIX. This is in this case posting scentiment against the wrong thing. There are layers here though. It can also be advertising. Sort of the dark side of diversity injection.
• Seems like an explore/exploit morning
• Autism on “The Leap”: Neurotypical and Neurodivergent (Neurodiversity)
• From a BBC Business Daily show on Elon Musk
• Thomas Astebro (Decision Science): The return to independent invention: evidence of unrealistic optimism, risk seeking or skewness loving?
• Examining a sample of 1,091 inventions I investigate the magnitude and distribution of the pre‐tax internal rate of return (IRR) to inventive activity. The average IRR on a portfolio investment in these inventions is 11.4%. This is higher than the risk‐free rate but lower than the long‐run return on high‐risk securities and the long‐run return on early‐stage venture capital funds. The portfolio IRR is significantly higher, for some ex anteidentifiable classes of inventions. The distribution of return is skew: only between 7‐9% reach the market. Of the 75 inventions that did, six realised returns above 1400%, 60% obtained negative returns and the median was negative.
• Myth of first mover advantage
• Conventional wisdom would have us believe that it is always beneficial to be first – first in, first to market, first in class. The popular business literature is full of support for being first and legions of would-be business leaders, steeped in the Jack Welch school of business strategy, will argue this to be the case. The advantages accorded to those who are first to market defines the concept of First Mover Advantage (FMA). We outline why this is not the case, and in fact, that there are conditions of applicability in order for FMA to hold (and these conditions often do not hold). We also show that while there can be advantages to being first, from an economic perspective, the costs generally exceed the benefits, and the full economics of FMA are usually a losing proposition. Finally, we show that increasingly, we live in a world where FMA is eclipsed by innovation and format change, rendering the FMA concept obsolete (i.e. strategic obsolescence).
• More Bit by Bit
• Investigating the Effects of Google’s Search Engine Result Page in Evaluating the Credibility of Online News Sources
• Recent research has suggested that young users are not particularly skilled in assessing the credibility of online content. A follow up study comparing students to fact checkers noticed that students spend too much time on the page itself, while fact checkers performed “lateral reading”, searching other sources. We have taken this line of research one step further and designed a study in which participants were instructed to do lateral reading for credibility assessment by inspecting Google’s search engine result page (SERP) of unfamiliar news sources. In this paper, we summarize findings from interviews with 30 participants. A component of the SERP noticed regularly by the participants is the so-called Knowledge Panel, which provides contextual information about the news source being searched. While this is expected, there are other parts of the SERP that participants use to assess the credibility of the source, for example, the freshness of top stories, the panel of recent tweets, or a verified Twitter account. Given the importance attached to the presence of the Knowledge Panel, we discuss how variability in its content affected participants’ opinions. Additionally, we perform data collection of the SERP page for a large number of online news sources and compare them. Our results indicate that there are widespread inconsistencies in the coverage and quality of information included in Knowledge Panels.
• White paper
• Note that belief maps are cultural artifacts, so comparing someone from one belief space to others in a shared physical belief environment can be roughly equivalent to taking the dot product of the belief space vectors that you need to compare. This could produce a global “alignment map” that can suggest how aligned, opposed, or indifferent a population might be with respect to an intervention, ranging from medical (Ebola teams) to military (special forces operations).
• Similar maps related to wealth in Rwanda based on phone metadata: Blumenstock, Joshua E., Gabriel Cadamuro, and Robert On. 2015. “Predicting Poverty and Wealth from Mobile Phone Metadata.” Science350 (6264):1073–6. https://doi.org/10.1126/science.aac4420
• Added a section about how mapping belief maps would afford prediction about local belief, since overall state, orientation and velocity could be found for some individuals who are geolocated to that area and then extrapolated over the region.

# Phil 5.31.18

7:00 – ASRC MKT

• Via BBC Business Daily, found this interesting post on diversity injection through lunch table size:
• KQED is playing America Abroad – today on russian disinfo ops:
• Sowing Chaos: Russia’s Disinformation Wars
• Revelations of Russian meddling in the 2016 US presidential election were a shock to Americans. But it wasn’t quite as surprising to people in former Soviet states and the EU. For years they’ve been exposed to Russian disinformation and slanted state media; before that Soviet propaganda filtered into the mainstream. We don’t know how effective Russian information warfare was in swaying the US election. But we do know these tactics have roots going back decades and will most likely be used for years to come. This hour, we’ll hear stories of Russian disinformation and attempts to sow chaos in Europe and the United States. We’ll learn how Russia uses its state-run media to give a platform to conspiracy theorists and how it invites viewers to doubt the accuracy of other news outlets. And we’ll look at the evolution of internet trolling from individuals to large troll farms. And — finally — what can be done to counter all this?
• Some interesting papers on the “Naming Game“, a form of coordination where individuals have to agree on a name for something. This means that there is some kind of dimension reduction involved from all the naming possibilities to the agreed-on name.
• The Grounded Colour Naming Game
• Colour naming games are idealised communicative interactions within a population of artificial agents in which a speaker uses a single colour term to draw the attention of a hearer to a particular object in a shared context. Through a series of such games, a colour lexicon can be developed that is sufficiently shared to allow for successful communication, even when the agents start out without any predefined categories. In previous models of colour naming games, the shared context was typically artificially generated from a set of colour stimuli and both agents in the interaction perceive this environment in an identical way. In this paper, we investigate the dynamics of the colour naming game in a robotic setup in which humanoid robots perceive a set of colourful objects from their own perspective. We compare the resulting colour ontologies to those found in human languages and show how these ontologies reflect the environment in which they were developed.
• Group-size Regulation in Self-Organised Aggregation through the Naming Game
• In this paper, we study the interaction effect between the naming game and one of the simplest, yet most important collective behaviour studied in swarm robotics: self-organised aggregation. This collective behaviour can be seen as the building blocks for many others, as it is required in order to gather robots, unable to sense their global position, at a single location. Achieving this collective behaviour is particularly challenging, especially in environments without landmarks. Here, we augment a classical aggregation algorithm with a naming game model. Experiments reveal that this combination extends the capabilities of the naming game as well as of aggregation: It allows the emergence of more than one word, and allows aggregation to form a controllable number of groups. These results are very promising in the context of collective exploration, as it allows robots to divide the environment in different portions and at the same time give a name to each portion, which can be used for more advanced subsequent collective behaviours.
• More Bit by Bit. Could use some worked examples. Also a login so I’m not nagged to buy a book I own.
• Descriptive and injunctive norms – The transsituational influence of social norms.
• Three studies examined the behavioral implications of a conceptual distinction between 2 types of social norms: descriptive norms, which specify what is typically done in a given setting, and injunctive norms, which specify what is typically approved in society. Using the social norm against littering, injunctive norm salience procedures were more robust in their behavioral impact across situations than were descriptive norm salience procedures. Focusing Ss on the injunctive norm suppressed littering regardless of whether the environment was clean or littered (Study 1) and regardless of whether the environment in which Ss could litter was the same as or different from that in which the norm was evoked (Studies 2 and 3). The impact of focusing Ss on the descriptive norm was much less general. Conceptual implications for a focus theory of normative conduct are discussed along with practical implications for increasing socially desirable behavior.
• Construct validity centers around the match between the data and the theoretical constructs. As discussed in chapter 2, constructs are abstract concepts that social scientists reason about. Unfortunately, these abstract concepts don’t always have clear definitions and measurements.
• Simulation is a way of implementing theoretical constructs that are measurable and testable.
• Hyperparameter Optimization with Keras
• Recognizing images from parts Kaggle winner
• White paper
• Storyboard meeting
• The advanced analytics division(?) needs a modeling and simulation department that builds models that feed ML systems.
• Meeting with Steve Specht – adding geospatial to white paper

# Phil 5.22.18

8:00 – 5:00 ASRC MKT

• EAMS meeting
• Rational
• Sensitivity knn. Marching cubes, or write into space. Pos lat/lon altitude speed lat lon (4 dimensions)
• Do they have flight path?
• Memory
• Retraining (batch)
• inference real time
• How will time be used
• Much discussion of simulation
• End-to-end Machine Learning with Tensorflow on GCP
• In this workshop, we walk through the process of building a complete machine learning pipeline covering ingest, exploration, training, evaluation, deployment, and prediction. Along the way, we will discuss how to explore and split large data sets correctly using BigQuery and Cloud Datalab. The machine learning model in TensorFlow will be developed on a small sample locally. The preprocessing operations will be implemented in Cloud Dataflow, so that the same preprocessing can be applied in streaming mode as well. The training of the model will then be distributed and scaled out on Cloud ML Engine. The trained model will be deployed as a microservice and predictions invoked from a web application. This lab consists of 7 parts and will take you about 3 hours. It goes along with this slide deck
• Slides
• Codelab
• Added in JuryRoom Text rough. Next is Research Browser
• Worked with Aaron on LSTM some more. More ndarray slicing experience:
import numpy as np
dimension = 3
size = 10
dataset1 = np.ndarray(shape=(size, dimension))
dataset2 = np.ndarray(shape=(size, dimension))
for x in range(size):
for y in range(dimension):
val = (y+1) * 10 + x +1
dataset1[x,y] = val
val = (y+1) * 100 + x +1
dataset2[x,y] = val

dataset1[:, 0:1] = dataset2[:, -1:]
print(dataset1)
print(dataset2)
• Results in:
[[301.  21.  31.]
[302.  22.  32.]
[303.  23.  33.]
[304.  24.  34.]
[305.  25.  35.]
[306.  26.  36.]
[307.  27.  37.]
[308.  28.  38.]
[309.  29.  39.]
[310.  30.  40.]]
[[101. 201. 301.]
[102. 202. 302.]
[103. 203. 303.]
[104. 204. 304.]
[105. 205. 305.]
[106. 206. 306.]
[107. 207. 307.]
[108. 208. 308.]
[109. 209. 309.]
[110. 210. 310.]]

# Phil 5.17.18

7:00 – 4:00 ASRC MKT

• How artificial intelligence is changing science – This page contains pointers to a bunch of interesting projects:
• Multi-view Discriminative Learning via Joint Non-negative Matrix Factorization
• Multi-view learning attempts to generate a classifier with a better performance by exploiting relationship among multiple views. Existing approaches often focus on learning the consistency and/or complementarity among different views. However, not all consistent or complementary information is useful for learning, instead, only class-specific discriminative information is essential. In this paper, we propose a new robust multi-view learning algorithm, called DICS, by exploring the Discriminative and non-discriminative Information existing in Common and view-Specific parts among different views via joint non-negative matrix factorization. The basic idea is to learn a latent common subspace and view-specific subspaces, and more importantly, discriminative and non-discriminative information from all subspaces are further extracted to support a better classification. Empirical extensive experiments on seven real-world data sets have demonstrated the effectiveness of DICS, and show its superiority over many state-of-the-art algorithms.
• Add Nomadic, Flocking, and Stampede to terms. And a bunch more
• Slides
• Extend SmartShape to SourceShape. It should be a stripped down version of FlockingShape
• Extend BaseCA to SourceCA, again, it should be a stripped down version of FlockingBeliefCA
• Add a sourceShapeList for FlockingAgentManager that then passes that to the FlockingShapes
• And it’s working! Well, drawing. Next is the interactions:
• Finally went and joined the IEEE

# Phil 5.14.18

7:00 – 3:00 ASRC MKT

• Working on Zurich Travel. Ricardo is getting tix, and I got a response back from the conference on an extended stay
• Continue with slides
• See if there is a binary embedding reader in Java? Nope. Maybe in ml4j, but it’s easier to just write out the file in the format that I want
• Done with the writer:
• Fika
• Finished Simulacra and Simulation. So very, very French. From my perspective, there are so many different lines of thought coming out of the work that I can’t nail down anything definitive.
• Started The Evolution of Cooperation

# Phil 10.11.18

Neural Network Evolution Playground with Backprop NEAT

• The genetic algorithm called NEAT will be used to evolve our neural nets from a very simple one at the beginning to more complex ones over many generations. The weights of the neural nets will be solved via back propagation. The awesome recurrent.js library made by karpathy, makes it possible to build computational graph representation of arbitrary neural networks with arbitrary activation functions. I implemented the NEAT algorithm to generate representations of neural nets that recurrent.js can process, so that the library can be used to forward pass through the neural nets that NEAT has discovered, and also to backprop the neural nets to optimise for their weights.

Thread on opacity and how we don’t know where our FB advertising is coming from

Meeting with Wayne

• Walked through the terms. I need to add citations
• Discussed What to do After the PhD. Setting up a program to study and implement trustworthy anonymous citizen journalism came up, which is very cool
• Quite a bit of logistical discussion on how to bridge from UMBC to UMD
• Showed Wayne my copy of Bit by Bit.

Continuous Profile Models (CPM) Matlab Toolbox and a matlab to python converter, as well as how to call MATLAB from python

# Phil 5.10.18

Worked on my post on terms

Navigating with grid-like representations in artificial agents

• Most animals, including humans, are able to flexibly navigate the world they live in – exploring new areas, returning quickly to remembered places, and taking shortcuts. Indeed, these abilities feel so easy and natural that it is not immediately obvious how complex the underlying processes really are. In contrast, spatial navigation remains a substantial challenge for artificial agents whose abilities are far outstripped by those of mammals.

7:30am – 8:00pm ASRC Tech conference

• Maybe generate an fft waveform that can be arbitrarily complex, but repeating and repeatable as a function to learn. We then find the simplest, smallest representation that we can then run hyperparameter tuning algorithms on.
• IoT marketplace is apparently a thing

# Phil 5.8.18

7:00 – 5:00 ASRC MKT

• Listening to an interview with Leonard Mlodinow. He talks about research that shows that even modest exposure to novelty results in more expansive thinking. Need to find that research.
• From Scientific American: The future belongs to the elastic mind. This is the argument behind best-selling author Leonard Mlodinow’s new book, Elastic, which examines the swirl of change we find ourselves living through, and the ways of thinking best suited to it. We all have what is needed for “elastic thinking”—to a greater extent, perhaps, than we realize. It’s just a matter of recognizing the needed skills, Mlodinow argues, and nurturing them
• Norm Change: Trendsetters and Social Structure
• In this paper, we focus on norm abandonment and examine the role played by the initiators of norm abandonment—“trendsetters”—in spearheading change. We highlight the characteristics that make someone a potential trendsetter, model a social norm game where choices are determined by such characteristics, and show with simulations based on our model how the network that trendsetters interact with may help or hinder norm change
• For Aaron:
• Bunch of discussion with Aaron on how to set up text NNs
• Reworking the embedded display to be less dumb. Yay! Less dumb. Had to figure this part out:
common = set.intersection(*setlist) # the '*' gives all the arguments as a tuple

5:00 – 8:00 ASRC Tech Conference

# Phil 5.7.18

7:00 – 5:00 ASRC MKT

• Content Sharing within the Alternative Media Echo-System: The Case of the White Helmets
• Kate Starbird
• In June 2017 our lab began a research project looking at online conversations about the Syria Civil Defence (aka the “White Helmets”). Over the last 8–9 months, we have spent hundreds of hours conducting analysis on the tweets, accounts, articles, and websites involved in that discourse. Our first peer-reviewed paper was recently accepted to an upcoming conference (ICWSM-18). That paper focuses on a small piece of the structure and dynamics of this conversation, specifically looking at content sharing across websites. Here, I describe that research and highlight a few of the findings.
• Matt Salganik on Open Review
• Spent a lot of time getting each work to draw differently in the scatterplot. That took some digging into the gensim API to get vectors from the corpora. I then tried to plot the list of arrays, but matplotlib only likes ndarrays (apparently?). I’m now working on placing the words from each text into their own ndarray.
• Also added a filter for short stop words and switched to a hash map for words to avoid redundant points in the plot.
• Fika
• Bryce Peake
• ICA has a computational methods study area. How media lows through different spaces, etc. Python and [R]
• Anne Balsamo – designing culture
• what about language as an anti-colonial interaction
• Human social scraping of data. There can be emergent themes that become important.
• The ability of the user to delete all primary, secondary and tertiary data.
• The third eye project (chyron crawls)

# Phil 5.6.18

Sentiment detection with Keras, word embeddings and LSTM deep learning networks

• Read this blog post to get an overview over SaaS and open source options for sentiment detection. Learn an easy and accurate method relying on word embeddings with LSTMs that allows you to do state of the art sentiment analysis with deep learning in Keras.

Which research results will generalize?

• One approach to AI research is to work directly on applications that matter — say, trying to improve production systems for speech recognition or medical imaging. But most research, even in applied fields like computer vision, is done on highly simplified proxies for the real world. Progress on object recognition benchmarks — from toy-ish ones like MNISTNORB, and Caltech101, to complex and challenging ones like ImageNet and Pascal VOC — isn’t valuable in its own right, but only insofar as it yields insights that help us design better systems for real applications.

Revisiting terms:

• Belief Space – A subset of information space that is associated with opinions. For example, there is little debate about what a table is, but the shape of the table has often been a source of serious diplomatic contention
• Medium – the technology that mediates the communication that coordinates the group. There are properties that seem to matter:
• Reach – How many individuals are connected directly. Evolutionarily we may be best suited to 7 +/- 2
• Directionality – connections can be one way (broadcast) or two way (face to face)
• Transparency – How ‘visible’ is the individual on the other side of the communication? There are immediate perception and historical interaction aspects.
• Friction – How difficult is it to use the medium? For example in physical space, it is trivial to interact with someone nearby, but becomes progressively difficult with distance. Broadcasting makes it trivial for a small number of people to reach large numbers, but not the reverse. Computer mediated designs typically try to reduce the friction of interaction.
• Dimension Reduction – The process by which groups decide where to coordinate. The lower the dimensions, the easier (less calculation) it takes to act together
• State – a multidimensional measure of current belief and interest
• Orientation – A vector constructed of two measures of state. Used to determine alignment with others
• Velocity – The amount of change in state over time
• Diversity Injection – The addition of random, factual information to the Information Retrieval Interfaces (IRIs) using mechanisms currently used to deliver advertising. This differs from Serendipity Injection, which attempts to find stochastically relevant information for an individual’s implicit information needs.
• Level 1: population targeted –  Based on Public Service Announcements (PSAs), information presentation should range from simple, potentially gamified presentations to deep exploration with citations. The same random information is presented by the IRIs to the using population at the same time similarly to the Google Doodle.
• Level 2: group targeted – based on detecting a group’s behaviors. For example, a stampeding group may require information that is more focussed on pointing at where flocking activity is occuring.
• Level 3: individual targeted –  Depending on where in the belief space the individual is, there may be different reactions. In a sparsely traveled space, information that lies in the general direction of travel might be a form of useful serendipity. Conversely, when on a path that often leads to violent radicalization, information associated with disrupting the progression of other individuals with similar vectors could be applied.
• Map – a type of diagram that supports the plotting of trajectories. In this work, maps of belief space are constructed based on the dimension reduction used by humans in discussion. These maps are assumed to be dynamic over time and may consists of many interrelated, though not necessarily congruent, layers.
• Herding – Deliberate creation of stampede conditions in groups. Can be an internal process to consolidate a group, or an external, adversarial process.

# Phil 5.3.18

7:30 – 5:00 ASRC MKT

# Phil 5.2.18

7:00 – 4:30 ASRC MKT

• I am going to start calling runaway echo chambers Baudrillardian Stampedes: https://en.wikipedia.org/wiki/Simulacra_and_Simulation
• GECCO 2018 paper list is full of swarming optimizers
• CORNELL NEWSROOM is a large dataset for training and evaluating summarization systems. It contains 1.3 million articles and summaries written by authors and editors in the newsrooms of 38 major publications. The summaries are obtained from search and social metadata between 1998 and 2017 and use a variety of summarization strategies combining extraction and abstraction.
• More Ultimate Angular
• Template Fundamentals (interpolation – #ref)
• Now that I have my corpora, time to figure out how to build an embedding
• Installing gensim
• By now, gensim is—to my knowledge—the most robust, efficient and hassle-free piece of software to realize unsupervised semantic modelling from plain text. It stands in contrast to brittle homework-assignment-implementations that do not scale on one hand, and robust java-esque projects that take forever just to run “hello world”.
• Big install. Didn’t break TF, which is nice
• How to Develop Word Embeddings in Python with Gensim
• Following the tutorial. Here’s a plot!
• I need to redo the parser so that each file is one sentence.
• sentences are strings that begin with a [CR] or [SPACE] + [WORD] and end with [WORD] + [.] or [“]
• a [CR] preceded by anything other than a [.] or [“] is the middle of  a sentance
• A fantastic regex tool! https://regex101.com/
• regex = re.compile(r"([-!?\.]\"|[!?\.])")
• After running into odd edge cases, I decided to load each book as a single string, parse it, then write out the individual lines. Works great except the last step, where I can’t seem to iterate over an array of strings. Calling it a day

# Phil 5.1.18

7:00 – 4:30 ASRC MKT

• Applications of big social media data analysis: An overview
• Over the last few years, online communication has moved toward user-driven technologies, such as online social networks (OSNs), blogs, online virtual communities, and online sharing platforms. These social technologies have ushered in a revolution in user-generated data, online global communities, and rich human behavior-related content. Human-generated data and human mobility patterns have become important steps toward developing smart applications in many areas. Understanding human preferences is important to the development of smart applications and services to enable such applications to understand the thoughts and emotions of humans, and then act smartly based on learning from social media data. This paper discusses the role of social media data in comprehending online human data and in consequently different real applications of SM data for smart services are executed.
• Explainable, Interactive Deep Learning
• Recently, deep learning has been advancing the state of the art in artificial intelligence to yet another level, and humans are relying more and more on the outputs generated by artificial intelligence techniques than ever before. However, even with such unprecedented advancements, the lack of interpretability on the decisions made by deep learning models and no control over their internal processes act as a major drawback when utilizing them to critical decision-making processes such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper, we review recent studies relevant to this direction and discuss potential challenges and future research directions.
• Building successful online communities: Evidence-based social design (book review)
• In Building Successful Online Communities (2012), Robert Kraut, Paul Resnick, and their collaborators set out to draw links between the design of socio-technical systems with findings from social psychology and economics. Along the way, they set out a vision for the role of social sciences in the design of systems like mailing lists, discussion forums, wikis, and social networks, offering a way that behavior on those platforms might inform our understanding of human behavior.
• Since I’ve forgotten my Angular stuff, reviewing UltimateAngular, Angular Fundamentals course. Finished the ‘Getting Started’ section
• Strip out Guttenburg text from corpora – done!