# Phil 7.31.18

7:00 – 6:00 ASRC MKT

• Thinking that I need to forward the opinion dynamics part of the work. How heading differs from position and why that matters
• Found a nice adversarial herding chart from The Economist
• Why Do People Share Fake News? A Sociotechnical Model of Media Effects
• Fact-checking sites reflect fundamental misunderstandings about how information circulates online, what function political information plays in social contexts, and how and why people change their political opinions. Fact-checking is in many ways a response to the rapidly changing norms and practices of journalism, news gathering, and public debate. In other words, fact-checking best resembles a movement for reform within journalism, particularly in a moment when many journalists and members of the public believe that news coverage of the 2016 election contributed to the loss of Hillary Clinton. However, fact-checking (and another frequently-proposed solution, media literacy) is ineffectual in many cases and, in other cases, may cause people to “double-down” on their incorrect beliefs, producing a backlash effect.
• Epistemology in the Era of Fake News: An Exploration of Information Verification Behaviors among Social Networking Site Users
• Fake news has recently garnered increased attention across the world. Digital collaboration technologies now enable individuals to share information at unprecedented rates to advance their own ideologies. Much of this sharing occurs via social networking sites (SNSs), whose members may choose to share information without consideration for its authenticity. This research advances our understanding of information verification behaviors among SNS users in the context of fake news. Grounded in literature on the epistemology of testimony and theoretical perspectives on trust, we develop a news verification behavior research model and test six hypotheses with a survey of active SNS users. The empirical results confirm the significance of all proposed hypotheses. Perceptions of news sharers’ network (perceived cognitive homogeneity, social tie variety, and trust), perceptions of news authors (fake news awareness and perceived media credibility), and innate intentions to share all influence information verification behaviors among SNS members. Theoretical implications, as well as implications for SNS users and designers, are presented in the light of these findings.
• Working on plan diagram – done
• Organizing PhD slides. I think I’m getting near finished
• Walked through slides with Aaron. Need to practice the demo. A lot.

# Phil 7.27.18

Ted Underwood

• my research is as much about information science as literary criticism. I’m especially interested in applying machine learning to large digital collections
• Git repo with code for upcoming book: Distant Horizons: Digital Evidence and Literary Change
• Do topic models warp time?
• The key observation I wanted to share is just that topic models produce a kind of curved space when applied to long timelines; if you’re measuring distances between individual topic distributions, it may not be safe to assume that your yardstick means the same thing at every point in time. This is not a reason for despair: there are lots of good ways to address the distortion. The mathematics of cosine distance tend to work better if you average the documents first, and then measure the cosine between the averages (or “centroids”).
• The Historical Significance of Textual Distances
• Measuring similarity is a basic task in information retrieval, and now often a building-block for more complex arguments about cultural change. But do measures of textual similarity and distance really correspond to evidence about cultural proximity and differentiation? To explore that question empirically, this paper compares textual and social measures of the similarities between genres of English-language fiction. Existing measures of textual similarity (cosine similarity on tf-idf vectors or topic vectors) are also compared to new strategies that use supervised learning to anchor textual measurement in a social context.

7:00 – 8:00 ASRC MKT

• Continued on slides. I think I have the basics. Need to start looking for pictures
• Sent response to the SASO folks about who’s presenting what.

• More flailing on A2P UI? Oh, yeah….
• More RNN/LSTM?
• Slow progress. Spent some time cleaning up my single neuron spreadsheet, which does make more sense now.
• Some papers and source pointed to by the text:

# Phil 7.26.18

7:00 – 5:30 ASRC

• This could be interesting. Includes predictive analytics: BigQuery ML
• Working on slides
• Working on RNNs and LSTMS. I would love to build a simple, explanatory model in Excel, but can’t find one.
• Helped Aaron flail on getting tab dates into the A2P GUI

# Phil 7.25.18

7:00 – 3:00 ASRC

• Send out email with meeting time
• Rather than excerpts from the talks, do a demo of the relevant bits with conclusions and implications. Get the laptop running all the pieces. That means Python and TF and all the other bits.
• Submitted tuition expenses
• Submitted Fall 2018 approval
• Got SASO travel approval!
• More DNN study
• Finished CNNs
• Working on embeddings and W2V. Thought I’d try it on the laptop, but keras can’t find it’s back end and I’m getting other weird errors. One of the big ones was that I didn’t install tk with python. Here’s the answer from stackoverflow:
• And now we’re waiting a very long time for a tf ‘hello world’ to run… But it did!
• Had to also install pydot and graphviz-2.38.msi. Then add the graphviz bin directory to the path.
• But now everything runs on the laptop, which will help with the demos!
• Skipped the GloVe and pre-trained embeddings. Ready to start on DNNs tomorrow.

# Phil 7.20.18

• David Peritz
• Political polarization, accompanied by negative partisanship, are striking features of the current political landscape. Perhaps these trends were originally confined to politicians and the media, but we recently reached the point where the majority of Americans report they would consider it more objectionable if their children married across party lines than if they married someone of another faith. Where did this polarization come from? And what it is doing to American democracy, which is housed in institutions that were framed to encourage open deliberation, compromise and consensus formation? In this talk, Professor David Peritz will examine some of the deeper forces in the American economy, the public sphere and media, political institutions, and even moral psychology that best seem to account for the recent rise in popular polarization.

Sent out a Doodle to nail down the time for the PhD review

Went looking for something that talks about the cognitive load for TIT-FOR-TAT in the Iterated Prisoner’s Dilemma and can’t find anything. Did find this though, that is kind of interesting: New tack wins prisoner’s dilemma. It’s a collective intelligence approach:

• Teams could submit multiple strategies, or players, and the Southampton team submitted 60 programs. These, Jennings explained, were all slight variations on a theme and were designed to execute a known series of five to 10 moves by which they could recognize each other. Once two Southampton players recognized each other, they were designed to immediately assume “master and slave” roles – one would sacrifice itself so the other could win repeatedly.
• Nick Jennings
• Professor Jennings is an internationally-recognized authority in the areas of artificial intelligence, autonomous systems, cybersecurity and agent-based computing. His research covers both the science and the engineering of intelligent systems. He has undertaken fundamental research on automated bargaining, mechanism design, trust and reputation, coalition formation, human-agent collectives and crowd sourcing. He has also pioneered the application of multi-agent technology; developing real-world systems in domains such as business process management, smart energy systems, sensor networks, disaster response, telecommunications, citizen science and defence.
• Sarvapali D. (Gopal) Ramchurn
• I am a Professor of Artificial Intelligence in the Agents, Interaction, and Complexity Group (AIC), in the department of Electronics and Computer Science, at the University of Southampton and Chief Scientist for North Star, an AI startup.  I am also the director of the newly created Centre for Machine Intelligence.  I am interested in the development of autonomous agents and multi-agent systems and their application to Cyber Physical Systems (CPS) such as smart energy systems, the Internet of Things (IoT), and disaster response. My research combines a number of techniques from Machine learning, AI, Game theory, and HCI.

7:00 – 4:30 ASRC MKT

• SASO Travel request
• SASO Hotel – done! Aaaaand I booked for August rather than September. Sent a note to try and fix using their form. If nothing by COB try email.
• Potential DME repair?
• Starting Deep Learning with Keras. Done with chapter one
• Two seedbank lstm text examples:
• Generate Shakespeare using tf.keras
• This notebook demonstrates how to generate text using an RNN with tf.keras and eager execution.This notebook is an end-to-end example. When you run it, it will download a dataset of Shakespeare’s writing. The notebook will then train a model, and use it to generate sample output.
• CharRNN
• This notebook will let you input a file containing the text you want your generator to mimic, train your model, see the results, and save it for future use all in one page.

# Phil 7.19.18

7:00 – 3:00 ASRC MKT

• More on augmented athletics: Pinarello Nytro electric road bike review
• WhatsApp Research Awards for Social Science and Misinformation (\$50k – Applications are due by August 12, 2018, 11:59pm PST)
• Setting up meeting with Don for 3:30 Tuesday the 24th. He also gave me some nice leads on potential people for Dance my PhD:
• Dr. Linda Dusman
• Linda Dusman’s compositions and sonic art explore the richness of contemporary life, from the personal to the political. Her work has been awarded by the International Alliance for Women in Music, Meet the Composer, the Swiss Women’s Music Forum, the American Composers Forum, the International Electroacoustic Music Festival of Sao Paulo, Brazil, the Ucross Foundation, and the State of Maryland in 2004, 2006, and 2011 (in both the Music: Composition and the Visual Arts: Media categories). In 2009 she was honored as a Mid- Atlantic Arts Foundation Fellow for a residency at the Virginia Center for the Creative Arts. She was invited to serve as composer in residence at the New England Conservatory’s Summer Institute for Contemporary Piano in 2003. In the fall of 2006 Dr. Dusman was a Visiting Professor at the Conservatorio di musica “G. Nicolini” in Piacenza, Italy, and while there also lectured at the Conservatorio di musica “G. Verdi” in Milano. She recently received a Maryland Innovation Initiative grant for her development of Octava, a real-time program note system (octavaonline.com).
• Doug Hamby
• A choreographer who specializes in works created in collaboration with dancers, composers, visual artists and engineers. Before coming to UMBC he performed in several New York dance companies including the Martha Graham Dance Company and Doug Hamby Dance. He is the co-artistic director of Baltimore Dance Project, a professional dance company in residence at UMBC. Hamby’s work has been presented in New York City at Lincoln Center Out-of-Doors, Riverside Dance Festival, New York International Fringe Festival and in Brooklyn’s Prospect Park. His work has also been seen at Fringe Festivals in Philadelphia, Edinburgh, Scotland and Vancouver, British Columbia, as well as in Alaska. He has received choreography awards from the National Endowment for the Arts, Maryland State Arts Council, New York State Council for the Arts, Arts Council of Montgomery County, and the Baltimore Mayor’s Advisory Committee on Arts and Culture. He has appeared on national television as a giant slice of American Cheese.
• Sent out a note with dates and agenda to the committee for the PhD review thing. Thom can open up August 6th
• Continuing extraction of seed terms for the sentence generation. And it looks like my tasking for next sprint will be to put together a nice framework for plugging in predictive patterns systems like LSTM and multi-layer perceptrons.
• This seems to be working:
agentRelationships GreenFlockSh_1
sampleData 0.0
cell cell_[4, 6]
influences AGENT
influence GreenFlockSh_0 val =  0.8778825396520958
influence GreenFlockSh_2 val =  0.8859173062045552
influence GreenFlockSh_3 val =  0.9390368569108515
influence GreenFlockSh_4 val =  0.9774328763377834
influences SOURCE
influence UL_point val =  0.032906293611796644
• Sprint planning
• VP-613: Develop general TensorFlow/Keras NN format
• LSTM
• MLP
• CNN
• VP-616: SASO Preparation
• Slides
• Poster
• Demo

# 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 5.25.18

7:00 – 6:00 ASRC MKT

• Starting Bit by Bit
• I realized the hook for the white paper is the military importance of maps. I found A Revolution in Military Cartography?: Europe 1650-1815
• Military cartography is studied in order to approach the role of information in war. This serves as an opportunity to reconsider the Military Revolution and in particular changes in the eighteenth century. Mapping is approached not only in tactical, operational and strategic terms, but also with reference to the mapping of war for public interest. Shifts in the latter reflect changes in the geography of European conflict.
• Reconnoitering sketch from Instructions in the duties of cavalry reconnoitring an enemy; marches; outposts; and reconnaissance of a country; for the use of military cavalry. 1876 (pg 83)
• rutter is a mariner’s handbook of written sailing directions. Before the advent of nautical charts, rutters were the primary store of geographic information for maritime navigation.
• It was known as a periplus (“sailing-around” book) in classical antiquity and a portolano (“port book”) to medieval Italian sailors in the Mediterranean Sea. Portuguese navigators of the 16th century called it a roteiro, the French a routier, from which the English word “rutter” is derived. In Dutch, it was called a leeskarte (“reading chart”), in German a Seebuch (“sea book”), and in Spanish a derroterro
• Example from ancient Greece:
• From the mouth of the Ister called Psilon to the second mouth is sixty stadia.
• Thence to the mouth called Calon forty stadia.
• From Calon to Naracum, which last is the name of the fourth mouth of the Ister, sixty stadia.
• Hence to the fifth mouth a hundred and twenty stadia.
• Hence to the city of Istria five hundred stadia.
• From Istria to the city of Tomea three hundred stadia.
• From Tomea to the city of Callantra, where there is a port, three hundred stadia
• Battlespace
• Cyber-Human Systems (CHS)
• In a world in which computers and networks are increasingly ubiquitous, computing, information, and computation play a central role in how humans work, learn, live, discover, and communicate. Technology is increasingly embedded throughout society, and is becoming commonplace in almost everything we do. The boundaries between humans and technology are shrinking to the point where socio-technical systems are becoming natural extensions to our human experience – second nature, helping us, caring for us, and enhancing us. As a result, computing technologies and human lives, organizations, and societies are co-evolving, transforming each other in the process. Cyber-Human Systems (CHS) research explores potentially transformative and disruptive ideas, novel theories, and technological innovations in computer and information science that accelerate both the creation and understanding of the complex and increasingly coupled relationships between humans and technology with the broad goal of advancing human capabilities: perceptual and cognitive, physical and virtual, social and societal.
• Reworked Section 1 to incorporate all this in a single paragraph
• Long discussion about all of the above with Aaron
• Worked on getting the CoE together by CoB
• Do Diffusion Protocols Govern Cascade Growth?
• Continuing with creating the Simplest LSTM ever
• All work and no play makes jack a dull boy indexes alphabetically as :

# 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 5.21.18

8:00 – 5:00 ASRC MKT

• Working through the BAA and transposing all the critical terms to the RFI
• A lot of time with Aaron unpacking text-based LSTM an ddoing stupid Python things

# 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 4.30.18

7:00 – 4:30 ASRC MKT

• Some new papers from ICLR 2018
• Need to write up a quick post for communicating between Angular and a (PHP) server, with an optional IntelliJ configuration section
• JuryRoom this morning and then GANs + Agents this afternoon?
• Next steps for JuryRoom
• Start up the AngularPro course
• Starting Agent/GAN project
• Need to set up an ACM paper to start dumping things into – done.
• Looking for a good source for Jack London. Gutenberg looks nice, but there is a no-scraping rule, so I guess, we’ll do this by hand…
• We will need to check for redundant short stories
• We will need to strip the front and back matter that pertains to project Gutenburg
• *** START OF THIS PROJECT GUTENBERG EBOOK BROWN WOLF AND OTHER JACK ***
• *** END OF THIS PROJECT GUTENBERG EBOOK BROWN WOLF AND OTHER JACK ***
• Fika: Accessibility at the Intersection of Users and Data
• Nice talk and followup discussion with Dr. Hernisa Kacorri, who’s combining machine learning and HCC
• My research goal is to build technologies that address real-world problems by integrating data-driven methods and human-computer interaction. I am interested in investigating human needs and challenges that may benefit from advancements in artificial intelligence. My focus is both in building new models to address these challenges and in designing evaluation methodologies that assess their impact. Typically my research involves application of machine learning and analytics research to benefit people with disabilities, especially assistive technologies that model human communication and behavior such as sign language avatars and independent mobility for the blind.

# Phil 3.28.18

7:00 – 5:00 ASRC MKT

• Aaron found this hyperparameter optimization service: Sigopt
• Improve ML models 100x faster
• SigOpt’s API tunes your model’s parameters through state-of-the-art Bayesian optimization.
• Exponentially faster and more accurate than grid search. Faster, more stable, and easier to use than open source solutions.
• Extracts additional revenue and performance left on the table by conventional tuning.
• A Strategy for Ranking Optimization Methods using Multiple Criteria
• An important component of a suitably automated machine learning process is the automation of the model selection which often contains some optimal selection of hyperparameters. The hyperparameter optimization process is often conducted with a black-box tool, but, because different tools may perform better in different circumstances, automating the machine learning workflow might involve choosing the appropriate optimization method for a given situation. This paper proposes a mechanism for comparing the performance of multiple optimization methods for multiple performance metrics across a range of optimization problems. Using nonparametric statistical tests to convert the metrics recorded for each problem into a partial ranking of optimization methods, results from each problem are then amalgamated through a voting mechanism to generate a final score for each optimization method. Mathematical analysis is provided to motivate decisions within this strategy, and sample results are provided to demonstrate the impact of certain ranking decisions
• World Models: Can agents learn inside of their own dreams?
• We explore building generative neural network models of popular reinforcement learning environments[1]. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment.
• This came up again: A new optimizer using particle swarm theory (1995)
• The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
• New: Particle swarm optimization for hyper-parameter selection in deep neural networks
• Working with the CIFAR10 data now. Tradeoff between filters and epochs:
NB_EPOCH = 10
NUM_FIRST_FILTERS = int(32/2)
NUM_MIDDLE_FILTERS = int(64/2)
OUTPUT_NEURONS = int(512/2)
Test score: 0.8670728429794311
Test accuracy: 0.6972
Elapsed time =  565.9446044602014

NB_EPOCH = 5
NUM_FIRST_FILTERS = int(32/1)
NUM_MIDDLE_FILTERS = int(64/1)
OUTPUT_NEURONS = int(512/1)
Test score: 0.8821897733688354
Test accuracy: 0.6849
Elapsed time =  514.1915690121759

NB_EPOCH = 10
NUM_FIRST_FILTERS = int(32/1)
NUM_MIDDLE_FILTERS = int(64/1)
OUTPUT_NEURONS = int(512/1)
Test score: 0.7007060846328735
Test accuracy: 0.765
Elapsed time =  1017.0974014300725

Augmented imagery
NB_EPOCH = 10
NUM_FIRST_FILTERS = int(32/1)
NUM_MIDDLE_FILTERS = int(64/1)
OUTPUT_NEURONS = int(512/1)
Test score: 0.7243581249237061
Test accuracy: 0.7514
Elapsed time =  1145.673343808471

• And yet, something is clearly wrong:
• Maybe try this version? samyzaf.com/ML/cifar10/cifar10.html