AI Brain Animated

Artificial Intelligence of Art

Let’s Make some Art

Learning through experience is one of the best ways to learn. So this is how I went about educating myself about machine learning, artificial intelligence and neural networks. I am an artist and programmer that became interested in how computers could make art. So lets use artificial intelligence to create new artwork.

I decided to use an open source project on GitHub called art-DCGAN by Robbie Barrat. His implementation was used recently in Christie’s first Artificial Intelligence art auction. But I first encountered Robbie Barrat’s work on

To start out we need some data to train the neural network. I wanted to use simple drawings to train my first model. I happen to have a library of art that I personally created. They are line drawings made using sound played through an oscilloscope. It turns out that most of the prebuilt code libraries utilize very small image sizes and you need at least 10,000 images to train them effectively.

Get Lots of Images

I happen to have movies of animated oscilloscope line drawings. Most of them come from this artwork called “Baby’s First Mobile”.

To generate a large number of images I turned the videos into individual frames using ffmpeg. Using commands like:

ffmpeg -i babyMobileVoice.mp4 -vf crop=500:500 thumb%04d.jpg -hide_banner

I was able to crop out the center of the video and produce individual frames. After running ffmpeg on all the oscilloscope videos I have and deleting all the black frames I ended up with over 10,000 images.

Make them Small

The next step is to make the images small. Some tools only accept 64 x 64 pixel images. The art-DCGAN uses 128 x 128 pixel images. I borrowed a python script from this blog at   To double the number of images I read that someone rotated them by 90 degrees for additional training examples. Using these directions I added a rotate feature and made a copy of each image.

import sys
import os
import numpy as np
from os import walk
import cv2
import imutils

# width to resize
width = int(sys.argv[1])
# height to resize
height = int(sys.argv[2])
# location of the input dataset
input_dir = sys.argv[3]
# location of the output dataset
out_dir = sys.argv[4]

if len(sys.argv) != 5:
print("Please specify width, height, input directory and output directory.")


# get all the pictures in directory
images = []
ext = (".jpeg", ".jpg", ".png")

for (dirpath, dirnames, filenames) in walk(input_dir):
for filename in filenames:
if filename.endswith(ext):
images.append(os.path.join(dirpath, filename))

for image in images:
img = cv2.imread(image, cv2.IMREAD_UNCHANGED)
h, w = img.shape[:2]
pad_bottom, pad_right = 0, 0
if h < w:
ratio = w / h
ratio = h / w

if h > height or w > width:
# shrinking image algorithm
interp = cv2.INTER_AREA
# stretching image algorithm
interp = cv2.INTER_CUBIC

w = width
h = round(w / ratio)
if h > height:
h = int(height)
w = round(h * ratio)
pad_bottom = abs(height - h)
pad_right = abs(width - w)
scaled_img = cv2.resize(img, (w, int(h)), interpolation=interp)
padded_img = cv2.copyMakeBorder(

cv2.imwrite(os.path.join(out_dir, os.path.basename(image)), padded_img)
rotate_img = imutils.rotate(padded_img, 90)
cv2.imwrite(os.path.join(out_dir, '90_'+os.path.basename(image)), rotate_img) 


Running this command

python 128 128 /origin /destination

will produce images of the proper size for the training to begin.

Setup an AWS GPU Instance

The GitHub art-DCGAN project has install directions but I will add to them here. In AWS I selected:

Ubuntu Server 14.04 LTS (HVM), SSD Volume Type – ami-06b5810be11add0e2 with the g3s.xlarge instance type.

Install all the Software

Once the instance started. I followed the directions on art-DCGAN install. Make sure to find the exact packages in the directions. On the Nvidia site you will need to press the legacy releases button to find the right version of the CUDA software. The deb file I used is cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb Newer updates of the software will not work with the art-DCGAN code.
Once it has finished building the code clone the art-DCGAN code. Now move your thousands of images into a folder called myimages/images. Make sure to have a folder called images inside the myimages folder as described in the project art-DCGAN was forked from.
Now you can run the command below but note the checkpoint options. This will cause it to save checkpoint models as it runs. The code tended to crash on me before it completed.
DATA_ROOT=myimages dataset=folder saveIter=10 name=scope1 ndf=50 ngf=150 th main.lua
saveIter will tell it to save a model for the discriminator and the generator after every 10 epochs. In my case an epoch was 318 training rounds.
But with these settings the training would constantly collapse. If the output of the training starts to show either the discriminator or the generator running at 0% your training has failed. To fix it I lowered the number of filters for the discriminator so it could keep up with the generator.
The GAN model is an adversarial training. A generator will create a random image and the discriminator will judge it based on the training source images. It repeats this process until the neural network is wired in such a way to produce images like the ones in the training set.
DATA_ROOT=myimages dataset=folder saveIter=10 name=scope1 ndf=12 ngf=150 th main.lua
is what worked for my particular artwork.
After running for five hours and making it to 90 epochs before failing I ended up with some images that look a lot like mine but with many variations.

Now the Artificial Intelligence model will make some new art

To generate a 36 image grid find the newest model file and use the command:
batchSize=36 net=./checkpoints/scope_90_net_G.t7 th generate.lua

AI Generated Oscilloscope Art

Artificial Intelligence Generated Oscilloscope Art

AI Generated Oscilloscope Art

AI Generated Oscilloscope Art

Now it becomes a matter of picking out the best work as an AI art curator.

With all of this work it becomes obvious that this is a very slow way to make art. It is still very much a human endeavor. But allowing a computer to generate lots of ideas for an artist to explore is helpful. Many artists have used randomized systems to generate art ideas. Artists such David Bowie, Brian Eno, Nam June Paik and John Cage have used mechanical and computer generators to automate some of their work and kick start the creative process.

In addition by making this model we have learned how to train other models that can be used for image recognition. You can also try out the other features of art-DCGAN that will download art from a public site and then generate new images based on that genre.

Future Fear by

Countering Computational Propaganda

Future Fear by

Countering Computational Propaganda

There is something new happening in computer science and social media. It is computational propaganda. Computational propaganda is the use of computer information systems for political purposes according to the journal Big Data. This would also include the efforts of governments to influence public opinion of another country in order to change the foreign relations and policy of that country or to cause dissent of citizens against their government.

Countering Propaganda

The first step and most important to take is to realize that propaganda is real. And then to admit that propaganda does impact the thought process of citizens. So I will start with the first step of describing computational propaganda and then ways to computationally identify it.

Elements of Computational Propaganda

There are various elements to computational propaganda that I will attempt to outline. Some of the elements include: algorithms, automation, human curation source, artificial intelligence, social media bots, sock puppets, troll farms, cyber attacks and stolen information, disinformation and data science.

This new generation of propaganda has gone through a process of computerized automation. It is not totally automated but computers, networks and the internet have made it possible to deliver it in an automated manner. It is also enabling the delivery in a personal manner, in a way that makes it seem to be coming from a real human. Technology is also making strides in the automatic production of propaganda.

Previously propaganda was slower moving and required getting news and editorials published. One used to have to own or control media outlets and then put people in place to spread disinformation. Our new modern way of spreading information online with social media has lowered the cost of spreading propaganda.

What is propaganda?

Propaganda is not just pure lies and conspiracy theories. It has come a long way from the Nazi and the communist USSR mode of operation that might obvious to us now. Current propaganda is infused with truth although it is also either partly true or out of context in hidden ways to individual audiences. This information can now be tailored and targeted at individuals. Often this information is geared to lead people to action as all good propaganda should.

Jacques Ellul in Propaganda The formation of Men’s Attitudes says that it is difficult to determine what propaganda is because it is a “secret action”. Ellul finds it impossible to pin down an exact definition of propaganda that did not take up an entire book. Our society has propaganda baked in that we are not aware of. In short, propaganda for Ellul covered the following areas:

  • Psychological Action – seeks to modify opinions by pure psychological methods
  • Psychological Warfare – destroy the morale of an adversary so that the opponent begins to doubt the validity of his beliefs and actions
  • Brainwashing – only used on prisoners to turn enemies to allies
  • Public and Human Relations – These group activities are also propaganda as they seek to adapt a person to a society. As we will see foreign governments use this technique in social media today.

Ellul says: “modern propaganda is based on scientific analysis of psychology and sociology… the propagandist builds his techniques on the basis of his knowledge of man” Then the effects of the propaganda are measured for results and refined.

These thoughts written in 1956 have only continued to be refined. They are now at work on the internet and in social media. And now there is a faster way to measure the results with computers and tracking of online activity.

In reality, there are competing propaganda efforts happening online. There is a democratic propaganda that competes with anti-democratic strains and militant islamic ones also.

Computational Propaganda in Action

But to take a step back I want to outline what this looks like online at the moment as I understand it.


Computational propaganda burst onto the awareness of the public during the 2016 US presidential election and continues today. This effort by Russian actors over many years and I will outline some of the features that are computational in nature. The entire propaganda system is co-dependant but attempts to hide as disconnected entities.

Recently it has been reported that Russian based accounts on Facebook and Twitter have been spreading propaganda to divide the American public. They used Facebook to support both sides of the Black Lives Matter protests, promote gun rights and anti-immigration. This has also been observed on Reddit and Twitter. Some accounts were bots or semi-automated bots and other accounts were used to purchase targeted advertisements.

Cyber War

One element was stealing information through cyber attacks and social engineering. This exhibited itself in the taking of information from the Democratic National Convention and utilizing classified leaks given to WikiLeaks.

Personal information on American citizens by foreign intelligence services has been stolen. Security questionnaires were taken by Chinese services. And recently credit information was appropriated in addition to voter rolls from an unknown number of states during the elections. This personal information is alleged to have enabled the more precise targeting of propaganda to specific populations.

Propaganda Generation

Then this information was selectively transformed into propaganda by taking it out of context and targeting it at select audiences. I can assume that producers of the propaganda used computer software to search the massive amounts of information that was stolen. Then they edited the most damaging of the information to refine it for maximum effect on specific audiences.

Targeting Propaganda

This was also the case for the efforts of the Trump campaign where they took comments damaging to an opponent and targeted voter suppression ads at specific people. Social media ad networks and databases were used in these efforts. These systems allow anyone to target a message at thousands of different personal identifiers, location and income brackets. This can be done on Facebook and Twitter.

Targeting Ad on Facebook

Targeting Ad on Facebook

The advertisement above is being targeted at people in NY City who engage with liberal political content and have a net worth between 1 million and 2 million.

Personal Propaganda

In addition there are stores of personal data that was correlated with computational algorithms to determine specific personality traits of individuals. Using this information messaging was crafted and target it at psychological vulnerabilities of individuals to change their thinking or push them to action. source

The Troll Factory

On top of this paid targeting there are places called troll factories where armies of people engage in social interactions on social media and blogs. There is one of these in St. Petersburg called The Factory. People will engage with online content to discredit it with propaganda techniques such as “whataboutism”. This is where people contest valid facts by pointing out a perceived hypocrisy on the other side of the issue. They will also attempt to generate fake news to cause panic in a particular community.

Bots and Sock Puppets

These efforts combine with a computational technique of online bots, trolls and sock puppets. Sock puppets are online fake personas. They appear to be real people online often appearing as Americans but they just broadcast propaganda.

Bots are pieces of software that perform online tasks. They may just pickup other people’s messages on line and rebroadcast them. Other bots are more human like and engage in conversations. They often search for keywords in conversations and generate a “whatboutism” or some disparaging message to confuse readers. There are other bots that are semi-automated. Once these are challenged as being bots or have a question they cannot respond to, a human will intervene and manually provide responses.

Bot networks

These bots and sock puppet accounts often work as a team. They will gang up on conversations and re-broadcast each others messages. The goal is to make their message seem mainstream by its volume or popularity. They can confuse rational conversations and arguments online by misdirecting them and triggering emotional responses.

Suppression of Speech Bots

Recently bots have been suppressing messaging from people who are attempting to get out messages to counter disinformation. In the case of Brian Krebs who was attacking the messaging of bots that support Putin they tricked Twitter into disabling Brians account. They did this by causing thousands of accounts to follow him and then retweet his tweets in mass. This effort caused Twitter to automatically assume that Brian was paying bots to promote his account. So they turned it off.

Artificial Intelligence

Artificial intelligence and machine learning are additional computation techniques being weaponized in this information battle. Not only is AI used to automate bots to appear human but it also can to create messaging content. Artificial intelligence processes source documents or training data. Then programmers configure the system to output new messages. It multiplies the efforts of a human to generate new content. This output is then curated or even tested out online. This validates the effectiveness of the content. This feedback loop is used to create more effective triggers for people.

Attacking the Disenfranchised

Often these efforts to trigger action leverage marginalized groups in society. The bots and troll factories can take domestic content and amplify it for their own purposes. It has been shown that Russian based bots often rebroadcast messages that attempt to increase divisions in US citizens. Hamilton68 illustrates these efforts.

Screen Shot 2017-09-16 at 10.43.50 AM.png
This is a dashboard that tracks known Russian bots and exposes what they are promoting. Often this is anything that breeds mistrust in the US government and pits groups against each other.

Some countries also invest in traditional media like newspapers,radio and television stations to broadcast messages. They attempt to make this look like real news but it is actually disinformation and propaganda. This “news” is picked up as legitimate by other news outlets and rebroadcast. People who buy into the messaging with use the source as proof of their opinions.

Conspiracy Theories

Propagandists often use current running conspiracy theories to oppose competing messages or true news. They neutralize the opposition by coming up with a secret conspiracy or amplifying one in circulation.

Live events

Recently The DailyBeast reported that Russian operatives organized and promoted rallies on Facebook. This illustrates the purpose of propaganda which is to move people to action. Once they are acting on the beliefs pushed by the propaganda it can tip into political action.


At the moment in the US propaganda from Russia seems aimed at changing the foreign policy of the US government. The Russians have secured the information space of their citizens in an authoritarian way that is not acceptable in American society. And they have leveraged the lack of privacy controls in the American capitalist system, where information about people is sold for marketing purposes. It seems that Russian propaganda efforts are only aimed at the destruction of democracy or western values according to Chris Zappone of The Age

Computational Countermeasures

There are hundreds of ideas on how to counter this online propaganda. Some are government policies, industry self policing and educational programs. But I want to focus on countermeasures that are computational. In effect an attempt to fight fire with fire.


One way to minimize the impact of propaganda is to have tools that alert an individual of the event. Another type of computational tool is one that allows a community to monitor what others in their community are being targeted with. This can prevent individual weaknesses from being exploited. There are efforts underway in this space but there are opportunities for continual improvement.

Current concepts

Ideas from Volume

  • A dark advertisement exposure network. Volunteers install browser agents to gather ads and put them in a public searchable database with the targeting criteria. Could also use fake personas to collect advertisements.
  • Public searchable databases of bots and sock puppets identified by computational techniques, such as time of day analysis, linguistic patterns and rebroadcasting behaviours.
  • The bot collection database would also hold the relationships between accounts, messages and motives.
  • Computational methods software package to identify bots that pretend to be human and out them as bots on the social media
  • Browser plug-in that will show a user the motives of a bot and expose a network of bots that link or help each other. It enables a person to ignore and discount ideas coming from that entity.

In a blog post Colin Crowell, Twitter’s vice president of public policy, government and philanthropy, said in a blog post that Twitter itself is a “powerful antidote to the spreading of all types of false information” and “journalists, experts and engaged citizens Tweet side-by-side correcting and challenging public discourse in seconds.”

The issue with this is that the bots are programmatically generating information using artificial intelligence or have armies of cheap labor. How can citizens and journalists keep up with researching and debunking half truths, obvious lies and nonsense?

Perhaps we need to build counter bot networks. Of course, ones that fit into the social media companies terms of service.

Bot Combat

(more ideas from Volume Integration)

  • Bots that disrupt bot networks by sending trigger words to them and keep them busy and away from meaningful conversations.
  • Bots that look for disinformation and hoaxes and broadcast information to debunk it
  • Artificial intelligence social bots that can automatically research messaging from propaganda bots and counter the messaging
  • Crafting fact checking messaging to target back at organizations running troll factories and bot networks
  • A force of vetted volunteers that could perform analysis tasks to find bots and propagandists and then write counter arguments to them


Of course this rests on the concept of analysis so we would need tools to visualize the data and perform the human effort needed to find relationships and motives of the actors. Some of this effort would be utilizing the many algorithms already designed to detect bots and online propaganda. In addition Volume Integration has a tool that can help monitor and alert on social media account activity and messaging. See our products.

At the end of the post is a list of papers on methods and analysis techniques to find automated bots online.

Prevention (CyberSecurity)

One factor in recent propaganda has been the ability for bad actors to obtain classified or private information. In this case we need better cyber security is needed. Sometimes the information is gathered via social engineering. Outside actors manipulate a person inside an organization into providing the information or access to the computer systems.

Ideas from Volume

  • Separate internal corporate networks from the internet
  • Increase Cybersecurity methods and policies (patching schedules, inventory control, multiple factor authentication, firewalls, packet inspection, audits)
  • Trust / Risk Verification Systems like Volume Analytics which monitor events on a computer network to alert on unauthorized or risky behaviour.


I am afraid that there is no real collusion. We are at the beginning of our abilities to counter computational propaganda. It is going to be an arms race as tactics and systems change. Technology and technique will breed more technology and technique. I hope we are able to separate out the false information and come a bit closer to truth. In the end, it will be the next phase of human conflict and manipulation to gain power and wealth over personal freedom.

Contact us if you want to know more about our work or follow us on Twitter, LinkedIn or Facebook.

Current Studies on Automating Analysis

I Code Like a Girl

I Code Like a Girl

Programmers Betty Jean Jennings (left) and Fran Bilas (right) operate ENIAC's main control panel at the Moore School of Electrical Engineering. (U.S. Army photo from the archives of the ARL Technical Library)

Programmers Betty Jean Jennings (left) and Fran Bilas (right) operate ENIAC’s main control panel at the Moore School of Electrical Engineering. (U.S. Army photo from the archives of the ARL Technical Library)

The American computer industry seems dominated by people like me – males. But it was not always this way, in fact I stand on the shoulders of women.

While writing about SAGE in previous posts, I learned that at the beginning of the computer age, most programmers were women. My skills, including programming languages and tools, have a foundation built on the contributions of those who came before me. It turns out that I code like a girl, using concepts pioneered by women.

Computers: Human Women or Machine?

In the 1940’s and 50’s, there was a peak in women working as computers. Mathematically intensive industries like nuclear research, ballistics, and engineering often employed women to perform computations.

Do you remember “When Computers Were Women?” The article reminds us that a “computer” was actually a human being until around 1945. Afterward, a computer was a machine and humans were called operators.


In 1946, the first electronic general-purpose computer called ENIAC (Electronic Numerical Integrator And Computer) was built. This began a process of reducing and automating manual calculations.

A select number of women operators and former “computers” were enlisted to become programmers for ENIAC. Notably, the first ENIAC programming team was comprised entirely of women: Kay McNulty, Betty Jennings, Betty Snyder, Marlyn Wescoff, Fran Bilas, and Ruth Lichterman.

Women programmer School of Electrical Engineering. (U.S. Army photo from the archives of the ARL Technical Library)

School of Electrical Engineering. (U.S. Army photo from the archives of the ARL Technical Library)

At the time, there was a strong division between the male domain of hardware and the female sphere of software. Male electronic engineers built the ENAIC system. But since software design and programming were considered clerical work, women instructed the the 27-ton hand-built mass of wires and vacuum tubes to perform calculations in sequence.

In true programmer fashion, the women learned by doing. The hardware engineers dropped the blueprints and wiring documents on them and said, “Here figure out how the machine works and then figure out how to program it.” So the women crawled around the massive frame and learned how each component worked. They successfully understood the interplay between hardware and software and how the computer’s behavior could be traced to a hardware or a software issue.

Unfortunately, all the credit for creating ENIAC went to the men who conceived it and built the hardware. The media covered the debut of ENIAC in February 1946, which showed off the centerpiece calculation of a trajectory. The program created by Betty Snyder and Betty Jennings impressed the VIPs because it allowed the computer to calculate faster than the projectile itself. But the women were not mentioned, seen in pictures of the event, nor invited to the press lunch with the men. In the end, the computer was the star of the show, depicted as an autonomous brain.

Gender Code

Words powerfully describe gender roles. What is now considered a male-dominated field, was once defined as “women’s work.” In the days of the first computers, the norms were as follows:

Male Female
Hard sciences Soft sciences
Engineering Programming
Hardware Software

Society was keen on recognizing men’s contributions, while neglecting those of women. In the book, _Recoding Gender: Women’s Changing Participation in Computing_, Janet Abbate found that publicity materials for ENIAC state that the machine reduced 25 man-months of human computer time to two hours on the ENIAC. However, it fails to mention that most of the human computers were really women. The materials also neglect to highlight the years of labor by both men (on the hardware) and women (writing software) to create the system. The only human labor noted in the press was the initial design of the machine, which was performed by men.

But even women of the time seemed to define their computer jobs as gender specific. Elise Shutt was a programmer on a later version of ENIAC called ORDVAC. When she was hired by Raytheon in 1953, she said, “It really amazed me that these men were programmers, because I thought it was women’s work.”

In another example, Grace Hopper compared programming to tasks like sewing clothes, making a recipe, and the work of a mother teaching a child. Thus, she defined programming as a female occupation. But this seems to have been lost on her supervisor, Howard Aiken who said in praise of Grace, “Grace was a good man.

Recruiting materials were also used to attract women to programming with various metaphors and generalizations. In the 1940s, MIT had a shortage of men and highlighted skills such as needlework and knitting as characteristics useful for programmers. Others noted that female pursuits like crosswords and puzzles would make good programmers.


The first women who pioneered programming on the ENIAC finally gained the recognition they deserved in 1997 when they were inducted into the Women in Technology Hall of Fame in 1997 and IEEE in 1997 and 2008.

It seems that we continue to struggle with metaphors and defining skills used to train and attract the next generation to computer work. From recent statistics, we are finding a wider gender gap in the computer industry. The reasons for this are inconclusive and give us a reason for self-evaluation and consideration of language used to hire and promote, treatment of women, and how skills are evaluated.

It becomes increasingly important to value each member of a programming team regardless of gender, age, race, or creed to attract and keep the best minds to build our future software. There seems to be no end to the amount of programming work needed. Code on!


Check out more of our work at Volume Integration and follow us on Twitter.

How I Learned to Stop Worrying and Love the Computer

How I Learned to Stop Worrying and Love the Computer

In the 1950s, the United States was worried about the threat of a Russian nuclear attack. But an Air Force computer helped allay those fears.


My last post about oscilloscopes mentioned the Semi-Automatic Ground Environment (SAGE), which was the technological savior of its time. SAGE was a system built in the 1950s to warn of incoming Russian bombers with nuclear bombs.

SAGE was composed of over 24 computers, and each contained a primary and backup CPU to provide a 99.6% reliability rate. But you have to keep in mind that each CPU took up 10,000 square feet. They were spread across the US plus one in Canada and connected by the first modems tied to standard AT&T phone lines.

In 1959, IBM created the 7090, a faster and smaller solid-state enhancement of the work done with the SAGE system. This computer appeared in the 1964 movie Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb. The movie would have been more realistic with the original SAGE system, but it was in use protecting the country.

Peter Sellers as Captain Lionel Mandrake surrounded by an IBM 7090

Computing Innovators

SAGE kicked computer research and the creation of programming methods into high gear. This machine was responsible for creating multiple companies and was the starting point for researchers who went on to create the forerunner of the Internet.

The info graphic below shows one branch of the tree that leads to present day companies that continue to provide research and development in the computer industry today.

Legacy of US Government Funded-Computer Industry (IBM, MITRE, Noblis, L-3 Communications, Lockheed Martin, Unisys, and MIT Lincoln Laboratory are all trademarks of their respective companies)

More Problems…More Innovation

Of course, it was not that easy to deter the Russians. Unfortunately, once the entire network of SAGE sites were in place around 1964, the threat of attack by bombers had been replaced by the threat of intercontinental ballistic missiles (ICBM). But the SAGE system was not fast enough to track incoming missiles from space. In fact, the system was not even fast enough to prevent a large-scale attack against radar jamming technologies that were in use. (A recently declassified Air Force training video confirms this.)

These new threats pushed the Air Force to continue building computers using successive generations of computer and software companies. These companies and their knowledge have spawned an increasing array of computers, software, and networks that are in use today.