BarnCamp 2017 notes

(still) re-using computers in our communities

We discussed the social and technical challenges of computer re-use. It was noted that there were multiple environmental advantages to re-use (as opposed to recycling) of all electronics; these are well-known.

Perhaps more interesting and providing opportunities for variety are the potential social/political benefits of re-use projects, including;

• opportunities for building community cohesion by getting (for example) people who can't afford new computers to cooperate with people who have tech jobs/knowledge
• providing a focus for commonality with established community groups, e.g. artistic, tech, green organisations
• merging 'after sales support' with basic skills education
• developing everyone's experience of localised mutual aid
• opportunities to have discussions with different people about consumerism and it's social/environmental harms

There are many obstacles to these kind of projects. Some of our experiences included;

• people wanting to get skills they can use "in the workplace", i.e. wanting to know how to do things in microsoft office so they can get a drudge office job because our options of how to get our material needs met still require this, for many of us
• even public sector organisations can be phobic about FLOSS options that could extend the useful life of machines
• data destruction and other regulatory requirements (WEEE licenses for example)
• demand for standardisation, whereas our strengths might be more to do with personalisation (focussing on our individual strengths and needs, and the particular needs of our customers/neighbours/mates)

The struggle continues...

Markov Chains and Bots

A brief background on Markov Chain processes and their applications was discussed. Eventually an attempt was made (mainly outside the talk).

• Some examples of their use were given, mainly with text generators:

https://filiph.github.io/markov/
http://rubberducky.org/cgi-bin/chomsky.pl
https://reddit.com/r/SubredditSimulator/

• An attempt to reproduce a Trump bot was made, first by downloading his tweets (6 months worth). A method found below was used:

http://trumptwitterarchive.com/howto/all_tweets.html

{Note: When saving these to UTF-8 format, a lot of special characters caused problems. So some data cleaning is required}

• Some Python code on github was found and cloned into a working directory (along with the tweet file):

https://github.com/codebox/markov-text

• The file was passed through the package to create a database with transitional probabilities, by:

python markov.py parse trumpDB 2 trump_tweet.csv {2 indicates the depth of the Markov Chain (e.g. takes 2 words either side into consideration for calculations) }

• Then to generate the text use:

python markov.py gen trumpDB 5 {5 is the amount of sentences generated}

Next steps:

• Automate the process
• Edit code to respond to replies ‘intelligently‘
• Link into chat applications (via APIs)
• Watch the choas ensue