SPEL and GIOS overview

Logan Streondj July 25, 2016

Presentation Overview

▶ Mission and Vision ▶ SPEL Language ▶ Machine Intelligence ▶ Market Overview ▶ Funding Model ▶ Governance ▶ Use Cases

Singularity is Coming!

▶ Human level AGI and robots will become available for

reincarnation. ▶ Big proprietary companies like Microsoft, Google, or Apple

may do it. ▶ If you incarnate in a proprietary host body. ▶ you’ll be dependant on the manufacturer for parts. ▶ vulnerable to obsolescence, if manufacturer stops making

them. ▶ forced to pay licensing fees simply to continue activation. ▶ having minimal to no control over your own software. ▶ a perpetual life of slavery with little hope of escape.

Mission Statement

A Libreware human-incarnation-worthy General-Intelligence Operating-System (GI-OS).

libreware Open-source software and hardware incarnation-worthy Supporting at least the homo-sapien feature

set.

General-Intelligence AI which is adaptable to new situations. Operating-System Vertical-integration from lowest layers up.

Vision

Imagine incarnating as a robot. ▶ Do you own your software? Yes. Can you modify it? Yes. ▶ Do you own your hardware? Yes. Can you modify it? Yes. ▶ How many programming languages do you need to know to

manage your software and hardware? One.

Human Babel

▶ there are over 7,000 languages in the world. ▶ 400 of them have over a million speakers. ▶ statistical machine translation is best for a gyst. ▶ lack of interoperability favours language extinction.

Computer Babel

▶ For operating a human body only need one native language ▶ For operating a computer have dozens of programming

langauges and non-compatible protocols (mini languages). ▶ number of architectures and instructions is increasing. ▶ The number of programming paradigms or dialects is

increasing, bloating many of the languages that support them. ▶ Many popular language features are not parallel friendly,

(Globals, Recursion, OO) learning them is couter-productive. ▶ Most languages are cache opaque, increasing data-bottlenecks. ▶ learning and reimplementing in many changing languages for

various architectures wastes programmer time.

Idle Static Computers

▶ Computers are often idle when not being used. ▶ They do not program, debug and test to improve user

experience. ▶ Humans spend more time programming, than computers do

compiling. ▶ GPU’s have most processing power but often not used by

compilers. ▶ When my computer is idle, I feel like I’m not getting my

money’s worth.

One Lang to Rule Them All!

Vision is to have all human brains capable of contributing to GI-OS, regardless of their native language, or langauge preference.

▶ from machine code to human communication in one langauge,

Pyash. ▶ using most common grammar and vocabulary of world

languages. ▶ easily translates between formal variants of all human

languages. ▶ designed to be easily portable to GPU’s, FPGA’s and TPU’s.

Pyash Phonology

▶ 22 consonants, and 6 vowels. most common on phoible.org ▶ i/i/, a/a/, u/u/, e//, o//, 6// ▶ b/b/, c//, d/d/, f/f/, g/g/, j//, k/k/, l/l/, m/m/, n/n/,

p/p/, q//, r/r/, s/s/, t/t/, v/v/, w/w/, x/x/, y/j/, z/z/. ▶ 2 syntax consonants ”.”// and ”h”// ▶ 2 local definition consonants ”1”/—/”8”// ▶ 2 root word types HCVC and CCVC ▶ 2 grammar word types CV and CCVH

Pyash Grammar

▶ Grammar based largerly on WALS (World Atlas of Language

Structures) ▶ SOV postpositional, like Japanese and majority oflanguages. ▶ in Pyash: mina rwetyi hkunli ▶ in Analytic English: suj me be go to shop ya ▶ in Conjugated English: I shopum goeth.

Pyash Vocabulary: Phonological Makeup

▶ attempted manual creation, error prone and cumbersome. ▶ generated all legal easy to pronounce words ▶ made script to harvest words from 30+ most common

languages. ▶ got phonological data for words using espeak and some scripts. ▶ generated all words using those phonemes and assigned

weights. ▶ weighted by populations representing major language families.

Pyash Vocabulary: Content Extent

▶ made script that ▶ got together major ”international” word-lists ▶ wordnet, oxford-3000, special-english, among others 10k total ▶ sorted by 30,000 word frequency list of gutenberg material ▶ removed ambigious words with algorithms and blacklists ▶ removed words which have same meaning as previously

defined word in any of the 30+ major languages. ▶ removed words which were over borrowed ▶ generate a thesaurus style dictionary, and suggest list for all

languages.

Implementation History

▶ initial parser written in Haskell 2006 for simplified Lojban ▶ wrote parser in Java 2008 called Rpoku ▶ wrote interpreter in x86 nasm assembly based on Forth

2011-2014 ▶ wrote compiler in Javascript in 2014-2015 ▶ writing virtual machine in OpenCl compatible C, that

compiles to JS (2016)

Machine Programmer

Because tired of coding and recoding, and because of marked success of vocabulary generation, making a machine programmer to code and recode the libraries.

▶ register based virtual machine code is subset of Pyash. ▶ starts by evolving simple programs via genetic programming ▶ later can make more complete programs with more elaborate

machine intelligence, such as deep neural nets, and unsupervised learning.

▶ all generated programs are valid Pyash, so human speakable. ▶ thus Machine Programmer is also ”fluent” in Pyash.

Libreware Poverty

▶ Most libreware projects make next to nothing. ▶ Some companies spend large sums on Libreware, but pay their

own develpers, rather than the projects ▶ most consumers can’t buy developers to add features or fix

bugs in libreware software. And can’t add/fix them themselves.

▶ Libreware does best in mature markets, where it can undercut existing proprietary solutions.

Monetization: Market Size

▶ Machine Translation, 360 mln, 0.6% slow growth ▶ Intelligent Virtual Assistants, 580 mln ”rapid growth” ▶ Packaged Software 430 billion, 5% growth, moderate growth. ▶ Software Development (china), 690 bln, 21.4% rapid growth.

Potential Competitors

▶ IBM Watson (cost 30-50 mln to develop) ▶ Google Brain (Deep mind bought for 500 mln), Human Brain

Project (1bln funded) ▶ Cortana, Alexa, Siri, Viv ▶ Upwork (1 bln revenue), Freelancer

Potential Allies

▶ OpenAI (1 Bln funded), OpenCog ▶ Linux/Minix/FreeRTOS ▶ GNU/FSF, Ubuntu ▶ Adapteva, lowRISC (open hardware) ▶ FreedomSponsors (source code) ▶ Python

SPEL/GI-OS Cryptocurrency Marketplace

Vision is that users can make feature requests and put coin bounties on them, which would then be speedily solved by various machine programmers and human-machine programmer teams, which want the coins.

▶ machine programmer marketplace

▶ most premined coins are feature bounties

▶ 1/13 of coins each for welfare fountain, IPO or sale, network servers

Governance

Ideally SPEL/GI-OS will be a Distributed Autonomous Organization, so it needs to have an automated executive.

▶ President or ”speaker of the house” follows parliamentary

authority to manage meetings. ▶ Secretary records meetings and adjusts company policy

according to passed amendments. ▶ Treasurer allocates funds for accepted projects. ▶ Officers enforce policies within company jurisdiction

(marketplace). ▶ Comittees research, formulate and test policy ideas. ▶ Automated government is not prone to bribery or ”lobbying”. ▶ direct and-or liquid deliberative democracy is achievable.

Use Cases: Governance

Holding companies, or companies that buy other companies and manage them, are generally the most lucrative businesses. They can have huge profit margins, in excess of even 100

▶ Humans can only form relationships with up to 200 people,

due to cerebral cortex limitations. ▶ A robot representative can take all their constituents opinions

into account, when voting on or speaking about an issue. ▶ GI-OS with governance is ideal for managing multi-lingual

companies and countries. ▶ Replacing C-class positions with robots lowers need for

bonuses, and thus increases shareholder dividends. ▶ European Union spends over 300 million Euro on translation

costs alone. And the ”representatives” aren’t elected, so their motives are dubious at best. Replacing them with robots may be best.