Linux Conference 2016: Reincarnating Robots

LNCA_2016-08-22_11-40-20So the Linux Conference 2016 in Toronto went fairly well.

I helped the FSF booth set up and was there for the first day of the actual convention (Monday).

My spiel to people consisted of:

“Do you know how computing power keeps getting cheaper?
By the mid 2020’s  we’ll be able to buy as much computing power as the human brain for a $1000 [1].
By the 2030’s that’ll be down to a few hundred dollars.
And with Integrated Information Theory  we know that machine have consciousness[2], it simply depends on the complexity of the software, and the capabilities of the hardware.
So eventually we’ll be able to reincarnate[3] into robots.
But you don’t want to reincarnate into a proprietary robot, where the manufacturer might stop making your parts, and you might have to pay licensing fees on your brain.
You want to have libre hardware and software, so you could make your own replacement parts, and update your own brain at your discretion.
So that is why you should support the FSF, stickers are by donation, and buttons are a few dollars.”

[1] Ray Kurzweil calculates by 2023 human brain computation for $1000 https://en.wikipedia.org/wiki/Predictions_made_by_Ray_Kurzweil#2023
[2] Integrated Information Theory 3.0  http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.100358

mechanisms, such as logic gates or neuron-like elements, can form complexes that can account for the fundamental properties of consciousness.

[3] quantum information (consciousness) can’t be deleted but can be moved http://arxiv.org/abs/quant-ph/0306044

The no-deleting principle states that in a closed sys-
tem, one cannot destroy quantum information. In closed
systems, quantum information can only be moved from
one place (subspace) to another.

Liberty Bodies, SPEL and GI-OS presentation #AGI #Translation #DAO #Automation #MachineLearning

cute robot

SPEL mascot by Samantha Iris

Made a presentation about SPEL and GI-OS at Bruce-Grey Linux Users Group today.
It covers Liberty Bodies, SPEL the language and how it is to integrate machine programming, virtual assitants, grid networking programming marketplace, and governance robots for it’s distributed autonomous organization.
It is still being updated, but here are some links for the detailed slides:

html
pdf
dvi
tex

 

20min Alpha Meditation Gateway Frequency 10hz brainwave 31Tet A@432hz

alphaMeditate_20min
Alpha Wave is known as the Gateway Frequency as it can help you power into other forms of meditation more easily, due to it’s great focus and clarity. Starts with a 3 minute entrainment song, using arpeggio chords in 31tet. then 5 minutes of silence, followed by a minute of the song, three times, ending with a minute of outro going up to 40hz gamma brainwave

It is free CC-BY-SA
Can listen to it, and download it at my Goblin Refuge channel.

Whirlpool Meditation Idea

whirpool-wave
Was meditating today, and some ideas dawned on me regarding meditation tracks.

One was a former idea, that has to do with making the track like a dream, where a short song, is followed by a double-length version of it, and then a triple length version of it, and so on, so with every pass it gets longer, but still of the same content, much like how we have dreams at night.

Another idea I had, was to make a song in the shape of a spiral. Like spiraling in towards some central note or chord.  When flattened a spiral is really a wave, so this particular wave would lower in amplitude over time, though this would be represented as having less frequency variation.

for-example if our song spanned three octaves,  then would cross from the top of the octave to the bottom in 8 steps, using a septimal major third which is 11 semitones,  then can cross the other way using a major third (10 semitones),  then cross again with a neutral third(9 semitones),  and so on, until we finish in undecimal diesis, which can fold back on itself until there is only 1 note.

The reason this is somewhat precious is that at the same time can have beat frequencies which entrain the brainwaves.  At first they may be high of the mark, then low of the mark, then end with the goal brainwave.   This kind of reminds me of casting a net, if we catch people at their current brainwave frequency, perhaps they are more likely to join us for the ride into the destination frequency.

Also it might be an interesting effect to have the brainwaves shifting in such a way.

Can have the opposite effect for the outro.

 

 

Native Language Population Weights for SPEL vocabulary generation #SPELdev #linguistics

map of world through language areas

map of the world based on native langauge areas, with different colours illustrating different language families of the world.

Hi All,

So I've decided to go with the native language weights after-all,
since it seems that including L2 makes things too variable over time.

I've used a combination of Ethnologue language families, and the
native language lists to come up with my weights.  It includes the top
100 languages by number of speakers.

Here is the language and family overview:

    /*
      Indo-European 45.72% {
        Indo-Aryan 18.76% {
          Central {
            Hindi   (hi)  4.46%,
            Urdu    (ur)  0.99%,
            Haryanvi  (bgc) 0.21%,
            Awadhi    (awa) 0.33%,
            Chhattisgarhi (hne) 0.19%,
            Dakhini   (dcc) 0.17%
          }
          Eastern {
            Bengali (bn)  3.05%,
            Odia    (or)  0.50%,
            Maithili  (mai) 0.45%,
            Bhojpuri (bho) 0.43%,
            Chittagonian (ctg) 0.24%,
            Assamese    (as)  0.23%,
            Magadhi   (mag) 0.21%,
            Sylheti   (syl) 0.16%,
          }
          North Western {
            Punjabi (pa)  1.44%,
            Saraiki   (skr) 0.26%,
            Sindhi    (sd)  0.39%,
          }
          Western {
            Gujarati (gu)  0.74%,
            Marwari   (mwr) 0.21%,
            Dhundari  (dhd) 0.15%,
          }
          Southern {
            Marathi (mr)  1.1%,
            Sinhalese (si)  0.25%,
            Konkani (kok) 0.11%,
          }
          Northern {
            Nepali    (ne)  0.25%,
          }
          Indo-Aryan Remainder (IAR) 2.24
        }
        Germanic {
          English (en)  5.52%,
          German  (de)  1.39%,
          Dutch   (nl)  0.32%,
          Swedish (sv)  0.13%
        }
        Italic {
          Spanish   (es)  5.85%
          Portuguese (pt) 3.08%
          French    (fr)  1.12%
            Haitian Creole (ht) 0.15%
          Italian   (it)  0.9%
          Romanian  (ro)  0.37%
        }
        Iranian {
          Persian (fa)  0.68%,
          Pashto  (ps)  0.58%,
          Kurdish (ku)  0.31%,
          Balochi (bal) 0.11%,

        }
        Slavic {
          Russian   (ru)  2.42%
          Polish    (pl)  0.61%
          Ukranian  (uk) 0.46%
          Serbo-Croation (hbs)  0.28%
          Czech     (cs)  0.15%
          Belarussian (be) 0.11%
        }
        Hellenic {
          Greek   (el)  0.18%
        }
        European Remainder  2.24
      }
      Sino-Tibetan 21.12% {
        Mandarin  (zh)  14.1%,
        Wu        (wu)  1.2%,
        Cantonese (zhy) 0.89%,
        Jin       (cjy) 0.72%,
        South Min (nan) 0.71%,
        Xiang     (hsn) 0.58%,
        Myanmar   (my)  0.50%,
        Hakka     (hak) 0.46%,
        Gan       (gan) 0.33%,
        North Min (mnp) 0.16%,
        East Min  (cdo) 0.14%,
        Sino-Tiberan Remainder (STR)  1.33
      }
      Hmong-Mien {
        Hmong   (hmx) 0.13%
      }
      Niger-Congo 6.93% {
        Swahili   (sw)
        Yoruba    (yo)  0.42%,
        Fula      (ff)  0.37%,
        Igbo      (ig)  0.36%,
        Chewa     (ny)  0.17%,
        Akan      (ak)  0.17%,
        Zulu      (zu)  0.16%,
        Kinyarwanda (rw)  0.15%,
        Kirundi   (rn)  0.13%,
        Shona     (sn)  0.13%,
        Mossi     (mos) 0.11%,
        Xhosa     (xh)  0.11%,
        Niger-Congo Remainder (NCR) 4.65
      }
      Afro-asiatic 6.33% {
        Arabic    (ar)  4.23%,
        Hausa     (ha)  0.52%,
        Amharic   (am)  0.37%,
        Oromo     (om)  0.36%,
        Somali    (so)  0.22%,
        Afro-Asiatic Remainder (AR) 0.63
      }
      Austronesian 4.99% {
        Indonesian  (id)  1.16%,
        Javanese    (jv)  1.25%,
        Sundanese   (su)  0.57%,
        Tagalog     (tl)  0.42%,
        Cebuano     (ceb) 0.32%,
        Malagasy    (mg)  0.28%,
        Madurese    (mad) 0.23%,
        Ilocano     (ilo) 0.14%,
        Hiligaynon  (hil) 0.12%,
        Austronesian Remainder (ANR) 0.5
      }
      Dravidian   3.5% {
        Telugu    (tl) 1.15%,
        Tamil     (ta) 1.06%,
        Kannada   (kn) 0.58%,
        Malaylam  (ml) 0.57%
        Dravidian Remainder (DR) 0.14
      }
      Turkic 2.64% {
        Turkish   (tr)  0.95%,
        Uzbek     (uz)  0.39%,
        Azerbajani (az) 0.34%,
        Turkmen   (tk)  0.24%,
        Kazakh    (kk)  0.17%,
        Uyghur    (ug)  0.12%,
        Turkic Remainder (TR) 0.43
      }
      Japonic 1.99% {
        Japanese  (jp)  1.92%
        Japonic Remainder (JR) 0.07
      }
      Austro-Asiatic 1.58% {
        Vietnamese  (vi)  1.14%,
        Khmer       (km)  0.24%
        Austro Asiatic Remainder (AAR) 0.2
      }
      Tai-Kadai 1.24% {
        Thai  (th) 0.86%
        Zhuang (za) 0.24%
        Tai-Kadai Remainder (TKR) 0.14
      }
      Koreanic 1.19% {
        Korean  1.14%
        Koreanic Remainer (KR)  0.05
      }
      Uralic 0.32% {
        Hungarian   (hu)  0.19
        Uralic Remainder (UR)   0.13
      }
      Kartvelian 0.08% {
        Georgian
      }
      total 97.63%

      Native Speakers

    */


And here are the weights as I managed to get them. Due to limitations
of both google translate and espeak-ng not all languages have their
words or phonemes available, however they were grouped with the
closest langauge family.

The most unfortunate is likely a complete abscense of the Tai-Kadai
family representation as it has no espeak-ng support, however it has
been grouped with the Austronesian languages which some believe it is
related to.
    NativeWeights = {
      "zh": 14.1 + /*wu*/ 1.2% + /*cjy*/ 0.72 + /*hsn*/ 0.58 + /*hak*/
0.46 +
            /*gan*/ 0.33 + /*mnp*/ 0.16 +/*cdo*/ 0.14 + /*hmx*/ 0.13 +
            /*STR*/ 1.33,
      "es": 5.85,
      "en": 5.52 + /*ER*/ 2.24,
      "hi": 4.46  + /*awa*/ 0.33 + /*bgc*/ 0.21 + /*hne*/ 0.19 +
            /*dcc*/  0.17 + /*IAR*/ 2.24,
      "ar": 4.23 + /*ha*/ 0.52 + /*AR*/ 0.63, "pt": 3.08,
      "bn": 3.05 + /*ctg*/ 0.24 + /*as*/ 0.23 + /*bho*/ 0.43 + /*mai*/
0.45 +
           /*or*/ 0.5  + /*mag*/ 0.21,
      "ru": 2.42 + /*uk*/ 0.46 + /*be*/ 0.11 + /*hbs*/ 0.28,
      "pa": 1.44 + /*skr*/ 0.26 + /*sd*/ 0.39 ,
      "de": 1.39,
      "id": 1.16 + /*jv*/ 1.25 + /*th*/ 0.86 + /*su*/ 0.57 + /*tl*/ 0.42
 +
            /*ceb*/ 0.32 + /*mg*/ 0.28 + /*mad*/ 0.23 + /*ilo*/ 0.14 +
            /*hil*/ 0.12 + /*ANR*/ 0.5 + /*TKR*/ 0.14,
      "te": 1.15,
      "ta": 1.06 + /*DR*/ 0.14,
      "vi": 1.14 + /*km*/ 0.24 + /*AAR*/ 0.2,
      "ko": 1.14 + /*jp*/ 1.92 + /*JR*/ 0.07 + /*KR*/ 0.05,
      "fr": 1.12 + /*ht*/ 0.15, "mr": 1.1 + /*kok*/ 0.11,
      "ur": 0.99,
      "tr": 0.95 + /*uz*/ 0.39 + /*tk*/ 0.24 + /*ug*/ 0.12 +/*TR*/ 0.43,
      "it": 0.9,
      "zhy": 0.89 + /*nan*/ 0.71,
      "gu": 0.74 + /*mwr*/ 0.21 + /*dhd*/ 0.15,
      "fa": 0.68 + /*ps*/ 0.58 + /*bal*/ 0.11,
      "pl": 0.61, "kn": 0.58,
      "ml": 0.57, "my": 0.50,
      "sw": /*yo*/ 0.42 + /*ff*/ 0.37 + /*ny*/ 0.17 + /*ak*/ 0.17 +
            /*rw*/ 0.15 + /*rn*/ 0.13 + /*sn*/ 0.13 + /*mos*/ 0.11 +
            /*xh*/ 0.11 + /*NCR*/ 4.65,
      "am": 0.37 + /*om*/ 0.36 + /*so*/ 0.22, "ro": 0.37,
      "az": 0.34, "nl": 0.32, "ku": 0.31,
      "ne": 0.25, "si": 0.25,
      "hu": 0.19 + /*UR*/ 0.13, "el": 0.18,
      "cs": 0.15, "sv": 0.13, "ka": 0.08
    },



So now future versions of the SPEL vocabulary will include the
percentage of world language speakers which may find something
reminiscent in the words.
— Logan Streondj,