LOCKSUIT

Releasing full AGI/evolution research in General Project Discussion

This is most of all my notes, images, algorithms etc summarized/unified in their recent forms. Here. I am 24 and started at 18 all this work. I make discoveries in my brain using mostly vision (visual language; shapes, cats, etc which has context, each explain each other like in a dictionary, a small world network of friendly connections).

https://www.youtube.com/watch?v=Us6gqYOMHuU

I have 2 more videos to share but not yet uploaded. And 2 more notes. The last note will have some more very recent good data but these not yet given are of less immediate importance. The long file though does have a lot of recent knowledge in it though still. It's better you know all it, at least the movie.

notes
https://paste.ee/p/mcnEk
https://paste.ee/p/kQLCx
https://paste.ee/p/CvSsB
https://paste.ee/p/lJYMP
https://paste.ee/p/EmCZt

Code-only of my advanced-ngram 'gpt2':
https://paste.ee/p/7DG3M
https://paste.ee/p/XvVp5
result:
The software was made on a
The software was made on a wide variety of devices, and operating apps and applications that users can easily read as an app for android. It is a bit of a difference, but i was able to get it. The developers are not going to make it through a web applications, and devices i have seen in the running for the mobile apps. Applications allows users to access applications development tools, and allow applications of the app store. A multimedia entertainment entertainment device, and allows platforms enabled access to hardware interfaces. Using a bit of html application app developers can enable users to access applications to investors, and provide a more thorough and use of development. The other a little entertainment media, and user development systems integration technology. Applications allows users to automatically provide access to modify, optimize capability allows users to easily enable. Both users and software systems, solutions allowing owners software solutions solutions to integrate widgets customers a day. And if you are accessing services product, and mobile applications remotely access to the software companies can easily automate application access to hardware devices hardware systems creators and technologies. Builders and developers are able to access the desktop applications, allowing users access allows users to
((I checked the 400MB, not too long copies pastes, like only 3-5 words at most))
https://www.youtube.com/watch?v=Mah0Bxyu-UI&t=2s

I almost got GPT-2 understood as shown at end of movie but need help, anyone understand it's inner workings? Looking to collaborate.

I recommend you do this as well and mentor each other.

more data by my top-of-mind pick:
AGI is an intelligent Turing Tape. It has an internal memory tape and an external memory tape - the notepad, the desktop, the internet. Like a Turing Tape it decides where to look/pay attention to, what state to be in now, and what to write, based on what it reads and what state it is in. The what/where brain paths. It will internally translate and change state by staying still or move forwards/backwards in spacetime. It'll decide if to look to external desktop, and where to look - notepad? where on notepad? internet? where on internet?

It's given Big Diverse Data and is trying to remove Big Diverse Data (Dropout/Death) so it can compress the network to lower Cost/Error and hence learn the general facets/patterns of the universe exponentially better while still can re-generate missing data despite having a small world network (all quantinized dictionary words explain each other). It uses Backpropagation to adjust the weights so the input will activate the correct node at the end. That node, can be activated by a few different sorts of images - side view of cat, front view of paw, cat ear, mountain, it's a multi dimensional representation space, Since the network Learns patterns (look up word2vec/Glove, it's same as seq2seq) by lowering error cost by Self-Attention evolution/self-recursion of data augmentation (self-imitation in your brain using quantinized visual features/nodes), it therefore doesn't modify its structure by adjusting existing connections (using weights/strengths) to remove nodes, it rather adjusts its structure by adjusting connections weights to remove error and ignores node count Cost.

Intelligence is defined as being flexible/general using little data, but walker bots are only able to solve what is in front of themselves, we need a thinker like GPT-2, and you can see the mind has evolved to simulate/forecast/predict the future using evolutionary mental RL self-imitation self-recursion of data. And intelligence is for survival, immortality, trying to find food and breed to sustain life systems, it's just an infection/evolution of matter/energy / data evolution.

MikeB

Pattern based NLP in General Project Discussion

This is a project I've been working on for a few years. In 2019 I was testing out the theory on Pandora Bots, and this year I'm converting it to plain C.

The main goal is to be as small as possible, solid-state (no algorithms, learning, knowledge calculation), fast as possible, multi-language.

It converts pattern-matched singular words into a symbolic/tokenised word to be matched again in symbolic sentences. They are then assigned an intention/topic/perspective. The chatbot code uses the Intention/topic/perspective to associate fixed response/s (with randomisation).

Everything from the spell check to the word-to-symbol tokeniser, and the sentence pickups, chatbot responses are all staged pattern matching.

The size and speed in pandora bots (1000 individual words with sentences) is ~500kb, and ~1 second response time. In Plain C (on an Arm Cortex M4 @ 120mhz) it's ~159kb and 15-100 milliseconds. After spell check and tokenisation, all actions are generally less than 1 ms.

Key features
-Less than 500kb including all word databases.
-Millisecond fast.
-100 max fixed chatbot responses make it easy to voice record and/or change personality.
-Private information automatically stripped during word compression (names of places and things).
-Native differentiation of Wondering, Questions, and Directions - "can you speak english" "do you speak english" "speak english".

The intention pickup allows you to write a general chatbot response (non specific) of a few varieties with confidence, and not have to look at the backend.

There is some short term memory for handling puzzles "If I did this, then what is this?" but as there's no knowledge reflection, it can still 1) tell it's a question, 2) scan for the topic, and 3) count logical words as opposed to emotional words. And be relevant that way.

So this solves the problem of chatbots in games due to:
-Too much data or processing power required.
-Cannot change the personality/no personality.
-Cannot change the language/only one language.
-Cannot acknowledge the user/escapes the topic.
-Wrong views or bad knowledge calculation.
-Cannot record audio/terrible voice synthesis.
-Not being white box/solid state/predictable.

Zero

Stop autonomous weapon in Future of AI

https://youtube.com/watch?v=9CO6M2HsoIA&feature=youtu.be

infurl

SwarmFarm agricultural robots in Robotics News

https://www.abc.net.au/news/2021-02-28/swarmfarm-agricultural-robots-queensland/13193394

Fourteen SwarmFarm robots are currently working in orchards, on cotton and grain properties, and at a turf farm. Soon a vineyard in South Australia will also have one slashing grass, spraying weeds, and detecting and killing snails.

infurl

Shakey with an "e" in General Robotics Talk

https://www.youtube.com/watch?v=7bsEN8mwUB8

In our daily lives we all rely on algorithms that were developed for Shakey.

Meet “Shakey” – the world’s first robot to embody artificial intelligence.

Shakey could perceive its surroundings, logically deduce implicit facts from explicit ones, navigate from place to place, make a plan to achieve a goal, monitor the execution of a plan in the real world, recover from errors in plan execution, improve its planning abilities through learning, and communicate in simple English.

Shakey was created from 1966-72 by the Artificial Intelligence Center at Stanford Research Institute (now SRI International).

yotamarker

the Android Studio alchemy series for the living grimoire in AI Programming

this series aims to add varies app physical abilities with as little code lines inside the MainActivity. it is what the android studio IDEs should have included as a default but they don't, so I did.

https://www.yotamarker.com/t347-android-studio-alchemy-series-timer

https://www.yotamarker.com/t348-android-studio-alchemy-series-b8triutil

https://www.yotamarker.com/t349-android-studio-alchemy-series-get-device-temprature

https://www.yotamarker.com/t350-android-studio-alchemy-series-compass

https://www.yotamarker.com/t351-android-studio-alchemy-series-tts-test-to-speech

https://www.yotamarker.com/t352-android-studio-alchemy-series-stt-speech-to-text

this took my main activity from 300 code lines to about 30 lines. and it just looks better.

Zero

Just another mildly interesting experiment in General Project Discussion

Well

Here is my current "hot topic". Using only well established algorithms that are good at what they do (that's new), striking right at knee (that's rare for me), and thinking out of the box as I usually do.

What I got so far:

- The avatar uses a browser version of MakeHuman. I can post pics of my blonde if you're interested. She looks a bit scary sometimes but I bet she'll be a nice Ai.
- The main mind structure is Levelgraph, which is a TripleStore implemented with the best technique (hexastores are unbeatable).
- The "meaning" part is stored as vector-objects, a simple but clever and neat data structure.
- A kD-tree is used for fast meaning-proximity searches.

Meet Jamie.

Zero

And... Touchdown! in General Chat

Perseverance landed safely on Mars a few minutes ago, congratulations to Nasa teams!!!
 

WriterOfMinds

Project Acuitas in General Project Discussion

I'm going to post updates re: my main project, Acuitas the semantic net AI, in this thread.

My focus this past month was on giving Acuitas the ability to learn more types of inter-word relationships.  He started with just class memberships (<thing> is a <thing>) and qualities (<thing> is a <adjective>), but now he can learn all of the following:

<thing> can do <action>
<thing> is for <action>
<thing> is part of <thing>
<thing> is made of <thing>
<thing> has <thing>

In the process I made extensive updates to the module behind the Text Parser that detects "forms," i.e. syntactic structures that encode these inter-word relationships.

I also upgraded the GUI library from Tkinter to Kivy, which is kind of boring but had to be done, because the old GUI was provoking frequent crashes.

More details on the blog: http://writerofminds.blogspot.com/2017/06/acuitas-diary-2-may-2017.html

The included diagram shows my vision for the conversation engine.  The upper half is implemented (though of course it still needs to mature a great deal); the lower half mostly does not exist yet.

gransee

Hello from Arizona in New Users Please Post Here

First post!

I plan on mostly lurking. Not sure if I have anything to contribute. Have been interested in AI for years.

Peter