AI project -- data collection
Budget: $15 – $25 USD
As an AI developer, I need to be able to make use of freemium compute to support my clients.
But I am also looking for simple tools for fast deployment to a local kubernetes cluster that is globally accessible. It has to be 100% as close to $0/yr domain name included.
What I need job wise is a job that allows for solving puzzles much higher than building Artificial intelligence from the ground up, brain and all. AI development is complexity 17. But the problems I am aiming to solve are much higher than complexity 17. But their are things much more complex than AI to be developed in the new dawn.
In and amongst the complexity plane I am looking to solve requires also a understanding of quantum physics. The type of problem I am aiming to solve is one that is complexity 23, quark level and antimatter level of difficult to see. This is the level of complexity where we have to consider the foundation our universe is based on.
What I need done is the networking, tests of code, and tests of nano plastic structures that can help regraph tissue via XNA and the bacterial equivalent of each cell type samples. What I need done is the test and results driven network that can KANBAN through the problems within a given network as teams of neurologists. What is also required is a group of genetists, botanists, and radiologists to work in the same lab. What I need done is to have their be a way for a person stuck in India to be able to get to the USA for free.
But what can I do for you in fair exchange for my time?
How can we help you make your project successful and what would you consider the time in that project worth?
How can we make your project successful and in what ways do you want it to succeed?
What would you want in exchange fairly for my time working on your project?
Required:
* ML engineering
* Numpy, scipy, matplotlib
* Pybrain3 ( look to tutorials point )
* Docker
* Flask ( learn Django first)
* brython.js, react.js, pyodide.js in conjunction with flask
* MRI data and FASTA data and ML training experience
* Architectural homiomorphic monoculture of AI framework development experience
* Oauth2.0 security modeling experience ( modelling experience, by hand )
* flask-JSONRPC and flask-SocketIO based application development experience ( either one )
* MAML & NLP modeling experience ( both are required )
* Prompt engineering experience
* Language integration to python ( even if it compiles golang first )
* Application reverse engineering experience ( 2 month minimum )
* Application hacking experience ( python is a language usable to simplify it; I bent Linux including x11 window compositor down to qt5 )
* Xml rpc experience ( you must separate the connection points at the stemming of the function biplane from the remote source )
Data you'll need to collect:
* MRI data ( . nii NIFTI format is common )
* FASTA data ( genetics data for protein modelling )
* BCI readouts in live time
* EEG readouts in live time
The last thing you'll need to underfit and median fit the networks. Each major region should have a unique outline and trained in separately. Each sub region is to be trained from those lively collected samples after each meets correct "just under fitness" fitness.
This also means normalization and flattening of data is not allowed. Mainly because the human head is not 2d or a flat series of wiggling lines. Meaning one must retain and accept 3d shape for each subregion. But this means accounting for each connection between them and its brain stem activation functions.
On top of that, you will need to resample multiple sets into a summed grouped set. This requires training on these samples synchronously from MRI and fasta data with a tool for handling spike function response normally observed during functional uptake of said protein used by the head. And lastly the biproduct that results as input to another network. This will have to include information on motor function, etc.
With all that in mind, this data is to not focus on MNist style classification. As instead it is to be used for, in live time, repriming to which the MRI data originated from. This means it has to work for by mirroring person whom's MRI data is used and from live data of the person ( BCI or EEG to MRI of activated connected regions; etc ). The end result should work when connected via xmlrpc to a VR/XR setup, so it has something to sanely look around in and operate around in, environment wise. While keeping its parts separated.
But I am also looking for simple tools for fast deployment to a local kubernetes cluster that is globally accessible. It has to be 100% as close to $0/yr domain name included.
What I need job wise is a job that allows for solving puzzles much higher than building Artificial intelligence from the ground up, brain and all. AI development is complexity 17. But the problems I am aiming to solve are much higher than complexity 17. But their are things much more complex than AI to be developed in the new dawn.
In and amongst the complexity plane I am looking to solve requires also a understanding of quantum physics. The type of problem I am aiming to solve is one that is complexity 23, quark level and antimatter level of difficult to see. This is the level of complexity where we have to consider the foundation our universe is based on.
What I need done is the networking, tests of code, and tests of nano plastic structures that can help regraph tissue via XNA and the bacterial equivalent of each cell type samples. What I need done is the test and results driven network that can KANBAN through the problems within a given network as teams of neurologists. What is also required is a group of genetists, botanists, and radiologists to work in the same lab. What I need done is to have their be a way for a person stuck in India to be able to get to the USA for free.
But what can I do for you in fair exchange for my time?
How can we help you make your project successful and what would you consider the time in that project worth?
How can we make your project successful and in what ways do you want it to succeed?
What would you want in exchange fairly for my time working on your project?
Required:
* ML engineering
* Numpy, scipy, matplotlib
* Pybrain3 ( look to tutorials point )
* Docker
* Flask ( learn Django first)
* brython.js, react.js, pyodide.js in conjunction with flask
* MRI data and FASTA data and ML training experience
* Architectural homiomorphic monoculture of AI framework development experience
* Oauth2.0 security modeling experience ( modelling experience, by hand )
* flask-JSONRPC and flask-SocketIO based application development experience ( either one )
* MAML & NLP modeling experience ( both are required )
* Prompt engineering experience
* Language integration to python ( even if it compiles golang first )
* Application reverse engineering experience ( 2 month minimum )
* Application hacking experience ( python is a language usable to simplify it; I bent Linux including x11 window compositor down to qt5 )
* Xml rpc experience ( you must separate the connection points at the stemming of the function biplane from the remote source )
Data you'll need to collect:
* MRI data ( . nii NIFTI format is common )
* FASTA data ( genetics data for protein modelling )
* BCI readouts in live time
* EEG readouts in live time
The last thing you'll need to underfit and median fit the networks. Each major region should have a unique outline and trained in separately. Each sub region is to be trained from those lively collected samples after each meets correct "just under fitness" fitness.
This also means normalization and flattening of data is not allowed. Mainly because the human head is not 2d or a flat series of wiggling lines. Meaning one must retain and accept 3d shape for each subregion. But this means accounting for each connection between them and its brain stem activation functions.
On top of that, you will need to resample multiple sets into a summed grouped set. This requires training on these samples synchronously from MRI and fasta data with a tool for handling spike function response normally observed during functional uptake of said protein used by the head. And lastly the biproduct that results as input to another network. This will have to include information on motor function, etc.
With all that in mind, this data is to not focus on MNist style classification. As instead it is to be used for, in live time, repriming to which the MRI data originated from. This means it has to work for by mirroring person whom's MRI data is used and from live data of the person ( BCI or EEG to MRI of activated connected regions; etc ). The end result should work when connected via xmlrpc to a VR/XR setup, so it has something to sanely look around in and operate around in, environment wise. While keeping its parts separated.