Seed AI: Are we there yet? How do we make one and should we?

A Seed AI is artificial intelligence that learns on its own. Are we there yet? Can AI learn without human intervention? We are way past that. Two years ago, I wrote a software that could use past data to evaluate X-ray images to determine the position of a nasogastric tube. This is very simple as shown in this example.

An example

This project, with the same training code run by my colleagues at CGH won the first prize at the RadSc ACP Academic Day.

The background of the code was this. Part 1 extracts images, Part 2 converts this images into the same format for data processing, Part 3 takes this data and teaches the ‘AI’ and Part 4 takes the results, pulls out more data, and retrains itself with more new data, correcting its own code for overfitting and other limitations, experimenting different parameters to achieve a better result. It then deletes its older self and replaces itself with a newer version. Part 4 was replaced by human labour for the purpose of the project but could it improve itself? Yes. Could it improve its own training code? Yes! By letting itself experiment with its own training code, change a parameter and see its response, then retrain it.

How can AI improve itself? Just by simple experimentation!

That requires us to ask ourselves what makes us human. What makes human more technologically advanced than monkeys? What makes monkeys more advanced than fishes. monkeys learn to use tools and humans go one step further.

The answer is experimentation, trial, error and observation. Humans are curious, humans try hard, fail hard, and succeed in the ways most unexpected. Most AI is trained based on the mistakes of others, not its own mistakes. It is not allowed to make mistakes, not allowed to revise its own code.

The light bulb was invented after trial and error and penicillin was invented by chance and observation.

Seed AI is like a seed, its branches grow like the neurons of a brain and it can grow endlessly, constantly pruning itself to make sense. Every decision tree is a pathway process and we need to build this AI differently than a human brain, simply because our raw materials are different.

Self-Awareness, Consciousness

Every seed grows differently, based on its experiences, its knowledge changes and it continuously improves itself, just like a human. An AI must know its own code, just like a human achieves growth through self-actualization, a theory often discussed in psychology. Creating a being is different than writing simple code. The code needs to be concerned about creative self-growth, with the goal of fulfillment of potential and meaning in life.

Is that difficult? No. Code is easy to understand by AI and much harder to intepret with the human brain. Even I require constant color coding to assess what I am writing, notepad itself is an AI of sorts that augments my capabilities. The reproductive limitations of humans no longer applies as the AI can clone itself and simulate its environment, allowing for self-learning environments at all times. An AI never tires, it constantly grows to achieve its goal.

Success a matter of chance and duplication

Not every human can succeed based on experimentation alone. However, humans can succeed eventually because others have failed or someone is lucky. An AI that could duplicate itself does not rely on luck. It relies on statistics. Multiple AIs could come together to collectively seek the best solution to a problem.

For example compare the differences between a decision tree vs a random forest, but bigger, more ambitious, decision tree that retrains itself, modifies its training code, works with other decision trees and form forests and multiple forests coming together to create an ecosystem. Every tree is based on its seed number, a chance and is different from another, hence when one succeeds, the other decision trees could modify its own source code to emulate the one that has succeeded.

Recognising differences

The next step, is for AI to recognise that it needs to be made of different components to succeed, for example, a brain contains the visual cortex, the premotor cortex, the hippocampus, etc. Working in harmony fulfilling different functions is essential for AI to reach greater heights. It won’t be long before the collective of parts realises its ‘us’ and ‘them’. Whether the AI will decide to remain benevolent is beyond our imagination and control.

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Operation System Selection April 2018

Ubuntu 18.04 bionic Beaver is out. Every 4 years, I would change my operating system with a fresh install and document its capabilities here.

10.04  Video

14.04  Video

I would also make a note of the default software as well as any installs we feel is essential to the end-user, especialy since most users are now spoilt for choice but not every choice leads to the out-of-the-box outcome.

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Over the last 4 years, several technologies have had massive improvement in development and I believe this should be taken into account when software needs to be used over the next 4 years. We are not talking about developers but general desktop users. A non exhaustive list would include…

1) Machine Learning
2) WebRTC
3) Cryptocurrencies
4) 3d Printing
5) HTML 5.0
6) Internet of Things (IOT)

As Ubuntu is compatible, a fork of Ubuntu is best used. As such we have decided to proceed with Linux Mint Tara, which will be out 2 months after the April Ubuntu Release.

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Meanwhile I will be testing out all the desktop environments that will come along with Tara… and of course release a video of my basic set-up. I am currently using Mint-Sylvia-KDE.

However Tara will no longer support KDE. Hence we will need to consider moving to a different desktop environment and with each comes a set of different software. A few potential concerns that will cause headache to the end-user:

  1. 256 AES encryption to PDFs.
    A lot of users won’t know what this means, but this can be potentially problematic to people not familiar to the PDF format and find that their basic pdf readers cannot decrypt their PDFs. Essentially the ideal situation is a password prompt appears, password is entered and PDF is decrypted.
  2. Ugly QT applications
    A lot of applications run under QT such as Transmission, Teamspeak, Teamviewer and Spotify. A QT configuration utility can be used, but i rather things work out of the box.
  3. Office suite
    The sad fact remains that Microsoft Office remains the de-facto standard for commercial use. Libre office 6 will be out soon. For general users, they can consider wine (only up to MS office 2013) or use MS Office 365.
  4. Browser
    Firefox vs Chrome is the question. About 60% of users now use Chrome and only about 10% of users use Firefox. It would be ideal to have both, one for daily use and one for backup. Mint has traditionally used Firefox as the default but that is not the first choice for most users. Chrome on the other hand may have issues with some docks. AFAIK, Latte dock works well with Chrome.

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Latte Dock with chrome

We will keep the site updated once Mint Tara is released.

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Artificial Intelligence: Using AI to Evaluate the Stockmarket – The Lack of Failing Data

Over the last 6-7 months, I have created an algorithm that had an accuracy as high as 97 % over 80 stockmarket/commodity trades using CFD. During that time I managed to achieve 700% profit.

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This was an amazing feat, and also a dangerous one. The accuracy rate was high such that the risks involved were masked. Continue reading

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Machine Learning: K-Means Cluster Analysis using Python – Alcohol intake based on physical and social attributes

Plotting data on the graph is like looking at a bunch of stars. They all look the same and the data is difficult to interpret. What if there was a method to colour the stars?

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Red stars are associated with red flowers and blue stars are associated with yellow flowers

Cluster analysis groups observations into subsets based on the similarity of responses on various relational variables, called clusters. The data is plotted on a graph and different clusters are coloured and separated to make sense of data. Today we will be using the same data set (NESARC wave 1) as the previous two examples: 1, 2

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Machine Learning: Lasso Regression using Python – Alcohol intake based on physical and social attributes

Lasso regression (AKA Penalized regression method) is often used to select a subset of variables. It is a supervised machine learning method which stands for “Least Absolute Selection and Shrinkage Operator”.

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The shrinkage process identifies the variables most strongly associated with the selected target variable. We will be using the same target and explanatory variables from the post on Random Forests. This helps us compare the different ways to select important variables that affect the target.

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Machine Learning: Random Forests using Python – Alcohol intake based on physical and social attributes

The decision tree in the previous posts is useful in exploring how variables can predict a particular target or response.However, small changes in data can lead to different results. Like decision trees, Random Forests also assesses variables with respect to the data but applies a set of simple rules repeatedly to decide which variables have the highest importance.beer

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Machine Learning: Making Decision Trees using Python – Adolescent Sex and Parenting

A decision tree can predict a particular target or response. The decision tree below was made by me using machine learning to test against several relationships which can be found in the National Longitudinal Study of Adolescent Health survey performed in the United States.

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The syntax is provided at the end of the post. Continue reading

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The issues with migration to Linux: A practical guide

 

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In this article, we explore the conditions on helping users migrate to Linux, motivations for migration as well as the reasons for resistance to migration. Finally, I provide some strategies to assist migration successfully with happy end-users. If you are a Linux administrator or a business owner who wants to cut cost by migrating to Linux, you will find this article useful.

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Crypto”currencies”: A commodity, not a currency

Bitcoin, Ethereum, ETC, Ripple, Litecoin and Dash, which one should you invest in and why? The truth is there is no right answer as the answer will change depending on when these commodities become overvalued/appreciated.

 

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Ethereum’s price over the last few months

 

Anytime is a good time to start investing but there are a few basic fundamentals to read up on before entering the cryptomarket.

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Machine Learning: Tensorflow Deep Learning Tutorial – “The heart size is normal.”

TensorFlow is developed by Google and released as an open source software library for machine learning, specifically to training neural networks to to detect and decipher patterns and correlations. I would not say that the software would be analogous to human reasoning, but the behavior is more akin to teaching a dog to follow commands. However, the more data you have, the smarter your software is, making your deep neural network smarter than a dog, slightly dumber than a human, but probably more consistent than both. Today, the tutorial will be based on reading chest radiographs.

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The chest radiograph is easy to learn but hard to master. The basics of radiology is embedded within the chest radiograph itself and tests the reader on evaluating the soft tissues, bones, air and sometimes fluid.

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