How to create Human Level Artificial Intelligence

The most important task is learning a large enough feature representation. This can also be called solving the perception problem or solving perceptual AI.

The representation can be learned by feeding enough data to a unsupervised learning algorithm. There are many unsupervised learning algorithms, Recently popular ones are stacked autoencoders (and their generalization - encoder graphs). It might be that only a single layer is required - in that case something similar to a large Singular Value Decomposition is all that is required.

The data for this would be videos (images+audio), audio, images, text. All of this does not need to be labelled. It is simply sufficient to scrape data from the web, such as from common crawl and feed it in.

To make a speaking computer, you would train a perceptron on the outputs. A perceptron is a simple machine learning algorithm that is trained to answer yes/no questions. A typical adult has a vocabulary of between 10,000-50,000. A perceptron would be trained for each of those words. The parameters for the perceptron would number in the millions to billions. Modern software such as vowpal wabbit can handle this.

The Steps

1. train large feature representation (unsupervised learning). Requires no labelled data.
2. train perceptrons to handle any of a number of different situations (supervised learning). Requires labelled data (input-output pairs)

Using the above approach, the prospect of unfriendly AI is not possible unless you train it on the supervised data set to explicitly be "evil" or to feed it a dataset of a person's life so that it carries out what a person would do.

The large feature representation only needs to be trained once. Afterwards, specific applications can be built on this base. This could be as powerful as any human ability. For example to train a "computer programmer", give it a dataset that consists of objectives and the stream of activity of a human programmer. For example,

• objective : "write a program that prints hello world in python"
• out : the keyboard character stream of programmer who completed this task.

You would get some more data of this type. You would then chop the keyboard stream into pieces, for example, all the characters of a keyboard. You would then have rows of data. Each row would have one output column, the character. It would have a large set of input columns, the preceding characters that were written before that particular instance of the character.

Example : A simple the cat sat on the mat speaking AI

Splitting the sentence "the cat sat on the mat at (sa)t would give

• output : t
• input : t,h,e, ,c,a,t, ,s,a

The input would be put through the "featurizer" and then the output of that, which would constitute millions/billions of columns would be fed into a perceptron for the character t which answers yes/no to whether a t should be typed.

In Summary

It it possible to create AI for any precise definition of the term by training a large enough feature representation once and then giving it datasets that correspond to the ability you would like it to achieve. It is possible to do this now with current supercomputers, datasets, and algorithms