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Combining Natural and Artificial Neural Nets for Better Search

We know of natural neural nets more by another name – animal brains (including human). Artificial neural nets are of course artificial intelligences built on the same basic rules as their natural counterparts, but much simpler and more focussed. Google, that pioneer of all manner of artificial intelligences designed for search, has posed the question as to whether or not artificial intelligences are capable of being taught to automatically label every song on the Internet.

Obviously the answer in principle, is yes, as fundamentally artificial neural nets are capable of being every bit as powerful as their natural counterparts. However, the ones we haver at the moment are nowhere even close, hampered by our incomplete understanding of how natural brains function. So, what if we were to use both natural neural nets and artificial in a single combined system? If it is turned into something of a game, will the human participants enjoy themselves at the same time as they are providing useful data, and training the artificial in what to expect?

That was the question posed in response by University of California, San Diego engineers. They set up a system using the internet whereby “game-powered machine learning,” would enable music lovers to search every song on the web well beyond popular hits, with a simple text search using key words like “funky” or “spooky electronica.”

The researchers, led by Gert Lanckriet, a professor of electrical engineering at the UC San Diego Jacobs School of Engineering, hope to create a text-based multimedia search engine that will make it far easier to access the explosion of multimedia content online. That’s because humans working round the clock labelling songs with descriptive text could never keep up with the volume of content being uploaded to the Internet. For example, YouTube users upload 60 hours of video content per minute. Only an artificial neural network dedicated solely to the task, could even hope to keep up with this sheer volume of information.

In Lanckriet’s solution, computers study the examples of music that have been provided by the music fans and labelled in categories such as “romantic,” “jazz,” “saxophone,” or “happy.” The computer then analyses waveforms of recorded songs in these categories looking for acoustic patterns common to each. It can then automatically label millions of songs by recognizing these patterns. Training computers in this way is referred to as machine learning. “Game-powered” refers to the millions of people who are already online that Lanckriet’s team is enticing to provide the sets of examples by labelling music through a Facebook-based online game called Herd It.

A Screenshot of Herd It in action


It’s the active feedback loop that combines human knowledge about music and the scalability of automated music tagging through machine learning that makes “Google for music” a real possibility. Although human knowledge about music is essential to the process, Lanckriet’s solution requires relatively little human effort to achieve success. Essentially the only real human participation is via casual gaming software, with the AI picking up the results and aggregating the data received - so it can weed out malicious attempts to lead the AI astray by "trolls" or "griefers". Through the active feedback loop, the computer automatically creates new Herd It games to collect the specific human input it needs to most effectively improve the auto-tagging algorithms, said Lanckriet. The game goes well beyond the two primary methods of categorizing music used today: paying experts in music theory to analyse songs – the method used by Internet radio sites like Pandora – and collaborative filtering, which online book and music sellers now use to recommend products by comparing a buyer’s past purchases with those of people who made similar choices.

Both methods are effective up to a point. But paid music experts are expensive and can’t possibly keep up with the vast expanse of music available online. Pandora has just 900,000 songs in its catalogue after 12 years in operation. Meanwhile, collaborative filtering only really works with books and music that are already popular and selling well. On the other hand, this system works with everything, and has next to no operating costs. At the end of the day, the highly trained AIs can be duplicated and used in multiple operations, and like humans, they will never stop learning.

There is absolutely no reason to believe a trained AI of this type would be limited to just music, and every reason to believe that if the trial is successful, they can be trained to index every media type going - and those not yet invented.



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