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WHY THIS MATTERS IN BRIEF

Today’s AI models are riddled with bias, plus they’re hard to develop, so Google have built an AI where evolution is the “Master Algorithm” in order to overcome these and other challenges.

 

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In the past I’ve talked about several Artificial Intelligence’s (AI) that have spontaneously evolved on their own – much to the surprise of their developers. These included Google’s AI’s that suddenly evolved and invented their own secret language, then encrypted it, Facebook’s AI’s that also developed their own language and learned how to collude with one another, as well as OpenAI’s AI’s that evolved to self-learn and then evolved again and spontaneously learned maths. And there have been others too.

However, in spite of all of this spontaneous evolution which so far no one has been able to explain how these AI’s evolved. Now though researchers at Google are embracing this “modern miracle” and are now testing how machine learning algorithms can be created from scratch, then evolve naturally by themselves, on purpose this time, based on simple math.

 

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Experts behind Google’s AutoML AI suite, which is the same AI suite that recently became the first AI in the world to create its own AI children, have now showcased fresh research which suggests the existing software could potentially be updated to “automatically discover” completely unknown algorithms while also reducing human bias during the data input process.

 

 

 

According to ScienceMag, the software, known as AutoML-Zero, resembles the process of natural evolution, with the AI code improving every generation with little or no human interaction – the new method could also prove to be a great breakthrough in creating self-evolving robots, something I’ve also written about before and another complimentary technology field that’s recently seen some of its own interesting breakthroughs.

Machine learning tools are ordinarily “trained” to find patterns in vast amounts of data while automating such processes and constantly being refined based on past experience but researchers say this comes with drawbacks that AutoML-Zero aims to fix. Namely the introduction of AI bias which so far has created a bunch of problems for people using them.

 

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“Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML,” their team’s paper states. “Innovation is also limited by having fewer options: you cannot discover what you cannot search for.”

The analysis, which was published last month on arXiv, is titled “Evolving Machine Learning Algorithms From Scratch” and is credited to a team working for Google Brain division.

“The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters, and is able to plug away 24/7 working on developing new algorithms,” Ray Walsh, a computer expert and digital researcher at ProPrivacy, told Newsweek.

As noted by ScienceMag, AutoML-Zero is designed to create a population of 100 “candidate algorithms” by combining basic random math, then testing the results on simple tasks such as image differentiation. The best performing algorithms then “evolve” by randomly changing their code.

 

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The results, which will be variants of the most successful algorithms, then get added to the general population, as older and less successful algorithms get left behind, and the process continues to repeat. The network grows significantly, in turn giving the system more natural algorithms to work with.

Haran Jackson, the Chief Technology Officer (CTO) at Techspert, who has a PhD in Computing from the University of Cambridge, told Newsweek that AutoML tools are typically used to “identify and extract” the most useful features from datasets—and this approach is a welcome development.

“As exciting as AutoML is, it is restricted to finding top-performing algorithms out of the, admittedly large, assortment of algorithms that we already know of,” he said.

“There is a sense amongst many members of the community that the most impressive feats of artificial intelligence will only be achieved with the invention of new algorithms that are fundamentally different to those that we as a species have so far devised.

 

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“This is what makes the aforementioned paper so interesting. It presents a method by which we can automatically construct and test completely novel machine learning algorithms.”

Jackson, too, said the approach taken was similar to the facts of evolution first proposed by Charles Darwin, noting how the Google team was able to induce “mutations” into the set of algorithms.

“The mutated algorithms that did a better job of solving real-world problems were kept alive, with the poorly-performing ones being discarded,” he elaborated.

“This was done repeatedly, until a set of high-performing algorithms was found. One intriguing aspect of the study is that this process ‘rediscovered’ some of the neural network algorithms that we already know and use. It’s extremely exciting to see if it can turn up any algorithms that we haven’t even thought of yet, the impact of which to our daily lives may be enormous.” Google has been contacted for comment.

 

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The development of AutoML was previously praised by Alphabet’s CEO Sundar Pichai, who said it had been used to improve an algorithm that could detect the spread of breast cancer to adjacent lymph nodes.

“It’s inspiring to see how AI is starting to bear fruit,” he wrote in a 2018 blog post.

The Google Brain team members who collaborated on the paper said the concepts in the most recent research were a solid starting point, but stressed that the project is far from over.

“Starting from empty component functions and using only basic mathematical operations, we evolved linear regressors, neural networks, gradient descent… multiplicative interactions. These results are promising, but there is still much work to be done,” the scientists’ preprint paper noted.

 

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Walsh told Newsweek: “The developers of AutoML-Zero believe they have produced a system that has the ability to output algorithms human developers may never have thought of.

“According to the developers, due to its lack of human intervention AutoML-Zero has the potential to produce algorithms that are more free from human biases. This theoretically could result in cutting-edge algorithms that businesses could rely on to improve their efficiency.

“However, it is worth bearing in mind that for the time being the AI is still proof of concept and it will be some time before it is able to output the complex kinds of algorithms currently in use. On the other hand, the research [demonstrates how] the future of AI may be algorithms produced by other machines.”

So as we look forwards into the future that then begs the next question – just how do you control or regulate AI’s that are designed, by their very nature, to evolve and evolve at digital speed?

About author

Matthew Griffin

Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, Bloomberg, CNBC, Discovery, RT, and Viacom, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Aon, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.

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