The AI wars have hit the next level. Google has announced the release of Bard, their answer to ChatGPT. Soon, you'll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web: whether that's seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner. These new AI features will begin rolling out on Google Search soon. Several internet commentators have been commenting on whether this Google's rollout of this model is too little too late.

Algorithms seem to be everywhere nowadays, working their magic behind recommendation engines or helping doctors detect rare genetic disorders. But while machine learning tools are gradually becoming more accessible to non-experts, designing machine learning algorithms themselves typically requires some expert know-how -- and even then, it's no guarantee that AI models will perform correctly, as sometimes their inner workings can be a mystery even to the initiated. So it's little wonder that the field of automated machine learning or AutoML has been garnering interest as of late, allowing non-experts to quickly apply machine learning to real-world problems without having to master its more arcane aspects first. And here's an even more mind-blowing idea: what if machine learning algorithms could be programmed to automatically evolve better versions of themselves? The notion of machine-evolved algorithms could potentially mean more creative solutions -- ones that surpass the ability of human minds to conceive -- in addition to dodging the limits that human bias might place on them.

Much of the work undertaken by artificial intelligence involves a training process known as machine learning, where AI gets better at a task such as recognising a cat or mapping a route the more it does it. Now that same technique is being use to create new AI systems, without any human intervention. For years, engineers at Google have been working on a freakishly smart machine learning system known as the AutoML system (or automatic machine learning system), which is already capable of creating AI that outperforms anything we've made. Now, researchers have tweaked it to incorporate concepts of Darwinian evolution and shown it can build AI programs that continue to improve upon themselves faster than they would if humans were doing the coding. The new system is called AutoML-Zero, and although it may sound a little alarming, it could lead to the rapid development of smarter systems - for example, neural networked designed to more accurately mimic the human brain with multiple layers and weightings, something human coders have struggled with.

"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. 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. Fun AutoML-Zero experiments: Evolutionary search discovers fundamental ML algorithms from scratch, e.g., small neural nets with backprop. Can evolution be the â Master Algorithmâ?

Automated machine learning – or AutoML – is an approach that cuts down the time spent in doing iterative tasks concerning model development. AutoML tools help developers build scalable models with great ease and minimal domain expertise. AutoML is one of the most actively researched spaces in the ML community. AutoML studies have discovered ways to constrain search spaces to isolated algorithmic aspects. This includes the learning rule used during backpropagation, the gating structure of an LSTM, or the data augmentation.

AutoML-Zero aims to automatically discover computer programs that can solve machine learning tasks, starting from empty or random programs and using only basic math operations. The goal is to simultaneously search for all aspects of an ML algorithm--including the model structure and the learning strategy--while employing minimal human bias. Despite AutoML-Zero's challenging search space, evolutionary search shows promising results by discovering linear regression with gradient descent, 2-layer neural networks with backpropagation, and even algorithms that surpass hand designed baselines of comparable complexity. The figure above shows an example sequence of discoveries from one of our experiments, evolving algorithms to solve binary classification tasks. Notably, the evolved algorithms can be interpreted.