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Given a set of objects , this ML engine “imagines” never seen before objects , each with several different and several similar characteristics. Essentially this can be thought of as a type of automated “Cross Domain Innovation”.
Used for centuries, Cross Domain Innovation aims to generate innovative ideas, experiences, and values through the convergence of knowledge from different, disparate domains. An awesome example of this is birds and trains. Japan’s bullet train was inspired by a bird’s beak. The resemblance is clear.Attribution: pixabay.comSo, to implement the above an AI technique known as “disentanglement” is used. The aim is to partially mimic human intuition, imagination and innovation. Disentanglement is not a new idea, and can be used on any type of knowledge and applied to a wide variety of real-world problems.Disentanglement is a machine learning model that breaks down any object’s characteristics and encodes them as separate dimensions. To explain this in non-technical terms, imagine a website developer that needs to add images of people to a site: but not real people so as to avoid any type of claim. So, an AI model is provided with a man’s picture and the expected output is a picture of an imaginary person who looks similar but is much taller. If the AI Model has learned the ‘height’ dimension independently, then this can be adjusted accordingly to get a picture of a similar-looking but taller person. If instead height and gender were encoded together then that request would result in a picture of a taller woman.A University of Southern California research team led by computer science Professor Laurent Itti and Ph.D. students Yunhao Ge, Sami Abu-El-Haija, and Gan Xin, have developed an AI model that imagines an object different from anything else ever seen before., an artificial intelligence research laboratory that conducts AI research with a view to promoting and developing “friendly AI in a way that benefits humanity as a whole”.Removing AI Bias Making AI less biased will always be of concern. In a serious incident a few years ago, the search term “gorilla” was blocked by Google’s image recognition service after it started tagging some non-Caucasian people as “gorillas”. Similarly, Microsoft and IBM’s face-analysis services performed near perfectly when identifying pictures of white males but not as good when analyzing images of black women, reflecting a bias in the training picture set. One way to address these kinds of issues would be to extract race and gender characteristics altogether from an image recognition model using the disentanglement technique.Heinz has a large number of products and “varieties”. A Disentanglement model could ingest information about all the products and propose new products, logos, and even taglines. These would then be filtered, shortlisted, and considered as potential new offerings.In medicine, disentangling the properties of drugs, for example, the core medicinal property as opposed to other properties, and then reassembling them to generate specifications could be used to propose new potential drugs or vaccines.Soccer was born in 1863 when England’s Football Association was formed and simultaneously defined a set of rules for both Soccer and Rugby. Today, AKQA, a digital product agency, has taught an AI Model how to create a new sports game by training it on hundreds of existing ones. The result is a game called “Speedgate”, imagined and created by the AI. It mixes soccer, rugby, and frisbees in a new game that was never thought of or played before. More on this in thisWith Speedgate, two opposing teams of six players pass, kick, and/or throw a ball through 3 gates at either end of a field, but excluding the middle one . A gate can be defended by only 1 of the 3 defenders playing. The other 3 players are forwards that try to get the ball through the gates. Unlike American Football, no pushing or bringing a player down is allowed. In addition to creating the game , the model generated the Speedgate Logo and the Speedgate tagline text But, of the games proposed by the AI model, some were clearly non-starters. For example, the model did not take into account the fact that a frisbee should not explode in midair , or that players should not be dangling from a pole. So, it will always be a team effort: AI and the R&D department.OpenAI's MuseNet is a deep neural net that predicts musical notes in music files. It generates songs with up to ten different instruments in ten different styles. OpenAI's Jukebox is an open-sourced algorithm that generates music with vocals. Trained on a million samples, the system is given a genre, an artist, and a snippet of lyrics from which it outputs song samples.
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