North-West University: Machine and deep learning are a MUST at the North-West University

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Our world is speeding up, and never in human existence have we been able to search as fast, travel as far or delve as deep. The last century alone has seen a meteoric increase in the accumulation of data and we are able to store unfathomable quantities of information to help us solve problems known and unknown. At some point the ability to optimally utilise these vast amounts of data will be beyond our reach, but not beyond that of the tools we have made. At the North-West University (NWU), Professor Marelie Davel, director of the research group MUST Deep Learning, and her team are ensuring that our ever-growing data repositories will continue to benefit society.

The team’s focus on machine learning and, specifically, deep learning, is creating magic to the untrained eye. Here is why.

“Machine learning is a catch-all term for systems that learn in an automated way from their environment. These systems are not programmed with the steps to solve a specific task, but they are programmed to know how to ‘learn’ from data. In the process, the system uncovers the underlying patterns in the data and comes up with its own steps to solve the specific task,” explains Professor Davel.

According to her, machine learning is becoming increasingly important as more and more practical tasks are being solved by machine learning systems: “From weather prediction to drug discovery to self-driving cars. Behind the scenes we see that many of the institutions we interact with, like banks, supermarket chains and hospitals, all nowadays incorporate machine learning in aspects of their business. Machine learning makes everyday tools – from internet searches to every smartphone photo we take – work better.”

The NWU and MUST go a step beyond this by doing research on deep learning. “This is a field of machine learning that was originally inspired by the idea of artificial neural networks, which were simple models of how neurons were thought to interact in the human brain. This was conceived in the early forties! Modern networks have come a long way since then, with increasingly complex architectures creating large, layered models that are particularly effective at solving ‘human-like’ tasks, such as processing speech and language, or identifying what is happening in images.”

She explains that, although these models are very well utilised, there are still surprisingly many open questions about how they work and when they fail.

“We work on some of these open questions, specifically on how the networks perform when they are presented with novel situations that did not form part of their training environment. We are also studying the reasons behind the decisions the networks make. This is important in order to determine whether the steps these models use to solve tasks are indeed fair and unbiased, and sometimes it can help to uncover new knowledge about the world around us. An example is identifying new ways to diagnose and understand a disease.”

The uses of this technology are nearly boundless and will continue to grow, and that is why Professor Davel encourages up-and-coming researchers to consider focusing their expertise in this field.

“By looking inside these tools, we aim to be better users of the tools as well. We typically apply the tools with industry partners, rather than on our own. Speech processing for call centres, traffic prediction, art authentication, space weather prediction, even airfoil design. We have worked in quite diverse fields, but all applications build on the availability of large, complex data sets that we then carefully model. This is a very fast-moving field internationally. There really is a digital revolution that is sweeping across every industry one can think of, and machine learning is a critical part of it. The combination of practical importance and technical challenge makes this an extremely satisfying field to work in.”

She confesses that, while some of the ideas of MUST’s collaborators may sound far-fetched at first, the team has repeatedly found that if the data is there, it is possible to build a tool to use it.

One can envision a future where human tasks such as speech recognition and interaction have been so well mimicked by these machines, that they are indistinguishable from their human counterparts. The famed science fiction writer Arthur C Clarke once remarked that any sufficiently advanced technology is indistinguishable from magic. At the NWU, MUST is doing their part in bringing this magic to life.

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