5 things Big Data can help with

Chris Aaron takes a look at five ways in which Big Data is transforming human activities in the scientific, political, commercial and personal worlds

As Big Data methods suffuse 21st-century economies, we can begin to discern some of the modes in which it and artificial intelligence/machine learning (AI/ML) are transforming existing services, products and processes. We could label three of these as: innovation by speed and completeness; innovation by discovery; and extension of existing methods. The other two areas covered relate to predictive analytics and household/personal management.

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1: INNOVATION BY SPEED AND COMPLETENESS

The first mode uses Big Data/AI techniques to improve the speed and completeness of existing processes, thereby creating outputs that were previously impossible. An example is Indonesia’s use of Big Data/AI in its diplomatic information service. The Ministry of Foreign Affairs (MFA), working with Big Data consultants from the United Nations’ Global Pulse office in Jakarta, has developed a system to take diplomatic reporting from its outposts around the world and visualize information extracted from these reports in different ways. The methods used range across document handling, image processing, machine learning, natural language processing and data visualisation. Developing the systems and creating training sets from document archives has been time-consuming, but MFA officials can now get a more complete, detailed understanding of their international partners’ situations and concerns, and can relate these to Indonesia’s diplomatic priorities using visualisation tools.

2: INNOVATION BY DISCOVERY

The second mode, innovation by discovery, results from perhaps the best-known characteristic of Big Data/AI – the extraction of meaningful patterns from very large, structured and unstructured datasets. A commonplace example might be Google or Facebook’s use of massive, volatile, unstructured data to sell you things or suggest your perfect date.

Another example is the discovery of new drugs. There are many bioinformatics startups, research labs and pharmaceutical companies active in this field, but research into a treatment for Idiopathic Pulmonary Fibrosis (IPF) at Nottingham University in the UK gives a flavour of how Big Data/AI methods are being applied. IPF has been increasing in prevalence in recent decades (it has also been seen in cases of ‘Long Covid’), and the search is on for drugs that can inhibit the biochemical process that causes scarring of the lung tissue. One approach is to simulate the impact of different molecules using a computer – a process now being transformed by Big Data methods. Nottingham’s Professor Jonathan Hirst tells NITECH that there are an estimated 1,060 molecules that might be useful in new-drug discovery, and that exhaustive computer testing of just 185,000 such compounds would take about eight months with current technology. In contrast, a Big Data/AI approach using an ‘active search’ algorithm enabled the Nottingham research team to identify dozens of promising molecules in a run of just 24 hours.

3: EXTENSION OF EXISTING METHODS

The third mode concerns how Big Data/ AI can extend exisiting technologies to new purposes or wider use. The proliferation of ultrasound imaging applications is a case in point. Although ultrasound scanning (US) is widespread in clinical settings, it can be timeconsuming, difficult to administer for less-experienced practitioners and can result in images of varying quality. Moreover, some of the images can be hard to interpret. AI/Big Data are helping to alleviate these limitations: real-time image analysis software now exists to guide clinicians in positioning a scanner to get the best images; automatic recognition of correct measuring points is reducing variability and speeding the process; and images can be analysed in real-time to highlight issues of concern.

4: PREDICTIVE ANALYTICS

In 2020, the Organisation for Economic Co-operation (OECD) reported on ‘the impact of Big Data and Artificial Intelligence in the Insurance Sector’. The report notes that “insurers can take advantage of Big Data to apply diagnostic and predictive analytics to predict the behaviour of policyholders and take action based on the outcomes”. Car insurance is one of the biggest business lines in most countries, and insurers group customers with similar risk profiles. Within those groups, riskier individuals are effectively subsidised by the more cautious. Big Data and AI offer the possibility of a tighter focus, potentially down to the level of individual risk assessment, meaning there would be less need for good drivers to subsidize less careful ones.

5: HOUSEHOLD AND PERSONAL MANAGEMENT

On an individual level, personal Big Data/AI systems that monitor our activities, our body chemistries and functions, and our physical and virtual environments on a 24/7 basis from birth will become as ubiquitous as smartphones. These will advise us what to wear (based on the weather), remind us what to take when we go out, tell us when clothing needs washing, and when to have a shower. They will tell us what to eat and when, and how much alcohol it is safe to consume. Alerts will be provided as to when household appliances need replacing, and even have them delivered by drone, and when the grass should be cut. They will advise us on personal grooming, what medicines to take for minor ailments, and whom we should be dating. Such multi-aspect monitoring and notification (MAMAN) systems are surely going to be the next big thing.

USING AI TO CREATE NEW CHEMICALS

In 2020, Jonathan Hirst (above), Professor of Computational Chemistry at the University of Nottingham’s Centre for Sustainable Chemistry was awarded a 10-year grant by the Royal Academy of Engineering to develop machinelearning techniques to design and manufacture new molecules in a more sustainable fashion. “At the moment, there are all kinds of inefficiencies, which are largely neglected in the search for new chemicals with specific desired properties,” explains Professor Hirst. “How do we find greener synthetic routes to chemicals, and how do we identify greener target molecules from the outset?”

The Royal Academy of Engineering ‘Chairs in Emerging Technology’ scheme awarded eight grants, worth 22 million GBP (circa 25.5 million EUR) in total, to pioneering academic teams during 2020. The research ranges from wearable ‘e-textiles’ to microbiological water-purification technologies.

On receiving the grant, Professor Hirst said, “I am most grateful to the Royal Academy of Engineering for this award, which builds on the fantastic research of many wonderful doctoral students and postdocs in our research group over the years. It will enable us to work ever more deeply with the industrial partners who have supported us, with the ultimate aim to deliver technology that goes beyond current machinelearning deployments to a new generation of dynamic interactive tools where AI and human experts are working hand in hand.”

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