How Data-driven Trend Intelligence Makes a Business Future-ready

Data-driven decision is the new master-key for unlocking profitable business avenues. But what are the right steps to capitalize on big data potentials? Let’s find out!

Big data has become the bread and butter for most businesses by now. The obvious answer to gain a competitive edge is data-driven intelligence. So how do new trends influence business activities in organizations and what are its long-term impacts? Let’s explore!

What is Data-driven Trend Intelligence?

Data-driven trend intelligence is a technique where business owners gain cognizance about ever-evolving market trends like falling raw materials prices, increase in dollar rates and new geo-politics activities with the help of software & technology platforms. This type of trend intelligence has been helping business owners to map long-term implications of the COVID-19 pandemic and its effects to overcome supply chain disruptions, logistics availability and product delivery.

How Trend Intelligence Makes a Business Future-ready?

Trend intelligence helps business organizations to accurately predict, monitor and act on possible future trends that tend to influence consumer behaviors. For instance, the ongoing veganism trend in the food industry is compelling manufacturers to innovate in ready-to-eat products using plant-based foods.

How AI and Big Data Makes Trend Intelligence More Lucrative for Profits

Since artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions, AI holds potentials for trend tracking to help market players gain a competitive advantage. Since data is the new oil and has surpassed the valuation of oil revenue in the world, big data helps to provide a wealth of insights that help to simplify business processes as per future trends.

Opportunities and Challenges of Data-driven Trend Intelligence

Though data science plays a monumental role in allotting financial budgets for long-term business activities, its challenges include inaccurate information since real-time changes in trends affect machine learning (ML) algorithms. To overcome these challenges, business organizations are now analyzing broad sets of data with many inputs to find key insights in real-time. Continuous troubleshooting in ML algorithms is another strategy to gauge accurate market trends. Data science experts are pulling up dashboards to acquire key information from broad data sets.

Thus, data-driven trend intelligence is paving the roadmap for intelligence as a service model that enables C-level executives and managers to excel in organizational leadership & generate high return on investments (ROIs).

Researchers successfully improve performance of Machine Learning(ML) to speed up drug discovery

To advance scientific discoveries and for their expanded application areas such as in pharmaceuticals and materials science, it is important to predict molecular properties quickly and precisely. Due to time-intensive and cost constraints involved with experiments and simulations to explore potential options, scientists have investigated using Machine Learning(ML) methods to assist research in computational chemistry.

However, most Machine Learning(ML) models are suitable to use only known or labelled data. This makes it nearly impossible to anticipate with accuracy the properties of novel compounds.

In fact, in industries such as drug discovery, there are millions of molecules to be selected from for use in potential drug candidate. A prediction inaccuracy of as small as 1% can result into misidentification of more than ten thousand molecules. In order to address this, improving the accuracy of Machine Learning(ML) models with limited data could play a key role to develop new treatment for diseases.

While the availability of labelled data is limited, the volume of feasible, yet unlabelled data is growing rapidly. A team of researchers at College of Engineering, Carnegie Mellon University reviewed if large volume of unlabelled molecules to construct Machine Learning(ML) models could perform better on property projections than other models.

The work of the researchers ended with the development of a self-supervised learning framework which they called Molecular Contrastive Learning of Representations with Graph Neural Networks. The findings of the study are published in Nature Machine Intelligence.

The framework boost performance of Machine Learning(ML) significantly by leveraging nearly 10 million unlabelled molecule data.

Meanwhile, for a simple explanation between labelled and unlabelled data, one of the research scientists suggested to image two sets of images of cats and dogs. Of the two sets, each animal in one set is labelled with the name of its species.

Machine Learning speeds up discovery of new materials for industrial processes

A research initiative by researchers at Northwestern University and University of Toronto employs machine learning to create best building blocks for the assembly of framework materials for use in a targeted application.

The findings of the initiative is published in Nature Machine Intelligence. It demonstrates that the using artificial intelligence approaches can help to propose novel materials for diverse applications. The use of machine learning to separate carbon dioxide from industrial combustion process is an example. In fact, AI approaches are promising to accelerate the design process of materials.

Meanwhile, in a bid to improve segregation of chemicals in industrial processes, a team of researchers at Northwestern University and University of Toronto in collaboration with experts at the University of Ottawa and Harvard University set out to find the best reticular frameworks.

The frameworks can be viewed as tailored molecular sponges. The frameworks are formed via self-assembly of molecular building blocks put in different arrangements. Furthermore, the frameworks represent a new family of crystalline porous materials that have proven to be promising to address several technology challenges.

Demonstrated use of automated platform aided build-up of Design Frameworks

“Earlier, to build the frameworks, it involved building an automated discovery platform. The platform generates the design of various molecular frameworks, thus significantly reducing the time required to find optimal materials for use in this process,” said the lead author of the study.

In fact, in the demonstrated use of the platform, frameworks that are discovered are strongly competitive against some of best-performing materials used for the separation of CO2 till date.

Nonetheless, the unpredictable amount of time and massive trial-and-error efforts needed to find new materials are some perennial challenges for addressing CO2 separation.

Machine Learning finds use to develop more accurate diagnostic tool for COVID-19

In a new development against the continued fight with COVID-19, a method of producing high-quality chest X-rays scans developed. These scans can be used to detect COVID-19 more accurately than currently used methods. The finding is scheduled to be demonstrated at the IEEE Big Data 2020 Conference of December 2020.

“Meanwhile, the availability of data is one of the most important elements of machine learning. By means of this, the research has taken a theoretical step forward for generating data using MTT-GAN,” explains the lead researcher.

In fact, the need for rapid and precise testing of COVID-19 is high, which includes testing to determine if COVID-19 is impacting the respiratory system of a patient. Currently, X-ray technology is used by many clinicians to distinguish scans of possible cases of COVID-19, however, data available is limited, and thus makes it challenging to accurately classify these images.

New tool superior than currently used one

Interestingly, the tool developed by the team of researchers at the University of Maryland is an extension of generative adversarial networks. Elaborately, generative adversarial networks are machine learning frameworks that quickly generate new data based on statistics from a training set. For extending this, the team uses more advanced method what is called Mean Teacher combined with Transfer Generative Adversarial Networks.

Following this, the lead research explains why Mean Teacher combined with Transfer Generative Adversarial Networks is superior to generative adversarial networks. This is because the former generates images that are much more similar to original scans produced by X-ray machines.

Furthermore, the MTT-GAN system has the potential for improving the accuracy of COVID-19 classifying scans. This makes it an important diagnostic instrument for physicians who are still striving to understand in the number of ways this complex disease is present in patients.