Today AI technologies are employed to both create and consume research. Scientists across the world are using AI to be able to conduct research that will solve the world’s most urgent problems—a goal they are able to march toward only by assimilating content that is sifted through for relevance and routed their way by powerful discovery algorithms. In the scholarly kitchen, AI is at once the main ingredient and the serving platter, and researchers, both the chef and the diner. It is this all pervasiveness that pre-defines any exploration of the role of AI in research.
The single biggest promise of AI is its potential to democratise research by lowering the barrier to access must-have support. Studies show that the average researcher is likely to spend four hours searching and up to five hours reading per week. Imagine an algorithm that curates a content feed matching the researcher’s interests, runs through each paper in it, and suggests summaries for each that she could quickly digest before deciding whether to read through 4000 words of the full paper. Now consider the fact that such personalised support is at her disposal at no cost.
Like a handful of others in a mix of tech startups, nonprofits, and behemoths on the AI solutions provider landscape, this is just one way in which CACTUS is challenging the status quo across the research workflow and helping redefine how science is disseminated and digested. A growing digital maturity buoyed by large and diverse training data sets, quantum computing power, and advances in natural language processing is allowing us to design an ecosystem of interoperable tools that support researchers every step of their way. Such support is helping transform how research is delivered to the world. Here is a sneak peek:
Synthesizing insights from big data: Machine learning allows synthesis of actionable insights from continuously expanding content corpora, which in turn informs R&D spending decisions to the tune of hundreds of millions of dollars.
Driving trust in science: To build trust in science, AI technologies are being used to assess the validity of scientific reporting and provide a quality standard that serves as an unbiased supplement to human-based authentication.
Improving current workflows: An engine trained on millions of published articles such that it can instantly identify the most common errors and omissions in a manuscript before it is submitted to a journal can reduce the chances of rejection or, in the very least, those of a long and uncertain wait.
Enabling new business models: Researchers spend a lot of time identifying a journal to submit to because they can only submit to one at a time. If rejected, they have to start the process all over again. An AI-powered article marketplace flips this limitation on its head and allows journals and researchers to choose each other on an open platform.
In research as in any industry, the essence of disruption is defined by the criticality of the problem(s) solved. As the technologies under the AI umbrella expand in scope and depth, their impact will become more meaningful and relevant to research dissemination and consumption. It is reasonable to expect that the coming years will see AI investments in research being increasingly focused on the consumer side. There will emerge challenges in training (quality of data sets) and deployment (data ownership and privacy), but there is little disputing that technological advancements will be of particular consequence to researchers from the Global South where R&D budgets are tight and research output is witnessing strong growth.
Put simply, it is AI that promises to level the playing field in a manner that researchers of tomorrow are defined by the strength of their ideas, and nothing else.