Artificial Intelligence in Research and Innovation

Artificial intelligence may greatly increase the efficiency of the existing economy but it’s also having an unexpected impact on law, research, R&D and the innovation cycle itself. If you enjoy articles like these, sign up to the Last Futurist, my speculative blog where we explore AI, stocks, tech innovation and breaking news.

Since the technology has so much hype, the ways in which it will impact society’s institutions, capitalism and democracy might also bring unexpected benefits and costs. AI's predictive powers for society and business may also have to be regulated by a global body, perhaps the United Nations, the EU and other new regulatory bodies.

Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making — and already it is transforming every walk of life. However it could also increase bias, racism and ingrained sexism and augment many of our social problems like wealth inequality.

Delphi AI

AI can also predict things that could impact how the scientific process of consensus building works. For example, an artificial intelligence system recently trained on almost 40 years of the scientific literature correctly identified 19 out of 20 research papers that have had the greatest scientific impact on biotechnology – and has selected 50 recent papers it predicts will be among the ‘top 5%’ of biotechnology papers in the future.

This introduces an AI bias into how we see data and research papers themselves. For example, some scientists say the system could be used to find ‘hidden gems’ of research overlooked by other methods, and even to guide decisions on funding allocations so that it will be more likely to target promising research. However others believe it will only augment bias that already exists within the framework.

The study describes a machine-learning system called Delphi – Dynamic Early-warning by Learning to Predict High Impact – that was ‘trained’ with metrics drawn from more than 1.6 million papers published in 42 biotechnology-related journals between 1982 and 2019. The scale of the analysis is impressive:

• The system assessed 29 different features of the papers in the journals, which resulted in more than 7.8 million individual machine-learning ‘nodes’ and 201 million relationships.
• The features included regular metrics, such as the h-index of an author’s research productivity and the number of citations a research paper generated in the five years since its publication.
•  But they also included things like how an author’s h-index had changed over time, the number and rankings of a paper’s co-authors, and several metrics about the journals themselves.

Delphi prototype can be easily expanded into other scientific fields, initially by including additional disciplines and academic journals, and potentially other sources of high quality research like the online preprint archive arXiv. AI is also becoming more relevant in pharmaceutical development, among other disciplines.

While society becomes more a human-AI hybrid system, the way we use data is changing. Delphi for example is viewed as an additional tool to be integrated into the researcher’s toolkit – and not as a replacement for human-level expertise and intuition.

How we use machine learning to predict high impact research is just in its infancy but one begins to see how AI will reframe how we view the scientific process and innovation itself. Scientists are critical of how Delphi might augment bias in the perception of research, but clearly machine learning will enable us to understand our own systems and biases better.

In the 21st century AI will slowly begin to have an even larger impact by serving as a new general-purpose “method of invention” that can reshape the nature of the innovation process and the organization of R&D. AI guided innovation is likely where we are heading. Could that result in a more exponential world of breakthroughs and increase the speed of technological change? At the Last Futurist, we think about these problems and possibilities a lot.

Much of the legal framework around the internet doesn’t exist yet and for AI it’s even less. The development of policies which encourage transparency and the sharing of core datasets across both public and private actors may be critical tools for stimulating research productivity and innovation-oriented competition going forward.

However for the most part the rules and regulations around AI and AI ethics and principles don’t yet exist in a coherent global manner. We might need the help of AI to create laws for the future of artificial intelligence itself.

The way AI will impact human systems is nearly beyond our intelligence to predict. But sooner or later machine learning will tackle those problems as well. Rapid advances in the field of artificial intelligence have profound implications for the economy as well as society at large. Yet in most instances today, we still live in the wild wild west of AI development and its global impact.