Artificial intelligence (AI) can assess far more data far more quickly than any single human can do. However, even with such immense pools of information, synergies between how humans and AI make predictions.
Predictive tasks are ubiquitous—any decision-making in any field or facet of life involves predicting the consequences of the available options before choosing them. Understanding the perils and promises of these assemblages and crafting a proper balance between the two is a major concern moving forward.
– Scott E. Page, Paper Author & Professor, Ross Business School, University of Michigan
The concern arises, from the relatively recent shift from predictions made on experience, some data and gut instinct to predictions made based on data and the considerations AI systems are programmed to make. The increased accuracy resulting from the application of ever more powerful algorithms to ever-larger databases, begs the question: “should humans remain in the predictive arena at all, or should we leave prediction to algorithms entirely?”
Humans approach predictions in a more nuanced way than AI methods, which can make the critical difference for an accurate forecast. AI handles big data well, while humans are better equipped to analyse what the researchers call “thick” data.
Rather than consisting of many data points of the same type of data, like big data, thick data’s fewer data points can tell a richer story. For example, years of statistical data may allow AI to predict how many home runs a baseball player may hit, but a human is more likely to understand how a well-liked team player may have a longer career.
Big data and thick data working together will produce more accurate collective predictions. Thick data can catch and draw attention to constellations of factors that might slip through the cracks between separated big data variables. Even though big data cast a wider net, that net contains holes.
The researchers put this idea to the test by mathematically testing how weighing human and AI inputs might result in different predictions. They found that in typical cases, meaning future outcomes depend on past outcomes, AI did not need human input to make accurate predictions. However, in atypical cases with more unknown or surprising factors, humans helped the AI reduce potential errors.
So long as humans can continue to identify different attributes, that is, continue to construct thicker data, or better understand atypical cases, they will continue to increase accuracy. Rather than a competition between humans and computers, the future of hybrid predictors will be a complex search for symbiosis. The researchers plan to continue exploring how partnered systems of AI and humans can help improve their predictions, including how multiple systems working together may give even more accurate results.
As reported by OpenGov Asia, U.S. researchers have been utilising AI for various purposes including finding COVID Antiviral Discovery. Since the beginning of the pandemic, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have been using AI to search through a vast number of small molecules to find usable drug candidates. Recently, they have utilised new computing hardware to speed the process, reducing searches that might have originally taken years to mere minutes.
The advantage of using AI, according to Brettin, is that it can quickly adapt to and accommodate chemical structures that it has never seen and that has never been synthesised and do not exist in nature. Artificial intelligence gives us both the speed and flexibility that pure physics-based computation would have a very hard time achieving.
In tests on a large dataset of small molecules, the researchers found they could achieve 20 million predictions, or inferences, a second, vastly reducing the time needed for each search. Once the best candidates were found, the researchers identified which ones could be obtained commercially and had them tested on human cells.
