AI and Machine Learning systems have proven a boon to scientific research in a variety of academic fields in recent years. They’ve assisted scientists in identifying genomic markers ripe for cutting-edge treatments, accelerating the discovery of potent new drugs and therapeutics, and even publishing their own research. Throughout this period, however, AI/ML systems have often been relegated to simply processing large data sets and performing brute force computations, not leading the research themselves.
But Dr. Hiroaki Kitano, CEO of Sony Computer Science Laboratories, has plans for a “hybrid form of science that shall bring systems biology and other sciences into the next stage,” by creating an AI that’s just as capable as today’s top scientific minds. To do so, Kitano seeks to launch the Nobel Turing Challenge and develop a AI smart enough to win itself a Nobel Prize by 2050.
“The distinct characteristic of this challenge is to field the system into an open-ended domain to explore significant discoveries rather than rediscovering what we already know or trying to mimic speculated human thought processes,” Kitano wrote in June. “The vision is to reformulate scientific discovery itself and to create an alternative form of scientific discovery.”
“The value lies in the development of machines that can make discoveries continuously and autonomously,” he added, “AI Scientist will generate-and-verify as many hypotheses as possible, expecting some of them may lead to major discoveries by themselves or be a basis of major discoveries. A capability to generate hypotheses exhaustively and efficiently verify them is the core of the system.”
Today’s AIs are themselves the result of decades of scientific research and experimentation, starting back in 1950 when Alan Turing published his seminal treatsie, Computing Machinery and Intelligence. Over the years, these systems have grown from laboratory curios to vital data processing and analytical tools — but Kitano wants to take them a step further, effectively creating “a constellation of software and hardware modules dynamically interacting to accomplish tasks,” what he calls an “AI Scientist.”
“Initially, it will be a set of useful tools that automate a part of the research process in both experiments and data analysis,” he told Engadget. “For example, laboratory automation at the level of a closed-loop system rather than isolated automation is one of the first steps. A great example of this is Robot Scientist Adam-Eve developed by Prof. Ross King that automatically generates hypotheses on budding yeast genetics, plan experiments to support or refute, and execute experiments.”