If you can conduct experiments thousands of times faster then you can make breakthroughs thousands of times faster – and that’s a game changer.


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Artificial Intelligence (AI) starting to master science – to the point that Robo-Scientists that can conduct and are a thing. And now researchers have developed what they say is a breakthrough robotic lab assistant, able to move around a laboratory and conduct scientific experiments just like a human.


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The machine, designed by scientists from the UK’s University of Liverpool, is far from fully autonomous: it needs to be programmed with the location of lab equipment and can’t design its own experiments. Yet. But by working seven days a week, 22 hours a day, with two hours to recharge every night, it allows scientists to automate time-consuming and tedious research they wouldn’t otherwise tackle.


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In a trial reported in Nature today, the robot’s creators, led by PhD student Benjamin Burger, say it was able to perform experiments 1,000 times faster than a human lab assistant, with that speed-up mostly due to the robot’s ability to work around the clock without breaks.

But Professor Andy Cooper, whose lab developed the robot, said that speed is not necessarily the point. The main benefit of a tool like this, he says, is that it allows scientists to explore avenues of research they wouldn’t waste a human’s time on.


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“The idea is not to do things we would do faster, but to do bigger, more ambitious things we wouldn’t otherwise try to tackle,” says Cooper.

For its showcase research, the robot was tasked with finding substances that can speed up chemical reactions that create hydrogen from light and water, an area of research useful to many industries, including green energy production. The robot was programmed with the basic parameters of the experiment but used algorithms to decide how to change 10 different variables, such as the concentration and ratio of chemical reagents.

Over an eight-day period, the machine carried out 688 experiments to find how to create more efficient reactions. It mixed samples in glass vials, exposed them to light, and analyzed the results using gas chromatography.


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The results of the tests are promising, but Cooper notes he wouldn’t have asked a human to even carry out the research, given how much time it would take and how it might distract them from their studies. “If you asked a human to do it they could lose their whole PhD,” he says. But for a machine, the potential benefits outweigh any loss of time.

The robot itself is not without its expenses, of course. The basic hardware costs between $125,000 and $150,000, says Cooper, and it took three years to develop the controlling software. The machine navigates labs using LIDAR, the same laser-based technology found in self-driving cars. That means it can operate in the dark, and it won’t get confused by changing lighting conditions. It manipulates lab equipment using an industrial arm built by German robotics firm Kuka, though some machines have to be adapted to its use.


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Lee Cronin, a professor of chemistry at the University of Glasgow who also uses automated equipment in his work, said the main advance of the research was the robot’s mobility and its ability to use human equipment. But he cautioned that such machines would still be “niche” in the future, as deploying them won’t always make sense in terms of costs.

“I’m not sure robotic assistants like this are going to be useful in a general sense but in repetitive experiments … they could be excellent,” Cronin told The Verge by email.

Cooper says that although the upfront costs are expensive, they’re not unusual compared to lab equipment, which often costs hundreds of thousands of dollars. He also says that while some scientific research can be automated using static machines, the flexibility of a robot that can be reprogrammed to take on a variety of tasks is ultimately more useful.


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“The idea was to automate the researcher, rather than the instrument,” says Cooper. “It’s a different paradigm.”

Cooper and his colleagues have already formed a spinoff company named Mobotix to commercialize the work, and they plan to have a “more fully commoditized product” ready in roughly 18 months. “We have an idea for a range of products,” he says. “A robot technician, a robot researcher, and a robot scientist, all with different levels of capabilities.”

Although the development of new robotic technology often leads to fears about loss of work through automation, Cooper says students who saw the robot were more likely to imagine how it could help them.


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People were sceptical at first, but there was “general amazement when it first started to work,” he says. “Now people are starting to think ‘if I don’t use this hardware I might be at a massive disadvantage.’”

About author

Matthew Griffin

Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, Bloomberg, CNBC, Discovery, RT, Viacom, and WIRED, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Aon, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.

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