Automation is the latest big thing at work. Bit by bit, human beings have watched as their work environment fills up with new technologies. Often, we have seen it as a zero-sum game, that humans lose and technology wins. We can certainly put this into context: we have been taking our money out of ATMs and drinking coffee from vending machines for decades. These technologies have been with us for many years, improving our lives.
However, logically, any large scale revolution naturally awakens a fear of change. How do we get past that? One way could be to take another look at how we, human beings, and they, new technologies, could live side by side. In spite of how things may seem, substituting manual workers with machines is not the future of the working world; working alongside one another to reach perfection is the reality. For a union where humans see the value of technology and don’t feel pushed out by it.
This dilemma is by no means a new one. In the late 90s, chess player Garry Kasparov famously lost his second match against the computer Deep Blue, a robot filled with millions of automated movements that checkmated the human brain. After the match, the Russian champion created a project called “Advanced Chess”. Three types of teams would compete: human-only, robot-only, and – watch out – mixed teams of artificial intelligence and human chess players.
The result? The mixed teams won.
It isn’t all that surprising. Since the mid 1960s, theorists have spoken of using the relationship between robots and humans as a way to “increase” the capabilities of both groups. Algorithms can carry out complex operations, but they don’t have the strategic vision or multiple intelligence of the human mind. The trick in all sectors of the economy is not to eliminate the human factor, replacing it with a “rough” technological facto, but rather to combine both, to take advantage their talents.
Artificial intelligence is important because it reduces subjectivity in data extraction and selection, but it could never substitute human judgment”, explains Giorgio de Paola, reservoir engineer at Repsol.
“Artificial intelligence is important because it gets rid of subjectivity in data extraction and selection, but it could never substitute human judgment”, explains Giorgio de Paola, reservoir engineer at Repsol. His job means that he knows what he’s talking about. De Paola works with many data and information compilations, extracted via automated resources, to identify new extraction wells, how to bore them, and how to reduce the environmental impact of these actions.
“A person will always have the final say. It is important to give people higher-quality and better processed data. However, human beings must be an integral part of the process of moving towards artificial intelligence“. De Paola feels that when reading information compiled through automated tasks, selecting which data to compile and choosing a long term strategy will always be down to the human brain: “Yes to symbiosis, no to substitution.”
Giorgio de Paola, reservoir engineer
This is what will create problems for tomorrow’s companies: human beings’ adaptation to AI. Maybe without reaching the extreme of “updating” our brain, work spaces do require a new type of human worker, capable of directing and exploiting the infinite potential of automated labor.
That is where José Miguel Seoane, senior scientist in biotechnology and low carbon technology, believes the future of employment lies. Seoane works in a laboratory that develops enzymes (biological catalysts) to provoke highly specific reactions that create value-added biomolecules. An example of this is biofuels with enhanced properties: “Our robots can do ten times what a human can do in a week. Routine, tedious, and programmable jobs”.
This same group also carries out machine learning projects, classifying exploration samples by analyzing the DNA of the microorganisms they contain, which generates large amounts of data. AI helps to work out the probability of a sample being hydrocarbon-positive, based on the results of all the previously analyzed samples.
Marilyn and Jack
Robots like Marilyn and Jack speed up the information compilation process. From there, scientists focus on qualitative, not quantitative investigation tasks. “They free up time so that people can focus on analyzing the results. They don’t replace a person because human analysis will always be more skilled and controlled.” A machine can detect a positive correlation, but you need a scientist to understand why that correlation has happened.
In this context, some have tried to surf the wave of automation, creating teams of humans and robots coordinated by artificial intelligence. An example of this is the Siemens factory in Berkeley. Thanks to the internal communication and automatic learning of the robots that are used, AI subdivides tasks by human and robot capabilities and skill sets. It is a “user manual” that better assigns tasks to machines and human workers.
The process could also have an even brighter side than we thought. Some people believe that coexistence with technology will help us to discover the “human” side of work: we do the things that we like doing, not the tasks that we are forced to do. Ultimately, the most specialized jobs will never be replaceable. “The machine proposes interpretations and solutions, and the human has to choose between the good and bad ones. It is a mutual learning process”, de Paola states. Or in other words, “heightened” intelligence for both humans and robots.