An interesting story appeared in the BBC News Online Business section. It featured a new report from the UK Institute for Public Policy Research which says that automation will not take all of our jobs, but will enormously increase wage inequality. Many workers—especially those in retail, transport, and manufacturing—will certainly lose their jobs but many, many, more will suffer wage stagnation while those who are capable of using advanced machines will see their wages climb. Well worth the read.
At the same time, other new research claims that the impact of machine learning on the economy could surpass that of previous AI applications put together.
What the researchers say: Machine learning computer systems, which get better with experience, are poised to transform the economy much as steam engines and electricity have in the past. They can outperform people in a vast number of tasks, though they are unlikely to replace people in all jobs (see BBC story above).
So says a Policy Forum commentary published in the journal Science. The researchers describe the criteria which can be used to evaluate whether a task or a job is amenable to machine learning (ML).
“Although the economic effects of ML are relatively limited today, and we are not facing the imminent ‘end of work’ as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound,” the researchers write. The skills people choose to develop and the investments businesses make will determine who thrives and who falters once ML is ingrained in everyday life, they argue.
ML is one element of what is known as artificial intelligence. Rapid advances in ML have yielded recent improvements in facial recognition, natural language understanding, and computer vision. It is already widely used for credit card fraud detection, recommendation systems, and financial market analysis, with new applications such as medical diagnosis on the horizon.
Predicting how ML will affect a particular job or profession can be difficult because ML tends to automate or semi-automate individual tasks, but jobs often involve multiple tasks, only some of which are amenable to ML approaches.
“We don’t know how all of this will play out,” acknowledged the lead author. Earlier this year, for instance, researchers showed that an ML program could detect skin cancers better than a dermatologist. That doesn’t mean ML will replace dermatologists, who do many things other than evaluating lesions.
“I think what’s going to happen to dermatologists is they will become better dermatologists and will have more time to spend with patients,” he said. “People whose jobs involve human-to-human interaction are going to be more valuable because they can’t be automated.”
Tasks that are amenable to ML include those for which a lot of data is available, according to the researchers. To learn how to detect skin cancer, for instance, ML programs were able to study more than 130,000 labeled examples of skin lesions. Likewise, credit card fraud detection programs can be trained with hundreds of millions of examples.
ML can be a game changer for tasks that already are online, such as scheduling. Jobs that don’t require dexterity, physical skills or mobility also are more suitable for ML. Tasks that involve making quick decisions based on data are a good fit for ML programs; not so if the decision depends on long chains of reasoning, diverse background knowledge or common sense.
ML is not a good option if the user needs a detailed explanation for how a decision was made, according to the authors. In other words, ML might be better than a physician at detecting skin cancers, but a dermatologist is better at explaining why a lesion is cancerous or not. Work is underway, however, on “explainable” ML systems—perhaps leading to unemployment among dermatologists.
So what? It seems to me that the question is not what AI alone, or ML and AI, can do, but rather what we want it to do and who should make the decision? Do we want a world where a privileged few benefit at the expense of the rest? That way lies Bannon and Trump, Murdoch and Packer. Should the decision be left to the market? The unfettered market leads mass underemployment, stagnant wages, rampant inequality, opioid addiction—and, possibly, to the superintelligent AI system which, as Stephen Hawking has predicted, will decide that it doesn’t need humans at all to perpetuate itself.
What now? Well, I guess, we have to start electing politicians who see the dangers and are courageous enough to actually do something. Unlikely but possible. The debate as to the kind of future and the kind of control systems we need to create that future will have to be held in every workplace, online, in print and in every meeting place. Maybe, just maybe, we might get it right.
By Dr Bob Murray