What Makes a Job Vulnerable to AI Automation?
For those that drinketh of it, it is certain that in the not-too-distant future artificial intelligence and-or robots will steal the vast majority of jobs currently occupied by human beings. Maybe in real life there are a great many jobs that we just don’t want machines to do-such as those in healthcare, the fastest-growing job sector by a wide margin-or even that machines fundamentally can’t do. “Although parts of many jobs may be ‘suitable for ML’, other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some.” The paper outlines eight general features that make a job SML. I won’t list them all here, but a few bear emphasizing. First, machine learning requires well-defined problems where input data can reliably be mapped to output predictions. Pictures of dogs go in, and predictions of dog breeds come out. On the other hand, we might actually be able to predict dog breeds based on pictures of dog owners in that case, a clear mapping wouldn’t exist because the causality behind the prediction would be buried somewhere in the ML model. To predict a medical diagnosis, a machine learning algorithm requires a load of training data consisting of patient records that have been labeled by humans with correct diagnoses. Only then can the algorithm look at new, unlabeled data and make accurate predictions. Machine learning models require relatively simple casual chains to make predictions. Also: machine learning doesn’t work in cases where wrong predictions are unacceptable. Emotional intelligence and empathy aren’t really SML. “The more unstructured task of interacting with other doctors, and the potentially emotionally fraught task of communicating with and comforting patients, are much less suitable for ML approaches, at least as they exist today,” Brynjolfsson and Mitchell note. What seems most likely is that SML tasks are not whole jobs or professions, but are instead components within those professions. Machine learning will continue to advance, but rather than stealing all the jobs, it will become a normal component of a great many jobs. Because an algorithm can predict a cancer diagnosis doesn’t mean it will become your new doctor.
4 trends to watch as IT automation expands
We recently examined some of the fundamental factors that have fueled IT automation to this point. “To this point” is the operative phrase – there’s not necessarily a finish line for automation, and even as it matures, automation is going to continue evolving from a technical, business, and people standpoint. With that in mind, we asked a variety of experts for their insights on the current trends to watch as automation grows inside of IT shops going forward, with a particular eye on the relationship between IT automation and business strategy. Here are four of the big trends likely to continue to shape automation and its impacts on how IT teams and businesses operate. As automation expands inside IT shops and especially along the spectrum of the software development lifecycle, it is likely to become more visibly and specifically linked to business strategy. In other words, companies will approach automation as a means – a tool for achieving specific business goals – rather than an end, as in automating certain processes and infrastructure components simply because they can. “Automation simply for automation’s sake yields no practical results,” says Wayne Ariola, CMO at Tricentis. IT leaders have a growing opportunity to show how automation can drive digital transformation plans and other big-picture business priorities. Much of the current discussion of automation focuses on machine learning, AI, robotics, and other technologies. Kirstein expects “Selective automation” to be the next trend that moves IT automation into the mainstream. This poses other benefits, too – including real automation pilots that will enable companies to more specifically link automation to specific business strategies early in their adoption. Greater alignment of people and process with automation technologies. Ariola of Tricentis notes above that automation for the sake of automation isn’t likely to yield results. He says, for example, that in Tricentis’ testing automation space, just dropping an automation tool into an existing team and its processes is a recipe for failure. There’s another upside to continuous learning, too – one that brings us full circle back to #1: Enabling continuous learning as IT automation increases can help spot new business opportunities.
Labelexpo Europe 2019
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