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4 Steps to Retraining Employees in the Age of A.I.
For humanity, major socio-economic transformation is nothing new, but the rise of artificial intelligence (AI) requires the world to adapt and realign to a new reality more rapidly than ever before.
Enabled by fast transportation, open foreign currency exchange, trade policies and communications, the economies quickly became global.
Expected to generate dramatic returns for society, the AI revolution is predicted to make a massive impact in 20 to 25 years, about half of a single work career.
In contrast, AI’s looming threat is to replace high-level, judgment-based skill-sets, such as complex analysis, discretionary decision-making and even creative ideation.
This is a pressing concern without clear answers, but doomsdayers neglect a crucial fact: Investments cannot capitalize on AI’s gains in the absence of human consumption.
If we take the whole of human history as our dataset, it is apparent that, ignoring local dislocations and the resulting need to migrate, humanity has typically landed on its feet and moved forward.
As we peer into the future, it’s difficult to predict exactly who society’s workers will be, but the past forecasts that human employment will be alive and well.
As a workforce strategy, it’s clearly more efficient and fiscally responsible to retain and retrain your employees in the age of AI, rather than engage in large-scale layoffs and rehiring.
Predictive analytics technology can help pinpoint three things: employees in your business that are most likely to leave, those who can benefit most from being re-skilled and the likely impact that re-skilling programs will bring to your workforce overall.
I recommend measuring inputs, tracking outcomes, modeling different policies and closely following early adopters to learn and emulate best practices.
recommend that businesses involve themselves in assisting their source schools in shaping the curriculum, providing teaching assistance, funding teacher development programs, sending their key innovators into the classrooms and developing teacher sabbatical exchange programs.
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Radiologists are increasingly asked to perform quantitative analysis on complex dynamic studies such as prostate and breast MRI, analyse multi-parametric imaging from MRI, PET, CT, and to follow new guidelines for lung cancer and other screening studies.
It offers numerous advantages over more “traditional” Computer Aided Design approaches including decreased time, and less specialised medical imaging expertise required for development as well as the potential for continuous and personalised refinement of algorithms.
This technology, however, is also fraught with limitations including the requirement for large amounts of annotated data, regulatory, medicolegal, and relative brittleness with regard to lack of generalizability from a few to a multitude of different scanners.
- On 20. oktober 2021
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