The MIT report says that 11.7% of User Fires can be replaced with existing AI
Last week, the Massachusetts Institute of Technology (Mit) published a study that said AI could already replace 11.7% of the current American workforce. Indeed, it is the kind of visual research that is guaranteed to get a lot of help in the work of researchers at a time of timid faith in AI, since the owners of the programs may want some assurance that their AI investment will pay off.
The report on this research is called “The Inteberg Index: Measuring the Exposure of Companies Focused on the AI Economy,” but it also has a dedicated page called “Project Iceberg” on the MIT website. Compared to the research paper, the project page has more Emoji. When a research paper receives a similar warning about AI tech, the project’s page, changed to “AI working with you?” He sounds more like an AI ad, in part thanks to the following:
“AI is changing the profession. We have spent years making AIS Smart – They can learn, the Profession of Smart AIS In cooperation with us?
The Titarg Index “comes from a pseudo-AI that uses what the paper calls “multi-human models” that apparently run at the Oak Ridge state lab and live at the Department of Energy.
Legislators and CEOs are seen as the target audience, and are designed to use Project Iceberg to “identify exposure prevention areas, prioritize training and infrastructure interventions, and evaluate interventions before spending billions.”
Large population model – should we start reducing this to LPM?
The director of AI programs at Oak Ridge explained the project to CNBC this way: “Basically, we are creating digital twins for the US Labor Market.”
The complete acquisition, the researchers say, is what AI accounts for the acquisition of 2.2% of the “BASHRAST MARK Market, but that 11.7% of the work is simply reduced with an understanding similar to that person currently can do.
It should be known that people in real jobs often work outside of their job descriptions, handle different and unfavorable situations, and are unable to deal with many social aspects of the work assigned to them. It is not clear that the model is an account of this, although it recognizes that the findings are not a guarantee, and says “external factors – Investment, infrastructure narrative, infrastructure narrative, infrastructure narrative, infrastructure orientation where the power is translated.”
However, the paper says, “Policy actors cannot wait for evidence of disruption before preparing responses.” In other words, Ai is too fast to be included with learning disabilities, according to the study.
