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" ... In the Lamb-Retherford experiment, electrons are excited by a beam from the 1S state into the 2S ... [+] state, then pumped with photons at a tuned frequency until many enter the 2P state. The effects can be seen at the detector, which is a thin piece of tungsten foil, sensitive to 2S electrons but not 2P or 1S electrons. The effect of the additional ~1 GHz photons show the effect of the Lamb shift. ... "
" ... In the experiment, his group served the exact same food to two different groups. One group was served the meal in typical fast-food fashion and the other was presented the same food in a fine dining setting. The only difference between the two groups was how the food was presented and served, not what the food was. The result: the fast-food dining group rated the food they were served 3 points on a 10-point scale. The fine dining group rated the food 8.4 points on the same scale. ... "
" ... Now in its 9th month, what was an experiment has become the new normal for many. Technology has enabled nimbleness at a level that very few thought was possible and as a result many organizations have remained productive and able to serve their customers and generate revenues. As technology continues to evolve and improve, business operations and communications will follow suit and resiliency will ensue. ... "
" ... Step Three - Identify your experiment methods/treatments to guide your research. This is one of the most important skills of an AI /data scientist researcher as understanding the different methods and applications of AI and where they are most valuable in solving specific use cases, as well as the types of algorithms best suited for different data set sizes/types will enable more robust model development. Often the most efficient AI methods selected are a combination of machine learning (ML) methods. Supervised and unsupervised machine learning methods, both have their value in solving different problems. What is key is that an AI data scientist must understand the differences and how and when to best apply each method to achieve the most effective results. It is not uncommon that a company selects one type of AI method, without exploring other methods and then in turn, they build sub-optimal AI models that could have been easily avoided had more research experimentation and third-party reviews been involved earlier in the research and design process to mitigate more risks. Providing ample time for research and discovery is key for AI data scientists to do their jobs effectively and efficiently, which is often contrarian to the corporate world where speed has become an activity mantra or frenzy in many North American cultures, where as in China, the vision is often over ten years out and their patience as a culture for the long-term may be their strongest AI skill competency they have in the war for AI IP to move to sustainable outcomes. ... "
" ... The results of the experiment indicated that workers were happier and there was also a 40% gain in productivity. You may, however, have to question the veracity of the self-reports. Workers may have tried to make the project successful so that they could have a permanent four-day workweek. The 40% productivity may not be realized once the shortened workweek is officially established and, subsequently, taken for granted. ... "