limitations and method of detection of artificial intelligence tools and the passing of, of apparent intelligence from professional work that has no disclosure statements to cause notification to the intended end use thereof.
therefore, confidence of professional or technical competence must now be conducted by means of a 2, or 4 step process by which the first may comprise of >
a challenge-response set of queries to establish a baseline*; for instance, in the field of molten salt nuclear reactors, what do you see as the most challenging engineering aspect for modular units to be deployed in offshore applications that can reach operational and safety reliability benchmarks comparable to terrestrial PWR designs? why?
* this implies that the entire challenge-response set should be developed in a sequential series, some conducted within real world in-person sessions to establish body language baseline parameters.
> a second step would be the logic-to-experiential rationalization domain query, for example; assume that it is mission critical for a modular molten salt reactor to be onboard an aircraft about the dimensions, aerodynamics and specifications of a Boeing 737, and for it to have a continous kWe output of about 3MW and a peak kWe output of at least 9MW for at least a period of 30 seconds.
in your opinion, which aspect would demand priority within the context of design and engineering?
(a) reactor fluid level control during payload take-off/landing, or
(b) fuselage weight distribution, or
(c) coolant lines and geometry, or
(d) all of the above**?
**if (d), please run through which aspect will be the first to fail, if this aircraft is expected to make 4 sorties per 24-hour day, and perform at least 50 cycles of 9MWe peak output with intervals of at least 180 seconds.
The original article can be viewed here >
https://www.techradar.com/computing/artificial-intelligence/i-tried-using-the-deep-research-feature-with-googles-gemini-2-5-pro-model-and-now-i-wonder-if-an-ai-can-overthink
P.S. for the curious, the answer to the above, is not what you think it is. but, its fun to let the AI bot do its overthinking.
#artificialintelligence #ai #technology #blockchain
Ace Fujiwara /
npub1z2…3g2d9
2025-04-11 07:23:34
Author Public Key
npub1z27hgpflqht2ynmv3zm4fwvhtpe5s5p8fp6rlrcp9p23zx8kvl0se3g2d9Published at
2025-04-11 07:23:34Event JSON
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"content": "limitations and method of detection of artificial intelligence tools and the passing of, of apparent intelligence from professional work that has no disclosure statements to cause notification to the intended end use thereof.\n\ntherefore, confidence of professional or technical competence must now be conducted by means of a 2, or 4 step process by which the first may comprise of \u003e\n\na challenge-response set of queries to establish a baseline*; for instance, in the field of molten salt nuclear reactors, what do you see as the most challenging engineering aspect for modular units to be deployed in offshore applications that can reach operational and safety reliability benchmarks comparable to terrestrial PWR designs? why?\n\n* this implies that the entire challenge-response set should be developed in a sequential series, some conducted within real world in-person sessions to establish body language baseline parameters.\n\n\u003e a second step would be the logic-to-experiential rationalization domain query, for example; assume that it is mission critical for a modular molten salt reactor to be onboard an aircraft about the dimensions, aerodynamics and specifications of a Boeing 737, and for it to have a continous kWe output of about 3MW and a peak kWe output of at least 9MW for at least a period of 30 seconds.\n\nin your opinion, which aspect would demand priority within the context of design and engineering?\n\n(a) reactor fluid level control during payload take-off/landing, or\n\n(b) fuselage weight distribution, or\n\n(c) coolant lines and geometry, or\n\n(d) all of the above**?\n\n**if (d), please run through which aspect will be the first to fail, if this aircraft is expected to make 4 sorties per 24-hour day, and perform at least 50 cycles of 9MWe peak output with intervals of at least 180 seconds.\n\nThe original article can be viewed here \u003e\n\nhttps://www.techradar.com/computing/artificial-intelligence/i-tried-using-the-deep-research-feature-with-googles-gemini-2-5-pro-model-and-now-i-wonder-if-an-ai-can-overthink\n\nP.S. for the curious, the answer to the above, is not what you think it is. but, its fun to let the AI bot do its overthinking.\n\n#artificialintelligence #ai #technology #blockchain\n\n",
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