AGI research has become a taboo in the early 21st century by 
Ciro Santilli 35 Updated 2025-04-18 +Created 1970-01-01
Ciro Santilli 35 Updated 2025-04-18 +Created 1970-01-01Due to the failures of earlier generations, which believed that would quickly achieve AGI, leading to the AI winters, 21st researchers have been very afraid of even trying it, rather going only for smaller subste problems like better neural network designs, at the risk of being considered a crank.
While there is fundamental value in such subset problems, the general view to the final goal is also very important, we will likely never reach AI without it.
This is voiced for example in Superintelligence by Nick Bostrom (2014) section "Opinions about the future of machine intelligence" which in turn quotes Nils Nilsson:
There may, however, be a residual cultural effect on the AI community of its earlier history that makes many mainstream researchers reluctant to align themselves with over-grand ambition. Thus Nils Nilsson, one of the old-timers in the field, complains that his present-day colleagues lack the boldness of spirit that propelled the pioneers of his own generation:Nilsson’s sentiment has been echoed by several others of the founders, including Marvin Minsky, John McCarthy, and Patrick Winston.Concern for "respectability" has had, I think, a stultifying effect on some AI researchers. I hear them saying things like, "AI used to be criticized for its flossiness. Now that we have made solid progress, let us not risk losing our respectability." One result of this conservatism has been increased concentration on "weak AI" - the variety devoted to providing aids to human
thought - and away from "strong AI" - the variety that attempts to mechanize human-level intelligence
composite particle made up of an even number of elementary particles, most commonly one particle and one anti-particle.
This can be contrasted with mesons, which have an odd number of elementary particles, as mentioned at baryon vs meson vs lepton.
Experiments that measure the gravitational constant by Ciro Santilli 35 Updated 2025-04-18 +Created 1970-01-01
Ciro Santilli 35 Updated 2025-04-18 +Created 1970-01-01Endohedral Fullerenes by Dom Burges (2016)
Source. Named after radio pioneer Heinrich Hertz.
Is fog computing more efficient than cloud computing? by Ciro Santilli 35 Updated 2025-04-18 +Created 1970-01-01
Ciro Santilli 35 Updated 2025-04-18 +Created 1970-01-01Advantages of fog: there is only one, reusing hardware that would be otherwise idle.
Disadvantages:
- in cloud, you can put your datacenter on the location with the cheapest possible power. On fog you can't.
- on fog there is some waste due to network communication.
- you will likely optimize code less well because you might be targeting a wide array of different types of hardware, so more power (and time) wastage. Furthermore, some of the hardware used will not not be optimal for the task, e.g. CPU instead of GPU.
All of this makes Ciro Santilli doubtful if it wouldn't be more efficient for volunteers simply to donate money rather than inefficient power usage.
Bibliography:
- greenfoldingathome.com/2018/05/28/is-foldinghome-a-waste-of-electricity/: useless article, does not compare to centralize, asks if folding the proteins is worth the power usage...
Step of electronic design automation that maps the register transfer level input (e.g. Verilog) to a standard cell library.
It can be seen as the limit case of an Einstein solid at high temperatures. At lower temperatures, the heat capacity depends on temperature.
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