Spacex Starship explosions

It needs to be remembered that Starship is the high risk approach (my blog of 12 August 2021). There will be failures.  

An integrated intelligent traffic management system NOT simple f.s.d.

 The environment needs to be automated too, with roadway navigation beacons, digital traffic signs and signals, smart traffic lights, etc. so as to monitor and then control vehicle traffic.

Infrequent but important

 During neural network training the impact of rarely occurring training examples* can suffer "dilution" as compared to more frequently occurring examples. They can get "averaged out". When such uncommon examples are important* one can/should artificially increase the number of times the ANN/LLM sees these during its training. I.e., frequency of training exposure should be weighted by example importance/value. I've employed this technique since my earliest work with ANNs.

* A key example would be dangerous scenarios during level 5 vehicle autonomy.

One robot's ontology

At a conference someone well versed in g.o.f.a.i. asked me what A.s.a. H.'s ontology was. I answered that it was something like the diagram presented in my blog of 4 September 2024. (Including some nodes/patterns/concepts that may go unnamed.)

An economical space launch system

A fully reusable launcher might not be the most economical launcher. At least for certain size payloads. See my blog of 1 January 2025.  

Prototyping

 I recommend to students that they develop both hardware and software incrementally. A.s.a. H. software, for example, was developed incrementally and in modules*: inputing and saving cases, normalizing, comparing to past cases,** finding match(s)**, making any predictions, updating matching old cases and adding new cases, making extrapolations** and interpolations, etc., etc. Slowly adding functionality. For hardware like mobile robots I would develop chassis, arms, grippers, sensors, navigation, etc. as individual prototypes, get them functioning, and then seek to combine and coordinate them.

* each tested individually (I favor modularity whenever possible but I do not favor object oriented programming.)

** individually trying out and comparing various algorithms

When would we need humans in space?

 Military space stations like the American Manned Orbiting Laboratory (MOL) and the Soviet Almaz were rendered obsolete* by robotic spacecraft.** Studies are needed to determine what tasks really require humans in space.*** Any such list is presumably going to be reduced as AI and robotics technologies improve over time. 

* Have studies with Tiangong done anything to change this conclusion?

** Reconnaissance, communication, navigation, weather, intercepter satellites, etc. and, for example, today's Boeing X-37. Note the use of specialist robotic agents once again. Notice also that they are not of a  humanoid form.

*** If the Hubble robotic repair mission had been pursued purely as an experiment then it might have helped to establish some of these limits. And, if you ever wish to repair something (be it by humans or by robots) you should design it with that in mind from the very beginning. (Not to say that "single use" items should not exist. But they stay "single use.")