I stand by my statements. The community, or even a handful of researchers haven't come up with a competent path to AGI. That's indisputable.
it's not like we have no idea how to build a general intelligence, or what such an architecture might look like
Show me one, I'd love to see it.
Listen, I love everyone working on them and many are my friends; but none of the attempts today have anything near the specificity of a project management roadmap to say with any certainty that AGI is even a probable outcome. Not OpenCOG, not Numenta, not MicroPsi.
That's not a hit on any of them either. The people and areas they are working on are awesome, amazing and fundamental to research but none of them would claim that they have a solid roadmap. Even the roadmap sessions at the conferences usually go nowhere because we just don't know enough about how generalizable intelligence works yet.
The keyword there is competent. You're making a subjective evaluation. Given your CV you must surely be aware that Goertzel has a 1,000 page book (two volumes, actually) laying out in great detail his roadmap to human-level intelligence. The leaders of other major projects in this space have their own ideas which they talk freely about at the AGI conferences, and are written down to varying degrees.
Notice a pattern?
Meanwhile, in deep learning (and FWIW I don't think any deep learning researchers are under the illusion deep learning provides a path to AGI), there are:
working systems that outperform humans at narrow visual tasks (image classification, segmentation, etc.), a working Go bot, early prototype systems that caption images, the list goes on and on.
None of those projects have yet shown any real progress towards AGI. A roadmap and "comprehensive architecture" are just plans and conjectures, not results. The AGI conference is also still fairly niche.
EDIT: Agreed with below. I also want to clarify that I'm not saying AGI is unworthy of research. It's just total early stages right now. Be aware that progress is incremental, and maintaining momentum (and research funding) is predicated on delivering tangible results and tools incrementally.
One thing to note though is these efforts are often put forth by some hard working folks and the product can be something great.
For example, "Thinking Machines" [1] [2], founded in 1983 in Cambridge and defunct in 1994. The folks who spent effort there would later take their knowledge of parallel programming and built a tool called An Initio as a successor company. The tool was way ahead of its time. It'd maximize resource usage on a machine in parallel without much technical knowledge needed by the developer. They made a boat load off of deploying this tool on internal datasets at many major companies for what was then called "data warehousing".
[1] https://www.technologyreview.com/s/406781/thinking-machines/
[2] https://en.wikipedia.org/wiki/Thinking_Machines_Corporation
But how can you say "we have no idea what the constituent parts of AGI are" or "they don't have a pathway (nobody does by the way)"? There is an active and vibrant (if sometimes eclipsed) AGI community. There is an annual AGI conference. There are a half dozen or so actively developed AGI projects with comprehensive architectures with attached roadmaps. It's an active area of research, but it's not like we have no idea how to build a general intelligence, or what such an architecture might look like.