I just published technology that some researchers called an 'AI pitch' about 'magic AI does everything' (https://news.ycombinator.com/item?id=12167097#12171264

I just published technology that some researchers called an 'AI pitch' about 'magic AI does everything' (https://news.ycombinator.com/item?id=12167097#12171264 https://news.ycombinator.com/item?id=12167097#12171264), even while they graciously pointed out some of its optimizations that add up to 'really good tech'. I appreciate all of the constructive and critical feedback, and I'm trying to bring it all together. Probably the greatest conflict I feel in communicating about such R&D is the challenge of resolving two objectives: (1) Explaining something technically complex to readers who are not generally interested in "what's under the hood", but still nonetheless appreciate high-level "outreach" about what's actually possible today (2) Defining the marginal benefits of limited research to a small group of peers who prefer to think only in terms of "what's under the hood", and thus are offended by higher-level connotations that they may associate with hollow marketing rather than rigorous science Every scientist who communicates to the general public faces the above challenge in some form, and thus struggles to maintain both clarity and integrity all around... One of my inspirations was Christopher Olah (colah.github.io), who I think strikes a wonderful balance of being very careful about jargon, while being very literal. In any case, a pernicious problem that we face here, and perhaps a premise for the OP's article, is the highly subjective, controversial, and polarizing definitions of artificial intelligence. Too often we forget that what we imagine to be artificial intelligence may feel trivial to another person, and vice versa.