This is nonsense.<p>Here is a (well-publicized) post which only serves to hype up and stake a claim on what is already valued and practiced, the exposition of research. The message seeks to capture an ignorant readership and practitioners with short-term memory, and have them walk away with the thought "This is the place where good research expositions will be."<p>The machine learning learning community has already benefited from the myriad great expositions provided online for free, in addition to the locations where even source code is given alongside research findings. I will not link to these sites, in hopes to not seem a salesman, but if you've taken an interest in the field and taken some time to search on a topic (for example neural nets), you will have likely already found one of several free, helpful resources. This includes online courses, online books, blogs, videos, &c.<p>These individuals give examples of already well-written expositions going back years (associating these well-received expositions with their own endeavour), yet the theme is one of "newness", helped by the usage of terms like "distillers" and "research debt". In Silicon Valley, it seems that if something's been given enough press and sounds new, you should at least hop on the bandwagon for a while, lest you risk missing out on being an "early adopter".<p>Publishers are not a new conception either. They're a funnel which selects what you see. In an era where the larger population is beginning to see how much power publishers have over what they think, new efforts should strengthen decentralization. Playing to the tune of publishers has gotten us into the mess we're in.<p>Peer review is a mainstay of science, and I am in complete support of it, but peer review and publishers do not need to coexist. I and others will happily use a decentralized system with peer review, additionally and crucially providing transparency.<p>Good exposition should occur. Good exposition does occur. Indeed, people are learning: Ask yourself when you last learned from someone's writing. Audiences get their information from many sources, and if they can't understand those sources, they don't go to them.<p>Addendum. I will not discuss at length the repercussions of teaching the population how to create intelligent systems, but that is a dangerous road for all of us, and not one easily traversed. Companies and other powerful persons have a strong interest in guaranteeing they have a large pool of subordinates who have skills that they can profit greatly from, so it's obvious why the push for software engineering and artificial intelligence teaching is so strong (the promise and hype has been strong). But this is heavy-handed and short-sighted. Businesses have functioned with this approach in the past however, so I wager they assume past performance can be used for prediction in this case as well. If anyone's doing any thinking at all.