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It's not so much a rise of evil as a rise of nonsense. It's a mass incompetence, as opposed to Skynet from the Terminator movies. That's what this type of AI turns into.

What's happened here is that translators haven't been made obsolete. What's happened instead is that the structure through which we receive the efforts of real people in order to make translations happen has been optimized, but those people are still needed.

In order to create this illusion of a freestanding autonomous artificial intelligent creature, we have to ignore the contributions from all the people whose data we're grabbing in order to make it work. That has a negative economic consequence.

If you talk to translators, they're facing a predicament, which is very similar to some of the other early victim populations, due to the particular way we digitize things. It's similar to what's happened with recording musicians, or investigative journalists—which is the one that bothers me the most—or photographers. What they're seeing is a severe decline in how much they're paid, what opportunities they have, their long-term prospects.

because of the mythology about AI, the services are presented as though they are these mystical, magical personas. IBM makes a dramatic case that they've created this entity that they call different things at different times—Deep Blue and so forth. The consumer tech companies, we tend to put a face in front of them, like a Cortana or a Siri

If you talk about AI as a set of techniques, as a field of study in mathematics or engineering, it brings benefits. If we talk about AI as a mythology of creating a post-human species, it creates a series of problems that I've just gone over, which include acceptance of bad user interfaces, where you can't tell if you're being manipulated or not, and everything is ambiguous. It creates incompetence, because you don't know whether recommendations are coming from anything real or just self-fulfilling prophecies from a manipulative system that spun off on its own, and economic negativity, because you're gradually pulling formal economic benefits away from the people who supply the data that makes the scheme work.

This idea that some lab somewhere is making these autonomous algorithms that can take over the world is a way of avoiding the profoundly uncomfortable political problem, which is that if there's some actuator that can do harm, we have to figure out some way that people don't do harm with it. There are about to be a whole bunch of those. And that'll involve some kind of new societal structure that isn't perfect anarchy. Nobody in the tech world wants to face that, so we lose ourselves in these fantasies of AI. But if you could somehow prevent AI from ever happening, it would have nothing to do with the actual problem that we fear, and that's the sad thing, the difficult thing we have to face.

To reject your own ignorance just casts you into a silly state where you're a lesser scientist. I don't see that so much in the neuroscience field, but it comes from the computer world so much, and the computer world is so influential because it has so much money and influence that it does start to bleed over into all kinds of other things.

Generally these papers are just pumped-up versions of the five-paragraph essay, with filler added. Thesis-driven, argumentative, like the newspaper editorials the genre is based on, this "researched writing" promises to solve big questions with little effort: "Reproductive rights resolved in five pages!"

Actual writing related to research is modest, qualified, and hesitant

our actual model involves elaborately respectful conversation, demonstrating sensitivity to the most nuanced claims of previous researchers

Academic, legal, medical, and business writing has easily understandable conventions. We responsibly survey the existing literature, formally or informally creating an annotated bibliography. We write a review of the literature, identifying a "blank" spot ignored by other scholars, or a "bright" spot where we see conflicting evidence. We describe the nature of our research in terms of a contribution to the blank or bright spot in that conversation. We conclude by pointing to further questions.

Millions of pieces of research writing that aren’t essays usefully circulate in the profession through any number of sharing technologies, including presentations and posters; grant and experiment proposals; curated, arranged, translated, or visualized data; knowledgeable dialogue in online media with working professionals; independent journalism, arts reviews, and Wikipedia entries; documentary pitches, scripts and storyboards; and informative websites.

real researchers don’t write a word unless they have something to contribute. We should teach our students to do the same

Google search results gives more weight to pxhere.com where the image has a mighty 4 views (some of which are me) over the original image, with almost 5000 views.

What kind of algorithm is that? It’s one that does not favor the individual. Image search results will favor sites like Needpix, Pixsels, Pixnio, Peakpx, Nicepic, and they still favor the really slimy maxpixel which is a direct rip off of pixabay.

did you know that the liberating world of “use any photo you want w/o the hassle of attribution” is such a bucket of questionable slime? And that Google, with all of their algorithmic prowess, gives more favorable results to sites that lift photos than to the ones where the originals exist?

So yes, just reuse photos without taking all of the severe effort to give credit to the source, because “you don’t have to.” Be a copycat. Show your flag of Lacktribution. Like everyone else. I will not. I adhere to Thanktribution.