leakydropout
Dennett denies Nagel's claim that the bat's consciousness is inaccessible, contending that any "interesting or theoretically important" features of a bat's consciousness would be amenable to third-person observation. For instance, it is clear that bats cannot detect objects more than a few meters away because echolocation has a limited range. He holds that any similar aspects of its experiences could be gleaned by further scientific experiments. -- https://en.wikipedia.org/wiki/What_Is_it_Like_to_Be_a_Bat%3F
Heterophenomenology ("phenomenology of another, not oneself") is a term coined by Daniel Dennett to describe an explicitly third-person, scientific approach to the study of consciousness and other mental phenomena. It consists of applying the scientific method with an anthropological bent, combining the subject's self-reports with all other available evidence to determine their mental state. The goal is to discover how the subject sees the world him- or herself, without taking the accuracy of the subject's view for granted. -- https://en.wikipedia.org/wiki/Heterophenomenology
The article builds a convincing point for itself (at the cost of a huge complexity), it contains no discernible contradictions to me. It is reasonable to prepare for the possibility of a future event (say, AGI, or Jesus returning to earth), by thinking about it now.
All interests and future predictions are different, but equally valid. To me, it feels like the Wright Brothers thinking about rotating safety valves in space, before they have even took off on their first flight, but that should not stop the author and supporters in any way: Science, futurism, and philosophy moves in small steps, and it may be a good time for some to start walking. Just make sure to properly define the end goal (AKA, the moment AI becomes AGI), or we may keep on truckin' forever, never closing the loop of our hostile AGI-created simulations creating a first friendly AGI.
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In 1870, Huxley conducted a case study on a French soldier who had sustained a shot in the Franco-Prussian War that fractured his left parietal bone. Every few weeks the soldier would enter a trance-like state, smoking, dressing himself, and aiming his cane like a rifle all while being insensitive to pins, electric shocks, odorous substances, vinegar, noise, and certain light conditions. Huxley used this study to show that consciousness was not necessary to execute these purposeful actions, justifying the assumption that humans are insensible machines. Huxley’s mechanistic attitude towards the body convinced him that the brain alone causes behavior.
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A large body of neurophysiological data seems to support epiphenomenalism. Some of the oldest such data is the Bereitschaftspotential or "readiness potential" in which electrical activity related to voluntary actions can be recorded up to two seconds before the subject is aware of making a decision to perform the action. More recently Benjamin Libet et al. (1979) have shown that it can take 0.5 seconds before a stimulus becomes part of conscious experience even though subjects can respond to the stimulus in reaction time tests within 200 milliseconds. -- https://en.wikipedia.org/wiki/Epiphenomenalism
The median of these final responses could then be taken as representing the nearest thing to a group consensus. In the case of the high-IQ machine, this median turned out to be the year 1990, with a final interquartile range from 1985 to 2000. The procedure thus caused the median to move to a much earlier date and the interquartile range to shrink considerably, presumably influenced by convincing arguments -- Analysis of the future: The Delphi Method. (1967)
- Reduces ability to catch errors. Linus: "given enough eyeballs, all bugs are shallow". But this does not work if everyone looks at things in the same way.
- Makes it easier for undesirable decision cascades to form: One or two people have an idea, and the other members in the group copy their decision, because they always agree. Smart well-weighted decisions are the product of disagreement.
- Stifles innovation. Do you really want your tech company to be all Stanford graduates? Where everyone knows the exact same algorithms, because they sat in the exact same classes, by the exact same professors for decades?
- Is inefficient in regards to Pareto optimality: It is impossible to hire someone that knows all. Hiring for diverse skill sets comes close. Hiring people with homogeneous skill sets is an expensive uphill battle, that relies on small random mutations.
- Reduces the motivation of your top performers. The really desirable job candidates don't always like working in a drone factory, where everybody dresses the same, there is no challenging of their ideas, and, for instance, women all have inferior roles.
https://www.slideshare.net/reed2001/culture-1798664 (17 million + views)
I am on the fence about the second part of your question. On the one hand: If they are free to grill you about hypotheticals and process, why can't you do the same? On the other hand, should you really? It is a very cheeky ("courageous") question that would catch me off guard. "Who are you to question the very job interview we are currently having? How is this relevant to your prospective job? Current interview not up to par for you?".
The other side of this advice is to create a dataset that is interesting to other researchers.
You are going to have a hard road in front of you if you want to seriously make a dent in AI research. ML research is a bit more accessible to outsiders than AI research, because a lot of fundamental AI research can be a bit "out there", and is praised/discarded depending on the tenure/authority of the authors. But you probably already knew this. Just be careful to avoid (meta-)theoretical research that is close to futurism or philosophy without any credentials: It is easier to label outsiders as kooks.
First, create a blog. Write articles in a way that they are accessible to your skill/knowledge level one year ago. Get in the habit of writing and performing write-ups of your experiments. Share good articles on social media (Twitter, DataTau, /r/machinelearning).
(Co-)author a workshop paper for an AI or ML conference. Workshops have lower bars for acceptance. If anything, you'll receive valuable feedback.
Find an (assistant) professor who is an expert in a topic/subject you are interested in (don't go for Hinton in the first try). Familiarize yourself with their work and send them a polite short email asking for (search term) pointers on your research, their research, or related works.
Replicate as many research papers as you can. Implement the papers that don't have accompanying source code. Get in the habit of running many experiments. Post these on Github. Publish on social media, mentioning original authors. Mint a DOI.
Benchmarks (competitions) don't care about your credentials. Win one / do very well, and you'll create a platform for your research and methods, based purely on practical results and in compared to many other techniques. Papers based on winning results are fairly easy to write and do well impact-wise.
Really, don't worry about getting scooped, or making an error that embarrasses your supervisor for years. Leave that for the PhD's. Just get something out there, a Wordpress blog is enough. If your research is useless, no harm done (it won't get any cites if you managed to publish it in a journal). If your research has value, you'll have plenty of researchers read it and get inspired: This is your contribution to science (and, unfortunately, don't expect much cites to a blog post).
Optionally, solve the college dropout problem instead. If you want to dedicate yourself to being a researcher on AI, give your "startup" some rocket fuel and get a higher education in the field of data science/physics/computer science/AI. The synergy of young smart people and older wiser academics is something that is very hard to replicate on your own.