For me, a big worry about AI is in its potential to further ease distorting or fabricating truth, while simultaneously reducing people's "load-bearing" intellectual skills in assessing what is true or trustworthy or good. You must be in the middle of this storm, given your profession and the investigations like this that you pursue.

Do you see a path through this?

Do you have any pointers to good (public) spaces like this? Your take sounds reasonable, and so I'm curious to see that middle-ground expression and discussion.

From time to time there are fun collaborative projects too, like https://app.astrobin.com/u/bagman?i=ey9s59#gallery.

Mark spent considerable time earning the trust of LA's skid row population – a large roadside tent community – and has a series of 1:1 interviews with a slice of the population, exploring their histories, challenges, preferences, and culture.

Mark doesn't believe that many (most?) of the skid row population would benefit from being provided with housing, and that issues of trauma, mental health, and childhood family environment are what he believes would have the highest leverage on the problem.

This is of course just one perspective on the problem, but Mark's perspective taught me quite a bit.

Took a day, but all's well now.

Unfortunately, this hasn't been my experience. As an example, I have a display-port-based Apple cinema display, and I bought this adapter to try to connect it to a new macbook pro 15":

  http://www.apple.com/shop/product/MMEL2AM/A/thunderbolt-3-usb-c-to-thunderbolt-2-adapter

If I remember correctly (and I might not), the apple store page for this adapter didn't originally carry a warning about this, but instead had text that made it easy to misunderstand whether this would work. The text on the page has changed.

As a weaker example, I have an LG 27UD88 Monitor. The USB-C connection from it works, but only carries 60W of charging "oomph," and the 15" macbook pro needs 85W of charging. So, even though the menubar icon on the mac signals that charging is happening, the battery is actually depleting over time. (Yeah, this one is more caveat emptor than the other, but still.)

Some folks excelled (though they put in 15+ hours per week on average, which is quite high), some cratered and I needed a backup plan for them to make progress.

   http://en.wikipedia.org/wiki/Paycheck_(film)

When I see these kinds of claims, my guess is that they are usually based on a failure analysis given a particular replication degree and estimates of failure probabilities of various components and failure domains. Underlying this analysis is usually an assumption about the independence of failures between the failure domains.

The good news is that industry has moved away from the computer as the unit of independent failure to a much larger failure domain: often a cluster within a data center, or an entire data center. This means that the analysis takes into account the infrequent occurrence of a large number of correlated failures within the failure domain.

The bad news is that there are inevitably correlated failures across the failure domains, regardless of how carefully you design to avoid them. Software bugs, coordinated attacks, operator errors, cascading failures caused by well-intentioned but runaway control loops and automated failover mechanisms, and so on, can be the culprit.

So, here's the problem. This statistic from Amazon, if taken at face value, would say that relying on Amazon to keep your data durable and safe is practically risk-free to the point of durability issues never happening in your lifetime (or, alternatively, to such a dramatically small fraction of objects that you might not care).

In practice, however, I suspect you do want to plan for the "unknown unknowns" that will cause data loss at low probability, but much higher probability than 0.000000001%.

Here's another way to look at it: I'd love it if Amazon posted some data about the rate at which they've experienced durability failures in the past year or two, rather than posting what I'm supposing (I might be wrong!) are calculations based on assumptions of dependent failure probabilities.

s/Google/Microsoft s/Microsoft/Linux s/Bing/Linux

I never would have predicted somebody cheering for "Microsoft the underdog."

But, factors like patent sharking, the ridiculous extension of copyright terms, the inability of patent offices to properly assess issues like novelty and obviousness, and so on, make it possible for all parties to abuse this compromise.

I think the spirit of the compromise is valid. I think its implementation today has become flawed, but not irreparably so.

Here's another complication. Often, faculty need multiple sources of funding over the years to support their research, including federal grants, university support (direct through money or indirect through facilities and staff), and industrial support through gifts and grants. As well, the intellectual heavy-lifting is done by the faculty and their students.

If you believe in intellectual property rights, then who of all of these stakeholders deserves ownership of those rights?

Personally, I lean against faculty patenting their inventions. I'd rather the IP is open to the public. Even if a faculty member wants to commercialize their research, I think they are better off competing in the marketplace rather than through IP arsenals.

But, I understand the other point of view. Patent licensing is a lucrative source of revenue for universities (e.g., UFL and its Gatorade revenue) and inventors. As well, it's at least uncomfortable and perhaps unfair if opportunistic companies were able to commercialize on a faculty member's invention without renumeration or attribution.

Complicated topic.