gliboc
Joined 8 years ago
Joined 8 years ago
Maybe you could do some research about the totalitarian mindset, and learn about yourself.
I do enjoy the irony of some Americans blaming big data companies when the president of their choice does not get elected, all the while advocating for less power to French news agencies (e.g. death by asphyxiation from Google news, without any recourse).
By the way, this is literally the most common foldr implementation in functional languages, so I have no idea why you would want to do a code review of it, or talk about performances in such a trivial setting, or pretend that you're confused by it so much that you have to refer to a library...
If you do not believe it is possible to empirically derive a way to interview properly, then rationalism is your best choice, i.e. Intuition/Deduction. And contrary to common belief, rationalism is not a prison. It's just a decision process, in which you're perfectly allowed to question yourself using available empiric data.
I'm just intervening to point out that what you're saying is not what the paper you linked implies. Its title is "Semantics derived automatically from language corpora contain human-like biases", and the general conclusion you could derive from it is that AI programs reflect the stereotypes ... of the data they are trained with. This is why they use the right word: stereotype, instead of the charged word you used: prejudice.
However, I think this metric could be irrelevant in the case of a home/work commute. The 11,000 miles average appears to have been obtained basically by randomly driving Waymo cars on Californian roads. But a usual commute is much less than 11,000 miles, and if your self-driving can do it by itself once then probably it can do it twice. As the article puts it:
"The value of the data is limited, however, as the figures don’t factor in the complexity of environments in which vehicles are tested–dense urban settings, versus low-speed suburbs or less complex highway driving–nor do they show conditions including weather, light or speed."
Nevertheless, you seem to have missed my point : I was arguing that coming up with a specific use-case example that may (or may not, actually) go wrong is an argument that goes both ways.
Manned vehicles are not coming anytime soon. They are a whole slew of problems etc. -- you got my point.
I think focusing too much on pesky details is very much a fallacy in this case - you do not want an "AI" to react like a human in all situations, you only want it to drive in a way that is conservative enough not to endanger people too much. And we clearly aren't that far from this goal right now.
Some thoughts:
- Putting 12000 big freezers (900 pounds at most) in orbit is never going to "crowd" the place; imagine these objects on Earth, then the claim that on a much higher radius sphere these could be seen at any point seems ridiculous. (I am not speaking about speed or dangerosity, which even up the numbers a bit, or powerful light-emitting projects)
- In that sense, the video displayed is annoyingly misleading.
- The danger of the Kessler effect is a long term one, and as such it seems purely economic to me. Increasing the number of space debris will gradually increase the probability of collisions, and raise the costs of space industry, in an upwards trend that may at some point represent a real financial burden. This is the only question: at which point does it become economically interesting to tackle this problem, and are we not underestimating the future costs at this point ?
- My take is not yet, and SpaceX will do fine managing their 12000 space fridges.
https://www.theguardian.com/science/blog/2013/nov/15/space-j...