Perhaps I was unduly harsh, but I disagree that I was shallow. I pointed out exactly where the problems lay; poor use of language and people operating outside of their domain of expertise.
I don't see any information in what you posted, just the most cliché of all dismissals in that category. That seems to me to be covered by what the guidelines call shallow. We don't need another boring "oh the humanities" go-round on Hacker News, even though some writers have problems with plain language.
Sorry but you haven’t addressed a single point in the article. If you’re going to criticize it, make actual points rather than resorting to handwavy “humanities are gibberish.”
>KW: It seems what we are dealing with here is the systematization of the possible: the target is something unknown that, by way of systematization, becomes a circumscribed and definite possibility – or rather an array of definite possibilities. The deployment of algorithms is thus ‘mobile’ and ‘flexible’ but, because it is oriented towards definite possibilities, not as much as it may seem; in fact there is only a definite and therefore limited spectrum of possibilities that algorithmic modelling can address. Does this situation change in the face of self-learning algorithms, given their inductive capacities to create semi-autonomous associations in an ever-growing range of possibilities?
When I read this, I get the strong sense that machine learning is being misconstrued, and most likely profoundly overestimated in its capabilities.
I'm impressed you were able to parse it at all. I had to translate it to normal human speech before I was able to even take a guess at what they were playing at.
The Professor being interviewed is using a very specific niche jargon. As such, what she's saying is largely unintelligible to those of us not initiated into that jargon.
Mickens' keynote from USENIX Security 2018, "Q: Why Do Keynote Speakers Keep Suggesting That Improving Security Is Possible?" might be more accessible https://www.youtube.com/watch?v=ajGX7odA87k
> The role of self-learning algorithms would seem to be very significant in this context, since – like capitalism – they also hinge upon movement
> If the systematization of the possible is somehow reconfigured by self-learning algorithms, are the capacities of indefinite potential then also reconfigured when it comes to evading this very systematization ...
> An associationism that can never be known, a life of associating with other things and people that is not amenable to and not incorporable by calculation – is this now changing through AI?
> However, the distance between the actual output signals of their algorithms and the target output represents what I call a space of play. Indeed, the algorithm designers described ‘playing with’ or ‘tuning’ the algorithm so that the output converges on the target. Here I think that deep machine learning is not circumscribed at all by a limited spectrum of possibility ...
I don't think all humanities are gibberish, but this is. I'm sure a Philosopher would find my opinions on Nietzsche to be similarly half-baked.
One could say everything that is trying to be said in those quotes a helluva lot more clearly if one threw away the thesaurus and spoke like a human being, rather than a high school senior trying to sound erudite in a college admissions essay.
> If the systematization of the possible is somehow reconfigured by self-learning algorithms, are the capacities of indefinite potential then also reconfigured when it comes to evading this very systematization ...
It just doesn't parse for me. What is doing the evading?
> KW: If the systematization of the possible is somehow reconfigured by self-learning algorithms, are the capacities of indefinite potential then also reconfigured when it comes to evading this very systematization? In other words: is a new, ‘intelligent’ systematization of indefinite potential arising in the context of AI?
> LA: You have really identified a crucial issue here. In the final chapter of Politics of Possibility, I proposed that potentiality continues to overflow and exceed the capacity for the calculation of possibles. However, I am worried that this evasive potentiality may also be under threat, and I do address that in my new book Cloud Ethics. With contemporary deep machine learning, there is a move to incorporate the incalculable and to generate potentials that need never be fully exhausted. Gilles Deleuze once wrote that ‘the problem gets the solution it deserves’, implying that the particular arrangement of a problem will systematize a solution. To my reading, today’s algorithms are reversing this, so that the solution gets the problem it deserves – in the sense that the potential pathways of the neural net are infinitely malleable in relation to a solution. Let us not forget that by ‘solution’ we mean an algorithm that may decide juridical processes, policing, security, employment and so on.
