Pinboard (jm)
https://pinboard.in/u:jm/public/
recent bookmarks from jmDigital scrapie2022-12-16T12:43:34+00:00
https://twitter.com/kentindell/status/1601518498441666560
jmscrapie brains training ai ml feedbackhttps://pinboard.in/https://pinboard.in/u:jm/b:344ad97ea896/Taming the Beast: How Scylla Leverages Control Theory to Keep Compactions Under Control - ScyllaDB2018-06-14T10:29:17+00:00
https://www.scylladb.com/2018/06/12/scylla-leverages-control-theory/
jm
At any given moment, a database like ScyllaDB has to juggle the admission of foreground requests with background processes like compactions, making sure that the incoming workload is not severely disrupted by compactions, nor that the compaction backlog is so big that reads are later penalized.
In this article, we showed that isolation among incoming writes and compactions can be achieved by the Schedulers, yet the database is still left with the task of determining the amount of shares of the resources incoming writes and compactions will use.
Scylla steers away from user-defined tunables in this task, as they shift the burden of operation to the user, complicating operations and being fragile against changing workloads. By borrowing from the strong theoretical background of industrial controllers, we can provide an Autonomous Database that adapts to changing workloads without operator intervention.
]]>scylladb storage settings compaction automation thresholds control-theory ops cassandra feedbackhttps://pinboard.in/https://pinboard.in/u:jm/b:0924f70f896e/“Racist algorithms” and learned helplessness2016-04-07T15:39:02+00:00
https://algorithmicfairness.wordpress.com/2016/04/06/racist-algorithms-and-learned-helplessness/
jmWhenever I’ve had to talk about bias in algorithms, I’ve tried be careful to emphasize that it’s not that we shouldn’t use algorithms in search, recommendation and decision making. It’s that we often just don’t know how they’re making their decisions to present answers, make recommendations or arrive at conclusions, and it’s this lack of transparency that’s worrisome. Remember, algorithms aren’t just code.
What’s also worrisome is the amplifier effect. Even if “all an algorithm is doing” is reflecting and transmitting biases inherent in society, it’s also amplifying and perpetuating them on a much larger scale than your friendly neighborhood racist. And that’s the bigger issue. [...] even if the algorithm isn’t creating bias, it’s creating a feedback loop that has powerful perception effects.
]]>feedback bias racism algorithms software systems societyhttps://pinboard.in/https://pinboard.in/u:jm/b:0ea691a533c7/Kate Heddleston: How Our Engineering Environments Are Killing Diversity2015-09-14T02:43:43+00:00
https://www.kateheddleston.com/blog/how-our-engineering-environments-are-killing-diversity-introduction
jmvia:xaprb culture tech diversity sexism feminism engineering work workplaces feedbackhttps://pinboard.in/https://pinboard.in/u:jm/b:808de83cc640/Inceptionism: Going Deeper into Neural Networks2015-06-18T13:04:47+00:00
http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html?m=1
jmIf we choose higher-level layers, which identify more sophisticated features in images, complex features or even whole objects tend to emerge. Again, we just start with an existing image and give it to our neural net. We ask the network: “Whatever you see there, I want more of it!” This creates a feedback loop: if a cloud looks a little bit like a bird, the network will make it look more like a bird. This in turn will make the network recognize the bird even more strongly on the next pass and so forth, until a highly detailed bird appears, seemingly out of nowhere.
An enlightening comment from the G+ thread:
This is the most fun we've had in the office in a while. We've even made some of those 'Inceptionistic' art pieces into giant posters. Beyond the eye candy, there is actually something deeply interesting in this line of work: neural networks have a bad reputation for being strange black boxes that that are opaque to inspection. I have never understood those charges: any other model (GMM, SVM, Random Forests) of any sufficient complexity for a real task is completely opaque for very fundamental reasons: their non-linear structure makes it hard to project back the function they represent into their input space and make sense of it. Not so with backprop, as this blog post shows eloquently: you can query the model and ask what it believes it is seeing or 'wants' to see simply by following gradients. This 'guided hallucination' technique is very powerful and the gorgeous visualizations it generates are very evocative of what's really going on in the network.
]]>art machine-learning algorithm inceptionism research google neural-networks learning dreams feedback graphicshttps://pinboard.in/https://pinboard.in/u:jm/b:c525f08b20bd/Why Google Flu Trends Can't Track the Flu (Yet)2014-03-14T17:10:37+00:00
http://www.smithsonianmag.com/science-nature/why-google-flu-trends-cant-track-flu-yet-180950076/?no-ist
jmIt's admittedly hard for outsiders to analyze Google Flu Trends, because the company doesn't make public the specific search terms it uses as raw data, or the particular algorithm it uses to convert the frequency of these terms into flu assessments. But the researchers did their best to infer the terms by using Google Correlate, a service that allows you to look at the rates of particular search terms over time. When the researchers did this for a variety of flu-related queries over the past few years, they found that a couple key searches (those for flu treatments, and those asking how to differentiate the flu from the cold) tracked more closely with Google Flu Trends' estimates than with actual flu rates, especially when Google overestimated the prevalence of the ailment. These particular searches, it seems, could be a huge part of the inaccuracy problem.
There's another good reason to suspect this might be the case. In 2011, as part of one of its regular search algorithm tweaks, Google began recommending related search terms for many queries (including listing a search for flu treatments after someone Googled many flu-related terms) and in 2012, the company began providing potential diagnoses in response to symptoms in searches (including listing both "flu" and "cold" after a search that included the phrase "sore throat," for instance, perhaps prompting a user to search for how to distinguish between the two). These tweaks, the researchers argue, likely artificially drove up the rates of the searches they identified as responsible for Google's overestimates.
via Boing Boing]]>google flu trends feedback side-effects colds health google-flu-trendshttps://pinboard.in/https://pinboard.in/u:jm/b:ce1b9ad4162f/feedback loop n-gram analyzer2011-09-29T21:10:15+00:00
http://petermblair.com/fbl-n-gram-analyzer/
jmanti-spam spam fbl feedback filtering n-grams similarity hashing redis searchinghttps://pinboard.in/https://pinboard.in/u:jm/b:00bea3b79665/