Pinboard (chriskrycho) https://pinboard.in/u:chriskrycho/public/ recent bookmarks from chriskrycho Investigating ad transparency mechanisms in social media: a case study of Facebook’s explanations 2018-10-16T03:48:17+00:00 https://blog.acolyer.org/2018/03/07/investigating-ad-transparency-mechanisms-in-social-media-a-case-study-of-facebooks-explanations/ chriskrycho When examining ad explanations provided by Facebook, the key finding is that explanations are often incomplete, and sometimes misleading. Suppose that an advertiser uses several attributes for targeting, the explanations will include at most one of those attributes. If you were going to pick just one attribute of course, then the one with the most explanatory power would probably be the one with the smallest population (i.e., fewer Facebook users with that attribute). But Facebook’s explanations appear to show only the most prevalent attribute (e.g., ‘people in the millennials audience’). This makes the explanations incomplete in a very unhelpful way. ]]> lying facebook adrian-colyer social-media algorithmism advertising the-morning-paper https://pinboard.in/u:chriskrycho/b:86bfe82326d3/ Artificial Intelligence — The Revolution Hasn’t Happened Yet 2018-04-20T00:14:38+00:00 https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7 chriskrychoOn the other hand, while the humanities and the sciences are essential as we go forward, we should also not pretend that we are talking about something other than an engineering effort of unprecedented scale and scope — society is aiming to build new kinds of artifacts. These artifacts should be built to work as claimed. We do not want to build systems that help us with medical treatments, transportation options and commercial opportunities to find out after the fact that these systems don’t really work — that they make errors that take their toll in terms of human lives and happiness. In this regard, as I have emphasized, there is an engineering discipline yet to emerge for the data-focused and learning-focused fields. As exciting as these latter fields appear to be, they cannot yet be viewed as constituting an engineering discipline. Moreover, we should embrace the fact that what we are witnessing is the creation of a new branch of engineering. The term “engineering” is often  invoked in a narrow sense — in academia and beyond — with overtones of cold, affectless machinery, and negative connotations of loss of control by humans. But an engineering discipline can be what we want it to be. In the current era, we have a real opportunity to conceive of something historically new — a human-centric engineering discipline. Entirely unconsidered here: whether creating this field is good—or whether treating it as *engineering* is good.]]> technology ai machine-learning ethics algorithmism michael-i-jordan engineering humanism humanities https://pinboard.in/u:chriskrycho/b:1b0d1ae563df/ The Shallowness of Google Translate - The Atlantic 2018-02-07T04:53:04+00:00 https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ chriskrychoI’ve recently seen bar graphs made by technophiles that claim to represent the “quality” of translations done by humans and by computers, and these graphs depict the latest translation engines as being within striking distance of human-level translation. To me, however, such quantification of the unquantifiable reeks of pseudoscience, or, if you prefer, of nerds trying to mathematize things whose intangible, subtle, artistic nature eludes them. To my mind, Google Translate’s output today ranges all the way from excellent to grotesque, but I can’t quantify my feelings about it. Think of my first example involving “his” and “her” items. The idealess program got nearly all the words right, but despite that slight success, it totally missed the point. How, in such a case, should one “quantify” the quality of the job? The use of scientific-looking bar graphs to represent translation quality is simply an abuse of the external trappings of science.]]> translation ai algorithmism technology art https://pinboard.in/u:chriskrycho/b:8f66e0f43282/ Future Historians Probably Won't Understand Our Internet, and That's Okay —Alexis C. Madrigal | The Atlantic “What’s happening? This has always been an easier question to pose—as Twitter does to all its users—than to answer.” 2017-12-11T01:59:03+00:00 https://www.theatlantic.com/technology/archive/2017/12/it-might-be-impossible-for-future-historians-to-understand-our-internet/547463/ chriskrychoStill, Seaver sees these technical systems not as totally divorced from humans, but as complex arrangements of people doing different things.
“Algorithms aren’t artifacts, they are collections of human practices that are in interaction with each other,” he told me. And that’s something that people in the social sciences have been trying to deal with since the birth of their fields. They have learned at least one thing: It’s really difficult. “One thing you can do is replace the word ‘algorithm’ with the word ‘society,’” Seaver said. “It has always been hard to document the present [functioning of a society] for the future.”
The archivist, Johnston, expressed a similar sentiment about the (lack of) novelty of the current challenge. She noted that people working in “collection-development theory”—the people who choose what to archive—have always had to make do with limited coverage of an era, doing their best to try to capture the salient features of a society. “Social media is not unlike a personal diary,” she said. “It’s more expansive. It is a public diary that has a graph of relationships built into it. But there is a continuity of archival practice.”
This is an interesting point of continuity.]]> libraries google twitter algorithmism alexis-madrigal facebook https://pinboard.in/u:chriskrycho/b:738d5947aa55/ A Brutal Intelligence: AI, Chess, and the Human Mind - Los Angeles Review of Books 2017-07-01T11:21:08+00:00 https://lareviewofbooks.org/article/a-brutal-intelligence-ai-chess-and-the-human-mind/ chriskrychoParticularly fruitful has been the deployment of search algorithms similar to those that powered Deep Blue. If a machine can search billions of options in a matter of milliseconds, ranking each according to how well it fulfills some specified goal, then it can outperform experts in a lot of problem-solving tasks without having to match their experience or insight. More recently, AI programmers have added another brute-force technique to their repertoire: machine learning. In simple terms, machine learning is a statistical method for discovering correlations in past events that can then be used to make predictions about future events. Rather than giving a computer a set of instructions to follow, a programmer feeds the computer many examples of a phenomenon and from those examples the machine deciphers relationships among variables. Whereas most software programs apply rules to data, machine-learning algorithms do the reverse: they distill rules from data, and then apply those rules to make judgments about new situations.
[1] A bit of all-too-human deviousness was also involved in Deep Blue’s win. IBM’s coders, it was later revealed, programmed the computer to display erratic behavior — delaying certain moves, for instance, and rushing others — in an attempt to unsettle Kasparov. Computers may be innocents, but that doesn’t mean their programmers are.
]]> algorithmism ai chess nick-carr machine-learning https://pinboard.in/u:chriskrycho/b:3d2157a3aaf1/ fleshers and stoics —Text Patterns 2017-05-11T15:07:00+00:00 http://text-patterns.thenewatlantis.com/2017/05/fleshers-and-stoics.html chriskrychoThe root of what I am calling our Anthropocene moment is the desperate hope that the very technological prowess that has put our natural world, and therefore the bodies of those who live in it, in such dreadful danger may also be turned, pivoted — as it were converted — to safeguard Life; that we may overcome by technical means the vulnerability of those bodies. It’s really the most sophisticated (and potentially insidious) version I know of Stockholm Syndrome. ]]> algorithmism humanism technology alan-jacobs https://pinboard.in/u:chriskrycho/b:f66b3334ea80/ How Google eats a business whole | The Outline 2017-04-18T17:17:12+00:00 https://theoutline.com/post/1399/how-google-ate-celebritynetworth-com chriskrychoGoogle’s push into direct answers has wide-reaching consequences for more than just small business owners who depend on search traffic. The email Google sent Warner in 2014 gives some insight into how Google selects reputable sources. Google wouldn’t answer questions about this, but based on the emails, the vetting was pretty thin; Google seemed more interested in whether the data was machine-readable than whether it was accurate. And the bar for featured snippets — the answers culled algorithmically from the web — is even lower, since it appears that any site good enough to rank in search results is good enough to serve as the source for Google’s canonical answers. That’s how you get erroneous answers that claim Barack Obama is organizing a coup, or that the Earth is flat, or that women are evil, or that this scam artist invented email.]]> algorithmism ai machine-learning google https://pinboard.in/u:chriskrycho/b:3bc6660d3600/ Evgeny Morozov: 'We are abandoning all the checks and balances' | Technology | The Guardian 2017-04-13T01:19:47+00:00 https://www.theguardian.com/technology/2013/mar/09/evgeny-morozov-technology-solutionism-interview chriskrychoThey are not bound to make us dumb, but the way they are currently implemented makes that a possibility. We need to know what we want from such devices: Do we want them to obviate problem solving? To make our lives frictionless? Or do we want these new devices to enhance our problem solving – not to make problems disappear but assist us with solving them? A lot of these devices seek to reward or punish in social currency. For instance, people from Silicon Valley say one way to improve voter turnout is to give people points for checking in with their smartphones at the voting booths – it might even work, people will show up because you show them coupons, but it risks recasting politics in a way that would make any further appeals to ethical behaviour impossible, once you use the language of coupons you need to talk to people in that language in all walks of life, whether it be picking up litter or turning off the lights. Do you want people to turn off the lights because they will get a coupon or because they have some ethical, environmental concerns? You don't hear people in Silicon Valley talk about the ethical and moral dimension. They are not concerned with anything like citizenship at all.]]> algorithmism solutionism evgeny-morozov https://pinboard.in/u:chriskrycho/b:6cfa8476ce8b/ Cancer on Nautilus: Cancer Isn’t a Logic Problem 2017-04-02T16:28:25+00:00 http://cancer.nautil.us/article/186/cancer-isnt-a-logic-problem chriskrychoThe theory of cancer as a logic problem, whereby cellular circuits go haywire and enable a cell to turn cancerous, has been the standard paradigm in cancer genetics. In the past decade, scientists have been looking not only to deduce the logic of molecular changes that can enable a cell to become a cancer, but have begun using targeted screens, employing such weaponry as the gene modification tool Crispr-Cas9, to disable each gene in a cancer cell one at a time and decipher a logic that can stop a cancer cell. But the impulse that disruptive technologies employed by software engineers can be applied to biology, as an analog to a machine or computer with bugs which can be hacked or solved—suggested in “hacking cancer”—is deeply engrained. The problem goes back centuries.  In 1747, French enlightenment thinker Julien Offray de La Mettrie published “L’homme Machine,” or “Man, a Machine.” The philosopher of science Karl Popper noted later that the “theory of evolution gave the problem an even sharper edge.” Meanwhile, adherents to the view of biology as mere clockwork grew. The “doctrine that man is a machine has perhaps more defenders than before among physicists, biologists and philosophers,” Popper observed, “especially in the form of the thesis that man is a computer.” The reason this view is popular with Silicon Valley and computer companies is that technologists sell “solutions,” which can become attractive if they eliminate a problem in a market space. But if all life, including cancer cells, continues to exploit niches, no solutions from technologists will be final.  Cancer cells are not simply a disorder or breakdown in a mechanism, but an organism going on a full-tilt offensive, using multiple, often shifting strategies to produce and use molecular fuel, win resources, and evade the immune system. If so, then the rules of the game may change—these insights suggest that the war on cancer may be endless. Still, we can get better at treating it as an evolving entity within the context on its ecology, through the idea of “living drugs,” such as engineering the body’s own immune cells to sense and mobilize an attack on cancer.]]> science big-data cancer algorithmism data-science https://pinboard.in/u:chriskrycho/b:e3cb9b2b7498/ Apple’s AI Director: Here’s How to Supercharge Deep Learning 2017-03-30T10:13:08+00:00 https://www.technologyreview.com/s/603912/apples-ai-director-heres-how-to-supercharge-deep-learning/ chriskrychoDeep learning—a technique that involves using vast numbers of roughly simulated neurons arranged in many interconnected layers—has produced dramatic progress in machine perception over recent years, but there are many ways in which these networks are limited. Salakhutdinov showed, for example, how image captioning systems based on the technology can label images incorrectly because they tend to focus on everything in the image. He then pointed to a solution in the form of so-called “attention mechanisms,” a tweak to deep learning that has been developed in the last few years. The approach can remedy these errors by having a system focus on specific parts of an image when applying different words in a caption. The same approach can help improve natural-language understanding, too, by enabling a machine to focus on the relevant part of a sentence in order to infer its meaning.]]> algorithmism machine-learning ai apple https://pinboard.in/u:chriskrycho/b:91cf0a007282/ Rationalizing Those 'Irrational' Fears of inBloom —Hack Education “This article first appeared on Points, a Data 2017-03-16T23:39:43+00:00 http://hackeducation.com/2017/03/16/inbloom chriskrychoIn the face of this long list of concerns, the public’s “low tolerance for uncertainty and risk” surrounding student data is hardly irrational. Indeed, I’d argue it serves as a perfectly reasonable challenge to a technocratic ideology that increasingly argues that “the unreasonable effectiveness of data” will supplant theory and politics and will solve all manner of problems, including the challenge of “improving teaching” and “personalizing learning.” There really isn’t any “proof” that more data collection and analysis will do this – mostly just the insistence that this is “science” and therefore must be “the future.” History – the history of inBloom, the history of ed-tech more generally – might suggest otherwise.]]> big-data pedagogy algorithmism education https://pinboard.in/u:chriskrycho/b:17d2b0da176c/ Systems Smart Enough To Know When They're Not Smart Enough | Big Medium 2017-03-16T23:25:44+00:00 https://bigmedium.com/ideas/systems-smart-enough-to-know-theyre-not-smart-enough.html chriskrychoHow can we add some productive humility to these interfaces? How can we make systems that are smart enough to know when they’re not smart enough? I’m not sure that I have answers just yet, but I believe I have some useful questions. In my work, I’ve been asking myself these questions as I craft and evaluate interfaces for bots and recommendation systems. When should we sacrifice speed for accuracy? How might we convey uncertainty or ambiguity? How might we identify hostile information zones? How might we provide the answer’s context? How might we adapt to speech and other low-resolution interfaces?
As I’ve wrestled with this, I’ve found the term “controversy” isn’t strong enough for those cases. Saying that women or Jews are evil isn’t “controverisal”; it’s hostile hate speech. Saying that Barack Obama isn’t an American citizen is a cynical lie. There are some cases where the data has been poisoned, where the entire topic has become a hostile zone too challenging for the algorithms to make reliable judgments.
People don’t click the source links, and that’s by design. Google is explicitly trying to save you the trouble of visiting the source site. The whole idea of the featured snippet is to carve out the presumed answer from its context. Why be a middleman when you can deliver the answer directly? The cost is that people miss the framing content that surrounds the snippet. Without that, they can’t even get a gut sense of the personality and credibility of the source. It’s just one click away, but as always: out of sight, out of mind. And the confidence of the presentation doesn’t prompt much fact-checking.
]]> ai google-home algorithmism siri alexa design ui machine-learning https://pinboard.in/u:chriskrycho/b:3bfe4f617d71/ Automated book-culling software drives librarians to create fake patrons to "check out" endangered titles / Boing Boing 2017-03-12T15:12:09+00:00 http://boingboing.net/2017/01/02/automated-book-culling-softwar.html chriskrychocory-doctorow algorithmism libraries https://pinboard.in/ https://pinboard.in/u:chriskrycho/b:6fc41a808ef6/ The Great A.I. Awakening - NYTimes.com 2017-03-12T15:11:16+00:00 https://mobile.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?_r=0&referer= chriskrychoThe issues Schuster had to deal with were tangled. For one thing, Le’s code was custom-written, and it wasn’t compatible with the new open-source machine-learning platform Google was then developing, TensorFlow. Dean directed to Schuster two other engineers, Yonghui Wu and Zhifeng Chen, in the fall of 2015. It took them two months just to replicate Le’s results on the new system. Le was around, but even he couldn’t always make heads or tails of what they had done.
As Schuster put it, “Some of the stuff was not done in full consciousness. They didn’t know themselves why they worked.”
]]> ai nyt algorithmism neural-networks translation google gideon-lewis-kraus https://pinboard.in/ https://pinboard.in/u:chriskrycho/b:5777fd8f5f4d/ Strong AI Isn’t Here Yet | Otium 2017-02-26T18:45:58+00:00 https://srconstantin.wordpress.com/2017/02/21/strong-ai-isnt-here-yet/ chriskrycho ai neural-networks epistemology ontology algorithmism https://pinboard.in/ https://pinboard.in/u:chriskrycho/b:069a50b56e51/