Dies geschieht in Ihren Datenschutzeinstellungen. But while catchy, it’s a curious and somewhat risky metaphor for marketers to use, conjuring as it does images of environmental pollution through leaks and spills as it finds its way to places it was never meant to be. May be so, but a balanced appraisal shows that while some researchers have not learnt this lesson, the fact is that AI is now well and truly here, embedded in our lives through smart devices and infrastructure. Despite antitrust investigations and a recession, Big Tech is doing great. Only then will we be able to address today’s most complex dilemmas. Because even Big Data sets are not the same, and our metaphors should reflect that. The gold rush now has a quaint feel to it, but captures some of the wild west prospecting spirit as it evokes spatial imagery of unmapped territories to be explored, and ground to lay claim to. Indeed, when it is possible to trawl the internet with a dragnet, it’s no longer a joke to ask if one can become addicted to data, and what the cure for such an addiction might be. The pioneering computer scientist, Fredrick Brooks, echoed this sentiment in 1994 in his acceptance lecture for the Allen Newell Award: “If indeed our objective is to build computer systems that solve very challenging problems, my thesis is that, That is, that intelligence amplifying [or augmenting] systems can, at any given level of available systems technology, beat AI systems. The resource metaphor also alerts us to other threats. The point is that we can reach an approximate understanding of the microscopic objects via analogies with the everyday macroscopic world. Is that true for any personal data, though? The news has hit him hard.

Similarly, organisations are increasingly aware of the importance of protecting their data assets. What happens to the likes of Sarah Cooper, Crooked Media, and the Lincoln Project in a Trumpless world? For instance, see Rowan Wilken’s work on metaphors in internet and mobile technology, documenting how metaphors are recruited by marketers and others as part of a deliberate rhetorical strategy. Because even Big Data sets are not the same, and our metaphors should reflect that. Why make an exception for those who want to sell a slice of their intellect and privacy rather than their bodies?”. Für nähere Informationen zur Nutzung Ihrer Daten lesen Sie bitte unsere Datenschutzerklärung und Cookie-Richtlinie. Among other things, they map across domains enabling us to think of the source domain in terms of the target.

): “Much of the power of artificial intelligence stems from its very mindlessness. In fact we’ve found many more Big Data metaphors. “Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care.” Memory Practices in the Sciences, Geoffrey Bowker. The kinds of data matter. Perhaps it is appropriate to invoke a different metaphor to conclude these reflections: one that is inspired by the notion of a robotic exoskeleton. Water and oil security remain major threats to world stability and peace.

As we become dependent on our technological slaves…we turn into slaves ourselves.”. “As soon as it works, it’s never AI.” We simply treat it as ‘stuff computers can do now’. Post-Snowden, we are acutely aware of data spills and leaks. Much of the confusion about whether or not computers can think arises from the use of inappropriate analogies to describe some of the more amazing things that computers can do — win at Jeopardy, play chess, drive cars , to name just a few.

The truth, however, is that computers don’t think, they merely compute much faster, and in different ways to us. While costs and expenses are more quantitative, this by no means equates to “objective”: we know that the actual numbers depend on specific accounting practices used in the organization.

Yet we know that things can go wrong when humans — even highly trained professionals — develop relationships with smart machines. Might we distinguish between different algorithmic genres, based upon these pattern differences?”. The flipside is that metaphors necessarily highlight certain aspects of the target domain while obscuring others — it is no coincidence that their evocative, narrative power brings metaphorical reasoning into collision with marketing. Irina Raicu is the Director of the Internet Ethics program at the Markkula Center for Applied Ethics, Santa Clara University. newsletter, Big Data, Big Questions: Metaphors of Big Data, Swimming or Drowning in the Data Ocean? Daten über Ihr Gerät und Ihre Internetverbindung, darunter Ihre IP-Adresse, Such- und Browsingaktivität bei Ihrer Nutzung der Websites und Apps von Verizon Media. The data collector (whether human or non-human) always makes a choice as to what data will be collected and what will not. She has split up with her boyfriend. The opacity of source code is a problem that has long been recognised by computer scientists. How can we develop a method of analysis that takes this mutual dependence into account?”. It leads to hype; people claim things that are not true.”.

They must be brought together in a specific context: “Further, questions abound about the distinction between “algorithms” and “data.” Access to an algorithm does not reveal what discoveries that algorithm might facilitate because those discoveries depend as much on the dataset’s features as on the pattern-discovery mechanisms. It can involve a person exaggerating or fabricating a person’s argument. Give someone a hand. The net effect is that there are huge volumes of personal information sloshing around the internet, blurring, if not quite washing away the distinctions between the public and private sphere. It’s essential that all Americans are able to access clear, concise information on what the outcome of the election could mean for their lives, and the lives of their families and communities. Picture a networked sensor streaming data from a physical source (e.g. For example: Love is nota fruit; however, the meaning of the comparison is easily understood. Rippled water texture: Courtesy D. Sharon Pruitt (https://www.flickr.com/photos/pinksherbet/3002844223/in/album-72157610551917961/), Wea00816: Courtesy NOAA -http://commons.wikimedia.org/wiki/File:Wea00816.jpg#/media/File:Wea00816.jpg, Big Data Watching You: Courtesy Jeremy Keith -https://www.flickr.com/photos/adactio/9276962702/, Big Data is the new oil: Courtesy Gerd Leonhard https://www.flickr.com/photos/gleonhard/8979555482, Raw Data Snapshot: Courtesy Paul Dineen https://www.flickr.com/photos/pauldineen/4529216647, Remote Sensing Illustration: Courtesy Arkarjun http://commons.wikimedia.org/wiki/File:Remote_Sensing_Illustration.jpg#/media/File:Remote_Sensing_Illustration.jpg, HAL Suit (Cyberdyne Japan): Courtesy Maria Konovalenko https://mariakonovalenko.files.wordpress.com/2011/01/robot-suit.jpg, Engelbart demo: Courtesy techpresident.com -http://techpresident.com/files/Douglas%20Englebart.png, Blue denim fabric texture: Courtesy D. Sharon Pruitt (https://www.flickr.com/photos/pinksherbet/6816223272/), Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Exactly the same critical eye should be brought to bear on Big Data analyses.

On the other hand, a bleak future is depicted by commentators such as Nicholas Carr. They are all things to which “Big Data” has been likened. He has a fiery temper. Look, no people! In this rhetoric, we had moved well beyond Data processing (which sounded terribly 1960s), via Information systems (hello 1970s-80s), finally to Knowledge Management (trumpet blast and angelic chorus!). They might miss him, too. Relevance. We would have balked. We can’t just discuss even this one subset of the Big Data phenomenon as if we all know what we’re talking about. This problem is discussed in an earlier article on learning analytics, and at length in a provocation piece around governing algorithms (see paragraph 8, one of many interesting points, emphasis added). “… focuses on the criteria that algorithms consider and the particular weights assigned to those criteria. . And yet, its meaning is (usually) abundantly clear. We shouldn’t unquestionably accept the argument that personal data is just like any other commodity and that most of our digital problems would disappear if only, instead of gigantic data monopolists like Google and Facebook, we had an army of smaller data entrepreneurs. A collage of … Universities are repositories of Knowledge which is passed on from generation to generation,’ etc., for your nervous activity is just your nervous activity and, alas, not mine.”. While you may not share such a dystopian view, it is certainly arguable that much of society is already dependent on Big Data algorithms, and that trend looks to accelerate. We’ll have more to say about this aspect of Big Data as resource metaphor later in this essay, but for now let’s explore a couple of implications of the fluid metaphor . from Twitter) we know also that this is usually a regulated stream, that does not necessarily contain the entire dataset . The machine intelligence metaphors we live by now cast much longer shadows.

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