Category Archives: Computing

Fully Automated Luxury … Dancing?  (A futuristic conspiracy theory)

Fully Automated Luxury … Dancing? (A futuristic conspiracy theory in the making)

Vic Grout, Professor of Computing Futures, Wrexham Glyndŵr University

Download the PDF version: Fully Automated Luxury Dancing – Download Version 1

[Note/Disclaimer: Some of the discussion in this piece is shockingly brief. A limit of 10,000 words was planned and (just) adhered to.]

We’ve encountered Michael Moorcock’s masterpiece, Dancers at the End of Time, before on these pages: both as an example of sci-fi doing what it does best (providing a blank canvas for a bigger discussion) and the problems futurologists have with not seeing key disruptive technology (the Internet, in Moorcock’s case).  But, for this post, an entirely different question to ponder: who exactly ARE ‘The Dancers’?

Because answering that puzzle (there aren’t that many clues to go on in the novel itself and obviously it is only a story) takes us to considering problems in (apparently) entirely different fields: environment, politics, economics, etc. (which is the important point really, of course) and may lead us to a view of the future quite at odds with current thinking right across the political spectrum.  Specifically, what’s usually wrong with long-term ‘futuristic’ political and economic prophesising?  Particularly the very well-intentioned left-wing stuff.  What’s the one thing that everything from Karl Marx’s Das Capital to Aaron Bastani’s Fully Automated Luxury Communism appear to take for granted?  (Spoiler alert: in simple terms it’s the belief that just because a political/economic system’s crap, it will naturally yield to something better – but we’ll come to that.)

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Advanced Machine Learning and Data Mining: A New Frontier in Artificial Intelligence Research

Special Issue Information: Advanced Machine Learning and Data Mining: A New Frontier in Artificial Intelligence Research (Big Data and Cognitive Computing Journal)

Call for Papers

Without data, there is no machine learning (ML), so there is no doubt that big data and ML are inextricably linked. However, much research to date has tended to treat them as separate areas of development. As we are confronted with today’s difficult problems and the wealth of held data continues to grow, it is vital that new, innovative ways of examining, testing, and using big data to produce useful information are both researched/developed and integrated. Whether this be for the social good (health diagnostics, for example) or corporate gain (competitive advantage), given the exponentially increase in both the volume of data and the velocity by which it is generated, the need for the expansion of direct cooperation of mining big data with ML is long overdue. For this Special Issue, as the individual fields of advanced machine learning and advanced data mining are well established, the focus will be specifically on their intersection: the point―or points―at which one aids, needs, or enhances the other.

This new frontier is almost boundless, but will eventually become the norm. Automatically learning and improving from experience without being explicitly programmed gives great opportunities. The quality of the data being used, its speed of acquisition, and the effectiveness of processing are all of vital importance―if Microsoft’s AI chatbot Tay taught us anything at all, it is certainly this.

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Effective Age-gating for Online Alcohol Sales (Final Report)

‘Surprised?  No, not really!’

The ‘Effective Age-gating for Online Alcohol Sales’ report, funded by Alcohol Change UK, is published today.  Written by Jess Muirhead and Vic Grout, it considers the question of how easy it is for UK under 18s to purchase alcohol via the Internet, and makes five key recommendations:

Recommendation 1: The law must be clarified

Despite its best intentions, the current law is ambiguous in relation to how and where safeguards are to be applied to prevent under 18s obtaining alcohol online.  If the intention really is to allow age-checking on delivery as a substitute for online verification then that should be published as official guidance by the relevant authorities.  However, knowing such measures to be as ineffective as they are, it is to be hoped that the necessary clarification would move the law in the other direction: that robust online age verification – at the transaction stage – becomes a clear legal requirement.

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AI/Machine Learning for Minefield Clearance

“Glyndwr University researcher seeks real world data to develop AI landmine clearance”

A landmine research project which aims to develop an artificial intelligence approach to mine clearance is being developed at Wrexham Glyndwr University.

The project – which is currently in its developmental stages – is being developed by Computing PhD student Alexander Bruckbauer who is working on building the model under the supervision of Professor of Computing Futures Vic Grout.

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