Skip to main content

Dream Machine: Appupen and Laurent Daudet ***

Every now and then we get a graphic novel designed to put across some aspect of science and technology while simultaneously entertaining the reader - and for the most part they try hard and then fail painfully. The most succesful I've seen so far is Robin Cousin's The Phantom Scientist - but I'm afraid Dream Machine doesn't come close.

The storyline features a small AI startup with an impressive generative AI, a startup with which a big, bad corporation is trying to get an exclusive contract. We see the head of the startup wrestling with whether or not to take the contract as he finds out more and more details about what generative AI is doing to people (most of which he surely knew anyway) and of the devious plan of the big corporation to gather vast amounts of data and eventually to be able to control whole countries. Move on folks, nothing dubious to see here.

The good news is that we do find out lots about generative AI along the way. And the underlying message that AI can be used for good or for bad purposes, while not exactly original (insert any tech of choice) is fine. But this is a terrible medium through which to deliver the message. With one exception (covered in a moment), hardly anything actually happens. Graphic novels are all about action - but apart from travelling from venue to venue there is no action here. It's all either conversations about AI or lectures about AI. The graphic part adds nothing to this - in fact the layout gets in the way of reading the text. The pictures rarely illustrate anything useful, while the wordy speech bubbles are so small it's hard to read what they say.

The exception I mentioned where it all comes alive are in the 'Super Hugo' sections. These are very short inserts that feature the main character's dreamworld alter ego as a caped superhero. Here he takes on the perils that face the technology in a literalised form. They are quite fun and at least something happens, even if it's not in the real world - but they aren't enough to save the whole. Another brave failure, I'm afraid.

Incidentally, Amazon puts this book at a reading age of 8-12, but it is definitely not aimed at children. Oh, and the Kindle version is in the original French, just for fun.

Paperback:   
Kindle 
Using these links earns us commission at no cost to you
These articles will always be free - but if you'd like to support my online work, consider buying a virtual coffee:
Review by Brian Clegg - See all Brian's online articles or subscribe to a weekly email free here

Comments

Popular posts from this blog

Math for English Majors - Ben Orlin *****

Ben Orlin makes the interesting observation that the majority of people give up on understanding maths at some point, from fractions or algebra all the way through to tensors. At that stage they either give up entirely or operate the maths mechanically without understanding what they are doing. In this light-hearted take, Orlin does a great job of taking on mathematical processes a step at a time, in part making parallels with the structure of language. Many popular maths books shy away from the actual mathematical representations, going instead for verbal approximations. Orlin doesn't do this, but makes use of those linguistic similes and different ways of looking at the processes involved to help understanding. He also includes self-admittedly awful (but entertaining) drawings and stories from his experience as a long-time maths teacher. To make those parallels, Orlin refers to numbers as nouns, operations as verbs (though he points out that there are some flaws in this simile) a

Everything is Predictable - Tom Chivers *****

There's a stereotype of computer users: Mac users are creative and cool, while PC users are businesslike and unimaginative. Less well-known is that the world of statistics has an equivalent division. Bayesians are the Mac users of the stats world, where frequentists are the PC people. This book sets out to show why Bayesians are not just cool, but also mostly right. Tom Chivers does an excellent job of giving us some historical background, then dives into two key aspects of the use of statistics. These are in science, where the standard approach is frequentist and Bayes only creeps into a few specific applications, such as the accuracy of medical tests, and in decision theory where Bayes is dominant. If this all sounds very dry and unexciting, it's quite the reverse. I admit, I love probability and statistics, and I am something of a closet Bayesian*), but Chivers' light and entertaining style means that what could have been the mathematical equivalent of debating angels on

2040 (SF) - Pedro Domingos ****

This is in many ways an excellent SF satire - Pedro Domingos never forgets that part of his job as a fiction writer is to keep the reader engaged with the plot, and it's a fascinating one. There is one fly in the ointment in the form of a step into heavy-handed humour that takes away its believability - satire should push the boundaries but not become totally ludicrous. But because the rest of it is so good, I can forgive it. The setting is the 2040 US presidential election, where one of the candidates is an AI-powered robot. The AI is the important bit - the robot is just there to give it a more human presence. This is a timely idea in its own right, but it gives Domingos an opportunity not just to include some of the limits and possibilities of generative AI, but also to take a poke at the nature of Silicon Valley startups, and of IT mega-companies and their worryingly powerful (and potentially deranged) leaders. Domingos knows his stuff on AI as a professor of computer science w