The first one I'd like to note is the attack on Tesla car's "autopilot" feature that was publicized in 2019. You can then apply the noise in the form of an adversarial patch, a real-world modification of the image data being scanned: dazzle face-paint to defeat face recognizers, strategically placed bits of tape on road signage, and so on.Īs AI applications are increasingly deployed in public spaces we're now beginning to see the exciting possibilities inherent in the leakage of human stupidity into the environment we live in. And they show the way towards deliberate adversarial attacks on recognizers-if you have access to a trained NN, you can often identify specific inputs that, when merged with the data stream the NN is searching, trigger false positives by adding just the right amount of noise to induce the NN to see whatever it's primed to detect. Trees are an example of a spurious image that deceives an NN into recognizing something inappropriately. Similarly, in the 90s, per legend, a DARPA initiative to develop automated image recognition for tanks that could distinguish between NATO and Warsaw Pact machines foundered when it became apparent that the NN was returning hits not on the basis of the vehicle type, but on whether there was snow and pine forests in the background (which were oddly more common in publicity photographs of Soviet tanks than in snaps of American or French or South Korean ones). One effect: face recognition in cameras is notorious for its racist bias, with some cameras being unable to focus or correctly adjust exposure on darker-skinned people. If you've been keeping an eye on AI you'll know that the real magic is all in how the training data sets are curated, and the 1950s axiom "garbage in, garbage out" is still applicable. Sure they need internet access and a server farm somewhere to do the real donkey work, but the effect is almost magically. So much so that we've come to take for granted the ability to talk to some of our smarter technological artefacts-and the price of gizmos with Siri or Alexa speech recognition/search baked in has dropped into two digits as of last year. And they deliver remarkable results at tasks such as image and speech recognition.
NNs, and in particular training Generative Adversarial Networks takes a ridiculous amount of computing power, but we've got it these days. In a scripting language inside a web browser on a 2020 laptop.)
#CHARLIE MURDER DIAMOND SCALE SOFTWARE#
Every time you add an abstraction layer to a software stack you can expect a roughly one order of magnitude performance reduction, so intuition would suggest that a WebAssembly framework (based on top of JavaScript running inside a web browser hosted on top of a traditional big-ass operating system) wouldn't be terribly fast but the other day I was reading about one such framework which, on a new Apple M1 Macbook Air (not even the higher performance Macbook Pro) could deliver 900GFlops, which would put it in the top 10 world supercomputers circa 1996-98. (I'm not exaggerating there: modern CPU/GPU performance is ridiculous.
Most of them centre on applications of neural networks, a subfield which stagnated at a theoretical level from roughly the late 1960s to mid 1990s, then regained credibility, and in the 2000s caught fire as cheap high performance GPUs put the processing power of a ten years previous supercomputer in every goddamn smartphone.
#CHARLIE MURDER DIAMOND SCALE SERIES#
This decade has seen an explosive series of breakthroughs in the field misleadingly known as Artificial Intelligence.
I don't often comment on developments in IT these days because I am old and rusty and haven't worked in the field, even as a pundit, for over 15 years: but something caught my attention this week and I'd like to share it. ( Blogging was on hiatus because I've just checked the copy edits on Invisible Sun, which was rather a large job because it's 50% longer than previous books in the series.)
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