Recent years have seen a surge in the popularity of acoustics-enabled personal devices powered by machine learning. Yet, machine learning has proven to be vulnerable to adversarial examples. A large number of modern systems protect themselves against such attacks by targeting artificiality, i.e., they deploy mechanisms to detect the lack of human involvement in generating the adversarial examples. However, these defenses implicitly assume that humans are incapable of producing meaningful and targeted adversarial examples. In this paper, we show that this base assumption is wrong. In particular, we demonstrate that for tasks like speaker identification, a human is capable of producing analog adversarial examples directly with little cost and supervision: by simply speaking through a tube, an adversary reliably impersonates other speakers in eyes of ML models for speaker identification. Our findings extend to a range of other acoustic-biometric tasks such as liveness detection, bringing into question their use in security-critical settings in real life, such as phone banking.

Building large AI fleets to support the rapidly growing DL workloads is an active research topic for modern cloud providers. Generating accurate benchmarks plays an essential role in designing the fast-paced software and hardware solutions in this space. Two fundamental challenges to make this scalable are (i) workload representativeness and (ii) the ability to quickly incorporate changes to the fleet into the benchmarks. To overcome these issues, we propose Mystique, an accurate and scalable framework for production AI benchmark generation. It leverages the PyTorch execution trace (ET), a new feature that captures the runtime information of AI models at the granularity of operators, in a graph format, together with their metadata. By sourcing fleet ETs, we can build AI benchmarks that are portable and representative. Mystique is scalable, due to its lightweight data collection, in terms of runtime overhead and instrumentation effort. It is also adaptive because ET composability allows flexible control on benchmark creation. We evaluate our methodology on several production AI models, and show that benchmarks generated with Mystique closely resemble original AI models, both in execution time and system-level metrics. We also showcase the portability of the generated benchmarks across platforms, and demonstrate several use cases enabled by the fine-grained composability of the execution trace.

There has always been sex in video games. As shocking as this revelation may be to those who have only ever played Call of Duty, Fifa or Pokémon Go, it's the truth. As soon as developers were able to put animated pixels on a screen, they were trying to make those pixels do rude things. In the early 1980s, for example, a publisher named Mystique released a series of "erotic" games for the Atari 2600, beginning with Burning Desire, in which you played a naked air rescue worker. From the very start, realism was important. Later, we were treated to Sam Fox Strip Poker on the Commodore 64 and Night Trap on the Sega Mega Drive, a sort of fuzzy interactive B-movie that was deemed so shocking that it became the subject of a congressional hearing.

Our spandex-clad saviors rule movies. They even appear in comics occasionally. Yet some of the most interesting stories about caped crusaders right now don't come with pictures or fancy special effects. They're in good old fashioned books. A new wave of authors is bringing fun, romance, and a spirit of adventure to superheroes--and they're doing it by focusing on the sorts of saviors big-budget tentpole movies tend to overlook.

This is a guest post by Erik Meijer (@headinthebox). He is an accomplished programming-language designer who runs the Cloud Programmability Team at Microsoft and a professor of Cloud Programming at TUDelft. There is a lot of hype and mystique around Machine Learning these days. The combination of the words "machine" and "learning" induces hallucinations of intelligent machines that magically learn by soaking up Big Data and then both solving world hunger and making us rich while we lay on the beach sipping a cold one. However, just as normal programmers can write code without needing to understand Universal Turing Machines, power domains, or predicate transformers, we believe that normal programmers can use Machine Learning without needing to understand vectors, features, probability density, Jacobians, etc.