Location data is almost never anonymous.
In what appears to be a first, a public figure has been ousted after de-anonymized mobile phone location data was publicly reported, revealing sensitive and previously private details about his life.
Monsignor Jeffrey Burrill was general secretary of the US Conference of Catholic Bishops (USCCB), effectively the highest-ranking priest in the US who is not a bishop, before records of Grindr usage obtained from data brokers was correlated with his apartment, place of work, vacation home, family members’ addresses, and more. Grindr is a gay hookup app, and while apparently none of Burrill’s actions were illegal, any sort of sexual relationship is forbidden for clergy in the Catholic Church. The USCCB goes so far as to discourage Catholics from even attending gay weddings.
Burrill’s case is “hugely significant,” Alan Butler, executive director of the Electronic Information Privacy Center, told Ars. “It’s a clear and prominent example of the exact problem that folks in my world, privacy advocates and experts, have been screaming from the rooftops for years, which is that uniquely identifiable data is not anonymous.” Read More
Daily Archives: July 29, 2021
Pretrained Transformers As Universal Computation Engines
We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning – in particular, without finetuning of the self-attention and feedforward layers of the residual blocks. We consider such a model, which we call a Frozen Pretrained Transformer (FPT), and study finetuning it on a variety of sequence classification tasks spanning numerical computation, vision, and protein fold prediction. In contrast to prior works which investigate finetuning on the same modality as the pretraining dataset, we show that pretraining on natural language can improve performance and compute efficiency on non-language downstream tasks. Additionally, we perform an analysis of the architecture, comparing the performance of a random initialized transformer to a random LSTM. Combining the two insights, we find language-pretrained transformers can obtain strong performance on a variety of non-language tasks. Read More
Cheat-maker brags of computer-vision auto-aim that works on “any game”
When it comes to the cat-and-mouse game of stopping cheaters in online games, anti-cheat efforts often rely in part on technology that ensures the wider system running the game itself isn’t compromised. On the PC, that can mean so-called “kernel-level drivers” which monitor system memory for modifications that could affect the game’s intended operation. On consoles, that can mean relying on system-level security that prevents unsigned code from being run at all (until and unless the system is effectively hacked, that is).
But there’s a growing category of cheating methods that can now effectively get around these forms of detection in many first-person shooters. By using external tools like capture cards and “emulated input” devices, along with machine learning-powered computer vision software running on a separate computer, these cheating engines totally circumvent the secure environments set up by PC and console game makers. This is forcing the developers behind these games to look to alternate methods to detect and stop these cheaters in their tracks. Read More
The 5th AI City Challenge
The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2) assessing tasks where the level of performance is enough to cause real-world adoption. Trans portation is a segment ripe for such adoption. The fifth AI City Challenge attracted 305 participating teams across 38 countries, who leveraged city-scale real traffic data and high-quality synthetic data to compete in five challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation being conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multitarget multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. Track 5 was a new track addressing vehicle retrieval using natural language descriptions. The evaluation system shows a general leader board of all submitted results, and a public leader board of results limited to the contest participation rules, where teams are not allowed to use external data in their work. The public leader board shows results more close to real-world situations where annotated data is limited. Results show the promise of AI in Smarter Transportation. State-of-the-art performance for some tasks shows that these technologies are ready for adoption in real-world systems. Read More
Rick's Cafe AI 