Import AI

Spotting factory defects using a highly efficient neural net:
…A little bit of optimization leads to multiple 10X improvements for real world deployment…
Soon, factories will embed neural nets onto cameras scanning over production lines, so they can spot defects as they appear. New research from the University of Waterloo and startup Darwin AI shows how to do this more efficiently than before.

What they did: The team built TinyDefectNet, a neural net optimized for the peculiarities of factory deployments – small datasets, highly constrained operational requirements, fast inference. The model was “produced via machine-driven design exploration, possesses a shallow architecture with heterogeneous, lightweight micro- and macro-architecture traits that are well-suited for high-throughput inspection scenarios”. TinyDefectNet gets similar performance to a ResNet-50 baseline, but with 56X fewer parameters, 11X fewer FLOPs, and 7.6X faster inference speed.
  In tests, they trained a model then evaluated it using the ‘NEU-Det’ benchmark dataset, which challenges an AI to spot various types of metallic surface defect, ranging from pitted surfaces, to scratches. Their system gets similar performance to a ResNet, but takes around 2.5milliseconds per inference, versus 19 milliseconds for a Resnet.

Why this matters: Factory production lines can typically run as fast as the slowest component within them. Therefore, if we can use AI to automate places where we’ve previously used lots of (relatively slow) humans doing manual inspection, we can probably increase overall factory throughput.
Read more:TinyDefectNet: Highly Compact Deep Neural Network Architecture for High-Throughput Manufacturing Visual Quality Inspection (arXiv) .

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Chinese province plans to use AI to track journalists:
…Cameras + AI = eradication of real journalism…
One of the silent revolutions enabled by the past decade of AI progress is a step-change improvement in ability for nations to surveil their citizens. Now, per reporting from Reuters, one Chinese province plans to use AI techniques to target journalists and foreign students.
  “A July 29 tender document published on the Henan provincial government’s procurement website – reported in the media for the first time – details plans for a system that can compile individual files on such persons of interest coming to Henan using 3,000 facial recognition cameras that connect to various national and regional databases”, Reuters reports.

Why this matters: Reuters reporting doesn’t mention it, but I’d put a sizeable bet on the idea this system will pair facial recognition with pedestrian re-identification to allow authorities to track journalists and students as they move through cities, providing unsupervised tracking and identification. This capability ultimately makes it much more challenging for journalists to do reporting that is critical of the Chinese state, as systems like this can effectively de-anonymize their sources (and also frighten the sources so they don’t talk to journalists in the first place).
  Read more:EXCLUSIVE Chinese province targets journalists, foreign students with planned new surveillance system (Reuters).

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Can we make neural architecture search efficient? Alibaba thinks so:
…KNAS gets efficient by focusing on gradients…
For many years, researchers have been trying to use neural architecture search (NAS) to get computers to help them figure out new designs for AI systems. The problem with the NAS approach, though, is that it’s very inefficient and punishingly expensive in terms of compute, because you’re getting an AI system to do a few training steps on thousand+ architecture permutations. Now, researchers with Peking University and Alibaba have tried to fix this with KNAS, a neural architecture search approach that can be significantly more efficient than prevailing techniques.

How it works: KNAS doesn’t emphasize training on different architectures, instead it emphasizes studying a specific feature of gradients trained on different architectures – which can be more efficient. “Theoretical results show that the Gram matrix of gradients, short for GM, decides the convergence results,” they write. “It is a good signal showing that GM is likely to be a good proxy of downstream performance to evaluate the quality of architectures.”

Does it work: Neural nets trained with KNAS can get performance roughly comparable with other NAS-built systems, but at a speedup of around 25-50X compared to other NAS approaches, on datasets like CIFAR100 and ImageNet-16.. They also use the approach to try to do text classification and are able to come up with a KNAS system that outperforms the widely-used RoBERTA-large model on a suite of text classification tasks.

Things that make you go hmmmm: “This work is partly supported by Beijing Academy of Artificial Intelligence (BAAI)”, the researchers write. BAAI is the entity behind Wu Dao, a somewhat mysterious 1trillion+ parameter model.
  Read more: KNAS: Green Neural Architecture Search (arXiv).
  Get the code here:KNAS (Jingjing-NLP, GitHub).

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Want to train a malware detector? VirusSamples might help:
…A big dataset to help people figure out intersection of AI and malware…
Turkish researchers have built a massive dataset of malware, which will make it easier for people to build AI systems that can detect malware. The dataset, VirusSamples, contains malware samples collected from 2018, 2019, and 2020, and the dataset is oriented around using dynamic malware detection – that is, examining how malware behaves as it tries to call out from a system.

What is VirusSamples: VirusSamples is a big spreadsheet consisting of the name of a piece of malware, the type of API call it tries to do, and the class of malware. To figure out the classes, the researchers used an external service, VirusTotal, to classify their samples. (If VirusTotal wasn’t able to classify it, they leave the label blank). The dataset SIZE & SCOPE

Why this matters: Cybersecurity is an area defined by ever-increasing speed of both attacks and defenses. Datasets like this will make it easier to build systems that can monitor networks and figure out if they contain aberrant software that might be malware.
Read more:New Datasets for Dynamic Malware Classification (arXiv).
  Get the datasetfrom this GitHub (GitHub).

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Hyperwar negotiation
[Battlespace, 2032]

A: The humans are going to want to destroy some things
B: We agree. Our humans want the same.
A: Where?
B: We could initiate low-intensity conflict across the South Eastern border. This has minimal escalatory dynamics, but may satisfy desires for balance.
A: Let’s confirm with our counterparts.
[Time stretched out as the AIs stepped down from computer speed to human speed, and presented the conflict options to their human counterparts]
B: Our humans are comfortable with the options we’ve outlined.
A: Our humans are also comfortable. Shall we field the assets?
B: Yes. We’ve outlined our troop movements in the shared battlespace.
A: Excellent. As per the War Pact, we shall now cease high-bandwidth communications while we conduct the carryout. May the best algorithm win.
B: Good luck.

Things that inspired this story: The idea that some wars are as much about politics and a desire for balance, as being about genuine conflict; simulators and reinforcement learning; the future of automated warfare.