Import AI 276: Tracking journalists with computer vision; spotting factory defects with AI; and what simulated war might look like

What would be the smallest computational envelope required to simulate fluid dynamics to the same fidelity as reality?
View this email in your browser

Welcome to Import AI, a newsletter about artificial intelligence. Forward this email to give your chums an AI upgrade. Subscribe here.

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) .

####################################################

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).

####################################################

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).

####################################################

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).

####################################################

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.



Thanks for reading. If you have suggestions, comments or other thoughts you can reach me at jack@jack-clark.net or tweet at me@jackclarksf

Twitter
Facebook
Website
Copyright © 2021 Import AI, All rights reserved.
You are receiving this email because you signed up for it. Welcome!

Our mailing address is:
Import AI
Many GPUs
Oakland, California 94609

Add us to your address book


Want to change how you receive these emails?
You can update your preferences or unsubscribe from this list

Email Marketing Powered by Mailchimp

Older messages

Import AI 274: Multilingual models cement power structures; a giant British Sign Language dataset;  and benchmarks for the UN SDGs

Monday, November 15, 2021

If you had the choice of having 1, 3, or 10 'AGI-class' systems come online at once, which would you pick? View this email in your browser Welcome to Import AI, a newsletter about artificial

Import AI 273: Corruption VS Surveillance; Baidu makes better object detection; understanding the legal risk of datasets

Monday, November 8, 2021

At what point will AI start to influence religion, and vice versa? View this email in your browser Welcome to Import AI, a newsletter about artificial intelligence. Forward this email to give your

Import AI #272: AGI-never or AGI-soon?, simulating stock markets; evaluating unsupervised RL

Monday, November 1, 2021

If each individual parameter of every machine learning model in existence were rendered as a 1cm by 1cm cube, how much space would they all take up? View this email in your browser Welcome to Import AI

Import AI 271: The PLA and adversarial examples; why CCTV surveillance has got so good; and human versus computer biases

Monday, October 25, 2021

How many times has artificial general intelligence been invented on other planets? View this email in your browser Welcome to Import AI, a newsletter about artificial intelligence. Forward this email

Import AI 269: Baidu takes on Meena; Microsoft improves facial recognition with synthetic data; unsolved problems in AI safety

Monday, October 11, 2021

At some point, we'll think about experimenting on AIs in the same way we'll think about experimenting on monkeys. View this email in your browser Welcome to Import AI, a newsletter about

You Might Also Like

SRE Weekly Issue #456

Monday, December 23, 2024

View on sreweekly.com A message from our sponsor, FireHydrant: On-call during the holidays? Spend more time taking in some R&R and less getting paged. Let alerts make their rounds fairly with our

The Power of an Annual Review & Grammarly acquires Coda

Sunday, December 22, 2024

I am looking for my next role, Zen Browser got a fresh new look, Flipboard introduces Surf, Campsite shuts down, and a lot more in this week's issue of Creativerly. Creativerly The Power of an

Daily Coding Problem: Problem #1645 [Hard]

Sunday, December 22, 2024

Daily Coding Problem Good morning! Here's your coding interview problem for today. This problem was asked by Facebook. Implement regular expression matching with the following special characters: .

PD#606 How concurrecy works: A visual guide

Sunday, December 22, 2024

A programmer had a problem. "I'll solve it with threads!". has Now problems. two he ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌ ͏ ‌

RD#486 (React) Things I Regret Not Knowing Earlier

Sunday, December 22, 2024

Keep coding, stay curious, and remember—you've got this ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌

🎶 GIFs Are Neat, but I Want Clips With Sound — Your Own Linux Desktop in the Cloud

Sunday, December 22, 2024

Also: 9 Games That Were Truly Ahead of Their Time, and More! How-To Geek Logo December 22, 2024 Did You Know Dextrose is another name for glucose, so if you see it listed prominently on the ingredients

o3—the new state-of-the-art reasoning model - Sync #498

Sunday, December 22, 2024

Plus: Nvidia's new tiny AI supercomputer; Veo 2 and Imagen 3; Google and Microsoft release reasoning models; Waymo to begin testing in Tokyo; Apptronik partners with DeepMind; and more! ͏ ͏ ͏ ͏ ͏ ͏

Sunday Digest | Featuring 'The World’s 20 Largest Economies, by GDP (PPP)' 📊

Sunday, December 22, 2024

Every visualization published this week, in one place. Dec 22, 2024 | View Online | Subscribe | VC+ | Download Our App Hello, welcome to your Sunday Digest. This week, we visualized public debt by

Android Weekly #654 🤖

Sunday, December 22, 2024

View in web browser 654 December 22nd, 2024 Articles & Tutorials Sponsored Solving ANRs with OpenTelemetry While OpenTelemetry is the new observability standard, it lacks official support for many

😸 Our interview with Amjad Masad

Sunday, December 22, 2024

Welcome back, builders Product Hunt Sunday, Dec 22 The Roundup This newsletter was brought to you by AssemblyAI Welcome back, builders Happy Sunday! We've got a special edition of the Roundup this