I had a paper at CRYPTO 2019 on cryptanalysis using neural networks that I thought I might share here, since there has previously been some interest in cross-domain work between cryptology and machine learning on this subreddit (e.g. CipherGAN, Learning the Enigma with Recurrent Neural Networks):

Paper (eprint version): https://ia.cr/2019/037

Github: https://www.github.com/agohr/deep_speck

Talk: https://youtu.be/weX1itU9VrM

tl;dr: Using neural networks to distinguish cipher output from random data together with an efficient search policy, we achieve a 200-fold speedup over the best previously published key recovery attack against a round-reduced (i.e. weakened) version of a modern block cipher. This is the first example of state of the art block cipher cryptanalysis using deep learning. The trained deep learning models are also compared to very strong distinguishers using traditional techniques and some partial insight into the source of the additional signal picked up by the DL model is provided.

I am developing a non profit tool or application using some of the medical datasets that are publicly available. Would like to know more about legal implications surrounding usage of such datasets. Or the permissions required.

I’m working on a school project where I am attempting to recreate a research paper. Most of the terms mentioned in the title of this post seem to be tightly coupled in other papers I’ve read and I have seldom run across instances where distinctions between any of them have been made. Most of the instances where I have found anyone attempting to make a distinction are in blog posts, which I regard with skepticism.

Sometimes I read something and think “oh, a sequence-to-sequence model is a many-to-many RNN architecture,” or “an encoder-decoder architecture is a sequence-to-sequence model”. Other times I wonder if there’s more nuance to these terms than what I have gleaned. Though it’s difficult to know since the papers that I’m reading are typically confined in scope and assume prior knowledge/familiarity (which I lack), so clarifiers like “model”, “architecture”, and “framework” seem to be used nearly interchangeably.

I have also reverted to several introductory texts but nowhere have I seen any clear boundaries between x is a type of y, or y is synonymous with z. I realize that some of this ambiguity seems to stem from nomenclature that originates in different areas or that morphs over time as it becomes utilized and “integrated” by practitioners. But as a novice, I don’t know what I “don’t know.” So, what’s the diff?

“Many-to-many” comes (to me) from Andrew Ng (in the context of NMT, where input and output sequences vary in length). Sequence-to-Sequence seems to originate from Sustkevar et al (2014) though sounds exactly like Ng’s “many-to-many” description. “Encoder-Decoder framework” also seems to originate from Sustkevar though it’s difficult to tell if there are earlier, more generalized instances of this concept. Sequence-to-sequence models seem like they are identical to encoder-decoder architectures/frameworks. Sequence-to-sequence models seem like a type of “End-to-End” model. I understand End-to-End models to be distinct from ML pipelines (is this truly the case?) in that they operate within a single instance/training context.

It could simply be the case that I’m just not clear what anyone means by framework vs model vs architecture since no clear distinctions appear anywhere and these terms seem to be used nearly interchangeably.

Plz halp.

I have some image manipulation models I’m thinking of making some side money from.

Should I simply sell it or do I expose it as an API?

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Is there a website I can go to where I can monetize my model?

Exposed in this Twitter thread: https://twitter.com/AndrewM_Webb/status/1183150368945049605

Text, figures, tables, captions, equations (even equation numbers) are all lifted from another paper with minimal changes.

Siraj’s paper: http://vixra.org/pdf/1909.0060v1.pdf

The original paper: https://arxiv.org/pdf/1806.06871.pdf

Edit: I’ve chosen to expose this publicly because he has a lot of fans and currently a lot of paying customers. They really trust this guy, and I don’t think he’s going to change.

Hello!
I work for an open-source team at IBM. For a year now we have been working on a project called Model Asset eXchange (MAX). The goal of this project is to standardize DL model deployment and consumption. The idea is to make it easier to integrate DL models into web apps and services or deploy it on any cloud platform. So far we have around 25 models as part of this project. Most underlying models themselves are SOTA open-sourced models from various sources and model zoos (Tf/PyTorch/google research/IBM research etc). The value addition that this project offers is a standardized interface to any model using REST API, containerization and optimizations during inference such as loading the graph just once but performing inference based on every API call. Each model has its own github repo and for convenience, we have also hosted the Docker container on a public endpoint for people to try it out. Where possible we have also extended deployment channels to other avenues, such as NodeRed (npm), CodePen, demo web apps, etc. I would like your feedback/suggestions and of course, welcome any issues/pull request on the underlying github repos as well!

Project link: https://developer.ibm.com/exchanges/models/

A model with all deployment options: https://developer.ibm.com/exchanges/models/all/max-object-detector/

Note: the GitHub link (marked “Get this Model”) for each model is separate

This might sound off-topic to this sub, but I feel like this is a problem that is way more common in the ML community. I’ve heard others peers who do research in ML complaining about the same thing.

Now to the problem: I’m from a little-known department, doing a PhD with a little-known advisor, and most of my research gets published in the main ML conferences (NeurIPS, ICML, ICLR, or CVPR). However, it seems that the community simply ignores that my research exists. There are two specific papers from big labs whose idea and experiments are extremely close to papers of mine from at least 1 year before — these papers have now over 500 citations and don’t mention my papers at all (which have less than 20 citations each, currently).

For example, last year I’ve published a computer vision paper that got strong results in segmentation and detection tasks. This year I’ve seen at least 4 papers being published that have weaker results on the same task, using the same base architecture, and do not compare nor reference my work from last year. All of these 4 papers claim to have the state-of-the-art, and compare against each other (the most recent ones compare their results against all the previous ones, that is). One of these papers has over 200 citations while my paper currently has 4.

Is there anything I can do to make my research more visible? I’m on the verge on quitting my PhD because not having my work acknowledged is simply terrible for me, especially since this happens with my research in general, and not only a few papers that I’ve published. I’ve tried contacting authors from these papers and typically get a reply saying that they were not aware of my work and agreed that it was extremely relevant, but they don’t go through the trouble of updating their submissions to reference me.

Article Link: https://medium.com/halting-problem/google-ai-research-division-to-issue-phd-degrees-8a6954293047

MOUNTAIN VIEW, CALIFORNIA — In a move that is completely unsurprising to many, Google’s AI research division has announced that they are issuing PhD degrees to select employees.

Industry research organizations like Google Brain, DeepMind, and FAIR are well known as heavy hitters in the artificial intelligence research community, publishing as many papers (if not more) as academic institutions like Stanford, Berkeley, and MIT. Many top professors from academia have migrated over to industry research labs as well, sacrificing the security of academic tenure for fat stacks of money. Although Google has previously experimented with research residencies, this is the first time that they have issued postgraduate degrees.

According to a representative, the tech giant decided to issue PhDs in order to attract scarce AI talent. “It’s extremely difficult to find people with talent in Machine Learning and Artificial Intelligence these days, and it pained us to see so many of our amazing interns decline our return offers to go back to school for PhDs. With the Google PhD program, we can continue to get the best talent while supporting our student-employees with world class resources and technology.”

For graduate students, the Google PhD program is a sweet deal. There is already a well-known pipeline where top graduate students do their PhDs at a university, intern at industry research groups during the summer, and then sign on full time after graduation with the promise of million-dollar compensation packages and the freedom to set their own research agendas.

However, aspiring AI researchers must still struggle through the difficulties of academia: four to six years of low pay, begging for funding, and advisors who can be sadistic, micromanaging, or absent. In contrast, Google offers researchers massive computational resources and motivates their PhD advisors with a potent combination of vegan snacks, kombucha, and equity refreshers. Instead of receiving a boring paper diploma at the end of their PhDs, Google PhD graduates receive a priceless note handwritten by Jeff Dean on a napkin that says, “You have a PhD now.”

Other tech companies are scrambling to build their own research groups so they are not left behind in the “AI revolution.” Halting Problem reached out to a representative from Salesforce AI Research who said, “What? Google is doing it? Well, guess we are too.”

In the meantime, Google is already planning for the next way to find top AI talent. According to a knowledgeable source, Google is in talks with Khan Academy to create a “machine learning kindergarten” program that teaches children Tensorflow.

It seems as though there is an Illinois state law that could be violated with the use of the MegaFace dataset. From the article:

By law, most Americans in the database don’t need to be asked for their permission — but the Papas should have been.

As residents of Illinois, they are protected by one of the strictest state privacy laws on the books: the Biometric Information Privacy Act, a 2008 measure that imposes financial penalties for using an Illinoisan’s fingerprints or face scans without consent. Those who used the database — companies including Google, Amazon, Mitsubishi Electric, Tencent and SenseTime — appear to have been unaware of the law, and as a result may have huge financial liability, according to several lawyers and law professors familiar with the legislation.

I’m just wondering how best to ensure researchers factor these concerns in when collecting massive datasets online. For example, I’ve recently collected a text dataset for my research. What do you all do to account for people’s privacy when collecting data?

Recently I came across a paper of Google that was describing how their recommendation algorithm works for Youtube. I wrote my own summary and key takeaways down. Check it out my paper review here.