Quantum encryption scheme broken with classical math

DAN GOODIN for ArsTechnica:

SIKE is the second NIST-designated PQC candidate to be invalidated this year. In February, IBM post-doc researcher Ward Beullens published research that broke Rainbow, a cryptographic signature scheme with its security, according to Cryptomathic, “relying on the hardness of the problem of solving a large system of multivariate quadratic equations over a finite field.”

Post-quantum encryption contender is taken out by single-core PC and 1 hour

One of the SIKE inventors conceded that many cryptographers “do not understand as much mathematics as we really should.”

One gets a sense that the AI’s are going to be really good at this though.

Discovery sanctions for GDPR redactions

An order by Judge Payne out of the Eastern District of Texas does not agree that redactions allegedly required by GDPR were proper:

To further demonstrate the alleged bad faith application of the GDPR, Arigna showed where Continental blacked out the faces of its Executive Board in a picture even though that picture was available on Continental’s public website without the redactions. Based on these redactions and failure to timely produce the ESI, Argina seeks an adverse inference instruction; an order precluding Continental from using any document that it did not timely produce, and Arigna’s costs and fees.

In response, Continental argued (but did not show) that it received an opinion letter from a law firm based in Europe stating the redactions were required by the GDPR, and that it had worked diligently to produce the ESI while also complying with the GDPR.

July 29, 2022 Memorandum Order, Case No. 22-cv-00126 (EDTX)

Wikipedia influences judicial decisions

Bob Ambrogi:

To assess whether Wikipedia impacts judicial decisions, the researchers set out to test for two types of influence: (1) whether the creation of a Wikipedia article on a case leads to that case being cited more often in judicial decisions; and (2) whether the text of judicial decisions is influenced by the text of the corresponding Wikipedia article.

Scientists Conclude that Wikipedia Influences Judges’ Legal Reasoning

They found that the addition of a case to Wikipedia increased the case’s citations by 20%.

They also purport to demonstrate with natural language analysis that “a textual similarity exists between the judicial decisions and the Wikipedia articles.”

I’m skeptical that this method proves actual influence by a Wikipedia article. But it’s easy to believe that case salience would have an impact.

Convenience vs Privacy

Very cool technology:

Delta Air Lines recently introduced a “Parallel Reality” system that lets travelers access individual flight information on a shared overhead screen based on a scan of their boarding pass — or their face. The twist is that 100 people can do this at a time, all using the same digital screen but only seeing their own personal details.

Unlike a regular TV or video wall, in which each pixel would emit the same color of light in every direction, the board sends different colors of light in different directions.

Coming to a giant airport screen: Your personal flight information

But it does require computers know exactly who and where you are.

Is ShotSpotter AI?

 A federal lawsuit filed Thursday alleges Chicago police misused “unreliable” gunshot detection technology and failed to pursue other leads in investigating a grandfather from the city’s South Side who was charged with killing a neighbor.

. . . . .

ShotSpotter’s website says the company is “a leader in precision policing technology solutions” that help stop gun violence by using sensors, algorithms and artificial intelligence to classify 14 million sounds in its proprietary database as gunshots or something else.

Lawsuit: Chicago police misused ShotSpotter in murder case

Some commentators (e.g., link) have jumped on this story as an example of someone (allegedly) being wrongly imprisoned due to AI.

But maybe ShotSpotter is just bad software that is used improperly? Does it matter?

The definition of AI is so difficult that we may soon find ourselves regulating all software.

Pilot confusion over computer control in Airbus planes

An Airbus A321 jet unexpectedly rolled hard left on takeoff in an April 2019 incident.

The NTSB concluded the incident was pilot error: too much rudder while taking off in a crosswind.

But the pilots were very confused:

A transcript of conversations between the captain and his first officer shows how the two were confused and didn’t realize how badly damaged the plane was as they continued to climb out of New York.

“Are we continuing?” the first officer asked, according to a transcript released by the NTSB. “ … I thought we were gone.”

“Well she feels normal now,” the captain said a few minutes later.

NTSB: Pilot described ‘near death’ experience after wing hit the ground

And at least one pilot blamed the confusing nature of the Airbus flight computers:

Cockpit Transcript at 18.

Thankfully the pilots safely returned to JFK airport.

“The internet is less free, more fragmented, and less secure”

The Council on Foreign Relations, described by Wikipedia as a “right leaning American think tank specializing in U.S. foreign policy and international relations,” has issued a report titled Confronting Reality in Cyberspace:

The major findings of the Task Force are as follows:

The era of the global internet is over.

U.S. policies promoting an open, global internet have failed, and Washington will be unable to stop or reverse the trend toward fragmentation.

Data is a source of geopolitical power and competition and is seen as central to economic and national security.

The report is a warning that the U.S. needs to get serious about a fragmenting internet or risk losing digital leadership entirely.

AI discoveries in chess

AlphaZero shocked the chess world in 2018.

Now an economics paper is trying to quantify the effect of this new chess knowledge:

[W]e are not aware of any previously documented evidence comparing human performance before and after the introduction of an AI system, showing that humans have learned from AI’s ideas, and that this has pushed the frontier of our understanding.

AlphaZero Ideas

The paper shows that the top-ranked chess player in the world, Magnus Carlsen, meaningfully altered his play and incorporated ideas from AlphaZero on openings, sacrifices, and the early advance of the h-pawn.

Carlsen himself acknowledged the influence:

Question: We are really curious about the influence of AlphaZero in your game.

Answer: Yes, I have been influenced by my hero AlphaZero recently. In essence, I have become a very different player in terms of style than I was a bit earlier and it’s been a great ride.”

Id. at 25 (citing a June 14, 2019 interview in Norway Chess 2019).

Bias mitigations for the DALL-E 2 image generation AI

OpenAI has a post explaining the three main techniques it used to “prevent generated images from violating our content policy.”

First, they filtered out violent and sexual images from the training dataset:

[W]e prioritized filtering out all of the bad data over leaving in all of the good data. This is because we can always fine-tune our model with more data later to teach it new things, but it’s much harder to make the model forget something that it has already learned.

Second, they found that the filtering can actually amplify bias because the smaller remaining datasets may be less diverse:

We hypothesize that this particular case of bias amplification comes from two places: first, even if women and men have roughly equal representation in the original dataset, the dataset may be biased toward presenting women in more sexualized contexts; and second, our classifiers themselves may be biased either due to implementation or class definition, despite our efforts to ensure that this was not the case during the data collection and validation phases. Due to both of these effects, our filter may remove more images of women than men, which changes the gender ratio that the model observes in training.

They fix this by re-weighting the training dataset so that the categories of filtered data are as balanced as the categories of unfiltered data.

Third, they needed to prevent image regurgitation to avoid IP and privacy issues. They found that most regurgitated images (a) were simple vector graphics; and (b) had many near-duplicates in the training set. As a result, these images were easier for the model to memorize. So they de-duplicated images with a clustering algorithm.

To test the effect of deduplication on our models, we trained two models with identical hyperparameters: one on the full dataset, and one on the deduplicated version of the dataset. . . . Surprisingly, we found that human evaluators slightly preferred the model trained on deduplicated data, suggesting that the large amount of redundant images in the dataset was actually hurting performance.

Given the obviously impressive results, this is an instructive set of techniques for AI model bias mitigation.