Usually I like to talk about making predictions about the future based on a reading of history.

However, I found two recent articles that forced me to think about more current publications helping to set a future course of science. Think Google, but not so evil because nothing to do with advertising.

First is a story about “Giant” that announced an index of 107 million papers in a way that cleverly navigates around present copyright laws.

Some researchers who have had early access to the index say it’s a major development in helping them to search the literature with software — a procedure known as text mining. Gitanjali Yadav, a computational biologist at the University of Cambridge, UK, who studies volatile organic compounds emitted by plants, says she aims to comb through Malamud’s index to produce analyses of the plant chemicals described in the world’s research papers. “There is no way for me — or anyone else — to experimentally analyse or measure the chemical fingerprint of each and every plant species on Earth. Much of the information we seek already exists, in published literature,” she says. But researchers are restricted by lack of access to many papers, Yadav adds. Malamud’s ‘General Index’, as he calls it, aims to address the problems faced by researchers such as Yadav.

Second is a paper on the prediction of research trends using computational analysis of available papers.

Here, we demonstrate the development of a semantic network for quantum physics, denoted SEMNET, using 750,000 scientific papers and knowledge from books and Wikipedia. We use it in conjunction with an artificial neural network for predicting future research trends. Individual scientists can use SEMNET for suggesting and inspiring personalized, out-of-the-box ideas. Computer-inspired scientific ideas will play a significant role in accelerating scientific progress, and we hope that our work directly contributes to that important goal.

It’s always tempting to invoke Douglas Adams’ famous “42” story when reading these types of articles.

The methods used look more mathematical, and rushed to conclusion, compared with something the seasoned historian might do to validate trends or meaning.

I gave another presentation this year at the ISACA SF conference. It felt like a guilty pleasure to be standing in person again with an audience.

Since so many people asked me for a copy of the slides, here they are:

3MB PDF: Ottenheimer-Auditing_AI-ISACASF2021

See also: My 2019 ISACA SF keynote presentation “Whose AIs Are On Your Data”

Yesterday I have a talk at ISACA-SF where I repeatedly emphasized how AI auditing is about testing things in a way that breaks them.

This shouldn’t be news to anyone used to testing things, and yet many of the platforms somehow are trying to respond to algorithm failure by telling people to stop the tests.

I documented in my talk Amazon and Tesla doing it especially plainly, showing that their preferred response to security flaws is for people to stop testing for them. It’s like the 1980s all over again, despite bug bounties and stunt hacking having become so popular.

Here’s a perfect example from Facebook.

In 2017, I got fed up. I filmed a little experiment with the now-co-host of my podcast, Luke Bailey. We made a brand new Facebook account and I spent the week manually liking conservative Facebook pages and then every subsequent page the platform recommended for me. The Right-wing Ryan radicalized and hard. My feed jumped from normal Republican content to creepy boomer posts about sexy women to Alex Jones posts within a week.

Facebook was very mad about this! Their response was, at the time, the most aggressive they had ever been with me: “This isn’t an experiment; it’s a stunt. It isn’t how people set up or use Facebook, and suggesting so is misleading.”

I should also point out in 2017 a researcher reporting a vulnerability would have expected a massive bug bounty payment in an infamous reward system of Facebook. However, in this story the security failure was so bad, the vulnerability so deep, Facebook security responded with the opposite — they told the researcher to stop doing things in ways that prove a systemic lack of safety on the site related to business logic flaws (BLF).