tingilinde

security and tiktok

TikTok clearly is a security risk to users, but the same can be said for Instagram, YouTube, FaceBook, Twitter (especially Twitter!), Amazon and so many other services that lawmakers are unwilling to address. I wouldn't argue for a TikTok ban, but rather for solid privacy and security rules.  Of coure hell would freeze over first.

 

on the space force

An interview (audio and transcript) with Lt. Gen. Nina Armagno.

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Moskowitz: What would it actually mean to go to war in space?

Armango: Well, a war in space would be devastating. Because it would probably destroy the very domain itself. And so, you know, the world as we know it would be very, very different.

Billings: Yikes. And what would it mean for regular people down on Earth if we did lose some of those capabilities in space?

Armango: I mean, you know, today, especially in modern American life, we benefit from that timing signal from the GPS constellation–that timing signal runs everything from, you know, the power that we're enjoying, to, you know, the blue dot on your cell phone.

I mean, that timing signal is everything, not to mention the positioning and navigation that GPS provides free to the world.

A war in space could, at the, at the low end, disrupt those services. At the high end, it could be destroyed completely and potentially for a very, very long time. Debris lasts in space forever. So I don't even want to put a year on it. It would be decades.

I think we Take it all for granted. I really do

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post quantum cryptography

Derick, with the help of some experts, has a clear description

'crypto trading app' seems to say it all

A self-deploying red flag.  But there are always suckers.

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Customers buying the bots - like Roxana - were told their investment would be handled by the company's artificial intelligence programme, guaranteeing high returns.

"I invested in a bot for one month," Roxana tells the BBC. "You could see in the app how many dollars the app was creating: there were graphics showing how the investment was progressing.

"It looked quite professional until, at some point, they announced maintenance." At that point, for some time, withdrawals from the app were frozen.

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a tip of the hat to Bjarne

 

where will startups get their money?

It's gong to be more difficult..  

Perhaps that's a good thing.

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When Liz Giorgi started her second business — an online platform that lets internet retailers get high-quality photos of their goods — she thought getting a bank account would be easy. After all, she didn’t have trouble getting credit cards, loans or checking accounts for her first venture, a production company she successfully sold after seven years.

This time was different. Her longtime bankers were nervous about working with a small internet software start-up. She tried the big national banks, to no avail. In total, 27 banks rejected her.

Then, in 2019, she found Silicon Valley Bank.

Giorgi got connected to the California bank through Techstars — a prestigious start-up mentorship program. SVB reps flew to Colorado, where the program took place, took her to lunch and courted her. Twenty-four hours later, Giorgi’s company, Soona, finally had a bank account.

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apple music classical

Apple's new classical music service is launching in two weeks.  Online music services are fairly hostile to the genre, so it'll be interesting if this will work with fans.

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According to the announcement, Apple Music Classical will offer an experience tailored for the classical music listener with the ability to search by composer, work, artist, or even catalogue number, and find specific recordings instantly, thanks to complete and accurate metadata. It will also offer ‘hundreds of curated playlists, thousands of exclusive albums, composer biographies, deep-dive guides for many key works, intuitive browsing features and much more’.

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medicare advantage

How people are cut from treatments.

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On the 17th day, her Medicare Advantage insurer, Security Health Plan, followed the algorithm and cut off payment for her care, concluding she was ready to return to the apartment where she lived alone. Meanwhile, medical notes in June 2019 showed Walter’s pain was maxing out the scales and that she could not dress herself, go to the bathroom, or even push a walker without help.

It would take more than a year for a federal judge to conclude the insurer’s decision was “at best, speculative” and that Walter was owed thousands of dollars for more than three weeks of treatment. While she fought the denial, she had to spend down her life savings and enroll in Medicaid just to progress to the point of putting on her shoes, her arm still in a sling.

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perhaps a bit harsh, but ...

 

a tip of the hat to Jim

 

A nice Quanta discussion with Arvind Narayanan

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You also gave a talk on detecting AI snake oil, which was also well received. How does that relate to fairness in machine learning?

So the motivation for this was that there’s clearly a lot of genuine technical innovation happening in AI, like the text-to-image program DALL·E 2 or the chess program AlphaZero. It’s really amazing that this progress has been so rapid. A lot of that innovation deserves to be celebrated.

The problem comes when we use this very loose and broad umbrella term “AI” for things like that as well as more fraught applications, such as statistical methods for criminal risk prediction. In that context, the type of technology involved is very different. These are two very different kinds of applications, and the potential benefits and harms are also very different. There is almost no connection at all between them, so using the same term for both is thoroughly confusing.

People are misled into thinking that all this progress they’re seeing with image generation would actually translate into progress toward social tasks like predicting criminal risk or predicting which kids are going to drop out of school. But that’s not the case at all. First of all, we can only do slightly better than random chance at predicting who might be arrested for a crime. And that accuracy is achieved with really simple classifiers. It’s not getting better over time, and it’s not getting better as we collect more data sets. So all of these observations are in contrast to the use of deep learning for image generation, for instance.

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chomsky and others on chatgpt

Language is thought - machine learning is neither.

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That day may come, but its dawn is not yet breaking, contrary to what can be read in hyperbolic headlines and reckoned by injudicious investments. The Borgesian revelation of understanding has not and will not — and, we submit, cannot — occur if machine learning programs like ChatGPT continue to dominate the field of A.I. However useful these programs may be in some narrow domains (they can be helpful in computer programming, for example, or in suggesting rhymes for light verse), we know from the science of linguistics and the philosophy of knowledge that they differ profoundly from how humans reason and use language. These differences place significant limitations on what these programs can do, encoding them with ineradicable defects.

It is at once comic and tragic, as Borges might have noted, that so much money and attention should be concentrated on so little a thing — something so trivial when contrasted with the human mind, which by dint of language, in the words of Wilhelm von Humboldt, can make “infinite use of finite means,” creating ideas and theories with universal reach.

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