Have you read John Carreyrou's award-winning book Bad Blood? Have you seen the HBO documentary directed by Alex Gibney that chronicles the rise and fall of Theranos and its blood-testing claims, The Inventor: Out for Blood in Silicon Valley. For my part, I have indeed read Carreyrou's book, and I watched Gibney's documentary, and I also followed the press coverage of Theranos and the criminal trial of Elizabeth Holmes. It is on the basis of all this evidence that I am speaking knowledgeably about the facts concerning Elizabeth Holmes, including the likelihood that she is a almost certainly guilty of the crimes for which she was convicted.

And what about you? Do you know anything about Holmes beyond the pseudo-documentary you just saw? The reason why I have replied to your question with a question is because you also seem to be talking in a "Holmesian" manner, taking everything she does and says at face value. So it seems like a valid question to inquire about your level of knowledge about her criminal record. Another interesting question is: Do you really believe, against all the evidence, that Elizabeth Holmes could be innocent?

There was a mountain of evidence against Holmes, and she was convicted in a criminal trial. So it seems like a safe assumption that she was guilty.

Does it follow then that, because she is almost certainly guilty, that she must be "incredibly self-aware"? Of course not. It's just as likely that she is totally lacking in self-awareness, and that she simply does not know when to shut her mouth because she has no idea how stupid she sounds.

Comments URL: https://news.ycombinator.com/item?id=50041997

Points: 3

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=49876367

Points: 4

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=49671043

Points: 23

# Comments: 41

Ian Ayres (2007) Super Crunchers: Why Thinking by Numbers is the New Way to Be Smart.

Joseph Healey (2020) Statistics: A Tool for Social Research [This is the textbook used in the class, which I highly recommend].

John Allen Paulos (1988) Innumeracy: Mathematical Illiteracy and Its Consequences.

Nate Silver (2012) The Signal and the Noise: Why So Many Predictions Fail, but Some Don't.

Nassim Nicholas Taleb (2005) Fooled by Randomness, second edition.

Nassim Nicholas Taleb (2010) The Black Swan: The Impact of the Highly Improbable, second edition.

Ian Ayres (2007) Super Crunchers: Why Thinking by Numbers is the New Way to Be Smart

Joseph Healey (2020) Statistics: A Tool for Social Research [This is the textbook used in the class, which I highly recommend]

Catherine Marsh and Jane Elliott (2008) Exploring Data: An Introduction to Data Analysis for Social Scientists, second edition

John Allen Paulos (1988) Innumeracy: Mathematical Illiteracy and Its Consequences

Nate Silver (2012) The Signal and the Noise: Why So Many Predictions Fail, but Some Don't

Nassim Nicholas Taleb (2005) Fooled by Randomness, second edition;

_______ (2010) The Black Swan: The Impact of the Highly Improbable, second edition

David Damrosch (2007) Buried Book: The Loss and Rediscovery of the Great Epic of Gilgamesh

https://www.amazon.com/Buried-Book-Rediscovery-Great-Gilgame...

It seems possible that AI will eventually become an abundant commodity, with cheap alternatives available from China and elsewhere. And if that happens, profits will be competed away, just like any other commodity in a competitive market.

One would imagine that a Silicon Valley legend like Larry Ellison - who built his business empire on the basis of proprietary software products and services - would understand this better than anyone.

Comments URL: https://news.ycombinator.com/item?id=48807530

Points: 8

# Comments: 0

https://forum.endeavouros.com/t/malicious-aur-checkup-script...

https://github.com/lenucksi/aur-malware-check

Yes, I agree, that's a good call. I would not try to check for anomalies manually with meatware. I would parse the data with python regex tools to establish a baseline and search for anomalous patterns.

I edited my post to reflect the change you suggested.

If you have a list of good CLI utilities, you could run them in a bash script (e.g., network-monitor.sh), which would run in the background, and then redirect the output data to another file (e.g., network-monitor.txt). The key concept here is "baseline" -- you need to know what normal baseline network activity looks like, so that you can identify anomalous behavior. The way to establish a baseline is to gather a lot of data from the system.

The following are a few useful command line utilities to use for a host intrusion detection system (HIDS) using a simple network monitoring bash script. However, I am not sure exactly how to tweak the options. Also need to find a way to check for data exfiltration:

-list open ports and processes that own them: netstat -lnp

-show open network ports: lsof -i; netstat -an | grep -i listen; netstat -nap

Then it would be relatively easy to write a python script with regex tools to parse the network-monitor.txt file, establish a baseline, analyze the data for patterns and search for anomalous behavior.

Besides network monitoring, there are other command line utilities you can use to check the system for possible intrusion, which you could run in a separate bash script as part of your Host Intrusion Detection System (e.g., hids-users.sh):

-show members of root group: cat /etc/group | grep root

-show users logged in: w #if you are the only user, you should not see more than one account logged in

-search for all accounts with UID of 0: grep :0: /etc/passwd #ideally there should be only one UID of 0 on the system, but attacker can create more.

-check that daemons who never log in have * or !, meaning no passwd: cat /etc/shadow

-look for orphaned files, possible sign of attacker temp account deleted: find / -nouser -print

-search for new user accounts that are not part of regular build: sort -nk3 -t: /etc/passwd #sort numerically third column (UID), colon delim (-t:)

EDITS: several small changes for clarity and also in response to comment below

US law enforcement agencies get much of their information from data brokerage firms, which collect and aggregate information from public records and private sources, e.g., drivers licenses, mortgages, social media, retail loyalty card purchases, professional credentials, charities’ donor lists, bankruptcies, payday lenders, warranty registrations, and other sources who sell personal data. This information is then sold to customers willing to pay for it. Not even the FTC can find out exactly where the data brokers get their information because brokerages cite trade secrecy as an excuse not to divulge their sources.

Law enforcement agencies not only uses data collected by private firms, they also use corporate IT platforms and proprietary software applications (e.g., Palantir, Microsoft, Amazon) to store, share and analyze data. Palantir provides an interface that runs on top of other data systems, including legacy systems, making it possible to link data points across separate systems.

Source: Sarah Brayne (2020) Predict and Surveil: Data, Discretion, and the Future of Policing, Oxford University Press, pp. 24-25, 41-42.

Comments URL: https://news.ycombinator.com/item?id=48083427

Points: 1

# Comments: 1

As a percentage of GDP, government debt is high by historical standards, and it is rising at a fairly rapid pace. That could be a problem if it continues unabated. But there are two solutions: reduce spending and/or raise taxes. Most U.S. federal expenditure goes toward either defense, entitlements or interest on the debt, so it seems unlikely that spending will fall very much. That means higher taxes. At some point, the country will need to find a political reformer who can sell tax increases to the electorate. Until then, the crisis just continues to quietly grow.

[1] Giovanni Arrighi (1994) The Long Twentieth Century: Money, Power and the Origins of Our Times

https://www.amazon.com/Long-Twentieth-Century-Money-Origins/...