# Profoundd archive — Epstein Files
# Bates number: EFTA00973641
# Title:        From: Jeffrey Epstein <jeevacation@grnail.com>
# Dataset:      9
# Pages:        3
# Tags:         epstein, doj, dataset-9
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=== SUMMARY ===
From: Jeffrey Epstein <jeevacation@grnail.com> To: Ari Gesher Subject: Re: MDF Date: Fri, 25 Oct 2013 11:49:25 +0000 the topological equivalents I believe show great promise. manifold transformations. just like immediately knowing that the girl who looks almost like your girlfriend ( twin sister ) isn't. the transform to self is skewed. On Wed, Oct 23, 2013 at 8:37 PM, Ari Gesher a wrote: The adaptive adversaries in the immune system context are parasites. I wrote a piece using biological parasi


=== EXTRACTED TEXT ===
From: Jeffrey Epstein <jeevacation@grnail.com> 
To: Ari Gesher 
Subject: Re: MDF 
Date: Fri, 25 Oct 2013 11:49:25 +0000 
the topological equivalents I believe show great promise. manifold transformations. just like immediately 
knowing that the girl who looks almost like your girlfriend ( twin sister ) isn't. the transform to self is skewed. 
On Wed, Oct 23, 2013 at 8:37 PM, Ari Gesher a wrote: 
The adaptive adversaries in the immune system context are parasites. I wrote a piece using biological parasites 
as a strong analogy for the problems cyber security (which also goes into the need to use side-channels to find 
and stop adaptive adversaries). The immune system does a horrible job of dealing with parasites 
(definitionally) and can be seen to be just like standard pattern-matching/data mining/statistical approaches as 
a defense mechanism - they're really good at stopping what they've seen before and pretty bad at stopping 
novel attacks. 
The self-recognition piece is interesting, though. 
On Oct 23, 2013, at 5:26 PM, Jeffrey Epstein leevacation®gmail.com>  wrote: 
I think the immune system might provide useful insights . Recognizing self , attacking everything that is not 
self 
On Wednesday, October 23, 2013, Ari Gesher wrote: 
On Oct 23, 2013, at 8:09 AM, Joscha Bach <e wrote: 
That being said, AGI will have trouble succeeding because it is 
following the scruffy tradition. Perhaps the main failing of this 
tradition is its refusal to define objective (and preferably 
quantitative) measures of success. 
The question of good benchmark tasks is haunting Al since its inception. Usually, when we 
identify a task that requires intelligence in humans (playing chess or soccer or Jeopardy, driving 
a car etc.) we end up with a kind of very smart (chess-playing, car-driving) toaster. That being 
said, Al was always very fruitful in the sense that it arguably was the most productive and useful 
field of computer science, even if it fell short of its lofty goals. Without a commitment to 
understanding intelligence and mind itself, Al as a discipline may be doomed, because it will 
lose the cohesion and direction of a common goal. 
So now this gets interesting and starts to point us towards both MDF and the study of deception. 
The smart toasters emerge because they're being designed to solve well-bounded problems (like playing 
chess). There is no deception in chess (I would put feints in a different category), no hidden information, 
no adaptive adversary that can breach the bounds of the rules of the game. Given that, either brute force or 
MI/statistical approaches works well enough to build things like Deep Blue or the Google self-driving car. 
EFTA00973641
At Palantir (where I work), we have, to date, stayed away from heavy machine learning or algorithmic 
approaches to data analysis, focusing instead on building better and better tools to connect human minds to 
data in a way that's rigorous and interactive. This is the only current way to detect adaptive adversaries 
like sophisticated fraudsters or state-sponsored cyber attackers - traditional ML approaches fail as tactics 
adapt faster than training data can be identified, tagged, and learned. I like to think of this class of 
problems as arms races, since automation is easily defeated by a change in tactics that use more 
advanced/harder to detect techniques. 
Let's take a look at fraud. Simple fraud, like stolen credit cards, is solved with simple automation looking 
for anomalies inside a big, validated training set (your legitimate transactions). The type of fraud is much 
more subtle I'm talking about goes something like this: 
1. Open accounts under fake identities 
2. Use accounts, pay balances, drive up credit lines 
3. Hit magic number, max out all cards 
4. Write fake checks to zero balances 
5. Max cards a second time before the checks bounce 
6. abandon the fake identities 
7. Goto step 1 
Instead, the state of the art is to use fairly simple data-mining to flag suspicious events with a low-
threshold - yielding a set of candidate events much smaller than the initial haystack but containing a 
relatively large number of false positives. In the above example, we use clustering that looks at caller ID 
data for calls to the bank, card transactions, payment methods, IP address data for access to the website, 
and account details. Seemingly unrelated accounts that are linked are scored by aggregate credit risk and 
queued up for human analysis in a rich, interactive analytic environment. This is how the final 
determination of fraud is made. The magic of our software is not magic at all - we integrate the data across 
multiple, disparate sources of data into a human-conceptual model based on a constrained ontology of the 
problem at hand. The interface is interactive (sometimes requiring some pretty serious engineering to pull 
off) and speaks a language that is familiar to the human experts on the problem, enabling them to move 
through large volumes of data very quickly. (For a simple but demonstrative workflow, take a look at 
this cybersecurity  demo) 
Our newest innovation is letting the human analysts specify the pattern matching algorithms in this 
ontologically -typed language (Conceptually: "two credit card accounts used at the same store to buy the 
same item within three minutes of each other") that is then translated into search jobs on the map-reduce 
cluster. This allows for a tight feedback loop and hopefully stay ahead of the fraudsters. 
What I've been noodling on is using a mix of MDF (these institutions fighting fraud are often combing 
through terabytes of data produced per day) and deep learning to see if you could train classifiers that 
would not only spot fraud but be able to see it as their tactics change. 
It's sort of an interesting twist on the Turing Test: can an AI detect the signature of a human that's actively 
trying to evade detection? 
The information contained in this communication is 
confidential, may be attorney-client privileged, may 
constitute inside information, and is intended only for 
the use of the addressee. It is the property of 
Jeffrey Epstein 
EFTA00973642
Unauthorized use, disclosure or copying of this 
communication or any part thereof is strictly prohibited 
and may be unlawful. If you have received this 
communication in error, please notify us immediately by 
return e-mail or by e-mail to jeevacation®gmail.com,  and 
destroy this communication and all copies thereof, 
including all attachments. copyright -all rights reserved 
The information contained in this communication is 
confidential, may be attorney-client privileged, may 
constitute inside information, and is intended only for 
the use of the addressee. It is the property of 
Jeffrey Epstein 
Unauthorized use, disclosure or copying of this 
communication or any part thereof is strictly prohibited 
and may be unlawful. If you have received this 
communication in error, please notify us immediately by 
return e-mail or by e-mail to jeevacation@gmail.com,  and 
destroy this communication and all copies thereof, 
including all attachments. copyright -all rights reserved 
EFTA00973643