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AI Is Everywhere, but Don't Ignore the Basics
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User Rank: Ninja
9/25/2019 | 7:08:32 PM
Re: Key points that were left out
When you get a chance, check out this article, it elaborates on the discussions we had about AI/ML.

They cover the examples you and I brought up in the discussions, it seems it just takes a small adjustment and the data is tainted, so to me that is not real AI but ML. Once AI becomes self-aware, then these problems will be a thing of the past, but there could be other things we need to address.


User Rank: Ninja
9/25/2019 | 6:49:04 PM
Re: Key points that were left out
Yes, there is no silver-bullet, it is still a work in progress but we have to continue to move forward because the future seems to be getting brighter and brighter (or the outcomes I should say).

Of course, in the security realm, laying solutions to make it harder for the assailant to penetrate your defenses is common-sense (onion and layered approach).

And yes, I do agree, that it is going to take time for AI to make decisions that are indicative of our expected outcomes, but I am curious about the validity of data and if that data is tainted in any way (biases), the results of AI could be skewed to affect the personal lives where it has been trained (like going into neighborhoods and opening fire on people of color, possibility). I would think we need to be able to filter data that is considered way out of the normal parameters, that is up for discussion. There will be one-offs.



User Rank: Ninja
9/12/2019 | 1:49:48 PM
Key points that were left out

1. Data: If AI/ML is a rocket, data is the fuel. AI/ML requires massive amounts of data to help it train models that can do classifications and predictions with high accuracy. Generally, the more data that goes through the AI/ML system, the better the outcome.

 I like the fact that you prefaced the statement with generally and in section 3 you addressed it quite nicely.

3. Domain experts: They play an essential role in constructing an organization's dataset, identifying what is good and what is bad and providing insights into how this determination was made. This is often the aspect that gets overlooked when it comes to AI/ML.

I do like the fact that you mentioned "what's normal, what's abnormal.". Now this statement, I am not so sure of because if we consider what is outside the various thresholds, in the human world, we have to take into consideration time or one offs. What if someone forgot to do something and they ran a task, that task was in the middle of the day but it was to go out, run a report and provide that report to the mgmt staff (that is not part of the norm from a business process standpoint but it is within the norm of normal business operations). The AI/ML could identify this task as being a threat.

However, I do like this statement you wrote, very perceptive:

2. "Wars have been won or lost primarily because of logistics," as noted by General Eisenhower. In the context of the AI/ML battleground, the logistics is the data and model pipeline. Without an automated and flexible data and model pipeline, you may win one battle here and there but will likely lose the war.

I would think it is the processes and planning that create the data (the logistics) and the pipeline is considered how the data is transferred, executed and delivered to right people at the right time, this is truly how wars are won.

"The more you sweat in peace, the less you bleed in war." - General Schwarzkopf

The details (data), planning (process) and execution (pipeline) are the key elements that are used to effectively address the issues that we see every day. The only time we are even close to winning this war on cyber-terror is when we start looking at people as human-beings and provide a roadmap to respect even the menial garbage worker, because no criminal (there are outliers) wants to remain in the same position in which they started.

User Rank: Apprentice
9/11/2019 | 2:17:10 AM
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