danielrm26

Rather than trying to remember all those commands for moving around in your command, I recommend turning on vim mode for the command line, and remapping ESC to "jk".

bindkey -v bindkey -M viins 'jk' vi-cmd-mode

Then you can edit your command line the same way you would edit a line in vim.

I think a key differentiation between ML and more common statistics is that ML is designed to improve itself based on data. Statistical methods don't do that.

So maybe they're trying to do very similar things in a lot of cases, but self improvement is a major differentiator.

The reason this works is because it uses Evolution Thinking vs. Design Thinking to find good people, which we need more of in tech.

https://danielmiessler.com/blog/why-you-should-hire-using-ev...

Derp.

You can write people in.

And most voting is at local levels.

So, false dichotomy.

Not likely to get a programming job out of this, but perhaps a job in information security where the game is to figure out how to abuse systems.

It's the same as cloaking a tank in a tank battle.

If you didn't take the armor off it's great. But if you did then you've put your eggs in the don't get hit basket.

The trick is figuring out if you could have bought more armor with the money you spent on the cloaking device.

Or, if enemy weapons are so good that armor doesn't matter, then it's time to think about cloaking very seriously.

It's a balance between probability and impact. With cloaking you're reducing likelihood. With armor you're reducing impact.

But in both cases you're reducing risk.

Ok, so this is going to sound crazy, but it's deeper than that.

When you put yourself on another port or make yourself invisible on a battlefield you didn't actually reduce your attack surface. You are equally vulnerable to a bullet or an 0day. If it comes towards you, it will hit you.

What was reduced was their ability to TARGET you. That's not reducing attack surface.

I'm thinking about doing a write-up on risk reduction methods that break all these down, where:

- Obscurity is hiding a secret that reduces your resilience if you are discovered - Avoidance is limiting the likelihood that you'll be targeted - Hardening makes you more survivable if you are hit

How does that grab you? You think that's the distinction we need?

Ultimately it's all security because it's reducing risk, i.e. probability and/or impact. But the ways in which that is done are important to understand.

So running your SSH daemon on a port other than 22 will never be better than doing something above patching, a solid config, and key or cert-based auth?

I disagree.

If you're listening you are potentially vulnerable to an 0day that is simply far less likely to hit you on 2222 than 22---especially before you hear about others getting pwned and have time to patch.

If you harden to a certain point (like the list above), the next best thing MAY be to get out of the line of fire, either by moving your listener or putting up a portknocker system or something.

Think of security as reducing risk.

If you're invisible your risk is lower because you have a lower chance of being targeted.

This is wholly separate from what happens if you're hit. That's resilience, or armor, or whatever we want to call that.

But both reducing the probability of being a target and being more resilient once you are a target qualify as security.

I finally figured out the best way to respond to this.

The central concept of Machine Learning is self-improvement of the models that are used, based on data alone.

That is NOT the central concept of Statistics.

So yes, Machine Learning may be related to, part of, semantically linked, a subset of, or whatever you want to say there, but the fact that Machine Learning is designed to self-improve is (in my opinion anyway) the reason it should be considered a "new" way of evaluating the world.

Saying it's all just Statistics sounds a lot like calling consciousness "just another information processing mechanism" (yawn). I know it's not that big of a difference, but it's similar.

Self-improvement of data analysis models matter enough to warrant the separate name and the attention that comes with it.

Machine Learning is generically defined as a method of data analysis that automates analytical model building.

That's the part that's going to have the impact---automated improvement of the way Statistics is applied to data analysis.

Is that not major enough to be considered and discussed separately?

The "insight" would be in the form of a falsifiable claim, i.e. that ML will be bigger than the advent of statistics in terms of its ability to improve our understanding of the world.

It's a claim, nothing more.

Hmm. Maybe you're right.

But let me try to push back.

If we're trying to draw human-consumable wisdom about the state of the world from data, simply capturing a snapshot and then applying some basic analysis is one thing.

Creating self-improving mechanisms for doing this is another, even if the latter use statistics in the process. Perhaps in a similar way that Statistics uses Algebra yet is distinct in its description and capabilities.

I'm not convinced you're wrong here, just trying to talk through it.

Because lasting things are boring things that don't pay well.

I recommend using Shodan for this kind of stuff.

https://shodan.io

My opinion of what we should do with Snowden. Not quite a pardon, but almost.

https://danielmiessler.com/blog/what-us-should-do-edward-sno...

I submit my Vim Primer for your consideration.

https://danielmiessler.com/study/vim/

Going to download and see what Prezto stuff breaks when I upgrade.

This will surprise nobody on Hacker News.

The allure of startups isn't a guaranteed 20% more than a regular company's salary; it's the possibility of 1000% more.

Human error comes from a lot more than just pure assessment of whether something is cancer or not.

Fatigue, willpower drain, emotion, and many other factors can make a top-tier human expert into a mediocre one (or worse) in a way that is very hard to detect.

Even if computers aren't perfectly matching the ideal human (which they will inevitably pass, of course) they're still massively valuable in that they can maintain their level of expertise when humans cannot.

The point is to apply all your real security and then add obscurity afterwards to lower your risk even further.

Wear the armor, then engage your cloaking device.

Don't substitute the cloaking device for the armor, but also don't dismiss the value of not being targeted.

It's a great point, but I think corporations have shown they're willing to sacrifice long-term benefit for next quarter's numbers.

Your points are solid regardless. They're just not good criticism of what was created, based on his description.

Still, great ideas on your part.

I read "why not?" as a rhetorical intro, similar to "now presenting!"

I don't think it was a question.

I read "why not?" as a rhetorical intro, similar to "now presenting!"

It's almost like if he wanted to do this and only include salary information he should have called it 'a simple spreadsheet of salaries'.

That way we'd know at least.

Maybe because it's called a simple spreadsheet of salaries. Including that other stuff would make it not just about salaries, and not simple.

So other than that, spot on.

This piece skirts perilously close to ad hominem. There is no actual addressing of the central point: that few people will have the money and most will not.

Does anyone actually disagree with this? And if not, why write an attack against that position?

Here's my primer for anyone interested:

https://danielmiessler.com/study/tcpdump/

What I find fascinating about this is that the system was programmed by people who were presumably not as good at Go as Lee Sedol.

So if the first comment in this thread (about how it's a completely non-human approach) is true, it's really interesting that humans can enable computers to come up with non-human ways of solving complex problems.

Seems like a big part of this story, if I'm not being completely dumb here.