He interned at the DoD when he was a college student. Maybe you are thinking of the NSA? Though the NSA is part of DoD, I don't think it was ever confirmed that Matt worked for the NSA.
Another trait, it took me a while to notice. I noticed the following facts about people who work with the door open or the door closed. I notice that if you have the door to your office closed, you get more work done today and tomorrow, and you are more productive than most. But 10 years later somehow you don't know quite know what problems are worth working on; all the hard work you do is sort of tangential in importance. He who works with the door open gets all kinds of interruptions, but he also occasionally gets clues as to what the world is and what might be important. Now I cannot prove the cause and effect sequence because you might say, ``The closed door is symbolic of a closed mind.'' I don't know. But I can say there is a pretty good correlation between those who work with the doors open and those who ultimately do important things, although people who work with doors closed often work harder. Somehow they seem to work on slightly the wrong thing - not much, but enough that they miss fame. -- Richard Hamming
> Over the weekend, he sent some messages that were inappropriate ... on Whatsapp
So, not during working hours, not using work-related tools, he send some inappropriate messages. It's not a big deal.
> these things aren't acceptable in the company we're building.
Sure. But this conversation did not happen in the company, not during work hours, and not using work tools. You were made privy of a very personal conversation happening between adults outside of work.
> The employee came to me in confidence
And you posted about it in great detail on Hacker News, making it a topic of water cooler conversation of about 80% of startups world-wide.
> she'll know and it will be a violation of the trust she placed in me
Exactly.
> So what do I do HN?
Preferably nothing at all. Don't make a big deal about what your employees do in their spare time.
> she shouldn't have to deal with it
She shouldn't have confided in you, because you can't seem to handle it. This dude is poison now. He may as well be fired. And your employee made it a business issue, which is a big thorny issue. You shouldn't have to deal with it, but now you do.
Such as the April 2013 Boston Marathon bombings. Or the rise of ISIL and Boko Haram in early 2013.
I still think the study was well executed, but the instant effect seems to disappear when you remove the 6-7 months before mid june 2013.
The long-term effect they claim to observe is more shaky. (I think one can find "statistically significant" groupings of say, Harry Potter articles, that show a similar peak and decline around the Snowden revelations.)
The marketing industry is moving beyond trackers and cookies. Hastened by the mobile/tablet revolution (users switch devices way more often) and the stricter privacy laws in Europe (anti-cookie laws).
Now they either require you to login to get the "mobile" experience, like Facebook or Twitter, or they use probabilistic statistics to identify you without cookies.
That guy reading a newspaper in the park with a paper bag over his head and 4 goons on the lookout, feeding us uninformative/unlikely data, that guy is with 90% certainty Jacques Mattheij.
(When cookie-tracking was more common we set up a cookie-swap program. Stopped after a few months out of security concerns.)
Indeed. But remedies exist. Statisticians can examine the validity of the data, analysts and detectives can be trained to interpret the results correctly, and social scientists can point out the dangers of relying solely on computer systems.
I have no strong view for or against breathalysers. I'll concede that there may be some errors in those tests. Does that completely invalidate these tools in judging if someone is too drunk to drive and may cause harm to self or others? Should we only opt for rigorous methods like drawing blood samples? My view is: no, we should not. These are valuable tools that work for the large majority of times and help save lives (at the inevitable cost of some errors and inconvenience).
In my world I believe in just intentions. Breathalysers are not introduced to imprison sober drivers, they are to combat drunk ("lazy, incompetent or malicious") drivers on our roads.
These methods are useful for catching the savvy criminals too. I am not ignoring that these systems are also useful to target activists and political dissidents. That's basically what they were build for in the first place (well, that and the terrorists, see DARPA LifeLog). It's just now that these tools are adopted by local Police departments.
A weapon stick can be used to subdue a suspect through non-lethal force, and it can be used to choke a peaceful protester. It will succeed in both tasks. It's not a stupid ineffective tool we should take away, because it can be used in bad ways. We should make sure to avoid the bad usage, and provide police with the best weapon stick possible for the good usage.
You assume that this system will be used to justify police brutality and that this system will be used by people who think that any black person is a "criminal". I have a higher opinion of the people who join the police. I rather reserve such judgment to the criminals themselves.
Apophenia is the _human_ tendency to see patterns in random _data_. Predictive _analytics_ is a machine that pulls non-random patterns out of data and presents this as _information_.
To say that other tools have a bad track record may count as a valid argument, but to me, it is a weak and fatalistic one. Judge each tool on its own merit or discard all tools as useless seems like an easy choice.
Predicting crime works in practice and theory. These models are not black boxes, they can be introspected. Bias can be detected and removed.
COINTELPRO was a program to infiltrate and disturb organizations that the state viewed as unwelcome. Monitoring social media activity is common detective work. The modern equivalent of an officer peeking over the fence in your back garden to see a stolen motorbike. Now they can use Google Maps for that. This is public information: The criminals feel free and safe enough to post and brag about their crimes on Facebook.
Removing or combating criminal elements in any community will improve that community, regardless of skin color. Black youth is helped, not suppressed, when gang recruiters are identified and punished.
Predictive tools are already used as probable cause. Prisoners in Guantanamo Bay can get a brain-wave reader test. This device will tell you what someone is thinking about and may reveal the plans of future terrorist attack.
They probably needed the software for this indeed. My local police department did not even cooperate with other local police departments. Profiles and reports for a criminal vanished, once they settled somewhere else. That is just throwing information away. This problem still exists in Europe. A sex offender from Belgium can move to Germany and become a janitor at a school.
Software is expensive to develop, but once developed it is actually very cost-effective. It can be copied over to other departments at a fraction of the cost of a detective salary.
Software will continue to eat the world. Criminals use new technology to stay ahead of the police, so the police has to stay up-to-date too. Data mining software helps the police do their jobs more efficiently and honestly. Factors don't lie, machine learning actively combats bias. While human intuition can be flawed and biased.
There is a danger than humans grant too much authority to computer systems, but there is also an opportunity to remove or dampen cognitive bias.
There may both be some cognitive dissonance and a difference between open government data (funded with tax money) and closed company data (funded with revenues).
- It is not required for a company to facilitate it.
- It is not a priori allowed to use company resources to discuss your salary.
- As a company you do not have to reward that unprofessional and irresponsible behavior.
What this Google employee did was obviously toxic for the culture. As a manager, deciding who gets the raise and who doesn't is already a hard decision (as Ben Horowitz says: Both actions will make some people feel left out), without your employees trying to undermine that.
I do not think this had anything to do with her being a female. If a male had set up an internal mailing list questionnaire he would have probably found himself in the exact same situation (people higher up do not like it when you are a trouble maker). How she leaves it ambiguous and yet hints at pay inequality for women at Google is lacking class and tact.
Google wants more equality. All things equal they hire minorities. If HR analysts find statistically significant inequality I doubt they would ignore that.
You don't use internal mailing lists to promote your religion, just like you should not use internal mailing lists to promote thorny political/social issues.
I am all for positive discrimination. For instance I do not think it is a bad idea to have all-women hackathons -- I do not think that's unfair. I furthermore wish you all the best with the business and I get the pitch: Besides the mainstream movies there is an entire untapped market of non-Hollywood movies with a lot of potential for growth. This is, after all, a business, not a political party (please avoid this debate entirely).
It is not racism. It is (positive) discrimination.
If we keep everything the same, but target a different demographic this service could be called: WhiteStream, a Netflix for whites and white-culture movies.
"The persuit of happiness" is classified as an Afro-American movie. Why is that? Does it portray black culture? Or is it because one or more actors have a black skin? I think it is the latter, because the theme from the movie is universal and transcends race.
James Bond is a hero, not because he is white, but because of his actions. Asking why there is no black James Bond is forcing race on an old fictitious English character (why do you care about the skin color of your heroes?). How to classify Morgan Freeman movies on the Afro-American scale? Add "Driving Miss Daisy" to the catalog, but leave out "Evan Almighty"?
"I’m going to stop calling you a white man. And, I’m going to ask you to stop calling me a black man. I know you as Mike Wallace and you know me as Morgan Freeman. You don’t say, "Well, ahem! This white guy named Mike Wallace." You don’t say it."
I firmly believe this business model can work. I know of a local business that is going into its 6th year of operations. Cleaning houses is a large part of their revenues. They match house cleaners, handymen, grocery shoppers, cooks and assistants with professionals starved for time. The model is subscription-based and they have some large companies which give out these subscriptions as a bonus/perk to their employees.
Perhaps Homejoy expanded too fast? They overdosed on funding? Deadlines and progress meetings became too dreadful? Or maybe there are regulatory issues they could not resolve?
I think there is more to this than a shoddy business model imitating Pets.com.
I've had complete opposite experience. Do the people who hire for this kind of work often bet on non-PhD candidates? Do they trust themselves to separate the wheat from the chaff?
Don't you want a colleague who is able to mention seminal papers for specific problems? Who is able to read and understand these papers and can distill useful features and optimizations from them?
People with PhD who go into business, usually end up in the better positions. They hire other PhD's for the good positions to keep the signal (mastery of the content) stronger.
As someone who did a lot of work with data I have little problem with my usefulness, but a lot of problems opening doors to the really interesting data companies (lacking a proper academic network). I wish I had gotten that PhD, because right now applying to Google, Microsoft, Facebook, Yahoo or eBay for data science positions makes me look like a fool.
I think you are right in that it sells many people short, but then again having no good academic credentials is selling yourself short.
Data science is not like security. There it is more accepted that good engineers/researchers do not necessarily have the best accreditation. It seems that data science/engineering is turning around to this though.
It's not that autodidacts can not build bridges, it is that the people with the data and money do not want their bridges build by autodidacts.
As a developer is it a good or a bad thing to have a high Truck Factor? There seems to be a trade-off. Management and business wants low Truck Factors, but talented developers do not want to be replaceable code monkeys. Do projects with a low Truck Factor lose specialist knowledge?
Then I wonder when the Truck Factor applies and if you would always want to lower it. For a few projects I worked on invoking this Truck Factor irked me. A low Truck Factor is insulting to your skills (for you ten others), but a high Truck Factor can be too. Who likes to hear that management is already planning to continue your work after you are found to be roadkill?
I don't even want to hear this metric in a start-up, because how would you even optimize this metric? Be glad you have something worth dying over.