Fat and Thin



Tobacco stained face

Yellowed teeth, stink of old neglect

Fat fat cheeks, like a little girl

That you are.

Fat and thin, short and oddly shaped

Little girl that you are

Ugly might as well be your middle name.

“No don’t look so hurt, disappointed”

: Those rights aren’t for you

“Cut her out they say”

It’s too harsh to bear

Your words hurt more than the blade.

Pictures are for pretty girls

Cameras are for good looks

And your white speckled additional eyes might not have let you see,

But you are neither

So stay stay little girl

Fat and thin.

Disorder, dismay and dismal stalked the photos of ‘mares

The lens saw too much, the mirror never cared

So I urged you

To hide, hide little girl

As long as you are the same.

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Progetto Sunrise, un nuovo modo per scoprire gli ambienti sottomarini | LifeGate


Le acque, gli oceani sono la nostra vita. Eppure ne sappiamo ancora troppo poco. Ecco la sfida del progetto Sunrise: creare una rete di comunicazioni sottomarine che ci permetta di conoscere e proteggere questo delicato habitat.

Source: Progetto Sunrise, un nuovo modo per scoprire gli ambienti sottomarini | LifeGate

How do you recruit data scientists?


How do you recruit data scientists? by John Salvatore

Answer by John Salvatore:

Step 1: Identify your needs.

For any hire, it's wise to start by identifying your needs. If you're unclear on exactly why you need a data scientist then you probably don't need a data scientist, or you're the wrong person to be making this important hiring decision.

It may help to start by asking yourself a few questions:

Why am I looking to hire a data scientist?
What will this person do on a day-to-day basis?
What will this person be expected to deliver?

Step 2: Prioritize

Prioritize the skills & knowledge that are critical to deliver on the expectations defined in step 1. I like to start by making a list of all the qualities an ideal candidate would have. If you don't have an in-depth knowledge of machine learning yourself, you'll definitely want to perform this step with someone who does.

Make sure you don't neglect culture fit and interpersonal characteristics. As with all hiring, you'll rarely find someone who fits all the requirements; finding the right fit is about prioritizing well and making smart tradeoffs. If a candidate is the field's leading expert on an an algorithm critical to your success, but is a disrespectful prick, you'll have to split hairs somewhere and make compromises.

If you're interested in a simple way of doing this empirically, I've provided a method below. If you're not, you can skip to the next paragraph.

Create a square matrix of these characteristics as both rows and columns. For each column, assume the person has the trait, and for each row, assume the person lacks the trait. Since you can't be both, fill the diagonal with zeros. Then, for each pair in the matrix, put a zero if you wouldn't and a one if you you would hire the person who has the column trait but lacks the row trait. When you're done with the whole matrix, sum along the columns to get a row vector, and along the rows to get a column vector. Transpose one of the vectors and subtract the "does not have trait" vector from the "has trait" vector. This effectively assigns positive value to the trait if you're likely to hire the person when the trait is present, and penalizes the value of the trait if you'd hire someone despite their lack of the trait. The highest number in the vector of trait values will corresponds to the highest trait you should prioritize, while the lowest number should be your trait with the lowest priority. Later, when you assess the candidate for each of these characteristics (see the next step), you can objectively compare candidates using a single number by taking the dot product of this "trait value" vector with their scores assigned to each trait.

It's important to get the prioritization right early on, so that during interviews you can avoid wasting time deciding what's important. Instead, you'll be able to effortlessly apply these heuristics without getting bogged down in analysis paralysis.

To narrow down the list of traits, ask yourself questions like 'What domains of knowledge are relevant to my product?' For example, a solid background in image processing and feature extraction algorithms may matter a lot if you're building an emotion recognition system from real-time video feeds of faces. It may be less relevant if you're a hedge fund trying to predict stock price fluctuations from tweets.

Step 3: Operationalize

For each of the traits, identify a quick, efficient way to assess whether your candidate sufficiently meets the criteria. Ideally, you'll want to use or develop a standardized set of questions or assignments designed to test the ranked list of traits you identified in step 2.

Try to  create questions that are clear and concise, but effective. For example, if you want to design a question that will specifically screen out applicants that don't have the "big picture" spatial intuitions underlying the decision boundaries of different classification algorithms, you might have a multiple choice question with a series of 2D plots requiring the applicant to pick the decision boundary most likely to be generated by an SVM classifier with a RBF kernel. Be sure to tune the depth of the questions to your needs.

Step 4: Automate

Finally, use a tool like HackerRank for Work as a pre-screening tool to automate this set of questions / problems. Automation has numerous benefits, and will pay dividends for years down the road in reduced work.

For example, automation will help reduce the amount of manual scoring, screening, and awkward under-qualified interviewing you'll need to do. As an added bonus, it may help eliminate the contribution of interviewer biases to ensure that you're hiring the most talented people, and not just the best interviewers.

Step 5: Source talent

Reach out to university career services or CS departments. Attend career fairs, hackathons, and meetups. Increase your visibility among the most talented candidates. If you're working on cool stuff, the best candidates will gravitate to you, but only if they know who you are. If you're unable to reach talent through traditional avenues, you may want to try sourcing candidates through online competitions like http://www.kaggle.com/ or https://www.hackerrank.com/.

Good luck!

How do you recruit data scientists?

What are Google’s most surprising product failures?


What are Google’s most surprising product failures? by Lewis Lin

Answer by Lewis Lin:

Here’s my list of Google product failures:

  • Google Wave. It could have been Slack.
  • Orkut. It could have been Facebook.
  • Google+. It could have been Snapchat or Whatsapp.
  • Google Hangouts on Air . It could have been Facebook Live or Periscope.
  • Google Answers. It could have been Quora.
  • Google Catalog Search. It could have been Pinterest.
  • Dodgeball. It could have been FourSquare or related social networking site.
  • Google Notebook. It could have been Evernote.
  • Google Page Creator. It could have been Squarespace.
  • Google Video. It wasn’t YouTube.
  • Google Glass. It should have waited until it was Google Contact Lens before it launched in the consumer market.
  • Google Knol. There’s plenty of information that can be Wiki-fied like dev documentation for open-source projects. Cloning Wikipedia was not the first thing that needed to be Wiki-fied.

Why did these products fail?

It’s not so much that the Googlers were lazy or incompetent. I’m positive they were hard working and committed. It’s more that product design is so hard that even the best companies can’t succeed 100% of the time.

Craig Lawrence pushed me to think a bit harder as to why Google failed. Despite hard work and commitment, here are reasons why Google failed so often:

  1. Lack of vision. There are only so many people who can predict the future. Sundar Pichai was one of those rare individuals who saw the Chrome browser and Chromebook OS opportunity, despite daunting odds and endless customer naysaying.
  2. Lack of resources. When I was at Google, I believe Google Notebook had half an engineer working on it a few months out of the year. Hard to defend the fort if the guard tower is empty.
  3. Lack of insight. The Google Wave and Google Glass team worked hard, but both teams missed the critical insight that others realized. That is, Slack realized work messages belong to channels. And Google Glass was too dorky to wear in public.
  4. Lack of focus. Google+ included everything but the kitchen sink. It was an authentication service. And a commenting plug-in. And an address book. And a multi-user video conferencing feature. It felt and was designed by committee.
  5. Lack of trying. I believe Marisa Mayer once said, “There are great (product) ideas that are executed poorly.” In other words, we shouldn’t conclude an idea is flawed because it failed. After Google Answers shut down, it was wrong to conclude that the Internet didn’t want a Q&A service. It was more appropriate to conclude that Google Answers just implemented Q&A the wrong way.

What’s the best way to avoid product failure?

From an organizational perspective, the best solution I’ve seen is the spinoff.

I’ve seen Expedia achieve good success after it was spun off from Microsoft.

And Expedia, apparently having seen the spinoff tactic work successfully, helped TripAdvisor flourish by spinning them off as well.

What are Google’s most surprising product failures?