What are some missing features from today’s social networks?

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What are some missing features from today's social networks? by Max Hilmer

Answer by Max Hilmer:

Each successful social network explores a certain element of social connection. They have a bouquet of well thought-out features, accompanied with a smart design, marketing strategy, and brand reputation. All this comes into play in order to make a unique user experience that convinces people to sign-up and keeps them coming back. In an interview with Neil Degrass Tyson, Biz Stone describes social networks more as an art form, and focuses on the feeling you get from using one. The Impact of Twitter on Society with Biz Stone – StarTalk Radio Show by Neil deGrasse Tyson"

Facebook

When I was in High School, if you didn't have a Facebook, you were missing out on an important paradigm of social interaction.  Even still, the majority of young teenagers use Instagram now, even NASA posts exclusives to the app; NASA Teams Up with Instagram To Debut Pluto Surface Photo

These are the 2014 statistics from the Social Networking Fact Sheet of the Pew Research Center.

As of September 2014:

  • 71% of online adults use Facebook
  • 23% of online adults use Twitter
  • 26% use Instagram
  • 28% use Pinterest
  • 28% use LinkedIn

Facebook Features, What's Missing?
  A new feature I'm excited to try is the ability to send and receive money via private messages. There are no fees, and it's as easy as sending a text. In the future, I'd like to see Facebook work on making it easier for people to easily crowdsource projects, kickstarters, and charities.

Facebook's digital assistant, "M", is another new feature that looks promising. Dave Marcus unveiled it as follows;

"Today we're beginning to test a new service called M. M is a personal digital assistant inside of Messenger that completes tasks and finds information on your behalf. It's powered by artificial intelligence that's trained and supervised by people.
Unlike other AI-based services in the market, M can actually complete tasks on your behalf. It can purchase items, get gifts delivered to your loved ones, book restaurants, travel arrangements, appointments and way more."

Twitter

Tagging on Twitter is pretty incredible. It's helped users raise the public voice, challenge corrupt businesses and officials, and even organize revolutions. Twitter Revolution: How the Arab Spring Was Helped By Social Media

Because of the way Twitter can raise the people's voice, public entities have less autonomy over their brand reputation. Advertising is said to be more democratic, even conversational between buyer and consumer. Twitter Marketing

Twitter, What's Missing?

Everything you need to know about Twitter's missing features, or what it could potentially be, you can find here; Twitter Live — STARTUPS + WANDERLUST + LIFE HACKING

And here – Introducing the Tweetstorm ™ — STARTUPS + WANDERLUST + LIFE HACKING

Or to see what Twitter's working on behind the curtains – Twitter's new 'Project Lightning' could dramatically change how people experience live events

Anti-Features

Sometimes, it's not what you can do, but what you can't do that matters, and restricting a user's ability has been a continual source of success in social networks.

  • Snapchat makes everything disappear, mimicking the style of a real-life conversation.
  • Twitter's 140 character limit turns posts into individual thoughts. A Tweet is like a single cell, the collection of which forms a multicellular organism that acts as the people's voice.

What are some missing features from today's social networks?

Fat and Thin

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AllOurRambles

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

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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?

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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?

Gabriella Mereu: se la conosci la eviti

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Contro le pseudoscienze

CatturaChi è?

Gabriella Mereu, classe 54, laureata in Medicina e Chirurgia nel 1983, è stata recentemente radiata dall’albo con un sospiro di sollievo da parte della comunità scientifica. Un sollievo che è stato, tuttavia, di breve durata, dopo che la Mereu ha impugnato la sentenza, ottenendo di essere riammessa all’albo. Insomma, una diatriba infinita. Ma perché l’ordine si è sollevato contro di lei? I morivi sono più che validi.

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