Untitled

[libr-tumbl-alternative]

Announcing Libr, the Tumblr replacement

Announcing Libr, the Tumblr replacement

As most of us know by now, Tumblr is not what it used to be. It’s no longer the home of radical free expression for art, alternative communities and dangerous ideas.

Everyone on Tumblr is affected by the new content policies, regardless of what content they post or view. Traffic is down 30% and the community is fracturing. Lots of people are upset and the downfall of Tumblr is being covered extensively in the media.

Like you, I missed how Tumblr was and the freedom it provided, so I built Libr to create a new, safe space for the community. Our community was never about Tumblr and always about us.

Like Tumblr, Libr allows you to filter what types of content you want to see and not see. But unlike Tumblr, we don’t do the filtering without your consent and opt in.

If you’re a content creator that supports freedom, join Libr and post as much content as you can :).

Because Libr is still small, your art will stand out more here than on Tumblr.

If you’d like to see some of the cool stuff that’s on Libr already, sign up :).

Libr will be the new hub for art and alternative communities. Let’s hope the community grows even bigger than it was on Tumblr.

To help that happen, please spread the word about Libr in your communities and everyone reblog this post!

Join Libr: https://librapp.com

[checkoutmycode]

Machine Learning Neural Network Cheat Sheet!

[aiweirdness]

The neural network generated pickup lines that are actually kind of adorable

So it occurred to me that the same neural network framework I’ve trained on recipes, Pokemon, superhero names, and Irish tune names might be able to write pick up lines as well.

Gathering the dataset was much more painful than I had expected - I hadn’t really read many of these before, and most were obscene, or aggressive, or kind of insulting. I began to regret the whole project.

But although the neural network figured out the basic forms “You must be a … because….” or “Hey baby, wanna…” it never learned to generate the worst lines - most of these were based on wordplay that it didn’t have a chance of reproducing. 

Instead, it began to generate lines that varied from incomprehensible to surreal to kind of adorable:

Are you a 4loce? Because you’re so hot! I want to get my heart with you. You are so beautiful that you know what I mean. I have a cenver? Because I just stowe must your worms. Hey baby, I’m swirked to gave ever to say it for drive.  If I were to ask you out? You must be a tringle? Cause you’re the only thing here. I’m not on your wears, but I want to see your start. You are so beautiful that you make me feel better to see you. Hey baby, you’re to be a key? Because I can bear your toot? I don’t know you. I have to give you a book, because you’re the only thing in your eyes. Are you a candle? Because you’re so hot of the looks with you. I want to see you to my heart. If I had a rose for every time I thought of you, I have a price tighting. I have a really falling for you. Your beauty have a fine to me. Are you a camera? Because I want to see the most beautiful than you. I had a come to got your heart. You’re so beautiful that you say a bat on me and baby. You look like a thing and I love you. Hello.

[mathblab]

Why Study Statistics? Careers in Mathematics

Thanks to the growth of computing power, statistics is a rapidly growing field but what is it good for?

Artificial Intelligence, Big Data, and Machine Learning

The ubiquity of computers have made piles of data readily available and people are using them to make useful predictions. This “big data” is changing the way we use statistics and it has given rise to some really cool applications:

1–Netflix Ratings. Predictive analytics uses data from yours and others’ viewing and rating patters to make predictions for content you haven’t even watched yet.

2–Facial Recognition Software. Photos are data too. Developer use a boat-load of sample photos to “train” a computer program to identify people’s faces. This is called machine learning, a form artificial intelligence that uses statistics to develop computer algorithms. These artificial neural networks are also used in Alexa, Siri and the thumb print reader on your iPhone.

Medicine

3–Medical Treatments. Survival analysis is used to find treatments that predict the highest levels of success for patients given their physical characteristics and medical backgrounds.

Engineering

4–Dependability. Reliability analysis is used to determine how long one can expect a part to last given temperature, thickness, material, etc. Applied to a system, analyses can determine which component is most likely to fail first.

Business

5–Marketing. Who would like our product? Who should we gear our ads to? These are questions advertisers want to know. Data mining is being used to sift through piles of customer data to find patterns and identify most likely customers. It isn’t all about numbers either, text mining uses statistics to find patterns in text-based data such as Amazon customer reviews.

6–Insurance Quotes. Actuaries use statistics to weigh risk and determine the optimal price to offer their insurance.

Social Science Research

7–Education. I thought I’d include this one because this is how I use statistics. I use statistical modeling to determine factors that lead to desirable student outcomes. I explore data to determine which teaching practices, after-school programs, courses, attitudes, etc. predict success in mathematics.

This is just a taste of what statistics can offer. I was compelled to write this because I feel statistics doesn’t get a lot of attention. Probably because of the fact that most people are lucky to take even one stats course in high school, but that’s an issue for another day.

[aiweirdness]

The neural network has bad ingredient ideas

I’m training a neural network to generate recipes based on a database of about 30,000 examples, and one great (not great?) thing about it is it comes up with new ingredients that I’m pretty sure aren’t in the list:

1 ½ teaspoon chicken brown water 1 teaspoon dry chopped leaves 1/3 cup shallows 10 oz brink custard ¼ cup bread liquid 2 cup chopped pureiped sauce ½ cup baconfroots ¼ teaspoon brown leaves ½ cup vanilla pish and sours ½ cup white pistry sweet craps 1 tablespoon mold water ¼ teaspoon paper 1 cup dried chicken grisser 15 cup dried bottom of peats ¼ teaspoon finely grated ruck

And this is a thing that it came up with repeatedly for some reason, and was quite adamant that I use:

1 cup plaster cheese

[theverge]

Google’s new browser experiment lets you learn about basic AI

Just how does machine learning work? You’ve probably read a primer or two on the subject, but often the best way to understand a thing is to try it out for yourself. With that in mind, check out this little in-browser experiment from Google named Teachable Machine. It’s a perfect two-minute summary of what a lot of modern AI can — and more importantly can’t— do.

Teachable Machine lets you use your webcam to train an extremely basic AI program. Just hit the “train green/purple/orange” buttons, and the machine will record whatever it can see through your webcam. 

Once it’s “learned” enough, it’ll output whatever you like (a GIF or a sound effect or some speech) when it sees the object or activity you trained it with. Read More

[weelitleladybug]

Alternative Music Zodiac Edition:

Aries: Pumped up kicks (Foster the people)

Taurus: Old boy (Brick + Mortar)

Gemini: Stolen Dance (Milky Chance)

Cancer: R U Mine (Arctic Monkeys)

Leo: Riptide (Vance Joy)

Virgo: Cigarette Daydreams (Cage the Elephant)

Libra: Take me to church (Hoizer)

Scorpio: Dangerous (Big Data)

Sagittarius: Skinny love (Birdy)

Capricorn: Do I wanna know (Arctic Monkeys)

Aquarius: Sugar, We’re goin’ down (Fall out boy)

Pisces: I wanna get better (Bleachers)

[crychan]

lauren shippen is the stan lee of podcasts

[diiscotechjuliet]

Visit Original Link

Dangerous | Big Data (feat. Joywave)

it must be fate, I found a place for us i bet you didn’t know someone could love you this much

[wolframalpha]

An Intro to Neural Nets

Neural Nets aren’t the same as brains, just inspired by them. [image: woman’s profile & network graph] Like how birds inspired planes, or burrs inspired Velcro.

A neuron in the human brain hears signals from thousands of other neurons, listens to each excitatory or inhibitory input, and synthesizes the great mass of information from all of them to yield… [image: neuron illustration]

… blip , or no blip. [image:  line graph with a spike on the y axis labeled “Fig. 1 ‘Blip.’” and a line graph with a slight bump on the y axes labeled “Fig. 2 'A lack of blip.’”]

Neural nets in machine learning are like that, just cleaner. [image: network graph with arrows]

After all, neurons in the human brain had to cobble together their structure over millennia of evolution, using neurotransmitters, ion channels, and precise voltage control to achieve… [image: illustration of an ion channel]

Keep reading

[hikimi-deactivated20250926]

Big data and machine learning with a human touch. 

[nixcraft]

Lmao @ Uber: That is not AI

(Via Nick)

[nixcraft]

Lol: Tech buzzwords explained.

Via https://twitter.com/random_walker/status/976836626121977858

[ibmblr]

Can we teach computers how to smell?

Researchers from IBM and Rockefeller University are trying to sniff out the answer. Smell may be the least understood of the five senses, so the team trained software to identify scents in order to learn more about how our brains perceive them. Their results prove for the first time that a scent can be predicted based on its molecular structure. Ultimately, as their database of scents grows, the predictions will become even more on the nose.

Learn how they did it →

[iloveluek]

[uncolored5sos]

[aiweirdness]

The neural network generated pickup lines that are actually kind of adorable

So it occurred to me that the same neural network framework I’ve trained on recipes, Pokemon, superhero names, and Irish tune names might be able to write pick up lines as well.

Gathering the dataset was much more painful than I had expected - I hadn’t really read many of these before, and most were obscene, or aggressive, or kind of insulting. I began to regret the whole project.

But although the neural network figured out the basic forms “You must be a … because….” or “Hey baby, wanna…” it never learned to generate the worst lines - most of these were based on wordplay that it didn’t have a chance of reproducing. 

Instead, it began to generate lines that varied from incomprehensible to surreal to kind of adorable:

Are you a 4loce? Because you’re so hot! I want to get my heart with you. You are so beautiful that you know what I mean. I have a cenver? Because I just stowe must your worms. Hey baby, I’m swirked to gave ever to say it for drive.  If I were to ask you out? You must be a tringle? Cause you’re the only thing here. I’m not on your wears, but I want to see your start. You are so beautiful that you make me feel better to see you. Hey baby, you’re to be a key? Because I can bear your toot? I don’t know you. I have to give you a book, because you’re the only thing in your eyes. Are you a candle? Because you’re so hot of the looks with you. I want to see you to my heart. If I had a rose for every time I thought of you, I have a price tighting. I have a really falling for you. Your beauty have a fine to me. Are you a camera? Because I want to see the most beautiful than you. I had a come to got your heart. You’re so beautiful that you say a bat on me and baby. You look like a thing and I love you. Hello.