There’s a reason why they coined the term “couch potato”. Excessive TV watching turns you into a potato in time. Research has shown that when you are watching TV, your higher brain regions shut down, and activities shift to the lower brain regions. Your lower brain is set in a

Shocking fact: Millennial men are less likely to work than any other age and gender demographic in America.

Today, there are 500,000 young men missing from the U.S. workforce.

Research suggests video games & improved leisure tech plays a role in the problem. 👇 Thread:

Following the 2007 to 2009 recession, 25 to 34 year old men exited high school with fewer middle-skill job opportunities than years prior.

During this time, we saw an increased number of men living with parents & choosing unemployment over lower paying jobs.

It's estimated that 24M millennials live w/ their parents.

1 in 4 living in their parents’ home neither go to school nor work.

What's more surprising? 9 in 10 who lived with their parents a year ago are still living there w/ no plans to leave.

Economists are calling millennial men a lost generation.

According to economist David Dorn:

“If you get to the point where you’re turning 30, you’ve never held a real job and you don’t have a college education, then it is very hard to recover at that point.”

Economists suggest this choosiness is a generational trait.

Forbes interview w/ a high school educated man:

"I’m very quick to get frustrated when people refuse to pay me what I’m worth."
“People feel that they have choice nowadays, and they

SRI KUMARIAMMAN, KANYAKUMARI (TN)
#bharatmandir #navratri2021

Created by Maharshi Parasurama and also one among the 108 Shakthi Peethas in the world. The name Kanyakumari stands for Kanya(Virgin) and Kumari(girl).
Devi took this form to vanquish the demon Banasura
@GunduHuDuGa

Once, Banasura(grand son of Mahabali) obtained a boon from Shiva that he could be vanquished only by a virgin.
So, Devi took the form of a kumari and started meditating with the desire to marry Shiva at Suchindram.
Pleased Shiva fixed the midnight hour for marriage with Devi.

Devarshi Narada knew that the marriage would end the chance of killing Banasura. So, he assumed the form of a cock and crowed. Shiva returned back to Suchindram thinking that it was dawn.
Angered, Devi cursed that all food and articles meant for wedding turn into sand and shells

When Banasura heard about Devi’s beauty, he tried to take her by force. Devi, then, killed him and granted the wish of Devas to remain there always protecting them.
The murti of Devi stands with a rosary in hand. Her diamond nose-ring is said to be visible even from the sea.

Once an ancient mariner mistook it for a lighthouse and sailing towards it, his ship wrecked upon the Kanya Kumari rocks. In order to prevent such a tragedy from happening again, the eastern door of the shrine is opened only on five special occasions throughout the year.

1/ new essay: What's next for marketplace startups? Reinventing the $10 trillion service economy, that's what. Co-authored with @ljin18 https://t.co/fgKbrHTnH5. A short thread 👇

2/ Marketplace startups have done incredibly well over the first few decades of the internet, reinventing the way we shop for goods, but have been less successful services. It's bc services are complex, subjective, fragmented, and often in real life. Makes it hard

3/ There's been 4 major eras at making the service economy work online. The Listings Era, the unbundled Craiglist era, the Uber for X era, and the Managed Marketplace era

4/ Each era has added more value than the last, and utilized technology innovations, from internet to social / "read/write web" to mobile. The "Unbundling Craigslist" era was particularly epic at generating startup ideas

5/ The problem is, all the low-hanging fruit has been picked off. The techniques that got us to here won't get us to the next phase. So we have to do some pretty different things. That's why "Managed Marketplaces" have been a big deal - hire folks as W-2s, certify quality, etc.

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

🕵️‍♂️ How Google's PageRank algorithm works

The PageRank algorithm gives each page a rating of its
importance, which is a recursively defined measure of importance, based on if important pages link to it.
It's recursive because the importance of a page refers back to the importance of other pages that link to it

Here's how it works in practice:
1⃣ We start with some pages and crawl them for links

2⃣ Each page has 1/N points (where N as the total number of pages)

3⃣ Add points to each page for the amount of links to it, divided by the number
of links emanating from the sources of these links