Everyone loves a good burger.

But what if we told you that the same burger moonlights as an economic tool?

A thread...

Well, back in 1986, The Economist introduced the “Big Mac Index” to evaluate the value of currencies across countries via McDonald’s ubiquitous Big Mac burger & this concept came to be known as Burgernomics.

So how does it work?
Suppose a Big Mac costs $5 in the US and 20 Yuan in China. The Big Mac exchange rate would then be 5:20 or 1:4.

However, if the actual exchange ratio was 1:5, investors might predict that the Yuan is cheaper/undervalued by 20%.
Hence, the burger based index is premised on the idea that burger prices around the world can help you judge whether a currency is too cheap or too dear.

Why just the Big Mac though?
Well, for starters, McDonald’s has a phenomenally wide reach in the world, and the survey itself includes ~120 countries

Moreover, the Big Mac is mostly produced to the same specifications around the world & hence, the costs of producing the burger should be relatively standard.
However, in India, McDonalds doesn’t sell the Big Mac due to local sensitivities towards beef.

So the Economist takes into account the Maharaja Mac (which has chicken) as India’s entry to the Big Mac Index.
While The Economist itself states that the Big Mac index “should be taken with a grain of salt”, many economists say that it's roughly accurate, since the pricing of a Big Mac takes into account local costs of raw materials, taxes, etc.
And unorthodox, as it may be, it has now become a popular way of comparing currencies against each other & is widely used by traders in the forex market.

What are your thoughts on this?

More from All

How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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@franciscodeasis https://t.co/OuQaBRFPu7
Unfortunately the "This work includes the identification of viral sequences in bat samples, and has resulted in the isolation of three bat SARS-related coronaviruses that are now used as reagents to test therapeutics and vaccines." were BEFORE the


chimeric infectious clone grants were there.https://t.co/DAArwFkz6v is in 2017, Rs4231.
https://t.co/UgXygDjYbW is in 2016, RsSHC014 and RsWIV16.
https://t.co/krO69CsJ94 is in 2013, RsWIV1. notice that this is before the beginning of the project

starting in 2016. Also remember that they told about only 3 isolates/live viruses. RsSHC014 is a live infectious clone that is just as alive as those other "Isolates".

P.D. somehow is able to use funds that he have yet recieved yet, and send results and sequences from late 2019 back in time into 2015,2013 and 2016!

https://t.co/4wC7k1Lh54 Ref 3: Why ALL your pangolin samples were PCR negative? to avoid deep sequencing and accidentally reveal Paguma Larvata and Oryctolagus Cuniculus?