simran

marketing × AI × internet culture.

currently in my diet coke phase.

[Vivek Raju @vivekraju93]

I have been laughing for 2 hours

[simran @dietcokesimran]

@dasrebel ad hominem nice

[simran @dietcokesimran]

i love his directness

[Deepinder Goyal @deepigoyal]

Dear Concerned Indian Doctors,

I see your rage against Temple on X. I have a few points
to make:

1. First and foremost, a business lesson: if Temple were a scam, and any of what we said weren't true, people would know it in just a few days of using Temple. We have 14-day, no-questions-asked returns (which even Apple doesn't offer in India). If the product doesn't work, it fails out of the gate and the company dies before recovering a tenth of its R&D.

2. We have already spent $30m building Temple, $20m out of which is my own money. Selling a few thousand devices recovers a small fraction of this investment. A scam that pays nothing is not a scam anyone would run.

3. I left the Eternal CEO role to chase purpose with Temple. This is not about money. I have enough of it to last for generations. My incentives are fully aligned with getting the science right. I am not staking a reputation earned over 20 years on making a quick buck.

4. Our employees bought into our round a few months ago. The round we're raising now is mostly our customers. Scams don't build that cap table.

5. Most wearable companies start with internal studies. Whoop shipped its band in 2015, and the first published validation followed a few years later. We hold ourselves to a higher standard; pre-prints are a step prior to peer reviewed publications, and they are coming up.

6. At some point you have to stick your neck out and say: we believe in our science, and the product is now valuable enough for people to pay for and use. We believe we are there. More validation continues in public.

7. We are confident that we are building something that the world is going to be positively astonished with. We stand by our science and our methods. Each of our claims is easily verifiable by individuals and institutions alike, and you will see a lot of independent studies come up on their own when the device becomes available to use.

Last but not the least, the most ground breaking features in Temple are not even public yet. And some of them, thankfully, have nothing to do with doctors 🤭

Hope this addresses some or all of your concerns. If not, keep raging 😘🤗

[simran @dietcokesimran]

@dasrebel i don’t have no horse in the game bro. i think our definition of arrogance is different. idc about this enough to defend him or attack him.

[simran @dietcokesimran]

@dasrebel https://t.co/YxsHNou2sK

[Laza @Senor_Mitch]

@MileseJonny Who are you again and why are you speaking to me like we know each other?

[simran @dietcokesimran]

@dasrebel false equivalence?

[simran @dietcokesimran]

@dasrebel idk that’s kinda harsh but whatever

“works doesn’t hate xyz” type of sentences are vacuous

you know what the world doesn’t hate enough? sexual violence

[simran @dietcokesimran]

what other kind is there anyway

[simran @dietcokesimran]

i wonder if lack of meta cognition is a pre-req for acting/performance.

[Gurpriya @GurpriyaSidhu]

I love Kareena Kapoor as an actor, I think she is a once in a lifetime talent, and so I never miss any of her interviews.
I am also curious about her as a person because she is nonchalant for someone who is also very good at her craft.
But the thing is, she is not articulate in her interviews, and that has bothered me for sometime. Barring a few with Anupama and Haroon where she talks about her craft more deeply, she does not give much to chew on in the rest of her media interactions. The sentences are not well formed, there's a lot of this and that, and filler words.
Only now I am beginning to realise that she thinks in emotions and dialogues. She tends to describe a scene or an experience or a decision, in how she felt, what she said, how she reacted, what someone else said, instead of explaining her thoughts about it. She does not like to think as much as she likes to feel. And I think that's what makes her amazing to watch as an actor, but not someone who is always fun to listen to as an artist.

[simran @dietcokesimran]

embodied intelligence

[Akanksha @akankshaluvcats]

master of none, larper of all.

[simran @dietcokesimran]

looker dashboards go brr

[Jeff Weinstein @jeff_weinstein]

You truly do not understand a metric until you've looked at it every. single. day. for six weeks. (Every time I've thought otherwise, I've been wrong.)

[Paul Crowley @ciphergoth]

Demanding that people read your work is like demanding they have sex with you. You are involved in a seduction, and if your seduction fails that's on you.

[Sidu Ponnappa @ponnappa]

no amount of cautious exploration teaches courage

[“paula” @paularambles]

everyone wants a high aura job until they find out it requires high aura hours

[Shweta Kukreja @ShwetaKukreja_]

Canva is laying off 22% of its workforce which is 1271 employees, and this is what their CEO said.

We need more founders like thissss.

[Andrej Karpathy @karpathy]

There are a few notable counter-examples (e.g. HAL) but this is basically right for a lot of pre-2000 sci-fi.

I think getting AI right from e.g. somewhere in the ~1970s would require a fairly complicated set of inferences, probably the biggest things would be:

Image the world in 50 years.
1. "Digital knowledge work / computing". The vast majority of knowledge work (which makes up a very large fraction of the economy) goes digital. As computers get cheaper, much of society’s work happens on screens, over billions of machines in homes and offices.
2. "Internet". Similar to ARPANET (and see Licklider’s “Intergalactic Computer Network” (1963)), this creates demand for a global network of computers where people work, argue, teach, joke, and socialize.
3. "Big data". This creates a very large, compounding repository of digital information, with traces of how people reason, how they create, what they know etc.
4. "Statistical learning". Trying to program intelligence by hand is extremely difficult. But Shannon's n-gram approximations of English (1948) show examples of how language has statistical structure that can be "learned". You could imagine doing so over extensions of cybernetic computing (e.g. in the style of Rosenblatt's Perceptron 1958). So instead of programming thought, we could fit a statistical model to the record.
5. "LLMs". This might lead to something along the lines of ELIZA (1966), but with enough data (per 1-3), and enough compute (see Moore's law 1965), the imitation might become of extreme hight fidelity to the point that it's economically extremely useful.
6. "Scaling laws". These networks may demand a huge amount of data / computing resources and expertise to train. It's plausible that the more of each you have, the better your ELIZA gets. The economics concentrate, where a few organizations build a few minds and then deploy copies of them digitally.

Something like that. Basically, it's a lot of leaps. You have to start with computers, to internet, to big data, to big compute (Moore's law), which then allow large-scale statistical learning approach based on neural networks, and then posit scaling laws, and then imagine the form factor of language models as a lot more sophisticated ELIZA. And you'd have to short everything else, most predominantly I think the idea that we'd figure out how to program robots by hand.

[counterculture barbie @thebigHBIC]

@qisty Exploit all your privileges as a woman