In March 2020, we redoubled our R&D efforts. I told our engineers we will invest in this however long it takes and produce a world-class platform.
1/ We just concluded our #ZohoDay2021 online event for industry analysts, with record participation.🙏
We hosted this on Zoho's own event platform with our home grown audio-video framework, a combination of @ZohoBackstage, @ZohoShowTime and @zohomeeting.
I will explain this.
In March 2020, we redoubled our R&D efforts. I told our engineers we will invest in this however long it takes and produce a world-class platform.
We used our own products and diligently reported issues encountered to engineers.
Today, we have concluded a successful event, due to painstaking efforts from our engineers and our events team who stood by them.🙏
More from Sridhar Vembu
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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)
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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I have spent 1.5 months on this app. You can make more $ in 2 days.
🤷♂️
I'm still happy that I launched a paid app bcs it involved extra work:
- backend for processing payments (+ permissions, webhooks, etc)
- integration with payment processor
- UI for license activation in Electron
- machine activation limit
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etc.
These things seemed super scary at first. I always thought it was way too much work and something would break. But I'm glad I persisted. So far the only problem I have is that mailgun is not delivering the license keys to certain domains like https://t.co/6Bqn0FUYXo etc. 👌
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📈 ~12000 vistis
☑️ 109 transactions
💰 353€ profit (285 after tax)
I have spent 1.5 months on this app. You can make more $ in 2 days.
🤷♂️
I'm still happy that I launched a paid app bcs it involved extra work:
- backend for processing payments (+ permissions, webhooks, etc)
- integration with payment processor
- UI for license activation in Electron
- machine activation limit
- autoupdates
- mailgun emails
etc.
These things seemed super scary at first. I always thought it was way too much work and something would break. But I'm glad I persisted. So far the only problem I have is that mailgun is not delivering the license keys to certain domains like https://t.co/6Bqn0FUYXo etc. 👌
omg I just realized that me . com is an Apple domain, of course something wouldn't work with these dicks