That is Rule #1 of being a Product Owner
A Startup versus A Project
I created multiple Startups in last 1 year and Quit them without making Money.
Is it possible?
Hell No!
All what I had done was creating multiple Projects.
Startup is a different ballgame altogether.
One of the biggest Mistakes Entrepreneurs makes is Quitting way too early.
As a Startup Entrepreneur, you are fighting against all odds.
You are picking up Ideas that have been rejected by Smart people at large Companies.
You are trying to build Products that the Customers don’t even know they need.
To succeed in that game you have to hole in and play the war.
You have to hole-in so deep that your adversary - even a Superpower - gets tired and gives up.
AND You need to stick long! Why?
The longer you stay, more achievements you can make and more seriously you will be taken by the players around. You will build more deeper expertise, connections, & brand. Thus, the biggest goal of a startup should be to stick long in an idea.
And for that to happen you should:
1. Find the most desperate customer who wants the solution and sell it to them. Fixing their problem is the key to survive. Later you can fix much more sexier markets.
2. Put all out emphasis on Cashflow - be fanatic about it. If you have the cashflow, Investors would come knocking. The only time investors would come knocking is when you don’t need them.
3. Be a penny-pincher. Even after you get the investment. Especially after you get the investment. The greatest entrepreneurs are those who greatly conserved other people’s money. That will improve your odds of Survival.
4. Shut out all kinds of Distraction and second doubts about your idea. Almost any idea can be Scaled or Morphed to become Monstrous. It is your Execution that counts. And if you become a great Executor you can always change your idea years from know.
5. Team with People who are in it for the long term. If you cannot stand them for more than a few weeks, you should not have started with them.
Only if you do the above 5 and Survive at least 1 year and generate Revenues from customers, you are a Startup - Until then, you are just building Projects.
Is it possible?
Hell No!
All what I had done was creating multiple Projects.
Startup is a different ballgame altogether.
One of the biggest Mistakes Entrepreneurs makes is Quitting way too early.
As a Startup Entrepreneur, you are fighting against all odds.
You are picking up Ideas that have been rejected by Smart people at large Companies.
You are trying to build Products that the Customers don’t even know they need.
To succeed in that game you have to hole in and play the war.
You have to hole-in so deep that your adversary - even a Superpower - gets tired and gives up.
AND You need to stick long! Why?
The longer you stay, more achievements you can make and more seriously you will be taken by the players around. You will build more deeper expertise, connections, & brand. Thus, the biggest goal of a startup should be to stick long in an idea.
And for that to happen you should:
1. Find the most desperate customer who wants the solution and sell it to them. Fixing their problem is the key to survive. Later you can fix much more sexier markets.
2. Put all out emphasis on Cashflow - be fanatic about it. If you have the cashflow, Investors would come knocking. The only time investors would come knocking is when you don’t need them.
3. Be a penny-pincher. Even after you get the investment. Especially after you get the investment. The greatest entrepreneurs are those who greatly conserved other people’s money. That will improve your odds of Survival.
4. Shut out all kinds of Distraction and second doubts about your idea. Almost any idea can be Scaled or Morphed to become Monstrous. It is your Execution that counts. And if you become a great Executor you can always change your idea years from know.
5. Team with People who are in it for the long term. If you cannot stand them for more than a few weeks, you should not have started with them.
Only if you do the above 5 and Survive at least 1 year and generate Revenues from customers, you are a Startup - Until then, you are just building Projects.
Source:
This post has been copied from an answer written by Balaji on Quora
Google Translate AI - can Automatically Translate between Languages Without being Explicitly Trained
In Nov 2016, Google posted a blog-post about its Google Translate's AI being able to automatically translate between languages, without being explicitly fed with their dictionaries - Lets try to understand it...
In the last 10 years, Google Translate has grown from supporting just a few languages to 103, translating over 140 billion words every day. To make this possible, Google needed to build and maintain many different systems in order to translate between any two languages, incurring significant computational cost.
In Sep 2016 Google Translate switched to a new system called Google Neural Machine Translation (GNMT) - an end-to-end Learning framework that learns from millions of Examples, and provided significant improvements in translation quality. However, while switching to GNMT improved the quality for the languages, scaling up to all the 103 supported languages presented a significant challenge.
Then came - Google’s Multilingual Neural Machine Translation System (GMNMT): Enabling Zero-Shot Translation - Translation between language pairs never seen explicitly by the system, where they addressed the mentioned challenge by extending their previous GNMT system, allowing for a single system to translate between multiple languages.
Here’s how it worked:
Let’s say we train GMNMT with Japanese ⇄ English and Korean ⇄ English examples, shown by the solid blue lines in the animation.
Now, a question:
Can this system translate between a language pair which the system has never seen before?
An example of this would be translations between Korean and Japanese where Korean ⇄ Japanese examples were not shown to the system.
Answer: Yes!!!
It can generate reasonable Korean ⇄ Japanese translations, even though it has never been taught to do so. This is called “zero-shot” translation, shown by the yellow dotted lines in the animation.
But How ???
The system is learning a common representation in which sentences with the same meaning are represented in similar ways regardless of language - i.e. an “interlingua”. Using a 3-dimensional representation of internal network data, we were able to take a peek into the system as it translates a set of sentences between all possible pairs of the Japanese, Korean, and English languages.
Part (a) from the figure above shows an overall geometry of these translations. The points in this view are colored by the meaning; a sentence translated from English to Korean with the same meaning as a sentence translated from Japanese to English share the same color. From this view we can see distinct groupings of points, each with their own color. Part (b) zooms in to one of the groups, and part (c) colors by the source language. Within a single group, we see a sentence with the same meaning but from three different languages. This means the network must be encoding something about the semantics of the sentence rather than simply memorizing phrase-to-phrase translations. We interpret this as a sign of existence of an interlingua in the network.
In the last 10 years, Google Translate has grown from supporting just a few languages to 103, translating over 140 billion words every day. To make this possible, Google needed to build and maintain many different systems in order to translate between any two languages, incurring significant computational cost.
In Sep 2016 Google Translate switched to a new system called Google Neural Machine Translation (GNMT) - an end-to-end Learning framework that learns from millions of Examples, and provided significant improvements in translation quality. However, while switching to GNMT improved the quality for the languages, scaling up to all the 103 supported languages presented a significant challenge.
Then came - Google’s Multilingual Neural Machine Translation System (GMNMT): Enabling Zero-Shot Translation - Translation between language pairs never seen explicitly by the system, where they addressed the mentioned challenge by extending their previous GNMT system, allowing for a single system to translate between multiple languages.
Here’s how it worked:
Now, a question:
Can this system translate between a language pair which the system has never seen before?
An example of this would be translations between Korean and Japanese where Korean ⇄ Japanese examples were not shown to the system.
Answer: Yes!!!
It can generate reasonable Korean ⇄ Japanese translations, even though it has never been taught to do so. This is called “zero-shot” translation, shown by the yellow dotted lines in the animation.
But How ???
The system is learning a common representation in which sentences with the same meaning are represented in similar ways regardless of language - i.e. an “interlingua”. Using a 3-dimensional representation of internal network data, we were able to take a peek into the system as it translates a set of sentences between all possible pairs of the Japanese, Korean, and English languages.
Source:
Zomato's founder DeepinderGoyal reveals Business & Profitability plans/secrets
6 Key takeaways of Zomato's Founder DeepinderGoyal interview with YourStoryMedia-
1. If you have a lot of Money in the bank, your Answers to Problems always revolve around Money. You think of the easiest answers, which generally involve Spending.
2. Perception of your size in the delivery business also comes from the delivery fleet Wearing your Brand colored Tshirts out on the Roads.
3. In Food Delivery, there is a big First-Mover-Advantage if you have a good Service. And we initially lost Hyderabad and Bangalore because of that.
4. About 20% of our cost is being focussed on new areas - Experiments - we are in Investment mode. We can cut down those experiments on new things and can be 20% positive on EBITDA today.
5. I think the biggest change was that we stopped spending mindlessly, not just on advertising, but everywhere. Costs flattened out and eventually came down.
6. We were launching in a lot of new countries. Every 2 months, there was a country launch, and that added to a lot of fixed costs, payroll costs, rental costs, etc. And when the market turned, we could not Sustain in the new markets that we had just launched in. We had to Layoff the teams in most of the countries we launched in during 2015 - mainly North America and Europe. There was no change in India and UAE. Actually, for about a month when we laid off people, nobody said anything and FoodTech was going great until TinyOwl imploded. And then food tech was in trouble, and we got dragged into it. Until that time, the Indian media did not care that we had to lay off some employees in the opposite side of the world.
1. If you have a lot of Money in the bank, your Answers to Problems always revolve around Money. You think of the easiest answers, which generally involve Spending.
2. Perception of your size in the delivery business also comes from the delivery fleet Wearing your Brand colored Tshirts out on the Roads.
3. In Food Delivery, there is a big First-Mover-Advantage if you have a good Service. And we initially lost Hyderabad and Bangalore because of that.
4. About 20% of our cost is being focussed on new areas - Experiments - we are in Investment mode. We can cut down those experiments on new things and can be 20% positive on EBITDA today.
5. I think the biggest change was that we stopped spending mindlessly, not just on advertising, but everywhere. Costs flattened out and eventually came down.
6. We were launching in a lot of new countries. Every 2 months, there was a country launch, and that added to a lot of fixed costs, payroll costs, rental costs, etc. And when the market turned, we could not Sustain in the new markets that we had just launched in. We had to Layoff the teams in most of the countries we launched in during 2015 - mainly North America and Europe. There was no change in India and UAE. Actually, for about a month when we laid off people, nobody said anything and FoodTech was going great until TinyOwl imploded. And then food tech was in trouble, and we got dragged into it. Until that time, the Indian media did not care that we had to lay off some employees in the opposite side of the world.
Deepinder Goyal
Source:
Yourstory.com/2017/08/zomato-deepinder-goyal-interview/
http://media2.intoday.in/btmt/images/stories/deepinder-goyal_660_050515060529.jpg
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