mongodude

Hope my founding team read this post! The bar to run a company is so high these days that you can't settle for anything mediocre! Also, if you have a product market fit in 2021, consider yourself lucky and focus on scaling it. It is getting very difficult to find a niche where you can scale as you would in 2013.

This is the problem in many emerging economies in SE Asia. Just because many people are struggling to arrange their daily meals, Governments get into socialist mode and taxes anything which does not classify as bare necessity. Sadly, it is a vicious loop and these economies are not modernizing enough at a rapid pace despite so much talent waiting for an opportunity.

What is the accuracy level you are seeing for your AI? What is the Data Review Service? Does it involve manual verification at your end?

I like Flair for three reasons:

- Easy to use

- Developers are very active

- State-of-the-art results using approaches that are easy to understand and works well for most of text classification tasks

I agree with you 100% and if you analyze the comments on HN everytime Google decides to mess around with their IM, I have seen similar sentiment around Gtalk. I was a regular user of Gtalk during college days 10 years ago and was pissed when they merge/transition them into hangouts. It is a poor product management decision driven by Google's philosophy of capitalizing the next big trend in the internet industry ignoring the preferences of their users.

NCs are a legacy management technique that needs to be replaced with continuous innovation, better work culture and a mentality to innovate or die!

Honestly, we the lesser mortals do not know what goes behind the closed doors when cheques like $1.5 billion are written.

People said the same thing when Microsoft valued Facebook at $15 billion. Now, in the hindsight, it seems so funny that people frowned upon those valuations.

Power of having many consumers engaged on your platform is too much in digital age.

Came across this article just as my training model overfitted! The type of AI that philosophers, media, government officials associate may not even come yet so much fuss and paranoia.

That will be great because marcaria is pretty shit

It's a tough market to be in but Spotify continues to hold its sway!

Google play music is pretty much dead, Apple Music may not innovate at the pace and Pandora will soon die, Spotify is in a good spot to grow

I'm a big fan :)

Experienced multiple times working on applied AI projects. Real-world datasets are messy and labeling them is an expensive task.

Enterprises are also not sure how to measure the RoI of their AI projects, especially since the accuracy is the mid-80s at best and someone needs to take ownership to teach the machine and improve the accuracy continuously.

Great - wonder how do you guys hold sway amidst tough competition from Amazon and Google for text analysis solutions? It's a tough world out there for startups in this space.

Hehe! You will be surprised how often this happens in best of the companies. A large fintech startup I work with had 5 GPU servers lying around idle before the CTO realized that they only need them while training the Machine Learning models and not during inference stage.

It's good but considering auto-reply feature of Gmail and integration with other apps like Calendar and Hangouts, I wouldn't pay for any such solution. Of course, assuming the organization use Gsuite else, maybe x.ai fits the bill.

Very classic one is x.ai or Amy or whatever they call it now. They overhyped their capability of building a smart assistant for reading and replying to emails and ended up being an average service that sort of works.

Earlier, they kept telling me that I have jumped their queue and will soon get access. Now they ask me to upgrade the plan to keep using it. Complete BS!

Your last line summarized the whole conversation of this thread. As a startup, you do need both!

Sendy's templating engine is a mess, best that worked for me is to design the template in Mail chimp, export it as HTML (Mail chimp provide this as free of cost) and then, use sendy for sending mails.

Since so long, I have been waiting for Indian universities especially IITs to invest and publish in building such corpora. Being a founder of AI/ML startup, I am surprised at the appalling lack of datasets available to work on Indian problems. Contrast this with Chinese universities where they have built some world class datasets to build NLP solutions in Mandarin. Our sentiment analysis works in 8 different languages but none of it is in Indian languages despite we being in India!

Very well! Considering the data Facebook has, I wonder why they haven't built anything like this or maybe they are.

But surely Googles and Apples of the world are not just replacing drivers but also automating creative tasks like writing or photography.

Score seems to work on some images but did not work on my dog's photo - how do you guys evaluate the accuracy?

I have used this service, does not work as advertised. We hardly got 2-3 testers to test our App.

Yes, I have heard of transfer learning and used them in practice. Very powerful but still primitive. One-shot learning techniques are still to mature but I agree with you that technologies like these will reduce our reliance on datasets and make the AI algorithms learn in more human like way.

I would disagree on this point, humans unlike current AI systems can learn from one or two data points, especially at easier tasks like identifying cats. Current AI algorithms need huge labeled data sets for solving narrow problems so one needs to build more generalization ability to our current AI systems.

I'll prefer utility over hype. One has to see how the community evolves around pytorch.

Squeeze and excitation network by momenta.ai has been a watershed moment for Chinese AI prowess and I'll watch out for such Chinese startups to dominate AI landscape for a while. What amuses me is why Google haven't participated in the last couple imagenets?

That was soon!

Share some great discussion on HackerNews or post about new frameworks/tools from ProductHunt.

Follow influencers in your area of interest and make sure to retweet them.

Think Bayes and Python Data Science Handbook are a good starting point. Below is the list of free books to learn ML/AI

http://blog.paralleldots.com/data-scientist/list-must-read-b...

Snap still has some of the best user engagement metrics in the industry which continues to attract top advertisement dollars. Whats worrying is the apathetic attitude of management to reduce cost and boost revenues rather than being blinded by the superiority of their product. When Facebook investors complained about poor revenues from mobile users, it took them no more than 2 quarters to show a remarkable increase in their ad revenue from mobile traffic. Snap would need this kind of aggressive revenue focus to stand a chance against the might of Facebook.

You didn't mention but the ability to add any photo and use it as a filter could be more killer feature of this app. You should also consider an ability to share filters.