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denisvlr | 1 year ago | on: Meta Movie Gen

Shameless plug: I just created a short AI film (1) and tried to tell an actual story and trigger emotions. I spent countless hours crafting the script, choosing the shots, refining my prompts, generating images, animating images, generating music, sounds, and so on... For me AI tools are just that - tools. True, you have to yield them some "control", but at the end of the day you are still the one guiding and directing them.

Similarily, a film director "just" gives guidance to a bunch of people: actors, camera operator, etc. Do you consider the movie is his creation, even if he didn't directly perform any action? A photographer just has to push a button and the camera captures an image. Is the output still considered his creation? Yes and Yes, so I think we should consider the same with AI assisted art forms. Maybe the real topic is the level of depth and sophistication in the art (just like the difference between your iPhone pictures and a professional photographer's) but in my opinion this is orthogonal to it being human or AI generated.

To be honest so far we have mostly seen AI video demos which were indeed quite uninteresting and shallow, but now filmmakers are busy learning how to harness these tools, so my prediction is that in no time you will see high quality and captivating AI generated films.

(1) https://artefact-ai-film-festival.com/golden-hours-66f869b36... Please consider liking it!

denisvlr | 6 years ago | on: The new business of AI and how it’s different from traditional software

This article really resonates with me: I'm currently working on bootstrapping an applied AI service and faced most of the challenges mentioned, to the point where I was doubting myself as I was not as efficient or productive as other Saas / Software founders. So this article is kind of reassuring to me.

A few more comments:

Cloud infra For a traditional web app you can quickly deploy it on a cheap AWS on Heroku VM for a few dollars/mo and later scale as you get more traffic. With AI you now need expensive training VMs. There are free options such as Google Collab but it doesn't scale for anything else than toy projects or prototyping. AWS entry point GPU instance (p2.xlarge) is at $0.900/hour i.e. $648/mo, and a more performant one (p3.2xlarge) at $2160/mo. Yes, you should shut them down when you are done with training but still. You can also use spot instances to reduce cost but it's not straightforward to set up.

For inference, you also need a VM with enough memory for your model to fit in, so again an expensive VM from day one.

Datasets if you rely on a publicly available dataset, chances are there are already 10 startups doing the same product. In order to have a somewhat unique and differentiated product, you need a way to acquire and label a private dataset.

Humans in the loop The labeling part is very tedious and costly both in terms of time and money. You can hire experts or do it yourself at great cost, or you can hire cheap outsourced labor who will deliver low-quality annotations that you will spend a lot of time controlling, filtering, sending back, etc.

For inference, depending on your domain, even with state-of-the-art performance you may end up with say 90% accuracy, ie 1 angry customer out of 10. that's probably not acceptable, but it gets worse: chances are you will attract early-adopter customers who are faced with hard cases, whose current solution doesn't work so that's why they want to use your fancy AI in the first place. In that context, your accuracy for this kind of customer might actually be much worse. So again you need significant human resources to control inferences in production. It will be hard to offer real-time results, so you may have to design your product to be async and introduce some delay in responses, which is maybe not the UX you initially had in mind.

I still think there are tremendous opportunities in applied AI products and services, but it's important to have these challenges in mind when planning a new product or startup.

denisvlr | 7 years ago | on: Updates from YC

I was in YC W16 batch.

Coming from abroad, part of the YC experience was, besides the program itself, renting a house in a residential area of Palo Alto where our team of 4 lived and worked. We didn’t have a car and the nearest bars & restaurants were at a 20 minutes bike ride. This set up felt like a retreat, and really pushed us to 100% focus on our startup. Basically the only things I did for 4 months is working, exercising in the surrounding parks and eating homemade food.

Had YC been located in SF it would have been a totally different experience. It’s a vibrant and exciting city specially for new comers.

However, I understand YC’s rationale: most local founders live in SF and the 1h commute is painful, the current building is not easily accessible, and doesn’t scale to bigger and bigger batches, etc.

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