Comment by allovernow | Hacker News Reader

Comment by allovernow | Hacker News Reader

I'm not sure about the pace of progress in research, but as an ML engineer at a startup who has been following developments, even if AI research stalls out completely, we've been given a huge set of amazing tools to apply to all kinds of technical problems for years to come.

I also think that even in the absence of massive breakthroughs, there's still plenty of work to be done during a "winter" in filling in the gaps of understanding between various SOTA advances. I think it may be the nature of science to advance in a sort of unbalanced tree of breakthroughs, where we drill down on certain popular and lucrative branches for a while before coming back to fill out and balance the width of the tree, if that analogy makes sense.

Just between transformers/autoencoders, GANs, classic classifiers, and combinations thereof I think we are already poised to see neural networks change society in the next ten or so years in a way similar to the influence of the internet. Especially if hardware and cloud computing continues to scale.

Can you give some examples of applications that you think will have big impacts? I see places where current AI techniques can make incremental improvements but I just don't see any applications that really seem game changing. The ones that come closest tend to be dystopian unfortunately, like most applications of facial recognition.

My startup analyzes historical sales via LSTM and makes future predictions based on geo location, description of products, price of products, weather and holidays. I did the similar work for IBM 10 years ago and we could hardly accomplish fraction of what my startup can achieve today.

Right inventory at right location in right quantity is essential to e-commerce and our ai is helping out a lot in this regard

That sounds like incremental efficiency improvements, not something that will radically change people's lives. Not that that's not valuable and may help us keep economic growth going at reasonable clip but it doesn't seem like the type of thing that will change the character of most people's lives in the way that the Internet and smartphones have.

Our equivalent is Amazon FBA. I think the warehousing and logistics of Amazon has changed the characteristics of most people's lives in the developed world. We aim to the do the same for selling from your own website

But people can already order almost anything from Amazon and have it delivered next day in many cases. No doubt it's a good thing for a business to be able to match that themselves from their own website but it's not like the change from no e-commerce to Amazon.

I am trying to de-centralize the ecommerce industry. Most people don't do it primarily because it's hard to find customers and also logistics is hard.

Matt; it's easy to say "it's incremental" - for example, an application that lets you order books online or communicate with groups of friends would be seen as incremental in 1990. The CEO's of Blackberry thought that the iPhone was good and feature rich, but they saw it as incremental as well!

My startup is using AI in many forms in order to build an accurate digital twin of the world cheaply, and extract valuable insights from it.

In a few years we will have an accurate digital twin of the world, almost indistinguishable from the real world. this would have been impossible or way too expensive without massive automation with AI

> In a few years we will have an accurate digital twin of the world, almost indistinguishable from the real world. this would have been impossible or way too expensive without massive automation with AI

Is the rent cheaper there?

By helping city and infrastructure planning it should help get more resource efficient, so hopefully yes?

> In a few years we will have an accurate digital twin of the world

No, you will not, unless you redefine what "accurate" means.

Google has 3D models of cities nowadays. 20years ago we only had 2D maps. Why do you think this trend will not continue?

Collecting more data scales far better than processing more data into information. That's one bottleneck right there.

While this is true about user data, this is definitely not true in the context of real physical world data.

Getting a picture from the tip of a wind turbine blade is done with a drone in the best case, or a rope access technician in the worst (legacy) case. Analyzing this picture with AI to identify anomalies takes a few seconds. That’s one of my company’s use cases.

In the same way, ask the google maps team about what scales better: flying real airplanes to capture photographic data, or just processing this data to get 3D models...

Edit: typo

Google Street View is full of artifacts, it's not even close to being accurate. The same goes for satellite imagery of rugged mountains. I'm not even mentioning the vegetation, snow cover, river levels, etc.

Accuracy is relative to the need. By accurate I mean accurate enough that we can extract actionable information from it.

For power plant critical structures we want 0.5mm, updated every 6months. For forest management we want 5m, updated every 2 years.

But this increase in spatial and temporal accuracy will keep on going. At some point in the future a small swarm of insect-sized drones will be able to capture a whole forest in a day for a super low cost. And a few people walking with basic smartphones for will be enough to map a whole city.

In 1980 you would have said that google maps 3D and google street view would never exist...

Well, you just redefined accurate to "accurate enough that we can extract actionable information from it". Your statement is true now.

BTW, I never say never.

I run a startup that analyzed litigation and judicial opinions. We can figure out what arguments are made, which were persuasive to the judge, which judges conform to the mean and which are outliers.

The long term potential is to make justice less expensive by being able to evaluate cases more cost effectively. It can also potentially identify judges that are outside of the norm.

I don't see a problem with this as long as the system is just identifying what is successful and normal. However, if judges themselves start using it and adjust their decisions, there is potential for it to create feedback loops that can move the norm into towards something that is not necessarily just. So this system can be used for good as long as there remains an independent check on it (i.e., independent judges).

This concern is not hypothetical. Today, judges are already using tools that calculate average prison times for offenders based on previous rulings. That should not be allowed as it creates feedback loops and bakes in earlier biases. However, the public should be able to use those tools to evaluate their legal position.

Yeah, on the applied side there's still a ton of work left to be done to make productionization of machine learning systems easier. We still lack good tooling for data annotation, experiment tracking, model calibration/evaluation and monitoring.

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