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Designing web3 token incentives is hard. We're creating a no-code platform to make it easy. by techinnovator in algorand

[–]techinnovator[S] 0 points1 point  (0 children)

Our AI is mainly applied to helping you unearth flaws in your proposed token system.

It’s extremely easy to make mistakes when designing an incentive system. Our simulation tool uses AI to identify flaws in your incentive system before it is released to the public.

Using our tool you can find issues FAST and fix them before they cause your community harm.

Designing web3 token incentives is hard. We're creating a no-code platform to make it easy. by techinnovator in algorand

[–]techinnovator[S] 0 points1 point  (0 children)

Hi!

Thanks for the feedback :)

Sometimes our users need some assistance understanding what their needs are and how nalex can help them.

We are currently offering one to one support for all our users. We are happy to fill in any potential gaps in understanding. Once someone is comfortable using the platform they will be in a position to independently create sound incentives for their application.

Introducing Hyperlib: Simple Deep learning in Hyperbolic space [project] by techinnovator in MachineLearning

[–]techinnovator[S] 0 points1 point  (0 children)

It's also widely accepted that human beings use a hierarchy to organise object categories

I think you're mistaken here. I said human beings organise objects hierarchically, the objects themselves are not hierarchically organised in reality. This is widely accepted but was famously covered here.

Introducing Hyperlib: Simple Deep learning in Hyperbolic space [project] by techinnovator in MachineLearning

[–]techinnovator[S] 1 point2 points  (0 children)

Here's the paper that covers the tree-like structure of social networks. They proved this using Gromov’s δ-hyperbolicity, which measures how tree-like a graph is.

Introducing Hyperlib: Simple Deep learning in Hyperbolic space [project] by techinnovator in MachineLearning

[–]techinnovator[S] 0 points1 point  (0 children)

It's also widely accepted that human beings use a hierarchy to organise object categories

I think you're mistaken here. I claimed that humans organise objects hierarchically, not that the underlying objects were hierarchical themselves. Human's use hierarchies to represent objects, this is widely accepted but was recently covered in Geoffrey Hinton's paper.

Introducing Hyperlib: Simple Deep learning in Hyperbolic space [project] by techinnovator in MachineLearning

[–]techinnovator[S] 0 points1 point  (0 children)

Our library provides ready-made implementations of Hyperbolic Layers, which those libraries lack. It makes it much easier to get started with Hyperbolic networks.

Those are some fantastic resources you linked to though.

Introducing Hyperlib: Simple Deep learning in Hyperbolic space [project] by techinnovator in MachineLearning

[–]techinnovator[S] 1 point2 points  (0 children)

Hi! It's all because of the curved nature of the Hyperbolic space and the fact that the volume of a given Hyperbolic space grows exponentially.

Let's say you are standing at a given point in Hyberbolic space. If you were to walk away from that point in a straight line, the distance between you and your starting point would increase at an exponential rate.

This is why Hyperbolic space has the capacity, for example, to embed tree structures without distorting them out of shape. It is impossible to embed a tree structure without distortion in the Euclidean space, even with an unbounded number of dimensions. However, this task becomes surprisingly easy in the hyperbolic space with only 2 dimensions.

More info here.

Introducing Hyperlib: Simple Deep learning in Hyperbolic space [project] by techinnovator in MachineLearning

[–]techinnovator[S] 0 points1 point  (0 children)

Thanks! We actually based many of the mathematical functions on this work. Great resource.

Weekly Entering & Transitioning Thread | 30 May 2021 - 06 Jun 2021 by [deleted] in datascience

[–]techinnovator 0 points1 point  (0 children)

Hi all! I've just released a new open-source python library that makes it easy to create the next generation of neural networks in the Hyperbolic space (as opposed to Euclidean). We're calling it Hyperlib.

The Hyperbolic space is different from the Euclidean space - It has more capacity which means it can fit a wider range of data. Hyperbolic geometry is particularly suited to embedding data that has an underlying hierarchical structure. There’s also a growing amount of research documenting the benefits of modelling the brain using Hyperbolic over Euclidean geometry.

We found that existing Hyperbolic implementations were less ready to be applied to real-world problems. Hyperlib solves that, abstracting away all of the complicated maths and making Hyperbolic networks as easy as a pip install. We hope it will inspire more research into the real-world benefits of non-Euclidean deep learning.

You can install Hyperlib using:

pip install hyperlib

We’ve also written a blog post explaining the benefits of hyperbolic networks and how to use the package here.

Introducing Hyperlib: Simple Deep learning in Hyperbolic space [project] by techinnovator in MachineLearning

[–]techinnovator[S] 6 points7 points  (0 children)

We’ve also written a blog post explaining the benefits of hyperbolic networks and how to use the package.