So I have this idea for creating AGI based off of some things I’ve been reading from Jürgen Schmidhubers.

Meta-learn

Lets say you have a Neural Network (the parent network) which can design arbitrary children networks and learn optimized design patterns for a given task. Of course your parent network won’t be super generalized for any type of network design, just relatively specific tasks. This is kind of why we don’t have “real” AI or AGI. The tasks are still relatively narrow.

Skimming the literature on meta-learning it looks like researchers have been able to get SOME generalization by training their meta-networks on multiple tasks. But of course data and identifying tasks might be a limitation for scale-ability and high levels of generalization. So I purpose a potentially more elegant way.

PowerPlay

This is where Jürgen Schmidhubers PowerPlay would come in. The PowerPlay algorithm is split into a solver and a problem generator. The problem generator generates novel problems which the solver has to try to solve. Novel problems are problems which are unsolvable by the current solver. The created problems are just a bit more complicated than the most complicated solvable problem. The solver has to be able to solve all previous problems the generator created plus the new one.

Meta-PowerPlay

Both the problem Solver and Generator have parent Networks which continually learn to design more sophisticated Solvers and Generators until you have much more general problem solvers, or rather a neural network that can design general problem solvers.

Its kind of similar how GANs work for deepfakes and image problems work, the networks try to outsmart eachother in a feedbackloop but instead of just doing a faceswap, it can generate a general purpose neural networks. Or at least one that is a lot more general than what we currently have.

Of course for this to work, this also assumes that Jürgen Schmidhubers idea that intelligence is actually far simpler than we think and that it could be expressed in a relatively small function once we fully understand it. And therefore, the Meta-Solver will be able to derive this function and encapsulate it in its children.

Hello guys!

I’ve just started working on a small project that involves analyzing a web cam generated image and compare it to images on a dataset folder to try and find a match.

There are some pre-trained models out there, which enables one-shot learning (like this GitHub for example: https://github.com/mohitwildbeast/Facial-Recognition-Using-FaceNet-Siamese-One-Shot-Learning)

However, it is not precise as I wanted, and I don’t really know why.

I was looking the FaceNet model by David Sandberg (https://github.com/davidsandberg/facenet) and it seems promising, however I don’t know how to use it for my case.

So, I was wondering if you guys have any advice for me, any link, that might help me!
The system should be simple, is just a proof of concept, so as long as the algorithm can compare the face it is detecting on the webcam, for example, to one on a images folder and return the embedded distance (distance between the faces, where smaller are similar faces and bigger otherwise).

I’m not sure if I was clear, as it is my first time writing on this sub.

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Thank you all in advance.

Classification of long time series using residual convolutions and GRU layer.

Used data from physionet2017 challenge.

It seemed to me strange that participants extracted features by other methods, instead of giving this task to model itself.

A pretty simple example, but I hope someone finds it useful.

Github with code

I’m attempting to wrap my pretrained BERT into an sklearn model with very limited success. I’ve managed to load input embeddings and labels using datasets, but I hit errors trying to pass more than one X to include the input masks and segment_ids. Any thoughts? Have people had success with this approach?

Recent news coverage would have you believe that the computer generated art revolution has finally come. Fabian Offert argues that it has been here all along.

Hey guys,

I am a computer engineer(software design and hardware-software engineering) and planning to get into ML/AI. I have studied the basics of ML/AI and wish to leverage my CE skills (memory optimization, speed optimization) to solve problems in the field of ML/AI (especially deep learning). All the algorithms in the machine learning domain are based on probabilities and on statistical methods (A strong generalization perhaps?, please excuse me if that’s the case. Any kind of contradiction to this statement is welcome). I learn that mathematical optimization is predominant in the field of ML/AI. How can something be built in the field of ML/AI with the focus on CE skills? I was looking for a direction of research or demand for novelty in the said field.

Please help me understand how to leverage computer engineering to enhance models in the field of AI.

I will appreciate anything which helps me understand the place for computer engineer in this field. I am primarily looking for things such as; research demands, current state-of-the-art work or concept extrapolation from CE to ML/AI.

Thank you.

AI safety and ethics is a hot topic. Besides fake news generation, and toxic comment generation, what other malicious tasks could language models be trained for?

So I carried out a random forest for regression, and I have one set of results. Would it make sense to run the algorithm on different seeds, but how would I compare the data for each run?

Or is the one run enough?

Also, if its for regression and not predicting, is there much to gain from running test vs training data?