Recently, villages from this year’s DEFCON (27) have put together a Reddit AMA. I was volunteering to answer community questions related to the Monero village at this year’s DEFCON. During discussion, I engaged u/aivillage in an inquiry on the progress and accessibility of deepfakes machine learning technology.

We had a brief discussion before both of my comments were deleted with no warning, notification, or explanation. I suspect the moderator in question was u/highwiz as he also engaged me in discussion elsewhere in the thread.

Link to the original thread

Link to specific tweet with pictures.

Machines is one of the hardest fields to study, from its capabilities up to its full potentials. In today’s generation, we all know the risks of relying to those untrusted machines but according to what I’ve read about this project, TRIAS, there are some significant we can rely on like:

>> Computers and the Internet significantly boost the efficiency of our work. However, untrusted applications can lead to data leaking or tampering.

>> Mobile phones meet our basic daily communication and entertainment needs…You can read more here: https://www.trias.one/whitepaper

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Trusthworthy and reliable systems are what we need now. There are so many resources out there but all we need is an eclectic source that will help us to understand and feel the presence of intelligent machine like TRIAS

Hello,

I am looking for some help with a project. The goal of the project is to use EMNIST data to train a model for translation that extracts certain fields and translate documents into data. I can offer a reasonable rate when we can negotiate.

Contact me if you are interested

Hi everyone,

While I was building an AlphaZero clone I had the opportunity to make a Python library for the Monte Carlo Tree Search algorithm that works both with an AI expert policy or without one. The existing Python libraries that I found were either too poorly functioning/documented or didn’t have a clear mechanism for deriving the probabilistic exploratory weighting to assign to a child node prior to determining its predicted win value.

Please let me know if there are any issues or if I can help clarify anything. I hope someone might find this useful!

https://github.com/ImparaAI/monte-carlo-tree-search

I have created a cool experiment in tensorflow that takes input from the user and does something very cool!

On my RTX 2080 Ti, the task in the web app completes in about 2 seconds. However, on my Linode (which does not have a GPU) the task takes nearly 1 minute!

Is there any web host similar to Linode where I can have a GPU?

StegaStamp: Invisible Hyperlinks in Physical Photographs

Video: https://youtu.be/E8OqgNDBGO0

Project Page: http://www.matthewtancik.com/stegastamp

Hi there! Me and my friends are working on the iOS app that voices any article from any source with the help of machine learning. The app is free and doesn’t contain ads at all. So if this sounds interesting for you, please find more information here: What is Peech app and how to use itThe link to download: Peech. Would be really helpful to hear any thoughts, ideas or feedback from ya.

I am using two Imagenet models and multiplying their outputs to create an ensemble model consisting of those two models.

My first attempt is using the same model, but with different images sizes as inputs.

However this ensemble model doesn’t seem to be converging. It gets stuck at 78% accuracy, no matter how many layers I unfreeze. While a single model is converging and over-fitting.

Is there something wrong with my model setup or something you suggest I try?

def full_model_5(model_keys): """prediction x, y, multiply them , activation softmax""" model_key, model_key2 = model_keys base_model, preprocess = basemodel(model_key) base_model2, preprocess2 = basemodel(model_key2) for layer in base_model.layers: # all base_layers are not trainable layer.name = layer.name + "dup" layer.trainable = False for layer in base_model2.layers: # all base_layers are not trainable layer.trainable = False # first model x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(2048, activation='relu')(x) x = BatchNormalization()(x) x = Dense(512, activation='relu')(x) x = BatchNormalization()(x) predictions_x = Dense(classes, activation='relu')(x) # second model y = base_model2.output y = GlobalAveragePooling2D()(y) y = Dense(2048, activation='relu')(y) y = BatchNormalization()(y) y = Dense(512, activation='relu')(y) y = BatchNormalization()(y) predictions_y = Dense(classes, activation='relu')(y) # combine the models together predictions = Multiply()([predictions_x, predictions_y]) predictions = Activation("softmax")(predictions) ##predictions = Maximum()([predictions_x, predictions_y]) model = Model(inputs= [base_model.input,base_model2.input], outputs=predictions) return model,( preprocess,preprocess2)