### Can AI predict the future of our universe?

I was wondering if AI if AI could predict if you’ll die soon then why can’t it predict the in future what changes would happen in universe?

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# Category Archives: Artificial Intelligence (AI)

### Can AI predict the future of our universe?

### What should the dimension of the mean and variance of the latent distribution in variational auto-encoders be?

### 3D Bounding Box Annotation Tool

### Is it possible to use AI for detecting the volume of a cup

### Can I shuffle image channel data as a form of data augmentation?

### faster rcnn loss problems

### what will i be able to do in the end of AI: modern approach?

### SVM versus Logistic Regression

### Does CNN Model accuracy increase if I fit it twice

### Can learned feature vectors be considered a good encryption?

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I was wondering if AI if AI could predict if you’ll die soon then why can’t it predict the in future what changes would happen in universe?

I’m having a problem to understand the needed dimensions of an VAE, especially for mu, logvar and z layer. Let’s say I have an input of 512×512, 1 color channel (CT images), batch size 32. Then my encoder/decoder looks like the following: self.encoder = nn.Sequential( nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1), # 32x512x512 nn.ReLU(True), nn.Conv2d(32, 32,…

I’m looking for free and easy to use annotation tool for 3D bounding box. There is a lot of annotation tools out there, but most of them only good for 2d boxes. Any recommendations?

I was just wondering if it’s possible to use Machine Learning to train a model from a dataset of images of cups with a given volume in the image and then use object detection to detect other cups and assume the volume of the cup, Basically the end goal is to detect the volume of…

If I want to augment my dataset, is shuffling or permuting the channels (RGB) of an image a sensible augmentation for training a CNN? IIRC, the way convolutions work is that a kernel operates over parts of the image but maintains the order of the kernels. For example, the kernel has $k \times k$ weights…

The RPN loss in faster rcnn is L({pi},{ti}) = 1/Ncls sigma(i)Lcls(pi,pi) + lambda 1/Nreg sigma(i)piLreg(ti,t*i) and Lreg=SmoothL1(t−t∗) which tx=(x−xa)/wa, ty=(y−ya)/ha, tw=log(w/wa), th=log(h/ha) (1) t∗x=(x∗−xa)/wa, t∗y=(y∗−ya)/ha, t∗w=log(w∗/wa), t∗h=log(h∗/ha) (2) x*,y*,w*,h* = GT(label) xa,ya,wa,ha = anchor(Rpn generated) x,y,w,h = predict box so how i can get predict box? And Lreg = SmoothL1(t−t∗), is that add up tx,ty,tw,th and minus the sum of tx,ty,tw,th?

i just started the book and i was wondering , what will i be able to do in AI by the end of the book ? and more particularly, what is my position with Reinforcement Learning, deep neural networks and NEAT algorithm, will i study them enough in the book will i need to read…

I have a question about logistic regression versus SVM. Can we say that training a logistic regression model is an example of an unconstrained optimization problem while training an SVM is an example of a constrained optimization problem?

As I train my CNN model I realised that fitting model twice, ie. running again this code history = model_2.fit([X_train_1,X_train_2], y_train, epochs = 120, batch_size = 256, validation_split = 0.2, callbacks = [keras.callbacks.EarlyStopping(monitor=’val_loss’, patience=20)]) after first trial will lead to better accuracy level most of the time. I don’t understand why this is happening. Is…

Considering I have some neural network that, using supervised learning, transforms a string into a learned feature vector where “close” strings will result into more close vectors. I know that since a NN is no one-way-function there is a way to retrieve the input data from my output if I have the entire network at…

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