• AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
  Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which roughly means AI that is as smart or smarter than humans. The dominant approach in the machine learning community is to attempt to discover each of the pieces required for intelligence, with the implicit assumption that some future group will complete the Herculean task of figuring out how to combine all of those pieces into a complex thinking machine. I call this the ``manual AI approach.'' This paper describes another exciting path that ultimately may be more successful at producing general AI. It is based on the clear trend in machine learning that hand-designed solutions eventually are replaced by more effective, learned solutions. The idea is to create an AI-generating algorithm (AI-GA)...

• GANs And Deepfakes Could Revolutionize The Fashion Industry
  AI will transform online commerce for retailers in an even more major way in the near future — realistic digital models may eventually replace humans. The technology that could make this happen is called GANs, short for generative adversarial networks...

• Building your own Deep Learning dream machine
  I’ve been geeking out on Deep Learning lately, taking Andrew Ng’s awesome Deep Learning specialization on Coursera and my friend Lukas Biewald’s awesome ML class. I wanted to build my own Deep Learning desktop so I can train models much faster than on my Mac laptop (or even than on an AWS Deep Learning AMI). With Lukas’ help & tutelage, we made it happen. In case you’re interested in doing the same, here’s the box we built...

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• Training language GANs from Scratch
  We show it is in fact possible to train a language GAN from scratch -- without maximum likelihood pre-training. We combine existing techniques such as large batch sizes, dense rewards and discriminator regularization to stabilize and improve language GANs...

• EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling
  EfficientNet is an open source library that uses a new compound model scaling method and leverages recent progress in AutoML to improve neural network scaling techniques, achieving state-of-the-art accuracy with up to 10x better efficiency....

• When algorithms mess up, the nearest human gets the blame
  Who or what gets blamed when you're harmed by AI? Well, it could be you. Humans act like a “liability sponge,” says Data Society, absorbing legal & moral responsibility in algorithmic accidents no matter how little they are involved...

• Testing TensorFlow Lite image classification model
  What if your model behaves differently on app and on Python env? I've just published new blog post about comparing TensorFlow and TensorFlow Lite models for easier debugging...

• Multi-Sample Dropout for Accelerated Training and Better Generalization
  Dropout is a simple but efficient regularization technique for achieving better generalization of deep neural networks (DNNs); hence it is widely used in tasks based on DNNs. During training, dropout randomly discards a portion of the neurons to avoid overfitting. This paper presents an enhanced dropout technique, which we call multi-sample dropout, for both accelerating training and improving generalization over the original dropout...

• No cloud required: Why AI’s future is at the edge
  For all the promise and peril of artificial intelligence, there’s one big obstacle to its seemingly relentless march: The algorithms for running AI applications have been so big and complex that they’ve required processing on powerful machines in the cloud and data centers, making a wide swath of applications less useful on smartphones and other “edge” devices. Now, that concern is quickly melting away, thanks to a series of breakthroughs in recent months in software, hardware and energy technologies that are rapidly coming to market...

• Game of Thrones Twitter Sentiment with Google Cloud Platform and Keras
  The final season of Game of Thrones apparently raised a lot of eyebrows, so I wanted to dig deeper on how people felt before, during and after the final episode of Game of Thrones by turning towards the ever non-soft-spoken Twitter community. In this blogpost, we’ll look at how an end-to-end solution can be built to tackle this problem, using the technology stack available on Google Cloud Platform...

• Machine learning startup Weights & Biases raises $15M
  Weights & Biases, a startup building development tools for machine learning, has raised $15 million in its second round of funding...

• Perceptual Straightening of Natural Videos
  Video is an interesting domain for unsupervised, or self-supervised, representation learning. But we still don't know what type of inductive biases will enable us to best exploit the information encoded in the temporal sequence of video frames...

• Create TensorFlow Name Scopes For TensorBoard
  Learn how to use TensorFlow Name Scopes (tf.name_scope) to group graph nodes in the TensorBoard web service so that your graph visualization is legible, via a screencast video and full tutorial transcript...

• Cold Case: The Lost MNIST Digits
  For all the people dealing/have dealt with MNIST, here's an extra never seen before 50,000 digits that you can test your models on! Check out...

• Geometric Deep Learning for Pose Estimation
  Theory and Pytorch implementation tutorial to find Object Pose from Single Monocular Image...

• Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin
  "Guesstimation enables anyone with basic math and science skills to estimate virtually anything--quickly--using plausible assumptions and elementary arithmetic"...
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