Accurate simulation of cancer-implicated proteins holds enormous promise for basic biomedical science and development of effective therapies, but the high computational cost required has long slowed progress. Recently a multi-institution research team developed a machine learning-based simulation for next-generation supercomputers capable of modeling protein interactions and mutations that play a role in many forms of cancer. Their work on simulating the RAS protein family will be published at SC19 and is a finalist for the Best Paper award. RAS proteins are implicated in roughly one third of cancers, and research to obtain a more detailed understanding of how they interact with the cell's lipid membranes and influence signaling pathways has long been pursued. One way to shortcut the simulations needed and to reduce the computational cost is to use ML to zoom in on areas of interest.

Tech leader Elon Musk is known for sounding the alarm bells on the risks of artificial intelligence. Musk has said that he believes that AI will soon manipulate social media if it hasn't already -- a concern that pales in comparison to his previous predictions of a future humanity governed by an intelligent machine dictator. A year ago, he told Recode Decode that the relative intelligence ratio between such a dictator and the rest of humanity would resemble the ratio between a person and a cat. The great Musk doesn't stand alone in fearing the risks of AI gone wrong. Stephen Hawking and other researchers have said that intelligent machines could become very dangerous.

Philippe Starck is a name that became a brand. From the habitation module on the new international space station to Steve Jobs' yacht, Philippe Starck's list of achievements speaks volumes. I interviewed one of most emblematic figures of design in the Peninsula's bar Felix in Hong Kong–a bar that he designed, of course. Philippe Branche: How do you see the future of design? Philippe Starck: I don't see one.

I have been trying yo reproduce the results of a SOTA paper regarding object detection. I have reimplemented their method and trained on the same dataset, based on the paper, however I was not able to achieve their results on the datasets they use for evaluation, no matter what I have tried. Then I also studied their referenced papers and realised that many of them use a train-test split strategy for evaluating their models. This means that they use a part of the evaluation dataset for finetuning their already trained model and then evaluate it on the testing part of the same dataset. In the case of these papers, this fact was explicitly mentioned.

A Chinese wildlife park has sparked outcry after making visitors submit to facial recognition scanning, with one law professor taking it to court. Professor Guo Bing is taking action against Hangzhou safari park, after it replaced its existing fingerprinting system with the new technology. "I [filed this case] because I feel that not only my [privacy] rights are being infringed upon but those of many others," Guo, from Zhejiang University of Sci-Tech, said according to an audio recording of an interview posted by state-run Beijing News. Guo is attempting to force the park to return the money he paid for an annual pass and highlight its misuse of data gathered by the software. A court in Fuyang has accepted his case.

Every technology revolution has a unique inflection point. The spark that ignited the artificial intelligence movement was a statistical data analysis system developed by Jim Goodnight when he was a statistics professor at North Carolina State University 45 years ago. He never imagined that the technology he created to improve crop yields would evolve into sophisticated data analytics software, a precursor to modern day AI. Back then computers could only compute 300 instructions a second and had 8K of memory. Today they can execute 3 billion instructions a second and contain multiple terabytes of memory.

Miguel Santiago, a senior at Baruch College in Manhattan, is graduating soon and already considering his next move -- maybe to a job at Goldman Sachs or somewhere else in banking. In at least six of his interviews, he's been questioned by a computer and not a live person. "They've basically replaced the first round with the HireVue," he said, referring to the video and artificial intelligence platform increasingly being used by employers for job interviews. When a candidate applies to a job at a company that uses HireVue, they are asked to go on to the platform, allow use of their webcam and respond to interview questions on video. The candidate's answers are recorded and then saved to the platform.

ICCV 2019 today announced its Best Paper Awards in three categories. The ICCV (IEEE International Conference on Computer Vision) is a top international biannual computer vision gathering comprising a main conference and several co-located workshops and tutorials. ICCV 2019 received 4,303 papers -- more than twice the number submitted to ICCV 2017 -- and accepted 1,075, for a reception rate of roughly 25 percent. Abstract: We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image.

Here we are in 2019, where we keep seeing State-Of-The-Art (from now on SOTA) classifiers getting published every day; some are proposing entire new architectures, some are proposing tweaks that are needed to train a classifier more accurately. To keep things simple, let's talk about simple image classifiers, which have come a long way from GoogleLeNet to AmoebaNet-A, giving 83% (top-1) accuracy on ImageNet. If we were to take an image and change a few pixels on it (not randomly), what looks the same to the human eye can cause the SOTA classifiers to fail miserably! I have a few benchmarks here. You can see how miserably these classifiers fail even with the simplest perturbations. This is an alarming situation in the Machine Learning community, especially as we move closer and closer to adopt the use of these SOTA models in real world applications. Let's discuss a few real-life examples to help understand the seriousness of the situation. Tesla has come a long way, and many self-driving car companies are trying to keep pace with them. Recently, however, it was seen that SOTA models used by Tesla can be fooled by putting simple stickers (adversarial patches) on the road, which the car interprets as the lane diverging, causing it to drive into oncoming traffic. The severity of this situation is very much underestimated even by Elon (CEO of Tesla) himself, while I believe Andrej Karpathy (Head of AI, Tesla) is quite aware of how dangerous the situation is. This thread from Jeremy (Co-Founder of Fast.ai) says it all. In this clip, @elonmusk tells @lexfridman that adversarial examples are trivially easily fixed.@karpathy is that your experience at @tesla? @catherineols is that what the neurips adversarial challenge found? A recently released paper showed that a stop sign manipulated with adversarial patches caused the SOTA model to begin "thinking" that it was a speed limit sign. This sounds scary, doesn't it? Not to mention that these attacks can be used to make the networks predict whatever the attackers want! Imagine an attacker who manipulates road signs in a way such that self-driving cars will break traffic rules.

Abstract: We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise).