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).

Aiming for treatment they have spent more than 30 years describing, understanding and diagnosing the most common hereditary form of stroke, CADASIL. For this, the four French neuroscientists are now receiving the world's most valuable prize for brain research – the Lundbeck Foundation Brain Prize, worth 1 million euros. Each year 17 million people worldwide suffer a stroke. Around 30 percent of these are mini strokes caused by changes in the small vessels of the brain. To begin with, these strokes cause temporary symptoms such as weakness, numbness and impaired coordination.

The ubiquity and sheer volume of data generated today give experts in virtually every domain ample information to track everything from financial trends, disaster evacuation routes, and street traffic, to animal migrations, weather patterns, and disease vectors. But using this data to build visualizations of complex predictive models using machine learning is a challenge to experts who lack the requisite computer science skills. A team at the NYU Tandon School of Engineering's Visualization and Data Analytics (VIDA) lab, led by Claudio Silva, professor in the department of computer science and engineering, developed a framework called VisFlow, by which those who may not be experts in machine learning can create highly flexible data visualizations from almost any data. Furthermore, the team made it easier and more intuitive to edit these models by developing an extension of VisFlow called FlowSense, which allows users to synthesize data exploration pipelines through a natural language interface. The research, "FlowSense: A Natural Language Interface for Visual Data Exploration with a Dataflow System" won the best-paper award at this year's IEEE Conference on Visual Analytics Science and Technology (VAST).

Mark Kingwell is a professor of philosophy at the University of Toronto. More than five decades ago, Marshall McLuhan argued that media are ecosystems, extensions of human consciousness. The famous adage that the medium is the message also means, as the often-misquoted title of McLuhan's famous book notes, that the medium is the mass age. We are all immersed in media and technology. Media have changed a lot since McLuhan wrote: less broadcast, more diffusion and unruliness.

After a hugely successful first day at the Deep Learning Summit and Responsible AI Summit, we were back in Montreal for day two. Sportlogiq were up first on the Deep Learning stage with Bahar Pourbabaee, Machine Learning Team Lead, discussing some of the main challenges in developing and deploying deep learning algorithms at scale. The sheer size of this scale was reiterated with Bahar suggesting that they are processing more than 60,000 sports videos from different sources, all of which include many thousands of frames. Bahar's first example of Sportloqiq's latest work depicted that of a fast moving Premier League Soccer game, with examples showing the sheer depth of analysis suggesting that both decisions and non-decisions alongside their consequences can be looked at and scrutinised, whilst individual joints of players and their lateral movement was observed under the microscope. Bahar then continued to detail some of the problems with the visual perception of their learning representation model which included player/object detection, player/team identification, state estimation and data association.

Students are then asked to focus on a topic, performing a form of meditation. The device and its software measures a student's level of concentration. This high tech gadget measures neurological impulses of each student, assigning scores to their level of attention or focus. Higher scores are awarded to those with greater concentration or attentiveness to the lesson. Teachers can view these scores at any moment throughout their lessons, adjusting their lesson delivery to the results.

Today's leading minds talk AI with host Byron Reese On this Episode of Voices in AI Byron speaks with futurist and author Alexandra Levit about the nature of intelligence and her new book'Humanity Works'. Listen to this episode or read the full transcript at www.VoicesinAI.com Byron Reese: This is Voices in AI brought to you by GigaOm and I'm Byron Reese. Today my guest is Alexandra Levit, she is a futurist, a managing partner at People Results and the author of the new book, Humanity Works. She holds a degree in psychology and communications from Northwestern University.