Europe, not the U.S., is now the most powerful regulator of Silicon Valley
Europe implemented a sweeping overhaul of digital-privacy laws on Friday that has reshaped how technology companies handle customer data, creating a de-facto global standard that gives Americans new protections and the nation’s technology companies new headaches.
Navigating the risks of artificial intelligence and machine learning in low-income countries
Based on ongoing research and interviews with aid workers and technology firms, we’ve learned five basic things to keep in mind when applying AI and ML in low-income countries
Facebook Is Designing Its Own Chips to Help Filter Live Videos
Facebook Inc. is working on designing computer-chips that are more energy-efficient at analyzing and filtering live video content, its chief artificial intelligence scientist Yann LeCun said.
Why AI Will Bring an Explosion of New Jobs
What if it’s not the end of work but the beginning of a massive job boom unlike anything we’ve ever seen in history?
Skill shift: Automation and the future of the workforce
Demand for technological, social and emotional, and higher cognitive skills will rise by 2030. How will workers and organizations adapt?
LOTS OF LOBBIES AND ZERO ZOMBIES: HOW SELF-DRIVING CARS WILL RESHAPE CITIES
some ideas for how urban planners of the future might reimagine those outdated layouts — and transform the city into a joyful mess of throughways and byways optimized not for cars but for people.
Does the brain store information in discrete or analog form?
New evidence in favor of a discrete form of data storage could change the way we understand the brain and the devices we build to interface with it.
Basic instincts
Some say artificial intelligence needs to learn like a child.
Growing up with AI: How can families play and learn with their new smart toys and companions?
what this experience will look like for today’s children, who are not just growing up with the web, but are also the first generation of kids to grow up with artificial intelligence (AI) in their daily lives.
Deep Convolutional Neural Networks as Models of the Visual System: Q&A
Q&A format to paint a fairly reasonable and accurate picture of the use of CNNs for modeling biological vision.
Standardizing a Machine Learning Framework for Applied Research — PyTorch vs MXNet
Until now, the Machine Learning (ML) frameworks we’ve used at Borealis AI have varied according to individual preference. But as our applied team grows, we’re finding that a preference-based system has certain shortcomings that have led to inefficiencies and delays in our research projects. As a result, we identified two main arguments in favour of standardizing a single framework for the lab.
Categorizing Listing Photos at Airbnb
Large-scale deep learning models are changing the way we think about images of homes on our platform.
“That’s Mental!” Using LDA Topic Modeling to Investigate the Discourse on Mental Health over Time
For this project I set out to investigate the contexts in which ‘mental health’ has been brought up over time. For this purpose, I collected ~30k New York Times articles from the 80s to present to analyze using topic modeling.
Russian Natural Language Processing
Is there really one NLP language model to rule them all?
Into a Textual Heart of Darkness
Going zero to not-quite-hero in NLP via hate speech classification
Self-Attention Generative Adversarial Networks
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.
A List of Chip/IP for Deep Learning (keep updating)
Machine Learning, especially Deep Learning technology is driving the evolution of artificial intelligence (AI). At the beginning, deep learning has primarily been a software play. Start from the year 2016, the need for more efficient hardware acceleration of AI/ML/DL was recognized in academia and industry. This year, we saw more and more players, including world’s top semiconductor companies as well as a number of startups, even tech giants Google, have jumped into the race. I believe that it could be very interesting to look at them together. So, I build this list of AI/ML/DL ICs and IPs on Github and keep updating. If you have any suggestion or new information, please let me know.
