Researchers have demonstrated that they can fool a modern face recognition system into seeing someone who isn’t there.
A team from the cybersecurity firm McAfee set up the attack against a facial recognition system similar to those currently used at airports for passport verification. By using machine learning, they created an image that looked like one person to the human eye, but was identified as somebody else by the face recognition algorithm—the equivalent of tricking the machine into allowing someone to board a flight despite being on a no-fly list. Read More
Daily Archives: August 6, 2020
GPT-3 Written Blog Got 26 Thousand Visitors in 2 Weeks
The future of online media
What does it mean when a computer can write about our problems better than we can?
People have been talking a lot about GPT-3, but more as a novelty than a tool (don’t know what GPT-3 is? look here). Some clever people have even figured out how to get it to generate code from descriptions. Yet, I think that the best use cases lie outside of tech. Read More
Hackers Broke Into Real News Sites to Plant Fake Stories
Over the past few years, online disinformation has taken evolutionary leaps forward, with the Internet Research Agency pumping out artificial outrage on social media and hackers leaking documents—both real and fabricated—to suit their narrative. More recently, Eastern Europe has faced a broad campaign that takes fake news ops to yet another level: hacking legitimate news sites to plant fake stories, then hurriedly amplifying them on social media before they’re taken down.
On Wednesday, security firm FireEye released a report on a disinformation-focused group it’s calling Ghostwriter. The propagandists have created and disseminated disinformation since at least March 2017, with a focus on undermining NATO and the US troops in Poland and the Baltics; they’ve posted fake content on everything from social media to pro-Russian news websites. In some cases, FireEye says, Ghostwriter has deployed a bolder tactic: hacking the content management systems of news websites to post their own stories. They then disseminate their literal fake news with spoofed emails, social media, and even op-eds the propagandists write on other sites that accept user-generated content. Read More
BART for Paraphrasing with Simple Transformers
BART is a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
– BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension –
Don’t worry if that sounds a little complicated; we are going to break it down and see what it all means. To add a little bit of background before we dive into BART, it’s time for the now-customary ode to Transfer Learning with self-supervised models. It’s been said many times over the past couple of years, but Transformers really have achieved incredible success in a wide variety of Natural Language Processing (NLP) tasks. Read More
Evolving IT environments require officials to plan for the next-generation SOCs.
Today’s hybrid IT environments, which incorporate cloud and on-premise infrastructure, demand structural changes to agency security operations centers, or SOCs, to be better able to operate within cyberspace versus simply reacting to it.
SOCs face plenty of challenges: serving the needs of remote and teleworking employees, managing multiple cloud platforms, and dealing with the exploding number of IT-configurable devices on emerging 5G networks. Read More
Can Your AI Differentiate Cats from Covid-19?Sample Efficient Uncertainty Estimation for Deep Learning Safety
Deep Neural Networks (DNNs) are known to make highly overconfident predictions on Out-of-Distribution data. Recent research has shown that uncertainty-aware models, such as, Bayesian Neural Network (BNNs) and Deep Ensembles,are less susceptible to this issue. However research in this area has been largely confined to the big data setting. In this work, we show that even state-of-the-art BNNs and Ensemble models tend to make overconfident predictions when the amount of training data is insufficient. This is especially concerning for emerging applications in physical sciences and healthcare where over-confident and inaccurate predictions can lead to disastrous consequences. To address the issue of accurate uncertainty (or confidence) estimation in the small-data regime, we propose a probabilistic generalization of the popular sample-efficient non-parametric kNN approach. We demonstrate the usefulness of the proposed approach on a real-world application of COVID-19 diagnosis from chest X-Rays by (a) highlighting surprising failures of existing techniques, and (b) achieving superior uncertainty quantification as compared to state-of-the-art. Read More
Generative Pretraining from Pixels
Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Trans-former to autoregressively predict pixels, without incorporating knowledge of the 2D input structure.Despite training on low-resolution ImageNet without labels, we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full fine-tuning, matching the top supervised pretrained models. An even larger model trained on a mixture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet,achieving 72.0% top-1 accuracy on a linear probe of our features. Read More
#image-recognitionWhy are so many AI systems named after Muppets?
An inside joke that says a lot about AI development
One of the biggest trends in AI recently has been the creation of machine learning models that can generate the written word with unprecedented fluidity. These programs are game-changers, potentially supercharging computers’ ability to parse and produce language.
But something that’s gone largely unnoticed is a secondary trend — a shadow to the first — and that is: a surprising number of these tools are named after Muppets. Read More
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