Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive their continuous relaxations, and perform gradient-based optimization with deep neural networks. Our results establish the new state-of-the-art for image retrieval by Hamming ranking in common benchmarks.

FOSTER CITY, Calif., March 27, 2018 -- GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite, today announced the immediate availability of GridGain Professional Edition 2.4, a fully supported version of Apache Ignite 2.4. GridGain Professional Edition 2.4 now includes a Continuous Learning Framework, which includes machine learning and a multilayer perceptron (MLP) neural network that enable companies to run machine and deep learning algorithms against their petabyte-scale operational datasets in real-time. Companies can now build and continuously update models at in-memory speeds and with massive horizontal scalability. GridGain Professional Edition 2.4 also enhances the performance of Apache Spark by introducing an API for Apache Spark DataFrames, adding to the existing support for Spark RDDs. GridGain Continuous Learning Framework GridGain Professional Edition 2.4 now includes the first fully supported release of the Apache Ignite integrated machine learning and multilayer perceptron features, making continuous learning using machine learning and deep learning available directly in GridGain.

As a guy who has his own deep learning library that aims to rival Tensorflow and PyTorch, the answer is: "chances are, no". Around this time last year, I was running a startup and as a side hustle, I was doing consulting work for any parties interested in machine learning. I get a lot of requests from businesses who want to empower their businesses with "AI". The results from any successful consults*Don't let terms like "consultancy" fool you - I spent 90% of the time getting rejections or me rejecting them. This is the typical representation of a neural network architecture, which I got from Wikipedia, released as public domain.

Obviously not the paper author, but this looks quite interesting. Mostly the fact that it finds all the local minima and can thus select the global minimum from them is nice. Though it would have been nice to see what the tradeoff is in terms of computational space and time complexity compared to error backpropagation.

This is about AlphaGo, Google DeepMind's Go playing AI that shook the technology world in 2016 by defeating one of the best players in the world, Lee Sedol. Go is an ancient board game which has so many possible moves at each step that future positions are hard to predict -- and therefore it requires strong intuition and abstract thinking to play. Because of this reason, it was believed that only humans could be good at playing Go. Most researchers thought that it would still take decades to build an AI which could think like that. In fact, I'm releasing this essay today because this week (March 8–15) marks the two-year anniversary of the AlphaGo vs Sedol match!

This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn't necessary to have prior knowledge of them to gain value from this series. Artificial intelligence will shape our future more powerfully than any other innovation this century. Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic. The rate of acceleration is already astounding.

If you haven't yet heard of generative adversarial networks, don't worry, you will. The hottest topic in deep learning, GANs, as they're called, have the potential to create systems that learn more with less help from humans. Just ask Ian Goodfellow, who hatched the idea for GANs in 2014 when he was still a Ph.D. student at the University of Montreal. Now a research scientist at Google, Goodfellow explained the workings and whys of GANs to a rapt crowd at the GPU Technology Conference last week. GANs remove one of the biggest obstacles to advancing AI, and particularly deep learning: the huge amount of human effort required.

Nvidia has unveiled several updates to its deep-learning computing platform, including an absurdly powerful GPU and supercomputer. At this year's GPU Technology Conference in San Jose, Nvidia CEO Jensen Huang unveiled the DGX-2, a new computer for researchers who are "pushing the outer limits of deep-learning research and computing" to train artificial intelligence. The computer, which will ship later this year, is the world's first system to sport a whopping two petaflops of performance. For some perspective: A Macbook Pro might have around one teraflop. A petaflop is one thousand teraflops.

Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings. That said, deep learning is rapidly advancing, and it's a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology.

In some applications of machine learning, predictive performance is all that matters. Indeed, in these cases it is often possible to build a large number of alternative models that, while different in structure, all make excellent near-optimal functional predictions. In biology, however, prediction is not enough. The key additional question is which of the many excellent predictive models is the one actually used by the living system, as optimized not by computation but by evolution. DCell provides proof-ofconcept of a system that, while optimizing functional prediction, respects biological structure.