We investigate the behavior of convolutional neural networks (CNN) in the presence of label noise. We show empirically that CNN prediction for a given test sample depends on the labels of the training samples in its local neighborhood. This is similar to the way that the K-nearest neighbors (K-NN) classifier works. With this understanding, we derive an analytical expression for the expected accuracy of a K-NN, and hence a CNN, classifier for any level of noise. In particular, we show that K-NN, and CNN, are resistant to label noise that is randomly spread across the training set, but are very sensitive to label noise that is concentrated. Experiments on real datasets validate our analytical expression by showing that they match the empirical results for varying degrees of label noise.

Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification. Many large-scale datasets are collected from websites, however they tend to contain inaccurate labels that are termed as noisy labels. Training on such noisy labeled datasets causes performance degradation because DNNs easily overfit to noisy labels. To overcome this problem, we propose a joint optimization framework of learning DNN parameters and estimating true labels. Our framework can correct labels during training by alternating update of network parameters and labels. We conduct experiments on the noisy CIFAR-10 datasets and the Clothing1M dataset. The results indicate that our approach significantly outperforms other state-of-the-art methods.

" Wait, what?? That's a horrible idea! Were the exact words of our leading NLP researcher when I first talked to her about this concept. Maybe she's right, but it's also definitely a very interesting concept which is getting more attention in the Javascript community lately. During the past year our team is building Bit which makes it simpler to build software using components. As part of our work, we develop ML and NLP algorithms to better understand how code is written, organized and used.

A few weeks ago, we covered ARM's announcement that it would be delivering a suite of AI hardware IP for Deep Learning, called Project Trillium. ARM announced at the time that third party IP could be integrated with the Trillium platform, and now ARM and NVIDIA have teamed up to do just that. Specifically the two companies will integrate NVIDIA's IP for the acceleration of Convolutional Neural Networks (CNNs), the bread and butter for image processing and visually guided systems such as vehicles and drones. Without a lot of fanfare, NVIDIA's Deep Learning Accelerator (NVDLA) was open-sourced last fall, providing free Intellectual Property (IP) licensing to anyone wanting to build a chip that uses CNNs for inference applications (inference, for those unfamiliar, is the processing of a trained neural network). The crying sound you're now hearing around the world is probably a bunch of well-funded startups and their investors who thought that a dozen guys in a garage could out-engineer NVIDIA when it came to CNN accelerator chips.

The Linux Foundation is throwing its hat into the artificial intelligence ring. On Tuesday, the open source organization announced the launch of the Deep Learning Foundation. The official goal is to promote open source innovation in AI, machine learning, and deep learning. Also like most of the foundation's projects, this one is hitting the ground running with a flagship software project, and sponsors that include Amdocs, AT&T, B.Yond, Baidu, Huawei, Nokia, Tech Mahindra, Tencent, Univa, and ZTE. The software, which is already available for download, is the Acumos AI Project and was supplied by AT&T and India-based Tech Mahindra, a supplier of IT for telecoms.

For this tutorial in my Reinforcement Learning series, we are going to be exploring a family of RL algorithms called Q-Learning algorithms. These are a little different than the policy-based algorithms that will be looked at in the the following tutorials (Parts 1–3). Instead of starting with a complex and unwieldy deep neural network, we will begin by implementing a simple lookup-table version of the algorithm, and then show how to implement a neural-network equivalent using Tensorflow. Given that we are going back to basics, it may be best to think of this as Part-0 of the series. It will hopefully give an intuition into what is really happening in Q-Learning that we can then build on going forward when we eventually combine the policy gradient and Q-learning approaches to build state-of-the-art RL agents (If you are more interested in Policy Networks, or already have a grasp on Q-Learning, feel free to start the tutorial series here instead).

This all sounds ludicrous until we realize that our algorithms are increasingly being made in our own image. As we've learned more about our own brains, we've enlisted that knowledge to create algorithmic versions of ourselves. These algorithms control the speeds of driverless cars, identify targets for autonomous military drones, compute our susceptibility to commercial and political advertising, find our soulmates in online dating services, and evaluate our insurance and credit risks. Algorithms are becoming the near-sentient backdrop of our lives. The most popular algorithms currently being put into the workforce are deep learning algorithms.

In a sign Macron's efforts to woo top scientists and businesses may be starting to bear fruit, Samsung Electronics, Japan's Fujitsu and London-based Google-owned Deepmind announced plans to beef up their operations in Paris earlier.

For quite a long time, a common opinion in academic world was that machine achieving human master performance level in the game of Go was far from realistic. It was considered a'holy grail' of AI – a milestone we were quite far away from reaching within upcoming decade. Deep Blue had its moment more than 20 years ago and since then no Go engine became close to human masters. The opinion about'numerical chaos' in Go established so well it became referenced in movies, too. Surprisingly, in march 2016 an algorithm invented by Google Deepmind called Alpha Go defeated korean world champion in Go 4-1 proving fictional and real-life sceptics wrong. Around a year after that, Alpha Go Zero – the next generation of Alpha Go Lee (the one beating Korean master) – was reported to destroy its predecessor 100-0, being very doubtfully reachable for humans.

The raspberry pi is a neat piece of hardware that has captured the hearts of a generation with 15M devices sold, with hackers building even cooler projects on it. Given the popularity of Deep Learning and the Raspberry Pi Camera we thought it would be nice if we could detect any object using Deep Learning on the Pi. Now you will be able to detect a photobomber in your selfie, someone entering Harambe's cage, where someone kept the Sriracha or an Amazon delivery guy entering your house.