The Convolutional Neural Networks (CNNs) have laid the foundation for many techniques in various applications. Despite achieving remarkable performance in some tasks, the 3D viewpoint generalizability of CNNs is still far behind humans visual capabilities. Although recent efforts, such as the Capsule Networks, have been made to address this issue, these new models are either hard to train and/or incompatible with existing CNN-based techniques specialized for different applications. Observing that humans use binocular vision to understand the world, we study in this paper whether the 3D viewpoint generalizability of CNNs can be achieved via a binocular vision. We propose CNN {2}, a CNN that takes two images as input, which resembles the process of an object being viewed from the left eye and the right eye.

Apple quietly announced four new versions of its iPad Pro and an upgraded MacBook Air on Monday. Instead of hosting a glitzy showcasing event, Apple revealed the devices in a news release. Starting next week, the tech giant will ship its latest iPad Pro in two sizes. There is an 11-inch model that starts at $799 and a 12.9-inch model that starts at $999. There are cellular-service-enabled upgrades to each of those, ringing up at $949 and $1,149, respectively.

The Bipartisan Press uses machine learning to assign webpages and texts a political bias score. Artificial intelligence can combat political bias. Yes, we all know that AI has had its own problems with bias in the past (and present). However, one news website is harnessing AI in the fight against overt political partisanship. And in an age when the world is becoming more politically polarised, its efforts couldn't be more timely.

This article is based on the keynote given by Tony Jebara at TensorFlow World in Santa Clara, California, October 2019. You can watch the presentation here. Machine learning is at the heart of everything we do at Spotify. Especially on Spotify Home, where it enables us to personalize the user experience and provide billions of fans the opportunity to enjoy and be inspired by the artists on our platform. This is what makes Spotify unique.

Artificial intelligence (AI) is one of the most exciting technological growth areas in recent years, with some investors like technologically-focused entrepreneur Tej Kohli predicting the sector will be worth $150 trillion (£125tn) by 2025, but why do we need the technology in our phones? Flagship devices today all come equipped with specialised AI processing chips, known and neural engines or neural processing units, from Apple's A12 Bionic CPU to Huawei's Kirin 980 or Qualcomm's Snapdragon 845, and more and more tasks are using their advanced processing capabilities. The most obvious artificial intelligence in our phones are the voice assistants that learn to understand our voice commands and then act appropriately from telling us the weather to playing our favourite song or adding an appointment to our calendar. Google, Apple, and Amazon have steered clear of labelling their services as AI so as not to scare away users fearful of a robot takeover, but these services rely on machine learning to function – understanding what you are telling them to do and then performing the right action. Possibly the most advanced implementation of any digital assistant is Google's Duplex service that will make calls and interact with other people and businesses on your behalf.

This is Part I of a two-part series on "How to assess Artificial Intelligence (AI) startups". Check out Part II here. During the past few years, we've witnessed a massive growth in the number of so-called Artificial Intelligence (AI) startups. Both sides of the table, investors and entrepreneurs, have jumped into the frenzy with intent. This new trend responds to significant advancements in AI research.

Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language applications such as question answering, text summarization, and machine translation. Early NER systems got a huge success in achieving good performance with the cost of human engineering in designing domain-specific features and rules. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

The recent advances in the field of deep learning have not been fully utilised for decoding imagined speech primarily because of the unavailability of sufficient training samples to train a deep network. In this paper, we present a novel architecture that employs deep neural network (DNN) for classifying the words "in" and "cooperate" from the corresponding EEG signals in the ASU imagined speech dataset. Nine EEG channels, which best capture the underlying cortical activity, are chosen using common spatial pattern (CSP) and are treated as independent data vectors. Discrete wavelet transform (DWT) is used for feature extraction. To the best of our knowledge, so far DNN has not been employed as a classifier in decoding imagined speech. Treating the selected EEG channels corresponding to each imagined word as independent data vectors helps in providing sufficient number of samples to train a DNN. For each test trial, the final class label is obtained by applying a majority voting on the classification results of the individual channels considered in the trial. We have achieved accuracies comparable to the state-of-the-art results. The results can be further improved by using a higher-density EEG acquisition system in conjunction with other deep learning techniques such as long short-term memory.

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To help autonomous vehicles and robots potentially spot objects that lie just outside a system's direct line-of-sight, Stanford, Princeton, Rice, and Southern Methodist universities researchers developed a deep learning-based system that can detect objects, including words and symbols, around corners. "Compared to other approaches, our non-line-of-sight imaging system provides uniquely high resolutions and imaging speeds," said Stanford University's Chris Metzler, on the Rice University post, Cameras see around corners in real time with deep learning. "These attributes enable applications that wouldn't otherwise be possible," he added. To achieve this, the system relies on a laser that can capture detailed images of objects around corners in real time. Specifically, a light from a high-speed laser is beamed onto a wall, the light from the hidden area bounces back to the wall, and that light is reflected to a camera.