Over this past summer, I was fortunate enough to be given the opportunity to deliver a speech to the State University of New York (SUNY) College of Optometry residency class of 2019. During this 20 minutes (which they likely perceived as just over an hour), I recommended that residents take a few moments and conduct a search of the world's literature using the key words "deep learning" with the disease of their choice. Conducting such a search myself gave me a better understanding of the likely direction of health care in my clinical lifetime. A study recently published in JAMA Ophthalmology describes a deep learning system which appears to show high sensitivity and specificity for the detection of glaucoma.1 Previously by Dr. Casella: Consider IOP fluctuations when diagnosing glaucoma Deep learning So, just what exactly is deep learning? In the arena of artificial intelligence, this subset of machine learning is based on so-called "neural networks" that process data into concepts.

Grammarly has found a niche suitable for the narrow capabilities of deep learning, and grown itself from a small app into the leading AI-based grammar checker.

Tests conducted in the Dell EMC HPC and AI Innovation Lab show that software can be virtualized in a containerized environment with no significant performance penalties.

An anonymous reader quotes a report from New Scientist: Yannis Assael at DeepMind and his colleagues trained a neural network, a type of AI algorithm, to guess missing words or characters from Greek inscriptions, on surfaces including stone, ceramic and metal, that were between 1500 and 2600 years old. The AI, called Pythia, learned to recognize patterns in 35,000 relics, containing more than 3 million words. The patterns it picks up on include the context in which different words appear, the grammar, and also the shape and layout of the inscriptions. Given an inscription with missing information, Pythia provides 20 different suggestions that could plug the gap, with the idea that someone could then select the best one using their own judgement and subject knowledge. "It's all about how we can help the experts," says Assael.

Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the target domain, covariate shift of source and target domain, different period of drifts in the source and target domains. Autonomous transfer learning (ATL) is proposed in this paper as a flexible deep learning approach for the online unsupervised transfer learning problem across many streaming processes. ATL offers an online domain adaptation strategy via the generative and discriminative phases coupled with the KL divergence based optimization strategy to produce a domain invariant network while putting forward an elastic network structure. It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain. The rigorous numerical evaluation has been conducted along with a comparison against recently published works. ATL demonstrates improved performance while showing significantly faster training speed than its counterparts.

While neural sequence generation models achieve initial su c-cess for many NLP applications, the canonical decoding procedure with left-to-right generation order (i.e., autoreg res-sive) in one-pass can not reflect the true nature of human revising a sentence to obtain a refined result. In this work, we propose XL-Editor, a novel training framework that enables state-of-the-art generalized autoregressive pretrainin g methods, XLNet specifically, to revise a given sentence by the variable-length insertion probability. Concretely, XL-E ditor can (1) estimate the probability of inserting a variable-le ngth sequence into a specific position of a given sentence; (2) execute post-editing operations such as insertion, deletion, and replacement based on the estimated variable-length insert ion probability; (3) complement existing sequence-to-sequen ce models to refine the generated sequences. Empirically, we first demonstrate better post-editing capabilities of XL-E ditor over XLNet on the text insertion and deletion tasks, which validates the effectiveness of our proposed framework. Fur - thermore, we extend XL-Editor to the unpaired text style transfer task, where transferring the target style onto a gi ven sentence can be naturally viewed as post-editing the senten ce into the target style. XL-Editor achieves significant impro ve-ment in style transfer accuracy and also maintains coherent semantic of the original sentence, showing the broad applic ability of our method.

Massive volumes of high-dimensional data that evolves over time is continuously collected by contemporary information processing systems, which brings up the problem of organizing this data into clusters, i.e. achieve the purpose of dimensional deduction, and meanwhile learning its temporal evolution patterns. In this paper, a framework for evolutionary subspace clustering, referred to as LSTM-ESCM, is introduced, which aims at clustering a set of evolving high-dimensional data points that lie in a union of low-dimensional evolving subspaces. In order to obtain the parsimonious data representation at each time step, we propose to exploit the so-called self-expressive trait of the data at each time point. At the same time, LSTM networks are implemented to extract the inherited temporal patterns behind data in an overall time frame. An efficient algorithm has been proposed based on MATLAB. Next, experiments are carried out on real-world datasets to demonstrate the effectiveness of our proposed approach. And the results show that the suggested algorithm dramatically outperforms other known similar approaches in terms of both run time and accuracy.

Endoscopic diagnosis is an important means for gastric polyp detection. In this paper, a panoramic image of gastroscopy is developed, which can display the inner surface of the stomach intuitively and comprehensively. Moreover, the proposed automatic detection solution can help doctors locate the polyps automatically, and reduce missed diagnosis. The main contributions of this paper are: firstly, a gastroscopic panorama reconstruction method is developed. The reconstruction does not require additional hardware devices, and can solve the problem of texture dislocation and illumination imbalance properly; secondly, an end-to-end multi-object detection for gastroscopic panorama is trained based on deep learning framework. Compared with traditional solutions, the automatic polyp detection system can locate all polyps in the inner wall of stomach in real time and assist doctors to find the lesions. Thirdly, the system was evaluated in the Affiliated Hospital of Zhejiang University. The results show that the average error of the panorama is less than 2 mm, the accuracy of the polyp detection is 95%, and the recall rate is 99%. In addition, the research roadmap of this paper has guiding significance for endoscopy-assisted detection of other human soft cavities.

For my capstone project at Metis, I decided to continue the work I was doing with natural language, film characterization and IBM Watson personality insights. In my previous project, I had successfully visualized the big five personality profiles of film characters across a single genre. However, for this project, I decided to take the work several steps further. Before embarking on my journey to become a data scientist, my work was primarily rooted in the immersive experience design industry. While I have been in the industry for nearly a decade, in 2015 I founded my own immersive experience company called Screenshot Productions.

Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it?