The KOSMOS platform employs multiple layers of applied deep learning … clinical value through the meaningful application of artificial intelligence.

Adopters of AI in the enterprise are focusing on specific production workloads centered around supervised and deep learning while the number of organizations using AI in production or evaluating the technology jumped to 85 percent of companies polled in an annual survey. Another indication of maturing enterprise AI initiatives is a heavier emphasis on data governance, according to an AI adoption survey released Wednesday (March 18) by O'Reilly Media. More than 26 percent of respondents said they are instituting formal governance processes as concerns about privacy and "trustworthy" AI grow. Nearly 35 percent of those surveyed said they expect to launch AI data governance efforts over the next three years, O'Reilly reported. "AI adoption is proceeding apace," wrote report authors Roger Magoulas and Steve Swoyer.

You already know that Deep Learning is good at vision, translation, playing games, and other tasks. But Neural Networks don't "learn" the way humans do, instead it's just really good at fast pattern matching. Today's research mainly focuses on bigger models with larger datasets, bigger models, and complicated loss functions. But the next revolution is likely going to be more fundamental. Let's take a look at two approaches: adding logic with Stacked Capsule Auto Encoders and Self-Supervised Learning at scale. This about sums up what most AI scientists already know: Deep Learning is really good at doing narrow, pattern based tasks such as object or speech recognition.

Though ERNIE 1.0 (released in March 2019) has been a popular model for text classification, it was ERNIE 2.0 which became the talk of the town in the latter half of 2019. Developed by tech-giant Baidu, ERNIE outperformed Google XLNet and BERT on the GLUE benchmark for English. ERNIE stands for Enhanced Representation through kNowledge IntEgration, and ERNIE 2.0 is an upgraded version of ERNIE 1.0. ERNIE 1.0 was pathbreaking in its own way – it was one of the first models to leverage Knowledge Graphs. This incorporation further enhanced training the model for advanced tasks like Relation Classification and NamedEntityRecognition (NER). Like its predecessor, ERNIE 2.0 brings another innovation to the table in the form of Continual Incremental Multi-task Learning.

Deep learning is currently the most widespread and successful technology in artificial intelligence. It promises to push the frontier of scientific discovery beyond current limits. However, skeptics have worried that deep neural networks are black boxes, and have called into question whether these advances can really be deemed scientific progress if humans cannot understand them. Relatedly, these systems also possess bewildering new vulnerabilities: most notably a susceptibility to "adversarial examples". In this paper, I argue that adversarial examples will become a flashpoint of debate in philosophy and diverse sciences. Specifically, new findings concerning adversarial examples have challenged the consensus view that the networks' verdicts on these cases are caused by overfitting idiosyncratic noise in the training set, and may instead be the result of detecting predictively useful "intrinsic features of the data geometry" that humans cannot perceive (Ilyas et al., 2019). These results should cause us to re-examine responses to one of the deepest puzzles at the intersection of philosophy and science: Nelson Goodman's "new riddle" of induction. Specifically, they raise the possibility that progress in a number of sciences will depend upon the detection and manipulation of useful features that humans find inscrutable. Before we can evaluate this possibility, however, we must decide which (if any) of these inscrutable features are real but available only to "alien" perception and cognition, and which are distinctive artifacts of deep learning-for artifacts like lens flares or Gibbs phenomena can be similarly useful for prediction, but are usually seen as obstacles to scientific theorizing. Thus, machine learning researchers urgently need to develop a theory of artifacts for deep neural networks, and I conclude by sketching some initial directions for this area of research.

Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodule in the CT image pose a challenging problem to the robust segmentation of the lung nodules. This article proposes U-Det, a resource-efficient model architecture, which is an end to end deep learning approach to solve the task at hand. It incorporates a Bi-FPN (bidirectional feature network) between the encoder and decoder. Furthermore, it uses Mish activation function and class weights of masks to enhance segmentation efficiency. The proposed model is extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. The U-Det architecture outperforms the existing U-Net model with the Dice similarity coefficient (DSC) of 82.82% and achieves results comparable to human experts.

Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in accelerometer and gyroscope data. A novel method to classify a lifting dataset using a 2D convolutional neural network (CNN) and no manual feature extraction is proposed in this paper; the dataset consisted of 10 subjects lifting at various relative distances from the body with 720 total trials. The proposed deep CNN displayed greater accuracy (90.6%) compared to an alternative CNN and multilayer perceptron (MLP). A deep CNN could be adapted to classify many other activities that traditionally pose greater challenges in industrial environments due to their size and complexity.

Supervised learning with irregularly sampled time series have been a challenge to Machine Learning methods due to the obstacle of dealing with irregular time intervals. Some papers introduced recently recurrent neural network models that deals with irregularity, but most of them rely on complex mechanisms to achieve a better performance. This work propose a novel method to represent timestamps (hours or dates) as dense vectors using sinusoidal functions, called Time Embeddings. As a data input method it and can be applied to most machine learning models. The method was evaluated with two predictive tasks from MIMIC III, a dataset of irregularly sampled time series of electronic health records. Our tests showed an improvement to LSTM-based and classical machine learning models, specially with very irregular data.

This paper investigates the idea of encoding object-centered representations in the design of the reward function and policy architectures of a language-guided reinforcement learning agent. This is done using a combination of object-wise permutation invariant networks inspired from Deep Sets and gated-attention mechanisms. In a 2D procedurally-generated world where agents targeting goals in natural language navigate and interact with objects, we show that these architectures demonstrate strong generalization capacities to out-of-distribution goals. We study the generalization to varying numbers of objects at test time and further extend the object-centered architectures to goals involving relational reasoning.

Melody is one of the most important components in music. Unlike other components in music theory, such as harmony and counterpoint, computable features for melody is urgently in need. These features are highly demanded as data-driven methods dominating the fields such as musical information retrieval and automatic music composition. To boost the performance of deep-learning-related musical tasks, we propose a set of interpretable features on monophonic melody for computational purposes. These features are defined not only in mathematical form, but also with some considerations on composers 'intuition. For example, the Melodic Center of Gravity can reflect the sentence-wise contour of the melody, the local / global melody dynamics quantifies the dynamics of a melody that couples pitch and time in a sentence. We found that these features are considered by people universally in many genres of songs, even for atonal composition practices. Hopefully, these melodic features can provide nov el inspiration for future researchers as a tool in the field of MIR and automatic composition.