YouTube presents an unprecedented opportunity to explore how machine learning methods can improve healthcare information dissemination. We propose an interdisciplinary lens that synthesizes machine learning methods with healthcare informatics themes to address the critical issue of developing a scalable algorithmic solution to evaluate videos from a health literacy and patient education perspective. We develop a deep learning method to understand the level of medical knowledge encoded in YouTube videos. Preliminary results suggest that we can extract medical knowledge from YouTube videos and classify videos according to the embedded knowledge with satisfying performance. Deep learning methods show great promise in knowledge extraction, natural language understanding, and image classification, especially in an era of patient-centric care and precision medicine.

--We consider higher-order linear-chain conditional random fields (HO-LC-CRFs) for sequence modelling, and use sum-product networks (SPNs) for representing higher-order input-and output-dependent factors. SPNs are a recently introduced class of deep models for which exact and efficient inference can be performed. By combining HO-LC-CRFs with SPNs, expressive models over both the output labels and the hidden variables are instantiated while still enabling efficient exact inference. Furthermore, the use of higher-order factors allows us to capture relations of multiple input segments and multiple output labels as often present in real-world data. These relations can not be modeled by the commonly used first-order models and higher-order models with local factors including only a single output label. We demonstrate the effectiveness of our proposed models for sequence labeling. In extensive experiments, we outperform other state-of-the-art methods in optical character recognition and achieve competitive results in phone classification. For instance, they have been successfully used for speech recognition [3], optical character recognition and natural language processing [4]. Due to several advantages, LC-CRFs achieve better performance compared to their generative counterparts, i.e. hidden Markov models (HMMs) [3]. While LC-CRFs are normalized over the whole sequence, thereby counteracting the label bias problem, MEMMs are normalized locally .

The problem of recognizing actions or activities has been widely addressed in the computer vision research community: it consists in the classification of a fully or partially observed action, typically observed through cameras or external motion capture [2]. In robotics, recognizing the human activity is paramount for enabling a proper interaction and providing assistance to the human: an assistive device or prosthetics could switch control modes depending on the current human activity (e.g., walking or sitting) [3], [4]; a mobile robot may adapt its navigation depending on the prediction of the human motion [5]. More generally, prediction is important to provide the robot with anticipation capabilities [6]. In collaborative robotics applications in manufacturing, such as in assembly lines, recognizing the current activity of the operator is necessary for ergonomics evaluations [7] and for the optimization of the robot actions. However, there are two critical issues that prevent the direct application of existing techniques in such scenarios. The first issue is the availability of external sensing devices (cameras or motion captures) that poses constraints on the application for many tasks and application scenarios.

Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algorithms like AlphaZero and Expert Iteration learn tabula-rasa, producing highly informative training data on the fly. However, the self-play training strategy is not directly applicable to single-player games. Recently, several practically important combinatorial optimization problems, such as the traveling salesman problem and the bin packing problem, have been reformulated as reinforcement learning problems, increasing the importance of enabling the benefits of self-play beyond two-player games. We present the Ranked Reward (R2) algorithm which accomplishes this by ranking the rewards obtained by a single agent over multiple games to create a relative performance metric. Results from applying the R2 algorithm to instances of a two-dimensional bin packing problem show that it outperforms generic Monte Carlo tree search, heuristic algorithms and reinforcement learning algorithms not using ranked rewards.

The pioneer deep neural networks (DNNs) have emerged to be deeper or wider for improving their accuracy in various applications of artificial intelligence. However, DNNs are often too heavy to deploy in practice, and it is often required to control their architectures dynamically given computing resource budget, i.e., anytime prediction. While most existing approaches have focused on training multiple shallow sub-networks jointly, we study training thin sub-networks instead. To this end, we first build many inclusive thin sub-networks (of the same depth) under a minor modification of existing multi-branch DNNs, and found that they can significantly outperform the state-of-art dense architecture for anytime prediction. This is remarkable due to their simplicity and effectiveness, but training many thin sub-networks jointly faces a new challenge on training complexity. To address the issue, we also propose a novel DNN architecture by forcing a certain sparsity pattern on multi-branch network parameters, making them train efficiently for the purpose of anytime prediction. In our experiments on the ImageNet dataset, its sub-networks have up to $43.3\%$ smaller sizes (FLOPs) compared to those of the state-of-art anytime model with respect to the same accuracy. Finally, we also propose an alternative task under the proposed architecture using a hierarchical taxonomy, which brings a new angle for anytime prediction.

We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking. Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns. Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training. We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines. We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs. Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies.

Britain's National Health Service (NHS) announced in a recent press release that it will anonymize patients' personal health data before sharing it with Alphabet's DeepMind. The process could help the pair more effectively train machine learning-based healthcare tools without the risk of compliance issues. As noted by our sister site ZDNet, the two companies use the data to analyze blood results and detect risk of acute kidney injuries or other illnesses. Back in 2016, the NHS and Google's DeepMind received major flack for personal data being shared without explicit consent from patients, but the anonymization of the data could help alleviate these concerns. "The new de-identification process (known as De-ID) will protect patient privacy by de-identifying a person's records in a consistent way," said privacy engineering company Privitar in the release.

In a competitive marketplace, insights from recorded call center audio can help companies improve employee training, enhance lead qualification, increase sales, improve customer satisfaction, and reduce churn and employee turnover. But because call scoring is labor-intensive and time-consuming, most call centers only listen to approximately 2% of their recorded calls. Deepgram, a deep learning enabled voice-to-text technology company, is helping solve this challenge for enterprises by unlocking a wealth of information buried within these call recordings. Related: DeepGram's Dr. Scott Stephenson presents "From Dark Matter to Deep Learning in the Enterprise" at GPU Technology Conference 2018. They also have deep expertise in other industries, including medical, legal, media, and emergency services. One of Deepgram's early customers saw a 3% increase in annual revenue using the solution.

As a fan of poker, I know there's no such thing as a sure bet. But randomly pick 100 tech startups and I'd confidently wager that the vast majority are calling themselves artificial intelligence (AI) companies or at least weaving AI heavily into their narrative. AI hype has become intense, and understandably so. The technology has become part of everyday life, whether it's Netflix predicting what shows we might like based on previous choices, Google's search results consistently improving based on millions of people's clicks, or conversational AI systems like Amazon's Alexa getting to know you. Meanwhile, virtually invisible to mainstream consumers, the business world is agog over harnessing AI to solve a range of problems.

Over the last years, companies have reaped the benefits of embracing data-driven methods. The data 2.0 strategy will be driven by new AI techniques like deep learning, reinforcement learning, and Bayesian machine learning. Deep learning is the state-of-the-art approach to AI across machine translation, computer vision, and natural language processing. Not using these techniques hurts bottom lines and limits the end-user experience. Approaches using deep learning, solve problems end-to-end.