Over the last fifty years, a large body of research in microeconomics has introduced many different mechanisms for resource allocation. Despite the wide variety of available options, "simple" mechanisms such as posted price and serial dictatorship are often preferred for practical applications, including housing allocation [Abdulkadiroğlu and Sönmez, 1998], online procurement [Badanidiyuru et al., 2012], or allocation of medical appointments [Klaus and Nichifor, 2019]. There has been considerable interest in formalizing different notions of simplicity. Li [2017] identifies mechanisms that are particularly simple from a strategic perspective, introducing the concept of obviously strategyproof mechanisms; under obviously strategyproof mechanisms, it is obvious that an agent cannot profit by trying to game the system, as even the worst possible final outcome from behaving truthfully is at least as good as the best possible outcome from any other strategy. Pycia and Troyan [2019] introduce the still stronger concept of strongly obviously strategyproof (SOSP) mechanisms, and show that this class can essentially be identified with sequential price mechanisms, where agents are visited in turn and offered a choice from a menu of options (which may or may not include transfers). SOSP mechanisms are ones in which an agent is not even required to consider her future (truthful) actions to understand that the mechanism is obviously strategyproof.

Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that provides: a) a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT; b) a feature-rich annotation interface for customizing and training IE models; and c) integrations to the broader CogStack ecosystem for vendor-agnostic health system deployment. We show improved performance in extracting UMLS concepts from open datasets ( F1 0.467-0.791 vs 0.384-0.691). Further real-world validation demonstrates SNOMED-CT extraction at 3 large London hospitals with self-supervised training over ~8.8B words from ~17M clinical records and further fine-tuning with ~6K clinician annotated examples. We show strong transferability ( F1 >0.94) between hospitals, datasets and concept types indicating cross-domain EHR-agnostic utility for accelerated clinical and research use cases.

The ability to predict multiple possible future positions of the ego-vehicle given the surrounding context while also estimating their probabilities is key to safe autonomous driving. Most of the current state-of-the-art Deep Learning approaches are trained on trajectory data to achieve this task. However trajectory data captured by sensor systems is highly imbalanced, since by far most of the trajectories follow straight lines with an approximately constant velocity. This poses a huge challenge for the task of predicting future positions, which is inherently a regression problem. Current state-of-the-art approaches alleviate this problem only by major preprocessing of the training data, e.g. resampling, clustering into anchors etc. In this paper we propose an approach which reformulates the prediction problem as a classification task, allowing for powerful tools, e.g. focal loss, to combat the imbalance. To this end we design a generative probabilistic model consisting of a deep neural network with a Mixture of Gaussian head. A smart choice of the latent variable allows for the reformulation of the log-likelihood function as a combination of a classification problem and a much simplified regression problem. The output of our model is an estimate of the probability density function of future positions, hence allowing for prediction of multiple possible positions while also estimating their probabilities. The proposed approach can easily incorporate context information and does not require any preprocessing of the data.

To develop and validate a deep learning (DL) algorithm to identify poor-quality lateral airway radiographs. A total of 1200 lateral airway radiographs obtained in emergency department patients between January 1, 2000, and July 1, 2019, were retrospectively queried from the picture archiving and communication system. Two radiologists classified each radiograph as adequate or inadequate. Disagreements were adjudicated by a third radiologist. The radiographs were used to train and test the DL classifiers.

A version of the PyTorch machine learning framework that incorporates a deep learning compiler to connect the Python package to cloud Tensor processors (TPUs) is now available on Google Cloud, the public cloud vendor and PyTorch co-developer Facebook announced. The general availability on PyTorch/XLA means users can access cloud TPU accelerators via a stable integration, the companies said Tuesday (Sept. Separately, promoters of the programming language R released a package that allows developers to use "PyTorch functionality natively from R." The new tool, dubbed "Torch for R," requires no Python installation. Meanwhile, Facebook and Google said PyTorch/XLA combines the machine learning library's APIs with XLA's linear algebra compiler that targets CPUs, GPUS and, now, cloud TPUs. While running on most standard Python programs, PyTorch/XLA defaults to CPUs for operations not yet supported on Tensor processors. That framework helps PyTorch users "find bottlenecks and adapt their programs to run more efficiently on cloud TPUs," said Craig Wiley, director of product development for Google's Cloud AI platform.

We've teamed up with O'Reilly for our newest bundle! Get ebooks like Learning SQL, 3rd Edition, Building Machine Learning Powered Applications, and Generative Deep Learning. Plus, your purchase will support Code For America! Normally, the total cost for the ebooks in this bundle is as much as $798. Here at Humble Bundle, you choose the price and increase your contribution to upgrade your bundle! This bundle has a minimum $1 purchase.

Deep in the bowels of the Smithsonian National Museum of American History in Washington, D.C., sits a large metal cabinet the size of a walk-in wardrobe. The cabinet houses a remarkable computer -- the front is covered in dials, switches and gauges, and inside, it is filled with potentiometers controlled by small electric motors. Behind one of the cabinet doors is a 20 by 20 array of light sensitive cells, a kind of artificial eye. This is the Perceptron Mark I, a simplified electronic version of a biological neuron. It was designed by the American psychologist Frank Rosenblatt at Cornell University in the late 1950s who taught it to recognize simple shapes such as triangles.

Founded in 2019, San Francisco-based Synthesis AI has developed technology that generates vast quantities of photorealistic images and pixel-perfect labels to optimize computer vision training. "The world is exploding with cameras," says Synthesis AI CEO Yashar Behzadi. This is great news for AI startups that specialize in computer vision, a field of AI that trains computers to interpret elements from digital images and videos. Up to now, computer vision has relied heavily on supervised learning, in which humans label key attributes in an image and then teach computers to do the same. But to Behzadi, this method has some pretty major setbacks.

Emerging technology is slowly finding a place in developing countries for its potential to plug gaps in ailing public service systems, such as healthcare. At the same time, cases of bias and discrimination that overlap with the complexity of algorithms have created a trust problem with technology. Promoting transparency in algorithmic decision-making through explainability can be pivotal in addressing the lack of trust with medical artificial intelligence (AI), but this comes with challenges for providers and regulators. In generating explainability, AI providers need to prioritise their accountability to patient safety given that the most accurate of algorithms are still opaque. There are also additional costs involved. Regulators looking to facilitate the entry of innovation while prioritising patient safety will need to look into ascertaining a reasonable level of explainability considering risk factors and the context of its use, and adaptive and experimental means of regulation. Artificial intelligence (AI) models across the globe have come under the scanner over ethical issues; for instance, Amazon's hiring algorithm reportedly discriminates against women,[1] and there is evidence of racial bias in the facial recognition software used by law enforcement in the United States (US).[2] While biased AI has various implications, concerns around the use of AI in ethically sensitive industries, such as healthcare, justifiably require closer examination. Medical AI models have become more commonplace in clinical and healthcare settings due to their higher accuracy and lower turnaround time and cost in comparison to non-AI techniques.

I started Practical Deep Learning for Coders 10 days ago. I am compelled to say their pragmatic approach is exactly what I needed. I started data science by learning Python, Pandas, NumPy, and whatever I needed in a short few months. I did whatever courses I need to do (e.g. Kaggle micro-courses) and whatever books I needed to read (e.g.