"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
In this article, you get to look over my shoulder as I go about debugging a TensorFlow model. I did a lot of dumb things, so please don't judge. You can see the final (working) model on GitHub. I'm building a model to predict lightning 30 minutes into the future and plan to present it at the American Meteorological Society. A model trained in this way can be used to predict lightning 30 minutes ahead in real-time given the current infrared and GLM data. I wrote up a convnet model borrowing liberally from the training loop of the ResNet model written for the TPU and adapted the input function (to read my data, not JPEG) and the model (a simple convolutional network, not ResNet).
Microsoft is the market leader when it comes to providing infrastructure as a service (IaaS) and platform as a service (PaaS) solutions. Microsoft Azure is the project that has not only benefitted the company in terms of ROI but has also changed the business dynamics of organizations around the globe. More and more companies are adopting Azure for their cloud and data products. Microsoft Azure AI was launched in 2018 and has emerged to be a success in the artificial intelligence services market too. Azure AI is a set of AI services built on Microsoft's breakthrough innovation from decades of world-class research in vision, speech, language processing, and custom machine learning.
With a dish of cells as a canvas, Anne Carpenter's collaborators apply layers of color. Each one highlights a different cellular feature: A fluorescent blue dye to stain the nuclei. Orange to label the cell membranes. This approach, called "Cell Painting," uses six biological dyes to stain eight major cell structures. Together, they create not just beautiful images, but also a detailed portrait of the cells' size, shape or morphology, and--if you can read the signs--physiological state.
Although artificial neurons and perceptrons were inspired by the biological processes scientists were able to observe in the brain back in the 50s, they do differ from their biological counterparts in several ways. Birds have inspired flight and horses have inspired locomotives and cars, yet none of today's transportation vehicles resemble metal skeletons of living-breathing-self replicating animals. Still, our limited machines are even more powerful in their own domains (thus, more useful to us humans), than their animal "ancestors" could ever be. It is easy to draw the wrong conclusions from the possibilities in AI research by anthropomorphizing Deep Neural Networks, but artificial and biological neurons do differ in more ways than just the materials of their containers. The idea behind perceptrons (the predecessors to artificial neurons) is that it is possible to mimic certain parts of neurons, such as dendrites, cell bodies and axons using simplified mathematical models of what limited knowledge we have on their inner workings: signals can be received from dendrites, and sent down the axon once enough signals were received.
Artificial Intelligence is surrounding us everywhere. Machine learning is a field of Artificial Intelligence which specializes in setting machine using algorithms to learn certain things by itself. Machine learning has a vast number of applications. We can approach machine learning systems by going out shopping, using our banking account or even in public transport. How much is machine learning changing things up?
Just like with the answer prediction feature, Jonathan and his team also didn't have any previous data to go on for the "Build it for me" feature. This meant they had to set up the conditions for another scale effect. There was never a precedence for the Build It For Me feature: you either chose to create a survey from scratch or you chose from one of the templates available. With the Build It For Me feature, you simply pick a target audience and a survey goal (which in Janice's case was getting feedback). Then you'd go on to choose the right template because that would then narrow down the options from 300 to 4. Because they had no previous data to work with, the data scientists weren't involved for this feature.
Tree-based machine learning models such as random forests, decision trees and gradient boosted trees are popular nonlinear predictive models, yet comparatively little attention has been paid to explaining their predictions. Here we improve the interpretability of tree-based models through three main contributions. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to (1) identify high-magnitude but low-frequency nonlinear mortality risk factors in the US population, (2) highlight distinct population subgroups with shared risk characteristics, (3) identify nonlinear interaction effects among risk factors for chronic kidney disease and (4) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model's performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains.
Valuations are relatively straightforward yet still involved exercises when similar properties in terms of hedonic variables[i] (also called comparables) transacted in the market close to the valuation date. In the absence of reliable comparable transactions, the possible value of a piece of real estate (be it residential or commercial) needs to be assessed using a valuation method. From back-of-the envelope cap rate models, transparent discounted cash-flow spreadsheets to sophisticated econometric models, any reliable valuation stands to benefit from accurate forecasts of expected levels of cash-flows and discount rates. The buying or selling decision is further influenced by the perceived current state of the real estate cycle but also the projected direction of the cycle. Predicting rents requires a good understanding of demand and supply forces at work in the space market, construction and how its financed, the evolution of the natural vacancy rate and possible migration flows of both firms and workers, among the more prominent determinants.