"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
Potentially toxic waste is just about the last thing you want to get in the mail. And that's just one of the opportunities for AI to make the business of analyzing wastewater better. It's an industry that goes far beyond just making sure water coming from traditional sewage plants is clean. Just about every industry on earth -- from computer chips to potato chips -- relies on putting water to work, which means we're all, literally, swimming in the stuff. It's also an industry that depends on moving samples through the mail to be analyzed by human experts.
After the explosive growth of open source machine learning and deep learning frameworks, the field is more accessible than ever. Thanks to this, it went from a tool for researchers to a widely adopted and used method, fueling the insane growth of technology we experience now. Understanding how the algorithms really work can give you a huge advantage in designing, developing and debugging machine learning systems. Due to its mathematical nature, this task can seem daunting for many. However, this does not have to be the way.
Data cleaning: Some people consider this feature engineering but it is really its own step. In short, you need to make sure the data is even useable before feature engineering is even possible. It involves fixing errors in the data, handling missing values, handling outliers, one-hot encoding, scaling features,and countless other things. In my opinion, data cleaning is the only step worse than feature engineering so anyone who finds a way to automate this step will be my new hero. Mean encoding: This step involves transforming categorical features like zip code into information useable by the model. For example, you might create a column that shows the average sales revenue for a zip code.
What does Timnit Gebru's firing and the recent papers coming out of Google tell us about the state of research at the world's biggest AI research department. The high point for Google's research in to Artifical Intelligence may well turn out to be the 19th of October 2017. This was the date that David Silver and his co-workers at DeepMind published a report, in the journal Nature, showing how their deep-learning algorithm AlphaGo Zero was a better Go player than not only the best human in the world, but all other Go-playing computers. What was most remarkable about AlphaGo Zero was that it worked without human assistance. The researchers set up a neural network, let it play lots of games of Go against itself and a few days later it was the best Go player in the world. Then they showed it chess and it took only four hours to become the best chess player in the world.
In the previous article, we introduced the concept of synthetic data and its applications in data privacy and machine learning. In this article, we will show you how to generate synthetic tabular data using a generative adversarial network (GAN). Tabular data is one of the most common and important data modalities. Enormous amounts of data, such as clinical trial records, financial data, census results, are all represented in tabular format. The ability to use synthetic datasets where sensitive attributes and Personally Identifiable Information (PII) are not disclosed, is crucial for staying compliant with privacy regulations, and convenient for data analysis, sharing, and experimenting.
Graph Neural Networks (GNNs) is a subtype of neural networks that operate on data structured as graphs. In an article covered earlier on Geometric Deep Learning, we saw how image processing, image classification, and speech recognition are represented in the Euclidean space. Graphs are non-Euclidean and can be used to study and analyse 3D data. GNN involves converting non-structured data like images and text into graphs to perform analysis. A graph is usually a representation of a data structure with two components -- Vertices (V) and Edges (E), which is generally put as; G Ω (V, E).
It's hard to feel connected to someone who's gone through a static photo. So a company called MyHeritage who provides automatic AI-powered photo enhancements is now offering a new service that can animate people in old photos creating a short video that looks like it was recorded while they posed and prepped for the portrait. Called Deep Nostalgia, the resulting videos are reminiscent of the Live Photos feature in iOS and iPadOS where several seconds of video are recorded and saved before and after the camera app's shutter is pressed. But where Live Photos is intended to be used to find the perfect shot and framing that may have been missed the exact second the shutter was pressed, Deep Nostalgia is instead meant to bring still shots, even those not captured on a modern smartphone, to life. The conversion process is completely automated. Users simply need to upload a photograph through the MyHeritage website where it's first sharpened and enhanced to not only improve the quality of the final animation but to also make it easier for the deep learning algorithm (created by a company called D-ID) to do its thing.
Deep Learning (DL) and Neural Networks (NN) not only automate the process of searching in huge volumes of information but also learn (store and use) from previously analysed data to improve the accuracy of overall searches. Today's research is focused on using deep learning and neural networks for classifying or categorizing the patents and finding similar patents. Natural language processing (NLP) is also being used to suggest contextually relevant keywords and their synonyms. This leads to improved concordance between the available knowledge and documents that users want to search for. AI can also help in deriving Insight into the strengths and weaknesses of a technology segment in certain geographies by cross referencing with the IP data and delivering an instant overview of the domain.
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition to near-human accuracy.
This post is part of a series covering the exercises from Andrew Ng's machine learning class on Coursera. The original code, exercise text, and data files for this post are available here. In part four we wrapped up our implementation of logistic regression by extending our solution to handle multi-class classification and testing it on the hand-written digits data set. Using just logistic regression we were able to hit a classification accuracy of about 97.5%, which is reasonably good but pretty much maxes out what we can achieve with a linear model. In this blog post we'll again tackle the hand-written digits data set, but this time using a feed-forward neural network with backpropagation.