Document Embedding Techniques

Word embeddings – the mapping of words into numerical vector spaces – has proved to be an incredibly important method for natural language processing (NLP) tasks in recent years, enabling various machine learning models that rely on vector representation as input to enjoy richer representations of text input. These representation preserve more semantic and syntactic information on words, leading to improved performance in almost every imaginable NLP task.

BERT is changing the NLP landscape

BERT is changing the NLP landscape and making chatbots much smarter by enabling computers to better understand speech and respond intelligently in real-time. What Makes BERT so Amazing?
• BERT is a contextual model.
• BERT enables transfer learning.
• BERT can be fine-tuned cheaply and quickly.

Introduction to Neural Networks and Their Key Elements (Part-B) – Hyper-Parameters

In the previous story (part A) we discussed the structure and three main building blocks of a Neural Network. This story will take you through the elements which really make a useful force and separate them from rest of the Machine Learning Algorithms. Previously we discussed about Units/Neurons, Weights/Parameters & Biases today we will discuss – Hyper-Parameters

Tutorial on Variational Graph Auto-Encoders

Graphs are applicable to many real-world datasets such as social networks, citation networks, chemical graphs, etc. The growing interest in graph-structured data increases the number of researches in graph neural networks. Variational autoencoders (VAEs) embodied the success of variational Bayesian methods in deep learning and have inspired a wide range of ongoing researches. Variational graph autoencoder (VGAE) applies the idea of VAE on graph-structured data, which significantly improves predictive performance on a number of citation network datasets such as Cora and Citesser. I searched on the internet and have yet to see a detailed tutorial on VGAE. In this article, I will briefly talk about traditional autoencoders and variational autoencoders. Furthermore, I will discuss the idea of applying VAE to graph-structured data (VGAE).

Automate Hyperparameter Tuning for your models

When we create our machine learning models, a common task that falls on us is how to tune them. People end up taking different manual approaches. Some of them work, and some don’t, and a lot of time is spent in anticipation and running the code again and again. So that brings us to the quintessential question: Can we automate this process?

Is the pain worth it?: Can Rcpp speed up Passing Bablok Regression?

R dogma is that for loops are bad because they are slow but this is not the case in C++. I had never programmed a line of C++ as of last week but my beloved firstborn started university last week and is enrolled in a C++ intro course, so I thought I would try to learn some and see if it would speed up Passing Bablok regression.

What it is really like to develop a model for a real-world business case. Have you ever taken part in a Kaggle competition? If you are studying, or have studied machine learning it is fairly likely that at some point you will have entered one. It is definitely a great way to put your model building skills into practice and I spent quite a bit of time on Kaggle when I was studying.

A Breakthrough for A.I. Technology: Passing an 8th-Grade Science Test

On Wednesday, the Allen Institute for Artificial Intelligence, a prominent lab in Seattle, unveiled a new system that passed the test with room to spare. It correctly answered more than 90 percent of the questions on an eighth-grade science test and more than 80 percent on a 12th-grade exam.

The Anthropologist of Artificial Intelligence

How do new scientific disciplines get started? For Iyad Rahwan, a computational social scientist with self-described ‘maverick’ tendencies, it happened on a sunny afternoon in Cambridge, Massachusetts, in October 2017. Rahwan and Manuel Cebrian, a colleague from the MIT Media Lab, were sitting in Harvard Yard discussing how to best describe their preferred brand of multidisciplinary research. The rapid rise of artificial intelligence technology had generated new questions about the relationship between people and machines, which they had set out to explore. Rahwan, for example, had been exploring the question of ethical behavior for a self-driving car – should it swerve to avoid an oncoming SUV, even if it means hitting a cyclist? – in his Moral Machine experiment.

Getting Started With Text Preprocessing for Machine Learning & NLP

Based on some recent conversations, I realized that text preprocessing is a severely overlooked topic. A few people I spoke to mentioned inconsistent results from their NLP applications only to realize that they were not preprocessing their text or were using the wrong kind of text preprocessing for their project. With that in mind, I thought of shedding some light around what text preprocessing really is, the different techniques of text preprocessing and a way to estimate how much preprocessing you may need. For those interested, I’ve also made some text preprocessing code snippets in python for you to try. Now, let’s get started!

Introducing Neural Structured Learning in TensorFlow

We are excited to introduce Neural Structured Learning in TensorFlow, an easy-to-use framework that both novice and advanced developers can use for training neural networks with structured signals. Neural Structured Learning (NSL) can be applied to construct accurate and robust models for vision, language understanding, and prediction in general.

2018 in Review: 10 AI Failures

• Chinese billionaire’s face identified as jaywalker
• Uber self-driving car kills a pedestrian
• IBM Watson comes up short in healthcare
• Amazon AI recruiting tool is gender biased
• DeepFakes reveals AI’s unseemly side
• Google Photo confuses skier and mountain
• LG robot Cloi gets stagefright at its unveiling
• Boston Dynamics robot blooper
• AI World Cup 2018 predictions almost all wrong
• Startup claims to predict IQ from faces

Uber has troves of data on how people navigate cities. Urban planners have begged, pleaded, and gone to court for access. Will they ever get it?

As the deputy director for technology, data, and analysis at the San Francisco County Transportation Authority, Castiglione spends his days manipulating models of the Bay Area and its 7 million residents. From wide-sweeping ridership and traffic data to deep dives into personal travel choices via surveys, his models are able to estimate the number of people who will disembark at a specific train platform at a certain time of day and predict how that might change if a new housing development is built nearby, or if train-frequency is increased. The models are exceedingly complex, because people are so complex. ‘Think about the travel choices you’ve made in the last week, or the last year,’ Castiglione says. ‘How do you time your trips? What tradeoffs do you make? What modes of transportation do you use? How do those choices change from day to day?’ He has the deep voice of an NPR host and the demeanor of a patient professor. ‘The models are complex but highly rational,’ he says.

Visualizing SVM with Python

In my previous article, I introduced the idea behind the classification algorithm Support Vector Machine. Here, I’m going to show you a practical application in Python of what I’ve been explaining, and I will do so by using the well-known Iris dataset. Following the same structure of that article, I will first deal on linearly separable data, then I will move towards no-linearly separable data, so that you can appreciate the power of SVM which lie in the so-called Kernel Trick.

How to generate neural network confidence intervals with Keras

Whether we’re predicting water levels, queue lengths or bike rentals, at HAL24K we do a lot of regression, with everything from random forests to recurrent neural networks. And as good as our models are, we know they can never be perfect. Therefore, whenever we provide our customers with predictions, we also like to include a set of confidence intervals: what range around the prediction will the actual value fall within, with (e.g.) 80% confidence?