I met Aman Naimat, Senior Vice President of Technology at Demandbase at their headquarters in San Francisco on my recent visit to California for Social Media Strategies Summit. Aman is working on leveraging the latest developments in Artificial Intelligence (AI) and data science for marketing and sales platforms. On this podcast, episode 150, Aman and I discuss how AI functions in account-based marketing (ABM). Before working with Demandbase Aman was previously founder and CTO of Spiderbook, a data-driven sales engine for account-based targeting. Aman has been building CRM systems since he was 19 and was the architect for the Oracle CRM Applications.

Language students are most engaged while reading texts at an appropriate difficulty level. However, existing methods of evaluating text difficulty focus mainly on vocabulary and do not prioritize grammatical features, hence they do not work well for language learners with limited knowledge of grammar. In this paper, we introduce grammatical templates, the expert-identified units of grammar that students learn from class, as an important feature of text difficulty evaluation. Experimental classification results show that grammatical template features significantly improve text difficulty prediction accuracy over baseline readability features by 7.4%. Moreover, we build a simple and human-understandable text difficulty evaluation approach with 87.7% accuracy, using only 5 grammatical template features.

Data and analytics leaders face a dilemma. Without data scientists, venturing into machine learning and data science is difficult. Without any successful pilots, convincing the business to hire data scientists is equally challenging. Enterprises don't have to have a large data science lab in order to take advantage of machine learning. "Many organizations are still in the early phases of their data science journey and struggle to understand what machine learning and data science can do for them," says Cindi Howson, research vice president at Gartner.

Successful Student has compiled the 25 Best Artificial Intelligence Colleges in the United States. Artificial Intelligence (AI), also known as machine learning, is a discipline within computer science. Artificial Intelligence is usually conceived of as doing more than just computing numbers (such as a calculator), but is more conceptual in nature (such as describing subjective qualities, or giving meanings to different contexts). An example of AI would be speech recognition and communicating, such as Apple's Siri, or Amazon's Alexa. Amazon has announced three new AI tools for anyone wanting to build apps on Amazon Web Services: Amazon Lex, Amazon Polly, and Amazon Rekognition. According to Amazon "This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them." For those interested in developing apps, see our 20 Best App Development Colleges article. Google, Facebook, Amazon, Apple and Microsoft are all working on AI. Facebook's FAIR (Facebook Artificial Intelligence Research) program engages with academia to assist in solving long term problems in AI. Facebook is hiring AI experts around the world to assist in their project.

In this tutorial, we will learn about OpenCV tracking API that was introduced in OpenCV 3.0. We will learn how and when to use the 6 different trackers available in OpenCV 3.2 -- BOOSTING, MIL, KCF, TLD, MEDIANFLOW, and GOTURN. We will also learn the general theory behind modern tracking algorithms. This problem has been perfectly solved by my friend Boris Babenko as shown in this flawless real-time face tracker below! Jokes aside, the animation demonstrates what we want from an ideal object tracker -- speed, accuracy, and robustness to occlusion.

Getting learners to read textbooks and use other teaching aids effectively can be tricky. Especially, when the books are just too dreary. In this post, we've compiled great e-resources for you digital natives looking to explore the exciting world of Machine Learning and Neural Networks. But before you dive into the deep end, you need to make sure you've got the fundamentals down pat. It doesn't matter what catches your fancy, machine learning, artificial intelligence, or deep learning; you need to know the basics of math and stats--linear algebra, calculus, optimization, probability--to get ahead.

Over this course you will build multiple practical systems using natural language processing (NLP), the branch of machine learning and data science that deals with text and speech. You'll start with a background on NLP before diving in, building a spam detector and a model for sentiment analysis in Python. Learning how to build these practical tools will give you an excellent window into the mechanisms that drive machine learning. Build a spam detector & sentiment analysis model that may be used to predict the stock market Learn practical tools & techniques like the natural language toolkit library & latent semantic analysis Create an article spinner from scratch that can be used as an SEO tool Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.

A machine learning algorithm designed to teach computers how to recognize photos, speech patterns, and hand-written digits has now been applied to a vastly different set a data: identifying different phases of condensed matter. In a project half-jokingly called "Phasebook," two Perimeter researchers showed that a neural network system – a standard part of today's powerful artificial intelligence (AI) algorithms – can also identify phase transitions between states of matter. The research, published today in the journal Nature Physics, validates the idea that the relationship between theoretical physics and AI can be a fruitful, two-way exchange. The fields have long been linked. AI research has often tapped physicists to help develop machine learning for industry.

The dynamic Boltzmann machine (DyBM) has been proposed as a stochastic generative model of multi-dimensional time series, with an exact, learning rule that maximizes the log-likelihood of a given time series. The DyBM, however, is defined only for binary valued data, without any nonlinear hidden units. Here, in our first contribution, we extend the DyBM to deal with real valued data. We present a formulation called Gaussian DyBM, that can be seen as an extension of a vector autoregressive (VAR) model. This uses, in addition to standard (explanatory) variables, components that captures long term dependencies in the time series. In our second contribution, we extend the Gaussian DyBM model with a recurrent neural network (RNN) that controls the bias input to the DyBM units. We derive a stochastic gradient update rule such that, the output weights from the RNN can also be trained online along with other DyBM parameters. Furthermore, this acts as nonlinear hidden layer extending the capacity of DyBM and allows it to model nonlinear components in a given time-series. Numerical experiments with synthetic datasets show that the RNN-Gaussian DyBM improves predictive accuracy upon standard VAR by up to 35%. On real multi-dimensional time-series prediction, consisting of high nonlinearity and non-stationarity, we demonstrate that this nonlinear DyBM model achieves significant improvement upon state of the art baseline methods like VAR and long short-term memory (LSTM) networks at a reduced computational cost.

The problem of automatically fixing programming errors is a very active research topic in software engineering. This is a challenging problem as fixing even a single error may require analysis of the entire program. In practice, a number of errors arise due to programmer's inexperience with the programming language or lack of attention to detail. We call these common programming errors. These are analogous to grammatical errors in natural languages. Compilers detect such errors, but their error messages are usually inaccurate. In this work, we present an end-to-end solution, called DeepFix, that can fix multiple such errors in a program without relying on any external tool to locate or fix them. At the heart of DeepFix is a multi-layered sequence-to-sequence neural network with attention which is trained to predict erroneous program locations along with the required correct statements. On a set of 6971 erroneous C programs written by students for 93 programming tasks, DeepFix could fix 1881 (27%) programs completely and 1338 (19%) programs partially.