If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
What do we do when we need any information? Simple: "We Ask, and Google Tells". But if the answer depends on multiple variables, then the existing Ask-Tell model tends to sputter. State of the art search engines usually cannot handle such requests. We would have to search for information available in bits and pieces and then try to filter and assemble relevant parts together.
I recently joined Google to edit this blog, and to explore the value of machine learning and big data in an intuitive and hands-on manner. Over the past couple years, I've been fortunate to work with engineers who design and tune ML algorithms, and I've even trained my own models on a couple occasions. But since joining Google, I've been truly humbled by the techniques, code, and expertise of the software engineers, product managers, customer engineers, solutions architects, and developer advocates within Google Cloud. Not to mention the venerable researchers who sit on DeepMind and all Google AI teams. Some of the most capable minds in the world dedicate every working moment to machine learning: the art and science enabling computers to make increasingly sophisticated analyses.
The Global Artificial Intelligence in Fintech Market is anticipated to grow rapidly and will post a CAGR of 40% during the forecast period. The availability of spatial data is a major factor driving the growth of the Artificial Intelligence in Fintech market. Sustaining in a competitive market has become crucial for the financial sector due to technological advancements. In order to achieve efficiency across business processes, enterprises need to design and layout a plan of action. This can be done by properly implementing AI practices into its operations.
There's an awful lot of text data available today, and enormous amounts of it are being created on a daily basis, ranging from structured to semi-structured to fully unstructured. What can we do with it? Well, quite a bit, actually; it depends on what your objectives are, but there are 2 intricately related yet differentiated umbrellas of tasks which can be exploited in order to leverage the availability of all of this data. NLP is a major aspect of computational linguistics, and also falls within the realms of computer science and artificial intelligence. Text mining exists in a similar realm as NLP, in that it is concerned with identifying interesting, non-trivial patterns in textual data.
At a recent conference in 2017, Microsoft CEO Satya Nadella used the analogy of a corn maze to explain the difference in approach between a classical computer and a quantum computer. In trying to find a path through the maze, a classical computer would start down a path, hit an obstruction, backtrack; start again, hit another obstruction, backtrack again until it ran out of options. Although an answer can be found, this approach could be a very time-consuming. They take every path in the corn maze simultaneously." Thus, leading to an exponential reduction in the number of steps required to solve a problem.
If you've been reading our blog regularly, you have noticed that we mention decision trees as a modeling tool and have seen us use a few examples of them to illustrate our points. This month, we've decided to go more in depth on decision trees--below is a simplified, yet comprehensive, description of what they are, why we use them, how we build them, and why we love them. A decision tree is a popular method of creating and visualizing predictive models and algorithms. You may be most familiar with decision trees in the context of flow charts. Starting at the top, you answer questions, which lead you to subsequent questions.
In today's complicated financial landscape accurate models are a necessity for banks to remain competitive, but developing accurate models is challenging. Models are inherently complex -- and if developed poorly can do more harm than good. Minimizing Model Risk with Automated Machine Learning will demonstrate how banks can use Automated Machine Learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements. We'll provide an overview of current trends and expectations for model risk management regulatory compliance, and how industry leading financial institutions are leveraging Automated Machine Learning to provide a much stronger framework for model development and validation than traditional manual efforts.
Deep Learning ultimately is about finding a minimum that generalizes well -- with bonus points for finding one fast and reliably. Our workhorse, stochastic gradient descent (SGD), is a 60-year old algorithm (Robbins and Monro, 1951) , that is as essential to the current generation of Deep Learning algorithms as back-propagation. Different optimization algorithms have been proposed in recent years, which use different equations to update a model's parameters. Adam (Kingma and Ba, 2015)  was introduced in 2015 and is arguably today still the most commonly used one of these algorithms. This indicates that from the Machine Learning practitioner's perspective, best practices for optimization for Deep Learning have largely remained the same.
In this article, I'll talk about Generative Adversarial Networks, or GANs for short. GANs are one of the very few machine learning techniques which has given good performance for generative tasks, or more broadly unsupervised learning. In particular, they have given splendid performance for a variety of image generation related tasks. Yann LeCun, one of the forefathers of deep learning, has called them "the best idea in machine learning in the last 10 years". Most importantly, the core conceptual ideas associated with a GAN are quite simple to understand (and in-fact, you should have a good idea about them by the time you finish reading this article).
Linear Regression, GLMs and GAMs with R demonstrates how to use R to extend the basic assumptions and constraints of linear regression to specify, model, and interpret the results of generalized linear (GLMs) and generalized additive (GAMs) models. The course demonstrates the estimation of GLMs and GAMs by working through a series of practical examples from the book Generalized Additive Models: An Introduction with R by Simon N. Wood (Chapman & Hall/CRC Texts in Statistical Science, 2006). Linear statistical models have a univariate response modeled as a linear function of predictor variables and a zero mean random error term. The assumption of linearity is a critical (and limiting) characteristic. Generalized linear models (GLMs) relax this assumption of linearity. They permit the expected value of the response variable to be a smoothed (e.g. GLMs also relax the assumption that the response variable is normally distributed by allowing for many distributions (e.g. Generalized additive models (GAMs) are extensions of GLMs. GAMs allow for the estimation of regression coefficients that take the form of non-parametric smoothers. Nonparametric smoothers like lowess (locally weighted scatterplot smoothing) fit a smooth curve to data using localized subsets of the data. This course provides an overview of modeling GLMs and GAMs using R. GLMs, and especially GAMs, have evolved into standard statistical methodologies of considerable flexibility. The course addresses recent approaches to modeling, estimating and interpreting GAMs. The focus of the course is on modeling and interpreting GLMs and especially GAMs with R. Use of the freely available R software illustrates the practicalities of linear, generalized linear, and generalized additive models.