Everyone today talks about AI, big data and machine learning, yet most do not delve into the fundamental properties of how they will operate and how they might be an actual threat to asset managers. Some view technological methods as tools to assist them instead of being such a threat, and it would help provide both perspectives of the argument. Deep learning is a branch of machine learning that uses particular architectures of neural networks. These are artificial networks that attempt to actually replicate how the neural structures in human brains operate. Such methods have successfully been applied to areas such as computer vision – i.e. image processing and classification – as well as speech recognition. The techniques are readily available to any undergraduate student willing to learn the process.

The next time you enter a query into Google's search engine or consult the company's map service for directions to a movie theater, remember that a big brain is working behind the scenes to provide relevant search results and make sure you don't get lost while driving. As Fortune's Roger Parloff wrote, the Google Brain research team has created over 1,000 so-called deep learning projects that have supercharged many of Google's products over the past few years like YouTube, translation, and photos. With deep learning, researchers can feed huge amounts of data into software systems called neural nets that learn to recognize patterns within the vast information faster than humans. In an interview with Fortune, one of Google Brain's co-founders and leaders, Jeff Dean, talks about cutting-edge AI research, the challenges involved, and using AI in its products. The following, done against the backdrop of the 50th annual Turing Award, an honor in computer science from the Association for Computing Machinery, has been edited for length and clarity. What are some challenges researchers face with pushing the field of artificial intelligence?

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NEW DELHI: HDFC Bank today announced the launch of an electronic virtual assistant (EVA), an artificial intelligence-driven chatbot, for customer services. Eva is India's first AI-based banking chatbot and can answer millions of customer queries across multiple channels instantly, HDFC Bank said in a statement. Eva can assimilate knowledge from thousands of sources and provide answers in simple language in less than 0.4 seconds, it said. "Within the first few days of its launch, Eva has answered over 1 lakh queries from thousands of customers from 17 countries across the globe," it claimed. With the launch of Eva, it said, bank's customers can get information on its products and services instantaneously and it also becomes smarter as it learns through its customer interactions.

Amazon.com Inc has launched a new programme to help students build capabilities into its voice-controlled assistant Alexa, the company told Reuters, the latest move by a technology firm to nurture ideas and talent in artificial intelligence research. The e-commerce company said it is paying for a year-long doctoral fellowship at four universities for an undisclosed sum. Working with professors, the Alexa Fund Fellows will help students tackle complex technology problems in class on Alexa, like how to convert text to speech or process conversation. Amazon, Alphabet Inc's Google and others are locked in a race to develop and monetize artificial intelligence. Unlike some rivals, Amazon has made it easy for third-party developers to create skills for Alexa so it can get better faster - a tactic it now is extending to the classroom.

What programming language should one learn to get a machine learning or data science job? It is debated in many forums. I could provide here my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data. I will use the trend search available on indeed.com.

Oxford University researchers have estimated that 47 percent of U.S. jobs could be automated within the next two decades. But which ones will robots take first? First, we should define "robots" (for this article only) as technologies, such as machine learning algorithms running on purpose-built computer platforms, that have been trained to perform tasks that currently require humans to perform. With this in mind, let's think about what you'll do after white-collar work. Oh, and I do have a solution for the short term that will make you the last to lose your job to a robot, but I'm saving it for the end of the article.

In this article, we will go through the intuition of linear regression and a straightforward implementation of the algorithm. This article is adapted from this booklet, in which you can find the mathematics behind the algorithm as well as detailed explanation and implementation details. Linear regression is a simple yet useful learning algorithm that can be seen as a statistical or an optimization problem. For simple regression, there are optimal analytical solutions; however, for high dimensions problems, there are not. Regression fits a function to a data set, so what we are trying to do is to find a representative function and fit it to our data set.

A good recommendation system will always boost your sales. When it comes to apparels or footwear, the use of better recommendations are always a matter of prime importance. Currently recommendation systems are implemented using machine learning algorithms. Algorithms like'Nearest Neighbour' provides easy way to implement recommendations. But have you ever verified how accurate are these recommendations?