As financial companies have begun employing automated advisors aimed at helping customers manage their money, and oncologists have started using the artificial intelligence system known as Watson to identify new treatments, the prominent role that sophisticated computer programs have begun to occupy in our lives has become undeniable. Government agencies are also harnessing the powers of automation. The U.S. Environmental Protection Agency and the U.S. Food and Drug Administration, for example, are starting to use complex computer models that can predict environmental exposure to chemicals and drug interactions across patient groups. As agencies begin to enter this brave new world of automation, questions have begun to emerge about how government officials should delegate important tasks to machines. For one, will automation negatively affect the level and quality of human deliberation and dialogue that are integral to democratic governance? Furthermore, when adjudicating individual determinations, like the awarding of disability benefits, will machines prove incapable of providing much needed empathy to claimants?

This post is'not' intended to teach people how to use popular predictive modelling APIs for free. Although, to your surprise, this isn't a far fetched possibility. Trained Machine learning models are basically a function that maps feature vectors to the output variable. Upon querying with a test instance, the model predicts an outcome, assigning probability scores to all the possible classes. Google, Amazon etc provides public facing APIs to train predictive models on the subscriber's data, the model can further be used for prediction purposes .

A new app can give you the answers to your math homework and even explain how to solve it just by taking a picture. Called Socratic, the free app uses artificial intelligence to determine what information you need, and returns'explainers' and videos to give you step-by-step help. The firm says it's like having a'digital tutor in your pocket,' generating answers from a community of teachers and students. A new app can give you the answers to your math homework and even explain how to solve it just by taking a picture. The tutor app can also help with questions in science, history, English, economics.

I recently gave a free webinar on Principal Component Analysis. We had almost 300 researchers attend and didn't get through all the questions. This is part of a series of answers to those questions. If you missed it, you can get the webinar recording here. Principal Component Analysis specifically could be used with a training and test data set, but it doesn't make as much sense as doing so for Factor Analysis.

At the heart of deep learning lies a hard optimization problem. So hard that for several decades after the introduction of neural networks, the difficulty of optimization on deep neural networks was a barrier to their mainstream usage and contributed to their decline in the 1990s and 2000s. Since then, we have overcome this issue. In this post, I explore the "hardness" in optimizing neural networks and see what the theory has to say. In a nutshell: the deeper the network becomes, the harder the optimization problem becomes.

Artificial intelligence (AI) on its own isn't enough to compete -- companies need industry-specific solutions to business problems. So said Martin Schroeter, IBM Corp. (NYSE: IBM)'s company senior vice president and chief financial officer, on the company's quarterly earnings call Thursday afternoon. Cognitive computing technology (IBM's term for AI) is just "table stakes," said Schroeter, claiming that his company is going the extra mile. IBM is building datasets for Watson to serve specific industries, including healthcare and finance. "You need more than public data or algorithms to solve real-world problems," Schroeter said.

Artificial intelligence (AI) has increasingly become one of the hottest topics in both business and science. More leading tech companies are showing their interest in AI investment, from Google's $400 million acquisition of DeepMind and Faraday Future's unveiling of self-driving supercars at CES 2017. These are just a few examples of the commitment companies have towards this cutting-edge technology, but one of the most promising areas for AI is in mobile. The idea of having a personal assistant to help tackle everyday tasks is becoming more appealing to users everywhere. However, intelligent apps are not just limited to digital assistants but for a variety of purposes from security to e-commerce.

Crowdfunding, blockchain and quantum computers: Why we've only just seen the start of the new...

Kristen Stewart – the actress best known for "Twilight" – has co-written a paper on machine learning. The paper outlines the use of neural style transfer in Stewart's directorial debut, "Come Swim", which is about to premiere at Sundance Film Festival. Neural style transfer turns normal images into impressionist art, and is used by popular photo app Prisma. The paper, first spotted by Quartz, is co-bylined with Adobe research engineer Bhautik J. Joshi and producer David Shapiro. It was published yesterday on ArXiv, a repository run by Cornell University for scientific papers that are not yet peer-reviewed.

Heading into HIMSS17, GE Healthcare is developing a range of technologies, from analytics and cloud-based imaging to machine learning. Building on its GE Health Cloud, in fact, the company is enabling care teams to store, view, analyze and share images in ways they could not before storage and compute power were available in the cloud. "We're working at building analytics into every part of our business and applying digital from a horizontal approach across all of GE Healthcare," GE Healthcare spokeswoman Kelley Sousa said. Take Project Northstar, for instance. GE's solution for helping practices transition to value-based care, which was announced last year, combines population health, care delivery, patient engagement and financial management in an integrated, interoperable software solution.