It's at the peak of the famous technology hype cycle, but don't let that make you cynical. Artificial intelligence is already making a difference in public service delivery. And unlike other technologies – it's designed to be a quick learner, so it doesn't rely on humans to figure everything out. The tool has potential in a huge number of areas, and will change how many of us live and work. Here are five ways that public servants can already use AI to make a difference.

How to get people interested in art? How to expose permanent-collection works that sit in storage? These are questions art museums constantly ponder. Recently, Tate Britain asked another one: How can artificial intelligence help? It put the question to anyone who wanted to compete for the 2016 IK Prize, which promotes the use of digital technology in the exploration of art at Tate Britain or on the Tate website.

Bruce Ratner, The Significant StatisticianTM, is President and Founder of DM STAT-1 Consulting, the ensample for Statistical Modeling, Analysis and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in all standard statistical techniques, and methods using machine-learning/statistics algorithms, such as its patented GenIQ Model, to achieve its clients' goals – across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, Risk Management, and Nonprofit Fundraising. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.

Some of the best minds in Australia and the U.S. have put their combined brain power to scientific (and evil) use. Researchers at CSIRO's Data61 and the Massachusetts Institute of Technology (MIT) Media Lab have built what they called the Nightmare Machine, an artificial intelligence designed to transform beloved childhood characters into downright monsters. Not only can their AI now create its own images, but it's specifically trying to freak us out. According to the CSIRO, the "spooky formula" is generated by two deep learning algorithms: One that takes a visual style from one image and adds it to another, and one that can create faces. More than 200,000 images have been assessed for their fear factor, it said in a blog post, to help the AI learn what is truly frightening to mere mortals.

With Amazon Machine Learning (Amazon ML), you can build and train predictive models and host your applications in a scalable cloud solution. In this project, you will use the visualization tools and wizards of Amazon ML to guide you through the process of creating a new machine learning (ML) model without having to learn complex ML algorithms and technology. To complete this project, you will download freely-available sample customer data and upload the data to an Amazon S3 bucket to create a datasource. You will then create an ML model from this datasource, from which you can then evaluate and adjust the ML model's performance, and then use it to generate predictions.

Databricks, corporate provider of support and development for the Apache Spark in-memory big data project, has spiced up its cloud-based implementation of Apache Spark with two additions that top IT's current hot list. The new features -- GPU acceleration and integration with numerous deep learning libraries -- can in theory be implemented in any local Apache Spark installation. But Databricks says its versions are tuned to avoid the resource contentions that complicate the use of such features. Apache Spark isn't configured out of the box to provide GPU acceleration, and to set up a system to support it, users must cobble together several pieces. To that end, Databricks offers to handle all the heavy lifting. Databricks also claims that Spark's behaviors are tuned to get the most out of a GPU cluster by reducing the number of contentions across nodes.

Today, the virtual assistant landscape is exploding with innovation: New applications and new forms of interaction are constantly emerging. Although the idea of a virtual assistant is decades old, it went mainstream with Apple's introduction of Siri. Siri was created at SRI International based on years of AI research, spun off as an independent venture-backed company in 2007, and acquired by Apple in 2010. The Siri that the world knows enables users to quickly find information and execute important device functions in a fast and friendly way. But Siri was first developed as a "do engine," similar to the emerging crop of AI assistants.

The CEO and founder of cloud-based accountancy software giant Xero says artificial intelligence (AI) and machine learning technologies will be "transformational" for finance over the next few years. Rod Drury told Business Insider during a recent interview in London: "We'll see more innovation in the next 2 years than we have in the last 10 years, all driven by AI." The Xero founder says: "We're getting a massive hit on the R&D we've done around machine learning and AI. We think it's going to be transformational for the industry. "If you capture information from the bank statement and the invoice and the bills that are flying through, you can actually programme things to do a whole lot of work for you and you're just checking and making fixes, which trains the machine." Xero's accountancy software helps small and medium-sized businesses manage their accountants in the cloud but Drury believes much of the management -- things like categorizing expenditure and sending accounts to be checked -- could be automated by "smart" AI and machine learning programmes, which learn the habits of your business. "You can build unique system for each business," says Drury. "The first innovation in cloud accounting was actually getting these transactions into the cloud.

Artificial Intelligence (AI) has always been the subject of dreams and visions about the distant future of humankind. Even though we are nowhere near a conscious robotic system, nowadays, AI systems are ubiquitous and showing tremendous successes in various fields of our everyday life. We are using these on a daily basis, often without even noticing. Whether it is the Virtual Personal Assistants on our mobile phones (such as Siri, Google Now, and Cortana), self-driving cars, the ranking of the web pages given your search query, or the classical textbook examples such as spam filtering and recommendation systems of online media providers and marketplaces like Amazon. Various fields of AI have made a major leap forward in the recent years. As most AI systems are too complex to be defined manually, we have to resort to automatically learning rules and patterns from data using sophisticated Machine Learning (ML) techniques.

One of the biological side effects of being a human is the will to live. Luckily for us, one of the ways in which our brain gives the heads up to inform us of potentially dangerous situations is by invoking that little old survival instinct called "fear". Have you ever been stuck sitting next to someone in a cinema, completely unfazed by a horror movie, while you diverted your attention to the closest escape door? Everyone gets spooked by out by different stimuli – whether rational or irrational – clowns, gigantic spiders, or even marshmallows. Since we know that stimuli can evoke varying psychological responses, one group of researchers from our team at Data61 and MIT Media lab, set out to find what unites us in our phobia and terrifies us on a universal scale.