Cloud computing in general, and Infrastructure-as-a-Service (IaaS) in particular, have become widely accepted and adopted paradigms for computing with the offerings of virtual machines (VM) on demand. By 2020, 67% of enterprise IT infrastructure and software spending will be for cloud-based offerings.16 A major factor in the increased adoption of the cloud by enterprise IT was its pay-as-you-go model where a customer pays only for resources leased from the cloud provider and have the ability to get as many resources as needed with no up-front cost (elasticity).2 Unfortunately, the burden of scaling was left for developers and system designers that typically used overprovisioning techniques to handle sudden surges in service requests. Studies of reported usage of cloud resources in datacenters19 show a substantial gap between the resources that cloud customers allocate and pay for (leasing VMs), and actual resource utilization (CPU, memory, and so on). Serverless computing is emerging as a new and compelling paradigm for the deployment of cloud applications, largely due to the recent shift of enterprise application architectures to containers and microservices.23 Using serverless gives pay-as-you-go without additional work to start and stop server and is closer to original expectations for cloud computing to be treated like as a utility.2 Developers using serverless computing can get cost savings and scalability without needing to havea high level of cloud computing expertise that is time-consuming to acquire. Due to its simplicity and economical advantages, serverless computing is gaining popularity as reported by the increasing rate of the "serverless" search term by Google Trends. Its market size is estimated to grow to 7.72 billion by 2021.10 Most prominent cloud providers including Amazon, IBM, Microsoft, Google, and others have already released serverless computing capabilities with several additional open source efforts driven by both industry and academic institutions (for example, see CNCF Serverless Cloud Native Landscapea).

At the start of the decade, deep learning restored the reputation of artificial intelligence (AI) following years stuck in a technological winter. Within a few years of becoming computationally feasible, systems trained on thousands of labeled examples began to exceed the performance of humans on specific tasks. One was able to decode road signs that had been rendered almost completely unreadable by the bleaching action of the sun, for example. It just as quickly became apparent, however, that the same systems could just as easily be misled. In 2013, Christian Szegedy and colleagues working at Google Brain found subtle pixel-level changes, imperceptible to a human, that extended across the image would lead to a bright yellow U.S. school bus being classified by a deep neural network (DNN) as an ostrich.

I'm trying to explain to Arthur I. Miller why artworks generated by computers don't quite do it for me. The works aren't a portal into another person's mind, where you can wander in a warren of intention, emotion, and perception, feeling life being shaped into form. What's more, it often seems, people just ain't no good, so it's transcendent to be reminded they can be. Art is one of the few human creations that can do that. No matter how engaging the songs or poems that a computer generates may be, they ultimately feel empty. They lack the electricity of the human body, the hum of human consciousness, the connection with another person.

On a warm day in April 2013, I was sitting in a friend's kitchen in Paris, trying to engineer serendipity. I was trying to get my computer to write music on its own. I wanted to be able to turn it on and have it spit out not just any goofy little algorithmic tune but beautiful, compelling, mysterious music; something I'd be proud to have written myself. The kitchen window was open, and as I listened to the sounds of children playing in the courtyard below, I thought about how the melodies of their voices made serendipitous counterpoint with the songs of nearby birds and the intermittent drone of traffic on the rue d'Alésia. In response to these daydreams, I was making a few tweaks to my software--a chaotic, seat-of-the-pants affair that betrayed my intuitive, self-taught approach to programming--when I saw that Bill Seaman had just uploaded a new batch of audio files to our shared Dropbox folder. I had been collaborating with Bill, a media artist, on various aspects of computational creativity over the past few years. I loaded Bill's folder of sound files along with some of my own into the software and set it rolling. I was thrilled and astonished.

Ross Goodwin has had an extraordinary career. After playing about with computers as a child, he studied economics, then became a speech writer for President Obama, writing presidential proclamations, then took a variety of freelance writing jobs. One of these involved churning out business letters--he calls it freelance ghostwriting. The letters were all pretty much the same, so he figured out an algorithm that would generate form letters, using a few samples as a database. The algorithm jumbled up paragraphs and lines following certain templates, then reassembled them to produce business letters, similar but each varying in style, saving him the job of starting anew each time. He thought he was on to something new but soon found out that this was a well-explored area.

The PlayStation 4 has dominated the video game market since its release six years ago. Sure, Microsoft's Xbox One is more powerful than the PlayStation 4, but Sony's console spent this generation producing incredible games. Putting software above hardware has been a winning strategy for Sony and it's sold more than 100 million consoles since the PlayStation 4's release in 2013. With more than 2,300 games for the system, it can be hard to pick which game to play. But TIME has you covered.

While this column has covered many areas of artificial intelligence (AI), there hasn't been much described about augmented reality (AR). This is a technology made famous and then forgotten by many via the overblown hype of Google glasses earlier this decade. The concept was that general use AR was just around the corner. What has slowly happened is that AR providers have focused more on narrow applications in the same way as early intelligence systems focused on specific domains rather than genera intelligence. As with other aspects of AI, including vision and robotics, one of the areas of AR focus is medicine.

Every year, our team of futurists, analysts, and consultants at Frost & Sullivan's Transformational Healthcare Group comes together to brainstorm and predict the themes, technologies, and global forces that will define the next 12 to 18 months for the healthcare industry. We also retrospect how we did each year, and each year we are becoming more accurate in the predictions we make. For the 2019 predictions that were released in November 2018, six out of eight predictions realized as anticipated, while the two remaining predictions have not panned out exactly the way we thought. The new vision for healthcare for 2020 and beyond will not just focus on access, quality, and affordability but also on predictive, preventive, and outcome-based care models promoting social and financial inclusion. As we are on the verge of entering a new decade of change globally, 2020 will be a reality check for long-pending national healthcare policies and regulatory reforms that must reinvigorate future strategies.

Subscribe to this episode via iTunes, Google Play, YouTube, and Spotify, or stream it and connect with hundreds of AI startups and influencers from our chatbot. A full text transcript can also be found at the end of this post. Over the last few years, we've seen the trade and supply of data increase, with more and more of it being generated and leveraged by all industries and organizations. Particularly in this era of innovation, data is essential, and as usage continues to skyrocket, the consideration of how it's being collected and how its being used is more important than ever. Everyone is talking about transforming their organization with artificial intelligence.

Just behind us, a giant industrial magnet powered up with warning signs dotted about its perimeter so we wouldn't scramble our phones. Before long, John Durrell, a specialist in superconductor engineering (who took apart more machines as a teenager than he can remember), arrived with a set of tools in his hands and a glint in his eye.