This week Microsoft and Alibaba stoked new fears that robots will soon take our jobs. The two companies independently revealed that their artificial intelligence systems beat humans at a test of reading comprehension. The test, known as the Stanford Question Answering Dataset (SQuAD), was designed to train AI to answer questions about a set of Wikipedia articles. Like the image-recognition software already deployed in commercial photo apps, these systems lend the impression that machines have become increasingly capable of replicating human cognition: identifying images or sounds, and now speed reading text passages and spewing back answers with human-level accuracy. Machine smarts, though, are not always what they seem.

Deep learning and artificial intelligence (AI) have moved well beyond science fiction into the cutting edge of internet and enterprise computing. Access to more computational power in the cloud, advancement of sophisticated algorithms, and the availability of funding are unlocking new possibilities unimaginable just five years ago. But it's the availability of new, rich data sources that is making deep learning real. If you want to get started right now, download the complete Deep Learning and Artificial Intelligence white paper. We are living in an era where artificial intelligence (AI) has started to scratch the surface of its true potential.

There's an AI capable of teleporting John Travolta and Uma Thurman into your living room and forcing them to dance for you. The machine takes a 2D image, like the dance scene from Pulp Fiction, and reimagines it in augmented reality as a 3D object. The tool is called Volume and it's being developed by artists Or Fleisher and Shirin Anlen. It's currently in the experimental stage, but the concept is simply incredible. Our experiment with Pulp Fiction allows users to step inside one the film's scenes in Augmented Reality, using Apple's ARKit framework on an iPad.

As we're nearing the end of 2017, we've come to the 5 year landmark of deep learning really starting to hit the mainstream. For me, I think of AlexNet and the 2012 Imagenet competition as the coming out party (although researchers have definitely been working in this field for quite a bit longer). It's been just 5 years and we've absolutely revolutionized the way we look at the capabilities of machines, the way we build software (Software 2.0), and the ways we think about creating products and companies (Just ask any VC or startup founder). Tasks that seemed impossible just a decade ago have become tractable, granted you have the appropriate labeled dataset and compute power of course. In this post, we'll overview the last couple years in deep learning, focusing on industry applications, and end with a discussion on what the future may hold.

Businesses have long used data analytics to help direct their strategy to maximize profits. Ideally, data analytics helps eliminate much of the guesswork involved in trying to understand clients, instead systemically tracking data patterns to best construct business tactics and operations to minimize uncertainty. Not only does analytics determine what might attract new customers, often analytics recognizes existing patterns in data to help better serve existing customers, which is typically more cost-effective than establishing a new business. In an ever-changing business world subject to countless variants, analytics gives companies the edge in recognizing changing climates, so they can take initiate appropriate action to stay competitive. Alongside analytics, cloud computing is also helping make business more effective and the consolidation of both clouds and analytics could help businesses store, interpret, and process their big data to better meet their clients' needs.

Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. At FAIR, Detectron has enabled numerous research projects, including: Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, and Data Distillation: Towards Omni-Supervised Learning. The goal of Detectron is to provide a high-quality, high-performance codebase for object detection research. It is designed to be flexible in order to support rapid implementation and evaluation of novel research.

Many of you installed Google's Arts and Culture app to see which famous portrait its AI though you most looked like. Microsoft' AI researchers have been working on something in the same vein. Instead of matching an uploaded images to other existing images, however, the team at the company's Deep Learning Technology Center have been training an AI to create images from scratch. All the AI needs is a list of keywords to go by. To be clear, what's going on in the image at the top is not what happens when you go to Google or Bing and perform a search.

Here at The Next Platform, we've touched on the convergence of machine learning, HPC, and enterprise requirements looking at ways that vendors are trying to reduce the barriers to enable enterprises to leverage AI and machine learning to better address the rapid changes brought about by such emerging trends as the cloud, edge computing and mobility. At the SC17 show in November 2017, Dell EMC unveiled efforts underway to bring AI, machine learning and deep learning into the mainstream, similar to how the company and other vendors in recent years have been working to make it easier for enterprises to adopt HPC techniques for their environments. For Dell EMC, that means in part doing so through bundled, engineered systems. IBM has strategies underway, including through the integration of its PowerAI deep learning enterprise software with its Data Science Experience. Both offerings are aimed at making it easier for enterprises to embrace advance AI technologies and for developers and data scientists to develop and train machine learning models.

Armughan Ahmad is SVP and GM of Solutions for Dell EMC. As we move furiously through 2018, media, analysts and tech enthusiasts are thinking about what will be'the next big thing' in technology. It was clear that 2017 was a hallmark year for artificial intelligence (AI) enthusiasm and awareness, where more industries and organizations embraced digital transformation and came to view their data as a critical corporate asset. But we've only scratched the surface. Building off that momentum, 2018 will be the year that AI adoption reaches critical mass among organizations and professionals.

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.