The billowing compute and data demands that spurred the oil and gas industry to be the largest commercial users of high-performance computing are now propelling the competitive sector to deploy the latest AI technologies. Beyond the requirement for accurate and speedy seismic and reservoir simulation, oil and gas operations face torrents of sensor, geolocation, weather, drilling and seismic data. Just the sensor data alone from one off-shore rig can accrue to hundreds of terabytes of data annually, however most of this remains unanalyzed, dark data.

Some of Google's top AI researchers are trying to predict your medical outcome as soon as you're admitted to the hospital. A new research paper, published Jan. 24 with 34 co-authors and not peer-reviewed, claims better accuracy than existing software at predicting outcomes like whether a patient will die in the hospital, be discharged and readmitted, and their final diagnosis. To conduct the study, Google obtained de-identified data of 216,221 adults, with more than 46 billion data points between them. The data span 11 combined years at two hospitals, University of California San Francisco Medical Center (from 2012-2016) and University of Chicago Medicine (2009-2016). While the results have not been independently validated, Google claims vast improvements over traditional models used today for predicting medical outcomes.

Deep learning is gaining traction across a broad swath of applications, providing more nuanced and complex behavior than machine learning offers today. Those attributes are particularly important for safety-critical devices, such as assisted or autonomous vehicles, as well as for natural language processing where a machine can recognize the intent of words based upon the context of a conversation. Like AI and machine learning, deep learning has been kicking around in research for decades. What's changing is that it is being added into many types of chips, from data centers to simple microcontrollers. And as algorithms become more efficient for both training and inferencing, this part of the machine learning/AI continuum is beginning to show up across a wide spectrum of use models, some for very narrow applications and some for much broader contextual decisions. "Some of this is in anticipation of what will be required in chips for autonomous vehicles," said Chris Rowen, CEO of Babblabs.

Coined in the 1950s, AI is a broad reference that includes a set of methods, algorithms and technologies that make the underlying software seem capable of exhibiting behavior or intelligence which is indistinguishable from a human. The most widely-known AI experiment is also one of the first: The Turing Test developed by Alan Turing in 1950 (the image below provides a brief AI development timeline) at the University of Manchester. The Turing Test contains criteria to determine whether a computer has human-like intelligence by convincing a human questioner that s/he is speaking to a human, not a computer. Since this time, the concept of AI has ballooned to include machine learning (deep learning), computer vision, natural language processing, robotics and more. But remember, all these AI technologies go to serve the goal of AI – to exhibit human-like behavior and intelligence.

Sundar Pichai, the chief executive of Google, has said that AI "is more profound than … electricity or fire." Andrew Ng, who founded Google Brain and now invests in AI startups, wrote that "If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future." There have been remarkable advances in AI, after decades of frustration. Today we can tell a voice-activated personal assistant like Alexa to "Play the band Television," or count on Facebook to tag our photographs; Google Translate is often almost as accurate as a human translator. Over the last half decade, billions of dollars in research funding and venture capital have flowed towards AI; it is the hottest course in computer science programs at MIT and Stanford.

Have you looked at Google Trends? It's pretty cool -- you enter some keywords and see how Google Searches of that term vary through time. I thought -- hey, I happen to have this arxiv-sanity database of 28,303 (arxiv) Machine Learning papers over the last 5 years, so why not do something similar and take a look at how Machine Learning research has evolved over the last 5 years? The results are fairly fun, so I thought I'd post. A good chunk of this post is about deep learning specifically, which is the subarea I am most familiar with.)

While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction – they can also combine with reinforcement learning algorithms to create something astounding like AlphaGo. Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. They can start from a blank slate, and under the right conditions they achieve superhuman performance. Like a child incentivized by spankings and candy, these algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – this is reinforcement. Reinforcement algorithms that incorporate deep learning can beat world champions at the game of Go as well as human experts playing numerous Atari video games.

There is increasing pressure in such fields as manufacturing, energy and transportation to adopt AI and machine learning to help improve efficiencies in operations, optimize workflows, enhance business decisions through analytics and reduce costs in logistics. We have talked about how industries like telecommunications and transportation are looking at recurrent neural networks for helping to better forecast resource demand in supply chains. However, adopting AI and machine learning comes with its share of challenges. Companies whose datacenters are crowded with traditional systems powered by CPUs now have to consider buying and bringing in GPU-based hardware that is better situated to handle machine learning inference work, and they have to find new employees in a relatively shallow pool of available AI talent. None of this is easy, but the trend is irreversibly toward AI, machine learning and deep learning, so decisions need to be made, according to Karim Beguir.

In this DZone Refcard, learn about TensorFlow, an open-source library with a rich set of application programming interfaces for most major languages and environments needed for deep learning programs like sentiment analysis and object detection.

We have come a long way to building intelligent machines, in fact, the rate of progress in Deep Learning and Reinforcement Learning, the two corner stones of artificial intelligence, is unprecedented. Alan Turing would have been proud of our achievements in computer vision, speech, natural language processing and autonomous systems. However, there are still many challenges and we are still some distance from building machines that can pass the Turing test. In this paper, we discuss some of the biggest questions concerning intelligent machines and we attempt to answer them, as much as can be explained by modern AI. Turing choose to avoid answering this question directly, however, it is important to have a clear and concise meaning of thinking that incorporates lessons from neuroscience and Artificial Intelligence.