This is a game built with machine learning. You draw, and a neural network tries to guess what you're drawing. But the more you play with it, the more it will learn. We made this as an example of how you can use machine learning in fun ways. Watch the video below to learn about how it works, and check out more A.I. Experiments here.

My name is Victoria, and I'll be your conference operator today. Welcome you to the NVIDIA Financial Results Conference Call. All lines have been placed on mute. After the speakers' remarks there will be a question-and-answer period. I will now turn the call over to Arnab Chanda, Vice President of Investor Relations at NVIDIA. You may begin your conference. With me on the call today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. You can hear a replay by telephone until the 17 November, 2016. The webcast will be available for replay up until next quarter's conference call to discuss Q4 financial results. The content of today's call is NVIDIA's property. It cannot be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These forward-looking statements are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Form 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All of our statements are made as of today, the 10th of November, 2016 based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary which is posted on our website. Revenue reached a record in the third quarter, exceeding $2 billion for the first time. Driving this was success in our Pascal-based gaming platform and growth in our datacenter platform, reflecting the role of NVIDIA's GPU as the engine of AI computing.

Nataliia Dmytriievska was 15 years old and enveloped by flames when she first heard the call of outer space. A year earlier her boyfriend had taught her the basics of poi, a Maori dance in which performers swing flaming, tethered weights to describe bright geometric shapes in the dark. Despite the burns and bruises she earned, Dmytriievska was a determined pupil. She would practice for hours each day, drawing flowers and other outlines around her body using dummy weights, before attempting the same perilous tricks using fire. Although money was never the primary motivation – "I simply love the fire; there is something magical when you feel like it's in your control," she said – after a few years Dmytriievska turned semi-professional. She joined a circus troupe in her home city of Kiev, Ukraine to help support her university studies. In June 2007, the troupe began rehearsals for an interpretation of Edgar Allan Poe's poem, The Raven. As the backing music sounded out for the first time – a pipe organ, played rhythmically, as if calling people to worship, soon joined by galloping guitars and a furious drumbeat – Dmytriievska took to the stage. But her mind was not on the performance. As soon as she finished the routine she left the stage, walked up to her friend on the mixing desk and asked: "Where is that music from?" Eve Online: how a virtual world went to the edge of apocalypse and back. The track, he said, came from Eve Online, a science-fiction video game. It is, he explained, a game set in a vast galaxy comprised of tens of thousands of stars and planets, and inhabited by half a million or so people from around the world, who explore and do battle together daily via the internet.

Google's work in machine learning and artificial intelligence is often interesting, but it can be a bit academic. People like to get their hands on these things -- as much as you can, anyway, with something intangible. To that end, Google is collecting a bunch of little demonstrations of this emerging category of tech in its AI Experiments showcase. The idea is simply to let people fool around with example of machine learning, or download the code themselves to see how it works. Giorgio Cam (best on mobile), which identifies objects seen by the device's camera and hypes them up in a rhyming fashion.

"Algorithm" might be one of the most popular terms that almost no one understands. Not many people have PhDs in data science, and even those experts don't always know what's happening. "It's not clear even from a technical perspective that every aspect of AI algorithms can be understood by humans," says Guruduth Banavar, IBM's chief science officer for cognitive computing, which is what IBM calls AI. Artificial intelligence is making decisions by reviewing people's medical tests in hospitals, credit histories in banking, job applications in some HR systems, even criminal risk factors in the justice system. Yet it's not always clear how the computers are thinking.

Most of my other projects are concerned with large-scale virtual screening applications: In collaboration with labs that do experimental biology, we develop and apply methods to predict candidate molecules that either inhibit (or activate, depending on the project) an individual protein, in absence or presence of a protein crystal structure. The interesting part about it is the interplay between predictions and feedback: I get to make predictions and (at some point), I get the experimental results to see whether I was right or wrong and to analyze why certain predictions worked better than others. Another exciting challenge in such projects is that one has to find ways to make this all computationally feasible -- if you have 15 million molecules, selecting 100 candidate molecules for experimental testing is a bit like searching for the needle in the haystack. Usually, it comes down to formulating specific hypotheses upfront as "filtering" steps since a brute-force docking, which computationally not feasible (since we also have time constraints). My projects require a certain amount of creativity and technical skills to put the ideas into action, but eventually, the approach (the hypotheses) also have to make sense to our collaborators (and the funding agencies).

Turns out that DJI's Mavik wasn't the company's only new airframe for the year. On Tuesday, a pair of updated drone models joined the family: the Phantom 4 Pro and the Inspire 2. The Phantom 4 Pro (P4P) is the upgraded version of the Phantom 4 (P4), which debuted earlier this year. The P4, if you recall, integrated a suite of obstacle avoidance systems: a pair of stereoscopic sensors mounted on the front of the drone. The P4P builds on that system, adding a second pair of stereoscopic sensors on the rear of the frame as well as an infrared sensor on either flank. These IR sensors have a 7m range -- shorter than the 30m range of the front and rear stereos but still plenty farsighted to avoid sideswiping trees and fences.

Microsoft is teaming up with Elon Musk's OpenAI, the non-profit artificial intelligence research company. OpenAi announced on Tuesday it will work with Microsoft to start running the cloud platform Azure, making it the primary cloud system that OpenAI uses for deep learning and artificial intelligence, a move Microsoft said it was "excited" about. "OpenAI chose Microsoft due to our deep learning research and ongoing commitment to AI, along with Azure's support for open source technologies and its unique combination of high performance computing, big data and intelligence capabilities such as Azure Batch, Azure Machine Learning and the Microsoft Cognitive Toolkit (formerly CNTK)," said Microsoft in a statement. The partnership will work on "democratizing AI and making it accessible to everyone," Microsoft says. The company plans to make this happen by taking that intelligence and infusing it into everyday devices and apps.

Machines are capable of understanding speech, recognizing faces and driving cars safely, making recent technological advancements seem impressively powerful. But if the field of artificial intelligence is going to make the transformative leap into building human-like machines, it'll first have to master the way babies learn. "Relatively recently in AI there's been a shift from thinking about designing systems that can do the sort of things that adults can do, to realizing if you want to have systems that are as flexible and powerful and do the kinds of things that adults do, you need to have systems that can learn the way babies and children do," developmental psychologist Alison Gopnik, a researcher at the University of California at Berkeley, told International Business Times. "If you compare what computers can do now to what they could do 10 years ago, they've certainly made a lot of progress, but if you compare them to what a four year old can do, there's still a pretty enormous gap." Babies and children construct theories about the world around them using the same approach scientists use to construct scientific theories.

Intel has moved to a new architecture called Kaby Lake for its PC chips, but it isn't done with the previous generation Skylake yet. The company will release new Xeon server chips based on Skylake in mid-2017, and they will boast big performance increases, said Barry Davis, general manager for the accelerated workload group at Intel. The Skylake Xeon chips will go into mainstream servers and could spark a big round of hardware upgrades, Davis said. Xeon chips aren't as visible as Intel's PC chips but remain extremely popular. Companies like Google, Facebook, and Amazon buy thousands of servers loaded with Xeon chips to power their search, social networking, and artificial intelligence tasks.