At this year's GPU Technology Conference, Facebook AI Research showed off a neural net that lets you generate unique images based on a text description. First they typed "beach" and what looked like a painting of a beach appeared. The image had clouds, so next they typed "beach -clouds" and a brand new beach image appeared with blue sky and no clouds. Lastly, they typed "sunset beach -clouds" and yet another beach image appeared with an orange-red sunset. Amid an impressive conference of photorealistic graphics, self-driving cars, and supercomputers, this was the presentation that drew the most "wow"s from the crowd, and for good reason.

In Avengers: Age of Ultron, the villain (Ultron) starts out as an artificial intelligence experiment gone wrong. Is this just Hollywood storytelling, or should we be worried about a future dictated by robot overlords? To help answer this question, we've teamed up with the awesome Rusty Ward over at Science Friction! Check out Science Friction's episode on the science of The Avengers! Where do YOU think artificial intelligence will take us?

Recently, an AI from Deep Mind called Alpha Go beat the 5th ranked Go player in the world, a feat that most thought was about a decade off. That's the way things are going these days in the world of machine learning and artificial intelligence. Deep Mind is a cutting edge AI company recently bought by Google and now owned by Alphabet, the holding company created by Google shortly after this purchase. It's one of many companies these days at the forefront of trying to teach computers how to think like humans. The learning that goes into teaching a computer how to win at Go isn't directly translatable into practical, day-to-day tasks like writing blog posts, but that doesn't mean that smart startups aren't learning from the big guys how to apply machine learning to solve many of the problems we face in our working lives… Not that writing blog posts is ever a problem, per se, but with the sheer amount of content being produced set to increase by 500% by 2020, there is a bit of a problem in the demand side of things.

Here, we discuss some methods for carrying out classification when only positive examples are available. The latter half of our discussion borrows heavily from W.S. Lee and B. Liu, Proc. Follow @efavdb Follow us on twitter for new submission alerts! Logistic regression is a commonly used tool for estimating the level sets of a Boolean function y on a set of feature vectors \textbf{F}: In a sense, you can think of it as a method for playing the game "Battleship" on whatever data set you're interested in. Consider now a situation where all training examples given are positive -- i.e., no negative examples are available.

About these poems: Self Published Amazon Poems By Marco Essomba (@marcoessomba) a Network and Security Expert, Self Confessed Geek, CTO/Cofounder of AMPS Intl, a leading UK based Application Delivery Infrastructures (ADI) Solutions Provider with niche expertise in the world's most advanced ADCs (www.amps-global.com). This poem is part of a collection of encrypted poems for which only the author has the keys. But for those who are brave, a long journey to attempt to decrypt the new digital transformation and information technology related topics based on the author real life experience in the network and security field and career spanning more than 10 years. In the spirit of open source poetry, all feedback (including bad ones) will be much appreciated. Keep reading those poems, for you may learn something about life on earth.

Artificial Intelligence, or AI, has made a lot of news recently. While much of that talk involves predictions of gloom and doom, that's not my focus here. When it comes to e-commerce, and really commerce in general, AI is poised to begin disrupting the entire industry. An interesting convergence is taking place; one that will have massive implications in the way merchants sell their products and services and the way consumers buy them. We are setting the stage for an era where transactions will take place with no human interaction at all.

The Internet of Things (IoT), Artificial Intelligence (AI) and cloud computing are three technologies that are converging to disrupt nearly every industry. IoT refers to a connected network of objects embedded with technology that enables the collection and exchange of data. Cloud computing is the storing and retrieval of data, and accessing application programs via the Internet. Artificial Intelligence is the simulation of human intelligence by machines. We are currently in the midst of the rise of the first wave of this technological convergence.

As I learned from my work in flight dynamics, to keep an airplane flying safely, you have to predict the likelihood of equipment failure. And today we do that by combining various data sets with real-world knowledge, such as the laws of physics. Integrating these two sets of information -- data and human knowledge -- automatically is a relatively new idea and practice. It involves combining human knowledge with a multitude of data sets via data analytics and artificial intelligence to potentially answer critical questions (such as how to cure a specific type of cancer). As a systems scientist who has worked in areas such as robotics and distributed autonomous systems, I see how this integration has changed many industries.

There are also many good books that focus on one particular topic. For example, Sutton and Barto's Reinforcement Learning is a classic. And Yoshua Bengio's Deep Learning book (available online) is almost becoming a classic before it is published. But, you need a few of those books in order to build a somewhat comprehensive and balanced understanding of the field.

Each of those SMs also contains 32 FP64 CUDA cores, giving us the 1/2 rate for FP64 and new to the Pascal architecture is the ability to pack 2 FP16 operations inside a single FP32 CUDA core, when under the right conditions.Nvidia has just announced a new GPU platform called the Tesla P100. NVIDIA NVLink for maximum application scalability – The NVIDIA NVLink high-speed GPU interconnect scales applications across multiple GPUs, delivering a 5x acceleration in bandwidth compared to today's best-in-class solution.Specifically, the DGX-1 can pump out 170 teraflops – that's 170,000 floating operations per second – with its eight 16GB Tesla P100 graphics chips.The hype for the upcoming next generation NVIDIA GeForce Pascal graphics processing units is now at an all-time high as majority of the reports are claiming that the GPUs might be making their way into the market in the coming months. The GPU is meant for data centers, scientific and technical research, or churning statistics. It features NVIDIA's new Pascal GPU architecture, the latest memory and semiconductor process, and packaging technology – all to create the densest compute platform to date.Other technologies employed in the DGX-1 include 16nm FinFET fabrication technology, for improved energy efficiency; Chip on Wafer on Substrate with HBM2, for maximizing big data workloads; and new half-precision instructions to deliver more than 21 teraflops of peak performance for deep learning.Nvidia's new GPU is the first to be based on its Pascal architecture. It provides the throughput of 250 CPU-based servers, networking, cables and racks – all in a single box. This core is clocked at 1,328 MHz with boost clocks up to 1,480 MHz.