The world's leading non-profit artificial intelligence research team needs the world's fastest AI system. "I thought it was incredibly appropriate that the world's first supercomputer dedicated to artificial intelligence would go to the laboratory that was dedicated to open artificial intelligence," Huang said. OpenAI's researchers will put the first production DGX-1 -- packing 170 teraflops of computing power, equal to 250 conventional servers -- to work on artificial intelligence's toughest problems. OpenAI's team is working at the cutting-edge of a field that promises incredible advances. Imagine artificial personal assistants that can coordinate our digital lives and autonomous cars and robots that are accessible to everyone.

Fluenty is an AI-driven messaging platform that is well positioned to deliver in the midst of this much hyped and growing wave. The company released its technology and published its patents before Smart Reply or Google Allo were even on the market. It is simple to integrate with other messaging apps and quick at acquiring users' taste and personal style. No need to open Fluenty when you need it. Unlike Google, it has the capacity to combine with other apps such as Facebook Messenger, WhatsApp, Google Hangout or KakaoTalk, as well as text messaging apps. By suggesting pre-written replies to messages, Fluenty does not interfere with the flow.

Robert Gordon has written a magnificent book on the economic history of the United States over the last one and a half centuries. His study focuses on what he calls the "special century" from 1870 to 1970--in which living standards increased more rapidly than at any time before or after. The book is without peer in providing a statistical analysis of the uneven pace of growth and technological change, in describing the technologies that led to the remarkable progress during the special century, and in concluding with a provocative hypothesis that the future is unlikely to bring anything approaching the economic gains of the earlier period. The message of Rise and Fall is this. For most of human history, economic progress moved at a crawl. According to the economic historian Bradford DeLong, from the first rock tools used by humanoids three million years ago, to the earliest cities ten thousand years ago, through the Middle Ages, to the beginning of the Industrial Revolution around 1800, living standards doubled (with a growth of 0.00002 percent per year). Another doubling took place over the subsequent period to 1870. Then, according to standard calculations, the world economy took off. Gordon focuses on growth in the United States.

The new machine, called a DGX-1, is optimized for the form of machine learning known as deep learning, which involves feeding data to a large network of crudely simulated neurons and has resulted in great strides in artificial intelligence in recent years. Language remains a very tricky problem for artificial intelligence, but in recent years researchers have made progress in applying deep learning to the problem (see "AI's Language Problem"). "This will allow us to train models on larger data sets, which we have found leads to progress in AI." OpenAI hopes to use reinforcement learning to build robots capable of performing useful chores around the home, although this may prove a time-consuming challenge (see "This Is the Robot Maid Elon Musk Is Funding" and "The Robot You Want Most Is Far from Reality").

Is it possible that the secret to building machine intelligence lies in spending endless hours reading Reddit? That's one question a team of researchers at OpenAI, a nonprofit backed by several Silicon Valley luminaries, hopes to answer with a new kind of supercomputer developed by chipmaker Nvidia. The researchers are also training robots do the dishes through experimentation, and they are building algorithms capable of learning to play a wide variety of different computer games. The new machine, called a DGX-1, is optimized for the form of machine learning known as deep learning, which involves feeding data to a large network of crudely simulated neurons and has resulted in great strides in artificial intelligence in recent years. The DGX-1 will let AI researchers train deep-learning systems more quickly using more data.

As athletes and coaches begin to adopt IoT technology, 21st century fans are beginning to expect more from sporting events. The world of robotics and AI have so much to offer in terms of event management, so why not bring bots to stadiums the world over? Concierge robots could bring so much to the sports fan experience. Fans come from far and wide to attend major events, such as the World Cup, the Super Bowl, the Tour de France, and so on. They stay in hotels, hostels, and shared-homes.

The sun was sparkling on the Mediterranean Sea on the afternoon when a graduate student from my lab tossed our prize robot into the water for the first time. I watched nervously as our electronic creation sank beneath the waves. But the bot didn't falter: When we gave it the command to swim, it filled its expandable mantle with water, then jetted out the fluid to shoot forward. When we ordered it to crawl, it stiffened its eight floppy arms in sequence to push itself along the sandy bottom and over scattered rocks. And when we instructed it to explore a tight space beneath the dock, the robot inserted its soft body into the narrow gap without difficulty.

These claims are not ludicrous on their own. We've seen rapid advancements in technology over the past decades; we know computers are growing more powerful and more accessible by the month. Already in 2011, a supercomputer named Watson won a game of Jeopardy against two former champions, using a mixture of AI and all-important natural-language processing. The future is here and it may soon outstrip us. Kurzweil's timeline of the technological singularity is based on the law of accelerating returns, wherein the more powerful computers become, the faster they advance.

Our control model was a well established machine learning model using features that are known to work well. For text, the features are essentially normalized word counts (TF-IDF: term frequency / inverse document frequency vectors). For images, we use HOG features (histogram of oriented gradients). These features were fed into a logistic regression model for training and prediction. Our test model used custom collection; we fed data, trained a model, and made a prediction using transfer learning for text and image analysis under the covers.

Imagine a'Persuade' button for every critical moment in your life. One click to send the perfect Tinder pickup line. One click to convince your boss it's time for a raise. One click to persuade your significant other that you're actually right this time. In the last 10 years, we've seen an explosion of communication apps.