It's tough to peruse the news these days without seeing yet another dystopian warning that robots are coming for your job. This meme even has its own gallows humor, and it's actually pretty good: Eventually there will be one worker left in the world programming all the robots, and his last job will be to write the code that replaces himself. But if this is really happening, where are the robots? On Aug. 9, the Bureau of Labor Statistics reported that second-quarter productivity declined by 0.5%, the third quarterly decline in a row. Productivity, defined as output per hour, has been weak since 2005, and especially weak since 2010 -- the time frame during which warnings of robots coming for your job have reached a fevered pitch.

This June, we published a big story--Shane Bauer's account of his four-month stint as a guard in a private prison. That's "big," as in XXL: 35,000 words long, or 5 to 10 times the length of a typical feature, plus charts, graphs, and companion pieces, not to mention six videos and a radio documentary. It was also big in impact. More than a million people read it, defying everything we're told about the attention span of online audiences; tens of thousands shared it on social media. The Washington Post, CNN, and NPR's Weekend Edition picked it up.

But once Feldman was hired to write the personality of a chatbot for Kasisto, a startup that focuses on artificial intelligence software for banks, she became vocal about the importance of taking gender out of the identity equation. Under her watch, myKai, the bot she was hired to craft a personality for, would be neither female nor male. Feldman's boss at Kasisto, Dror Oren, says the work the team has done with the bot made him more outspoken about the need for equality in tech than he'd have imagined going into the project, and he's a self-proclaimed feminist to begin with. Now, he's hyperaware of the differences between the personality of Kai and overly feminine answers inside similar products made by most large tech companies. Kasisto is on to something.

Intel has pulled open the curtain on a secretly developed mega-chip called Knights Mill, a key component in its artificial-intelligence strategy. The chip--which belongs to the family of high-performance Xeon Phi processors--gives Intel a legitimate opportunity to tackle machine learning. It is targeted at servers and workstations, and will be available in 2017. Intel was caught off-guard with the emergence of artificial intelligence as a way to analyze and present data. Knights Mill, introduced on Wednesday at the ongoing Intel Developer Forum, will fill a big hole in company's chip lineup.

Word just started going around the rumormill that Apple has acquired Turi, a company that describes itself as a "machine learning platform for developers and data scientists." We reached out to Apple for confirmation, and sure enough -- we got the standard reply they give when they're confirming an acquisition but not saying much else: "Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans." Apple declined to comment on the financial terms of the deal, but Geekwire suggests that it was upwards of 200 million. This isn't the first acquisition Apple has made in the AI/machine-learning space. It acquired Perceptio, a company that specialized in machine learning and image recognition, back in September 2015. In addition to its machine-learning products, Turi also runs the Data Science Summit -- a two-day conference focusing on, as the name implies, data science.

Instead, Descartes relies on 4 petabytes of satellite imaging data and a machine learning algorithm to figure out how healthy the corn crop is from space. Grain elevator operators, ethanol producers, commodities traders, hedge funds, insurance companies, and even the farmers growing the corn will all look to the USDA's August crop report being released August 12th to try and understand how the supply side of the corn market will behave. Descartes says it can consistently out-predict the USDA's corn estimates Descartes, which launched in 2014, began releasing corn yield estimates ahead of the USDA's August crop report last year. Now new nanosatellite constellations, like the one run by satellite imaging startup Planet, are taking snapshots of the entire globe at 3- to 5-meter resolutions every day.

The MegaFace dataset contains 1 million images representing more than 690,000 unique people. It is the first benchmark that tests facial recognition algorithms at a million scale.University of Washington In the last few years, several groups have announced that their facial recognition systems have achieved near-perfect accuracy rates, performing better than humans at picking the same face out of the crowd. But those tests were performed on a dataset with only 13,000 images -- fewer people than attend an average professional U.S. soccer game. What happens to their performance as those crowds grow to the size of a major U.S. city? University of Washington researchers answered that question with the MegaFace Challenge, the world's first competition aimed at evaluating and improving the performance of face recognition algorithms at the million person scale.

Telenor Group today announces two specific initiatives to support entrepreneurship and competence building in Norway. The ambition is to strengthen the nation s competitiveness and to stimulate job creation by Norwegian startups. In collaboration with the Norwegian University of Science and Technology (NTNU) and the leading research institute SINTEF, Telenor will establish a lab focused on artificial intelligence and big data at NTNU in Trondheim, Norway. As the second initiative, Telenor will develop and launch a dedicated, next-generation Internet of Things (IoT) network in several Norwegian cities. Norwegian startups and students will get cost-free access to the IoT network in order to develop and test their products and services. The first pilot will be located in Oslo, in collaboration with StartupLab.

However, the idea of building a generative model via an energy function is a good one. Since it is a probabilistic model, training a Boltzmann Machine often involves maximizing its likelihood function, which is proportional to the energy divided by the total energy of all configurations. This total energy is formally called the partition function, and is generally intractable. By restricting the connections of a Boltzmann Machine to be bipartite, people came up with Restricted Boltzmann Machine (RBM), whose inference can be approximated by MCMC and similar methods. Using RBMs to pre-train a deep feedforward nets is one of the main breakthroughs in Deep Learning several years ago.

At Snips, we have spent the past year building technologies for assistants, from analyzing your location patterns to extracting relevant stuff from your email and chat messages. But we don't want to be just a technology company. We want to build products on top of our technology, simplifying the way we interact with our devices on a daily basis. Compared to product categories like calendars or photo-sharing apps, assistants are relatively new. As such, designers around the world have been experimenting with various ideas, from bots to voice assistants, proactive suggestions or physical devices.