Co-working robots are the near future of work. As long as we still need humans somewhere on the factory floor, we need robots that can safely operate next to those humans. The most famous example of a co-working robot is Rethink Robotics' Baxter. But, of course, co-working can't be a category of one, and this new Tend.ai Basically, with Tend.ai you buy a regular robot arm, and attach a regular webcam to it, and then you let Tend.ai supply the smarts.

In the demo video below that simple command actually meant, "Help me buy new soccer cleats for my son." Powered by USAA's Savings Booster, a feature that the company has offered to its banking customers since mid-2015, Alexa quickly responded with a suggested action. Next thing you know, the happy Mom watches her child head out the door wearing the new shoes. According to Darrius Jones, AVP of Enterprise Innovation at USAA, the company built a crack team to rise to the Alexa Challenge, a contest conducted by PYMNTS.com, To build its demo, USAA's developers were able to create the Alexa-based conversational interface to Savings Booster, an existing feature for banking customers, in a matter of 10 hours. It then took 22 days to implement and be up-and-running on the Alexa platform.

Recently, neural networks have become the tool of choice for a variety of tough computer-science problems: Facebook uses them to identify faces in photos, Google uses them to identify everything in photos. Apple uses them to figure out what you're saying to Siri, and IBM uses them for operationalizing business unit synergies. Can neural networks help you find the emoji when you really need it? This post will outline some of the engineering behind Dango, allowing us to automatically learn from hundreds of millions of real-world uses of emoji, and distill this down to a tool small and fast enough to predict emoji for you in real time on your phone. Dango is a floating assistant that runs on your phone and predicts emoji, stickers and GIFs based on what you and your friends are writing in any app.

This year at Cannes Lions, judges are recognizing work at the intersection of AI and advertising. The Next Rembrandt, a campaign by JWT Amsterdam for the Dutch bank ING, used "deep learning" algorithms to analyze more than 168,000 painting fragments by the 17th century master to create a digital image based on his existing work. Then, a 3D printer fabricated a painting based on the image, replicating the depth and texture of original Rembrandt works to a surprising degree. The campaign took home a Grand Prix at Cannes in the Cyber Lions category, which recognizes "ideas indigenous to, or enhanced by, the digital environment," and an additional Grand Prix in the Creative Data Lions category. This year's entries were more sophisticated than those in the past, said Cyber Lions jury president Chloe Gottlieb, SVP and executive creative director at R/GA. "The data is not an output from the creativity," said Gottlieb.

A group of researchers from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have developed a way to train artificial intelligence to read and interpret pathology images. Scientists tested the artificial intelligence (AI) during a competition at the annual International Symposium of Biomedical Imaging, where it was tasked to look for breast cancer in images of lymph nodes. It turns out it can detect breast cancer accurately 92 percent of the time and won in two separate categories during the contest. Andrew Beck from BIDMC says they used the deep learning method, which is commonly used to train AI to recognize speech, images and objects. They fed the machine with hundreds of slides marked to indicate which parts have cancerous cells and which have normal ones.

Just a few weeks back, scientists at Google's artificial intelligence division DeepMind announced they were developing a "kill switch" to ensure that intelligent machines do not go all Terminator on us. Now, it seems, Google's AI-related concerns …… Read More Google released a new paper on a highly controversial topic: safety rules for Artificial Intelligence. Artificial intelligence is either the bright shining future of technology or an insidious threat that could endanger all of mankind, depending on your point of view. The words Artificial Intelligence can bring to mind far-fetched, sci-fi ideas and a society where robots have replaced humans. Well, this idea may not be too far off given Google's recent innovations.

Washington (AFP) - If it has to make a choice, will your autonomous car kill you or pedestrians on the street? The looming arrival of self-driving vehicles is likely to vastly reduce traffic fatalities, but also poses difficult moral dilemmas, researchers said in a study Thursday. Autonomous driving systems will require programmers to develop algorithms to make critical decisions that are based more on ethics than technology, according to the study published in the journal Science. "Figuring out how to build ethical autonomous machines is one of the thorniest challenges in artificial intelligence today," said the study by Jean-Francois Bonnefon of the Toulouse School of Economics, Azim Shariff of the University of Oregon and Iyad Rahwan of the Massachusetts Institute of Technology. "For the time being, there seems to be no easy way to design algorithms that would reconcile moral values and personal self-interest -- let alone account for different cultures with various moral attitudes regarding life-life tradeoffs -- but public opinion and social pressure may very well shift as this conversation progresses."

Since 2009, Microsoft has engaged with academic pioneers of deep learning and has created industry-scale successes in speech recognition as well as in speech translation, object recognition, automatic image captioning, natural language, multimodal processing, semantic modeling, web search, contextual entity search, ad selection, and big data analytics. Much of these successes are attributed to the availability of big datasets for training deep models, the powerful general-purpose GPU computing, and the innovations in deep learning architectures and algorithms. In this talk, a selected overview will be given to highlight our center's work in some of these exciting applications, as well as the lessons we have learned along the way as to what tasks are best solved by deep learning methods.

So, again: this is the principle, below is only a discussion of the nature of the signals or representations!

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