Fox News Flash top headlines for June 7 are here. Check out what's clicking on Foxnews.com Videos of Boston Dynamics' robots have been the stuff of both awe and inspiration, as well as nightmares. Now, it appears the robots will be doing more than just performing parkour or dancing around on YouTube. According to The Verge, who interviewed Boston Dynamics' CEO Marc Raibert at Amazon's Re:MARS conference in Las Vegas, Spot, the company's dog-like robot and arguably its cutest machine, will be available for purchase "within months" and certainly before the end of 2019.

Based on the book "A Human's guide to Machine Intelligence" by Kartik Hosanagar, Professor of Technology and Digital Business & Professor of Marketing, The Wharton School of the University of Pennsylvania.

A new MIT-developed technique enables robots to quickly identify objects hidden in a three-dimensional cloud of data, reminiscent of how some people can make sense of a densely patterned "Magic Eye" image if they observe it in just the right way. Robots typically "see" their environment through sensors that collect and translate a visual scene into a matrix of dots. Think of the world of, well, "The Matrix," except that the 1s and 0s seen by the fictional character Neo are replaced by dots -- lots of dots -- whose patterns and densities outline the objects in a particular scene. Conventional techniques that try to pick out objects from such clouds of dots, or point clouds, can do so with either speed or accuracy, but not both. With their new technique, the researchers say a robot can accurately pick out an object, such as a small animal, that is otherwise obscured within a dense cloud of dots, within seconds of receiving the visual data.

Otter CEO and Silicon Valley-based serial entrepreneur Sam Liang created a cloud-based artificial intelligence engine to power its automated speech to text transcription service. In my 25-plus-year career--and counting!--as a journalist, I've done thousands of interviews and attended even more meetings, either face to face or over the phone which, for lots of them, I had to manually transcribe the conversations to make sure that I got my interlocutors' comments right--a chore that I dreaded. So when my friend Marie Domingo introduced me to Otter.ai at the TechCrunch Disrupt conference in San Francisco last fall--she was handling their public relations at that time--I was, of course, curious to know more about how this mobile app works but highly skeptical that it would actually help me. After more than six months using the free version of the service that includes 600 minutes of free transcriptions per month, Otter.ai Not only does the app (for iOS and Android) do an excellent job in transcribing my live interviews and meetings from speech to text with great accuracy--and letting me focus on the actual conversations rather than taking notes verbatim--but it made the notes totally searchable which is an amazing time saver when I'm looking for specific keywords.

LONDON - An American billionaire has given Oxford University £150 million ($188.6 million) for a new institute that will study the ethical implications of artificial intelligence and its vast potential to change society as we know it. The donation from Stephen Schwarzman, CEO of the private equity firm Blackstone, will also fund a center to house all of the university's humanities subjects in a single space to encourage collaborative study. The idea is to bring together those working on projects that make life worth living with those trying to make sure that the technology of the future works for the interest of society. "AI can be an enormous force for good," Schwarzman told The Associated Press. "But on the downside it can lead to high unemployment. It could destabilize society if it happens too fast."

WASHINGTON - A majority of Americans are concerned that a foreign government might interfere in some way in the 2020 presidential election, whether by tampering with election results, stealing information or by influencing candidates or voter opinion, a new poll shows. The poll from The Associated Press-NORC Center for Public Affairs Research finds Democrats far more likely to express the highest level of concern, but Democrats and Republicans alike have at least some concerns about interference. Overall, half of Americans say they're extremely or very concerned about foreign interference in the form of altered election results or voting systems, even though hackers bent on causing widespread havoc at polling places face challenges in doing so. An additional quarter is somewhat concerned. Similarly, about half are very concerned by the prospect of foreign governments influencing political candidates or affecting voters' perceptions of the candidates, along with hacking candidate computer systems to steal information.

Robots can do all sorts of things that humans can: they can deliver packages, drive cars, make lattes, and now, rather disconcertingly, they can bleed. In a paper published in Nature, researchers from Cornell University and the University of Pennsylvania explore the use of'electrolytic vascular systems for energy-dense robots' which in this particular case means, a robotic fish that relies on a form of'blood.' As reported by Gizmodo, the specimen uses a type of human-engineered circulatory system to pump a synthetic'blood' -- an electrolyte solution used as hydraulic fuel -- to provide its propulsion and power. Using a'blood' as fuel, researchers say a robotic fish could is able to swim for 36 hours straight Researchers are employing the use of a'blood' like substance to power a robotic fish. The solution is infused with electrolytes to power the bot and is also used to hydraulicly propel it through the water.

Assessment of mental workload in real world conditions is key to ensure the performance of workers executing tasks which demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end. However, EEG correlates of mental workload vary across subjects and physical strain, thus making it difficult to devise models capable of simultaneously presenting reliable performance across users. The field of domain adaptation (DA) aims at developing methods that allow for generalization across different domains by learning domain-invariant representations. Such DA methods, however, rely on the so-called covariate shift assumption, which typically does not hold for EEG-based applications. As such, in this paper we propose a way to measure the statistical (marginal and conditional) shift observed on data obtained from different users and use this measure to quantitatively assess the effectiveness of different adaptation strategies. In particular, we use EEG data collected from individuals performing a mental task while running in a treadmill and explore the effects of different normalization strategies commonly used to mitigate cross-subject variability. We show the effects that different normalization schemes have on statistical shifts and their relationship with the accuracy of mental workload prediction as assessed on unseen participants at train time.

Exchanging gradients is a widely used method in modern multi-node machine learning system (e.g., distributed training, collaborative learning). For a long time, people believed that gradients are safe to share: i.e., the training data will not be leaked by gradient exchange. However, we show that it is possible to obtain the private training data from the publicly shared gradients. We name this leakage as Deep Leakage from Gradient and empirically validate the effectiveness on both computer vision and natural language processing tasks. Experimental results show that our attack is much stronger than previous approaches: the recovery is pixel-wise accurate for images and token-wise matching for texts. We want to raise people's awareness to rethink the gradient's safety. Finally, we discuss several possible strategies to prevent such deep leakage. The most effective defense method is gradient pruning.

In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can suppress virtually all the detected objects in the image. That is, we can place the patch anywhere in the image, causing all existing objects in the image to be missed entirely by the detector, even those far away from the patch itself. This in turn opens up new lines of physical attacks against object detection systems, which require no modification of the objects in a scene. A demo of the system can be found at https://youtu.be/WXnQjbZ1e7Y.