Recently there has been interest in using satellite images as investing tools. Hedge funds are looking at construction in Beijing to bet on concrete demand, or they are counting cars in Walmart parking lots to get an early estimates on profits. Here I discuss a project to determine land use (i.e. is an area a building or not a building) from satellite images. The idea was to measure the change in land use over time as an economic indicator. This project was a proof of concept for the Insight Data Fellows Program.

One of the most significant developments in cyber espionage in recent years has been the number of groups adopting "living off the land" tactics. That's our shorthand for the use of operating system features or legitimate network administration tools to compromise victims' networks. The purpose of living off the land is twofold. By using such features and tools, attackers are hoping to blend in on the victim's network and hide their activity in a sea of legitimate processes. Secondly, even if malicious activity involving these tools is detected, it can make it harder to attribute attacks.

Wachter told his captive Bay Area audience, "the future of AI and digital transformation will not be created in Mountain View or Cupertino. They simply do not and cannot know enough about the way the healthcare system works. Nor will it be created at UCSF, because we don't have the business expertise or scale." Such transformation, he said, will only occur with partnerships. Beyond academic, enterprise, and startup collaboration, Fernando noted, "markets like China and India, maybe Asia and Africa, are going to come up with new ways to deliver high quality care that we will import into the US--and that might be the biggest disruption." Eron elaborated on this, saying foreign markets have fewer regulations and thus might have access to more, diverse data sets to train algorithms.

When a CIA-backed venture capital fund took an interest in Rana el Kaliouby's face-scanning technology for detecting emotions, the computer scientist and her colleagues did some soul-searching -- and then turned down the money. "We're not interested in applications where you're spying on people," said el Kaliouby, the CEO and co-founder of the Boston startup Affectiva. The company has trained its artificial intelligence systems to recognize if individuals are happy or sad, tired or angry, using a photographic repository of more than 6 million faces. Recent advances in AI-powered computer vision have accelerated the race for self-driving cars and powered the increasingly sophisticated photo-tagging features found on Facebook and Google. But as these prying AI "eyes" find new applications in store checkout lines, police body cameras and war zones, the tech companies developing them are struggling to balance business opportunities with difficult moral decisions that could turn off customers or their own workers.

Disney Research has unveiled an amazing prototype of a robot that moves just like a real dog would. The company has released a new robotic kit that is capable of creating a variety of'robotic manipulators and legged robots'. A video shows the development of the robots from the very first conceptual stages. Disney has unveiled a new robotic kit that can create a'Puppybot' machine. The computer system uses a library of components to create potential robotic designs.

When a CIA-backed venture capital fund took an interest in Rana el Kaliouby's face-scanning technology for detecting emotions, the computer scientist and her colleagues did some soul-searching - and then turned down the money. 'We're not interested in applications where you're spying on people,' said el Kaliouby, the CEO and co-founder of the Boston startup Affectiva. The company has trained its artificial intelligence systems to recognize if individuals are happy or sad, tired or angry, using a photographic repository of more than 6 million faces. Rana el Kaliouby, CEO of Affectiva, demonstrates their facial recognition technology. Recent advances in AI-powered computer vision have accelerated the race for self-driving cars and powered the increasingly sophisticated photo-tagging features found on Facebook and Google.

GENEVA – Major U.S. trading partners including the European Union, China and Japan voiced deep concern at the World Trade Organization (WTO) on Tuesday about possible U.S. measures imposing additional duties on imported autos and parts. Japan, which along with Russia had initiated the discussion at the WTO Council on Trade in Goods, warned that such measures could trigger a spiral of countermeasures and result in the collapse of the rules-based multilateral trading system, an official who attended the meeting said. Over 40 WTO members, including the 28 countries of the European Union -- warned that the U.S. action could seriously disrupt the world market and threaten the WTO system, given the importance of cars to world trade. The United States has imposed tariffs on European steel and aluminum imports and is conducting another national security study that could lead to tariffs on imports of cars and car parts. Both sets of tariffs would be based on concerns about U.S. national security. U.S. President Donald Trump said on June 29 that the probe would be completed in three to four weeks.

The case of 12 boys and their football coach who were found alive after being trapped in a cave in northern Thailand for nine days has gripped the world. Rescuers are now trying to work out how to safely get the group out of the cave, with the Thai military warning that the children could be trapped for up to four months. They have now received food and medical attention, but what will the impact of their ordeal be on their mental health? Dr Andrea Danese is a consultant child and adolescent psychiatrist at the National and Specialist CAMHS Trauma and Anxiety Clinic in London. He says that in the short term, many of the children facing such a traumatic incident may be fearful, clingy, jumpy or moody.

In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system. In contrast to previous work which augments an utterance without considering its relation with other utterances, we propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance's same semantic alternatives in the training data. A novel diversity rank is incorporated into the utterance representation to make the model produce diverse utterances and these diversely augmented utterances help to improve the language understanding module. Experimental results on the Airline Travel Information System dataset and a newly created semantic frame annotation on Stanford Multi-turn, Multidomain Dialogue Dataset show that our framework achieves significant improvements of 6.38 and 10.04 F-scores respectively when only a training set of hundreds utterances is represented. Case studies also confirm that our method generates diverse utterances.

We propose distributed solutions to the problem of Robust Subspace Recovery (RSR). Our setting assumes a huge dataset in an ad hoc network without a central processor, where each node has access only to one chunk of the dataset. Furthermore, part of the whole dataset lies around a low-dimensional subspace and the other part is composed of outliers that lie away from that subspace. The goal is to recover the underlying subspace for the whole dataset, without transferring the data itself between the nodes. We first apply the Consensus-Based Gradient method to the Geometric Median Subspace algorithm for RSR. For this purpose, we propose an iterative solution for the local dual minimization problem and establish its r-linear convergence. We then explain how to distributedly implement the Reaper and Fast Median Subspace algorithms for RSR. The proposed algorithms display competitive performance on both synthetic and real data.