Inc. in June pitched its facial recognition technology -- which can identify people from surveillance footage using image databases -- as a tool for U.S. Immigration and Customs Enforcement, showing that Amazon continued to push the software to law enforcement agencies as criticism swirled from the company's workforce and civil liberties groups. Employees in the Amazon Web Services cloud-computing unit met with the federal agency in California to present its artificial intelligence tools, according to emails obtained by the nonprofit Project on Government Oversight. Those tools include Rekognition, which uses artificial intelligence to quickly identify people in photos and videos. The software enables law enforcement to track individuals from cameras in public places. The American Civil Liberties Union in May criticized the use of the technology by police departments in Oregon and Florida, saying it threatened civil rights.

This paper presents a new meta-modeling framework to employ deep reinforcement learning (DRL) to generate mechanical constitutive models for interfaces. The constitutive models are conceptualized as information flow in directed graphs. The process of writing constitutive models are simplified as a sequence of forming graph edges with the goal of maximizing the model score (a function of accuracy, robustness and forward prediction quality). Thus meta-modeling can be formulated as a Markov decision process with well-defined states, actions, rules, objective functions, and rewards. By using neural networks to estimate policies and state values, the computer agent is able to efficiently self-improve the constitutive model it generated through self-playing, in the same way AlphaGo Zero (the algorithm that outplayed the world champion in the game of Go)improves its gameplay. Our numerical examples show that this automated meta-modeling framework not only produces models which outperform existing cohesive models on benchmark traction-separation data but is also capable of detecting hidden mechanisms among micro-structural features and incorporating them in constitutive models to improve the forward prediction accuracy, which are difficult tasks to do manually.

Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We design a task-specific evaluation for a question answering task and evaluate how well a model interpretation improves human performance in a human-machine cooperative setting. We evaluate interpretation methods in a grounded, realistic setting: playing a trivia game as a team. We also provide design guidance for natural language processing human-in-the-loop settings.

Would-be travelers of the galaxy, rejoice: The Chinese tech giant Baidu has invented a translation system that brings us one step closer to a software Babel fish. For those unfamiliar with the Douglas Adams masterworks of science fiction, let me explain. The Babel fish is a slithery fictional creature that takes up residence in the ear canal of humans, tapping into their neural systems to provide instant translation of any language they hear. In the real world, until now, we've had to make do with human and software interpreters that do their best to keep up. But the new AI-powered tool from Baidu Research, called STACL, could speed things up considerably.

Amazon.com pitched its facial-recognition system in the summer to Immigration and Customs Enforcement officials as a way for the agency to target or identify immigrants, a move that could shove the tech giant further into a growing debate over the industry's work with the government. The June meeting in Silicon Valley was revealed in emails as part of a Freedom of Information Act request by the advocacy group Project on Government Oversight; the emails were published first in the Daily Beast. They show that officials from ICE and Amazon Web Services talked about implementing the company's Rekognition face-scanning platform to assist with homeland security investigations. An Amazon Web Services official who specializes in federal sales contracts, and whose name was redacted in the emails, wrote that the conversation involved "predictive analytics" and "Rekognition Video tagging/analysis" that could possibly allow ICE to identify people's faces from afar -- a type of technology immigration officials have voiced interest in for its potential enforcement use on the southern border. "We are ready and willing to support the vital (Homeland Security Investigations) mission," the Amazon official wrote.

The US military is chasing a'third wave' of artificial intelligence (AI) that will see robots endowed with the basic common sense of a 10-year-old child. Its research branch the Defense Advanced Research Projects Agency, or DARPA, is calling for researchers to breed a new type of AI that can solve complex problems. The goal is to build AI that can'communicate more effectively with people' and'understand new situations' better than any previous machines. The project, called The Machine Common Sense Program, is part of a $2 billion investment in AI by Darpa - the military research branch that pioneered the internet. The US military is chasing a'third wave' of artificial intelligence that will see robots endowed with common sense.

Amazon has faced pushback, both internally and externally, for selling its Rekognition facial recognition technology to law enforcement. It's a move the company's own employees said would "serve to harm the most marginalized." Now, The Daily Beast reports that Amazon met with ICE officials in June, and it pitched the agency on Rekognition. According to internal emails obtained through a FOIA request, Amazon Web Services representatives met with ICE officials on June 12th. A salesperson then followed up with ICE, laying out "action items" based on their meeting, one of which says "Rekognition Video tagging/analysis, scalability, custom object libraries."

Added: Oct 19, 2018 11:50 am DARPA is soliciting innovative research proposals in the area of machine common sense to enable Artificial Intelligence (AI) applications to understand new situations, monitor the reasonableness of their actions, communicate more effectively with people, and transfer learning to new domains.

Artificial intelligence (AI) is changing society as profoundly as the steam engine and electricity have done. But unlike past technological revolutions, the AI revolution offers a unique chance to improve lives without opening up and exacerbating global inequalities. That will require widening of the locations where AI is done. The vast majority of experts are in North America, Europe and Asia. Africa, in particular, is barely represented.

In the spring of 2016, an artificial intelligence system called AlphaGo defeated a world champion Go player in a match at the Four Seasons hotel in Seoul. In the US, this momentous news required some unpacking. Most Americans were unfamiliar with Go, an ancient Asian game that involves placing black and white stones on a wooden board. And the technology that had emerged victorious was even more foreign: a form of AI called machine learning, which uses large data sets to train a computer to recognize patterns and make its own strategic choices. Still, the gist of the story was familiar enough.