The U.S. must offer a "measured response" to Iran's downing of an American drone over the Strait of Hormuz, according to a Republican member of the House Homeland Security Committee. Failure to respond to Iranian aggression would incentivize future attacks, U.S. Rep. Mark Green, R-Tenn., told Shannon Bream on Thursday on "Fox News @Night." "I think we clearly need a measured response here," Green said. "I think the world needs to see, honestly, smoke and fire. I think Kim Jong Un needs to see smoke and fire. There's been an attack on the U.S. military and if we don't respond, we are incentivizing future attacks."

The main story of this edition of the Tech Diaries is the Deepfakes issue that has gotten the U.S Congress freaking out. It represents the class of synthetic media generated by AI and represents another dark side of technology -- ringing alarm bells about what the implications of a sudden digital transformation can have on the society as a whole. The disruption caused by deepfakes can have serious consequences on how we differentiate right from wrong -- as if the "fake news" issue on the social media & other platforms isn't enough headache already. U.S lawmakers have started hearings on the issue, fearing the disruptive & deceptive technology may unfairly affect the upcoming U.S Presidential elections in 2020 -- as we saw, how just a simple low tech manipulation of videos of the U.S President & the House Speaker by rival groups earlier this year created headlines. The real problem starts when advanced Deep Learning algorithms are employed to create real-life images.

The new stage of the race for AI domination I. Let use your imagination (an ability only humans have). Imagine you are in the Library of Congress and need to find a book that contains one specific sentence. You may get lucky, but most probably you die before you find it. But you can hire thousands of interns, and they will be flipping through pages in books and comparing the sentence on a piece of paper you gave them with sentences in a book, and one will find the book rather soon. This is exactly what AI does these days, and will do for many years to come; AI is just an intern with thousand hands and eyes, and a brain strong just enough to learn some patterns, although for AI learning takes much more time than for a human intern.

New tentative contract for Canada's scientists would enshrine right to speak to media Rouge National Urban Park, nearly 95% complete, becomes North America's largest urban park Academics join outcry sparked by Hong Kong's contentious extradition bill Africa's science academy leads push for ethical data use Attend the Journées de la relève en recherche to get tips and tricks to succeed in your graduate studies or to enter the job market!

There is great potential for the use of Artificial intelligence (AI) by cities. With the help of AI, we can provide more responsive services to citizens. However, AI poses ethical issues that need special attention, as Chief Digital Officers of London and Helsinki here we explain why and suggest an approach for city government. The use of automation and machine learning systems is not a new phenomenon in public administration, but it is being transformed in how it is being used -- from automating simple transactions to more complex problem-solving. Today we see adoption across a range of municipal services -- chatbots in customer services, prioritisation of housing repairs, traffic signalling, demand-responsive transport, even library book management systems.

Both AI and cybersecurity are broad and poorly understood fields. This book helps give you an overview of the various technologies that make up AI, where they have come from, and what AI has evolved into today. Cybersecurity is another field that has evolved over the last few decades. Dive into the world of cybersecurity and then learn how AI is being applied to the battle. When you're done reading this book, you will be spouting terms like cognitive computing, machine learning, and deep learning, and know how they apply to the cybersecurity space.

With the lag of tech in healthcare, will AI/ML improve patient care or remain a smart idea? Technology experts have promised artificial intelligence (AI) and machine learning (ML) will revolutionize healthcare. Applications have the potential to streamline workflows and reduce human errors, speeding drug discovery, assisting surgery, and provisioning better billing and coding methods. But, in an industry that typically lags in digital maturity by as much as 10 years, according to a 2017 study, is AI in healthcare an empty promise or truly a forward-thinking and innovative reality? Technology experts have promised artificial intelligence (AI) and machine learning (ML) will revolutionize healthcare.

WASHINGTON/DUBAI, UNITED ARAB EMIRATES - U.S. President Donald Trump played down Iran's downing of a U.S. military surveillance drone on Thursday, saying he suspected it was shot by mistake and "it would have made a big difference" to him had the remotely controlled aircraft been piloted. While the comments appeared to suggest Trump was not eager to escalate the latest in a series of incidents with Iran, he also warned: "This country will not stand for it." Tehran said the unarmed Global Hawk surveillance drone was on a spy mission over its territory, but Washington said it was shot down over international airspace. "I think probably Iran made a mistake -- I would imagine it was a general or somebody that made a mistake in shooting that drone down," Trump told reporters at the White House. "We had nobody in the drone. It would have made a big difference, let me tell you, it would have made a big, big difference" if the aircraft had been piloted, Trump said as he met Canadian Prime Minister Justin Trudeau in the Oval Office.

Trump calls the strike a'foolish move'; national security correspondent Jennifer Griffin reports. It's time for the U.S. to take military action against Iran – not to start a war, but to blow some things up in retaliation for Iran shooting down a U.S. surveillance drone Thursday in international air space, just days after setting off explosives that damaged two oil tankers. President Trump gave Iran a pass after the recent tanker attacks. But instead of reassessing their strategy and trying to de-escalate tensions, the Iranians escalated significantly by shooting down the American drone – a high-flying unmanned aircraft that costs about $130 million. I don't see how President Trump can let Iran's latest attack pass without action if he expects Iran and other nations to respect the U.S. and not conclude they can attack our forces at will, without fear of retaliation.

There has been rapidly growing interest in the use of algorithms for employment assessment, especially as a means to address or mitigate bias in hiring. Yet, to date, little is known about how these methods are being used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and assess the claims and practices of companies offering algorithms for employment assessment, using a methodology that can be applied to evaluate similar applications and issues of bias in other domains. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their techniques for detecting and mitigating bias. We find that companies' formulation of "bias" varies, as do their approaches to dealing with it. We also discuss the various choices vendors make regarding data collection and prediction targets, in light of the risks and trade-offs that these choices pose. We consider the implications of these choices and we raise a number of technical and legal considerations.