The computer engineer fired by Google for suggesting women are less suited to certain roles in tech and leadership is considering taking legal action against the company. James Damore, a chess master who studied at Harvard, Princeton and MIT and worked at the search engine's Mountain View HQ in California, caused outrage when he circulated a manifesto at the weekend complaining about Google's "ideological echo chamber" and claiming women have lower tolerance of stress and that conservatives are more conscientious. He was fired on Monday after the search giant's chief executive, Sundar Pichai, said portions of Damore's 10-page memo "violate our code of conduct and cross the line by advancing harmful gender stereotypes". Damore has now said he would "likely be pursuing legal action". "I have a right to express my concerns about the terms and conditions of my working environment and to bring up potentially illegal behaviour, which is what my document does," he said in an email reported by the New York Times. In a further email to the rightwing website Breitbart, he reportedly said: "They just fired me for'perpetuating gender stereotypes'."

Before the meeting, roughly three dozen activists from various groups -- including the Stop LAPD Spying Coalition, Black Lives Matter and Los Angeles Community Action Network -- stood outside the LAPD's downtown headquarters, denouncing the use of drones by police. The Police Commission should "completely reject LAPD's latest attempt to revive its drone program," said Hamid Khan, founder of the Stop LAPD Spying Coalition, an anti-surveillance group that frequently criticizes the LAPD. Earlier this year, L.A. County Sheriff Jim McDonnell announced his agency's plans to use a $10,000 drone to help deputies responding to arson scenes, suspected bombs and hostage situations. On July 27, the majority of the Civilian Oversight Commission also expressed their desire for McDonnell to stop flying the drone, citing concerns over surveillance and safety.

A global system of networked devices now generates terabytes of data each second. Affordable storage makes it possible to record seemingly arbitrary amounts of information. And machine learning algorithms, together with distributed computing, increasingly rise to the task of extracting actionable intelligence from this information. But what precisely does "big data" mean? As the importance of data science has grown, so too has the body of jargon associated with it.

LONDON, July 28 (Thomson Reuters Foundation) - A new technique using artificial intelligence to predict where deforestation is most likely to occur could help the Democratic Republic of Congo (DRC) preserve its shrinking rainforest and cut carbon emissions, researchers have said. Congo's rainforest, the world's second-largest after the Amazon, is under pressure from farms, mines, logging and infrastructure development, scientists say. Protecting forests is widely seen as one of the cheapest and most effective ways to reduce the emissions driving global warming. But conservation efforts in DRC have suffered from a lack of precise data on which areas of the country's vast territory are most at risk of losing their pristine vegetation, said Thomas Maschler, a researcher at the World Resources Institute (WRI). "We don't have fine-grain information on what is actually happening on the ground," he told the Thomson Reuters Foundation.

As training data, drawn from planes_features.csv,

The violent android may not be far from reality in China, where'legal robots' are being used in one province to help decide thousands of cases. The machines stand three-feet-tall (90 centimetres) and are used to review cases, check facts, and offer sentencing opinions. 'Legal robots' (pictured) are being used in China to help decide thousands of cases. China's case management robots are not the first machines designed to help with legal issues. DoNotPay, a robot that has been helping people to dispute around 160,000 parking tickets since 2015, expanded its capabilities last month.

Tech companies pitch chatbots to businesses as a way to keep customers coming back for more. A new bot built by Microsoft employees in their spare time is designed to do exactly the opposite. The chatbot, tested recently in Seattle, Atlanta, and Washington D.C., lurks behind fake online ads for sex posted by nonprofits working to combat human trafficking, and responds to text messages sent to the number listed. The software initially pretends to be the person in the ad, and can converse about its purported age, body, fetish services, and pricing. But if a would-be buyer signals an intent to purchase sex, the bot pivots sharply into a stern message.

'Legal robots' have been deployed on thousands of cases in China to help decide sentencing. The robots - which are about three feet tall and have heads shaped like toasters - review documents and identify problems with cases. They also advise on sentencing, and can generate arrest warrants and "approve indictments", said prosecutors in the eastern province of Jiangsu, where the robots are being piloted. Almost 15,000 legal cases have been reviewed by the robots since they were deployed last September, officials said at a press conference this week. They have detected issues and corrected mistakes in more than half the cases, and 541 convictions were commuted.

Suspected money laundering enterprises are in the headlines again, ensnaring the state-controlled Industrial and Commercial Bank of China as well as . Yet for financial institutions that want to identify, track and stop money laundering and other financial crimes, the technological tools of banking's yesteryear no longer cut it. A new wave of tech tools can help banks and other financial firms discover dispersed and complex webs of illegal activity. Increasingly, financial firms are turning to artificial intelligence and sophisticated network analysis to detect such criminal enterprises. The legacy tools banks use to find suspicious dispersals of money often rely on logical "if-then" rules to spot criminal activity, as Wired reports.

Recently software development companies started to embrace Machine Learning (ML) techniques for introducing a series of advanced functionality in their products such as personalisation of the user experience, improved search, content recommendation and automation. The technical challenges for tackling these problems are heavily researched in literature. A less studied area is a pragmatic approach to the role of humans in a complex modern industrial environment where ML based systems are developed. Key stakeholders affect the system from inception and up to operation and maintenance. Product managers want to embed "smart" experiences for their users and drive the decisions on what should be built next; software engineers are challenged to build or utilise ML software tools that require skills that are well outside of their comfort zone; legal and risk departments may influence design choices and data access; operations teams are requested to maintain ML systems which are non-stationary in their nature and change behaviour over time; and finally ML practitioners should communicate with all these stakeholders to successfully build a reliable system. This paper discusses some of the challenges we faced in Atlassian as we started investing more in the ML space.