Artificial intelligence is already an integral part of our lives. From Google Maps to Alexa, AI makes our lives more convenient. Less visibly, AI has helped streamline operations across a range of industries by automating mundane tasks. But as AI expands its applications, many have expressed concerns that it will exacerbate existing inequalities. Numbers from the World Economic Forum suggest that more than half of the 1.4 million US workers expected to be affected by tech disruption will be women. And there are questions about ways that algorithms reflect human biases.

Mark Zuckerberg would have known about concerns of data breaches at Facebook as early as 2010, MPs today heard. The tech giant's former operations manager Sandy Parakilas said the media giant handed over'highly personal' data on its users to app developers. But once the information was handed over the company had no way of keeping track of it - meaning it could be harvested and used by firms like Cambridge Analytica. Mr Parakilas, who worked for the firm in 2011 and 2012, said he gave top executives a briefing on the dangers that data could be breached. But he said Facebook effectively turned a blind eye to these concerns and did not carry out audits of where the data was going.

Swarms of firefighting drones could one day be deployed to tackle hugely destructive megafires that are becoming increasingly frequent in the Mediterranean region because of climate change, arson and poor landscape management. It's one of a number of initiatives looking at how best to fight large fires from the air – a challenge that's becoming more and more common. A 2017 report on forest fires by the EU's Joint Research Centre said that the year would'likely be remembered as one of the most devastating wildfire seasons in Europe since records began', after the destruction of nearly 700,000 hectares of land in the EU by early September. Such fires are dangerous not only for people who live in the area but also for the crews of people whose job it is to put the fires out. But using intelligent robots to scout the area and drop water can allow humans to stand further back from the danger zone, only looking at the drones' data to make decisions from the safety of a command and control centre.

If you go to college and take a course "Machine learning 101", this might be the first example of machine learning your teacher will show you: Imagine you work for a real estate agency, and you want to predict, for how much a house will sell. You have some historical data -- you know that house A has been sold for $500 000, house B for $600 000, and house C for $550 000. You also know something about properties of the houses -- you know the size of the house in square meters, number of rooms in the house, and the year the house was build. The goal of the real estate agency is to predict, for how much a new house D will sell, given its known properties (size, age and number of rooms of the house). In ML terminology, the known properties of the house are called "features" or "indicators" (we use the term "indicators" in Signals, because this term has been historically used in trading).

An academic who developed the app used by Cambridge Analytica to harvest data from millions of Facebook users said Wednesday that he had no idea his work would be used in Donald Trump's 2016 presidential campaign and that he's being scapegoated in the fallout from the affair. Alexandr Kogan, a psychology researcher at Cambridge University, told the BBC that both Facebook and Cambridge Analytica have tried to place the blame on him for violating the social media platform's terms of service, even though Cambridge Analytica ensured him that everything he did was legal. "My view is that I'm being basically used as a scapegoat by both Facebook and Cambridge Analytica," he said. "Honestly, we thought we were acting perfectly appropriately, we thought we were doing something that was really normal." Authorities in Britain and the United States are investigating the alleged improper use of Facebook data by Cambridge Analytica, a U.K.-based political research firm.

Here's how a data firm helped Donald Trump get elected as president. Image of a Facebook logo taken on a mobile phone. LONDON (AP) -- An academic who developed an app used by Cambridge Analytica to harvest data from millions of Facebook users said Wednesday he had no idea his material would be used in Donald Trump's 2016 presidential campaign and that he's being scapegoated in the affair. Alexandr Kogan, a psychology professor at Cambridge University, told the BBC that both Facebook and Cambridge Analytica have tried to place the blame on him for violating the social media platform's terms of service, when he had been assured that everything he did was appropriate. Kogan told the BBC that Cambridge Analytica approached him about the app and, in retrospect, he should have asked the company more questions about how the data would be used.

Award winners in robot competitions held by the were named on 14 March 2018, during this year's European Robotics Forum (ERF), held in Tampere, Finland on 13–15 March. Awards for the ERL's 2017-18 season were presented at a Gala Dinner to winning teams that took part in all ERL competitions: Service Robots (ERL-SR), Industry Robots (ERL-IR) and Emergency Robots (ERL-ER). ERL-SR is for robots that could provide assistance in homes, particularly for people with reduced mobility. ERL-ER is for robots in simulated emergency situations and ERL-IR tackles automation in industry. Dozens of teams from around Europe took part in the 2017–18 ERL competitions, which stimulate innovation by and collaboration among robotics researchers by setting tasks in simulated real-life conditions, for completion against the clock.

A UK-based academic whose app harvested the data of 50 million Facebook users has claimed he is being made a scapegoat by the social media company and Cambridge Analytica. Aleksandr Kogan, a psychology lecturer at Cambridge University, developed a personality app which amassed a huge cache of personal information from Facebook for the British political consultancy accused of an illegal data grab. Cambridge Analytica (CA) is alleged to have used the information to help Donald Trump's 2016 presidential campaign and on Tuesday suspended its chief executive, Alexander Nix, after he was secretly recorded boasting about the firm's pivotal role in the US election. MPs have summoned Facebook founder Mark Zuckerberg to give evidence over the "catastrophic failure of process" behind the breach and have accused the social media giant of misleading Parliament about how companies acquired and held user data. Facebook, which also faces an investigation by the US Federal Trade Commission, has suspended activity for CA and Dr Kogan for violating its policies.

In this post I'm going to show what streaming ETL looks like in practice. My first job from university was building a data warehouse for a retailer in the UK. Back then, it was writing COBOL jobs to load tables in DB2. We waited for all the shops to close and do their end of day system processing, and send their data back to the central mainframe. From there it was checked and loaded, and then reports generated on it.

Every time you read a trade journal, an article on LinkedIn or attend a conference you can bet there'll be something about AI and Big Data (it's always capital B and capital D too). It's also probable that many businesses will be able to get along fine without either. However, anyone wanting to profit from these innovations will be finding out exactly how they can assist them. On the one hand, AI will undoubtedly help in processes, transactions and compliance. Machine learning will reduce time, cost and complexity from many arduous jobs within companies, businesses and firms.