But turbulence could be a thing of the past thanks to new technology that promises to predict the type that lurks unseen - and steer planes around it. That's according to Boeing, which will be trialing remote-sensing technology that can detect clear-air turbulence early next year. The concept, which they hope could be rolled-out across all commercial carriers, comes from a seven-tear collaboration with the Japan Aerospace Exploration Agency (JAXA) and has huge potential for travellers. According to the Federal Aviation Administration, the number of turbulence-related injuries doubled in 2016, from 21 to 44. But this could be slashed by lidar (light detection and ranging technology), which will work by emitting pulses of laser light from the plane's nose, scattering small dust and other particulates.

Craig Federighi, Apple's senior vice president of software engineering says there are two things you can do to stop nefarious actors from forcing you into FaceID. According to Federighi, "If you don't stare at the phone, it won't unlock," & "If you grip the buttons on both sides of the phone when you hand it over, it will temporarily disable Face ID." Clearly, iPhone X owners will have to practice their squeezing techniques. It would be painful and costly to be held up and discover that you were squeezing it all wrong. The ACLU & the EFF recently sued the DHS for searching the phones and laptops of 11 plaintiffs at the US border without a warrant. The group of plaintiffs includes 10 US citizens and one lawful permanent resident, several of whom are Muslims or people of color.

On 6th October 15 top level speakers from companies such as Google, Gartner, Unilever, Global Future of Work Foundation and Ricoh USA will take part in this event which once again makes Barcelona the global centre for new game-changing and innovative trends in HR. Is digital selection the way to ensure equal opportunities without personal biases? Could new technologies be a weapon or tool in Middle Eastern women's fight for equality? How can information security and privacy be guaranteed in increasingly digital environments? All of these questions will be answered at the fourth edition of the International HR Conference Barcelona, organised by Advantage Consultores, which will take place on 6th October in the Telefónica Auditorium in Barcelona. We maintain our commitment to Barcelona as an international reference point for Human Resources.

Pattern recognition is identified as a key human skill that has supported the rise of people to become the dominant species. However, there are limits to this crucial ability, especially when confronted with masses of information that differ only slightly. Small, but significant, variations are more easily recognised by machines that can minutely inspect and compare differences without fatigue and with low margins of error. Humans are, thus, using machines to augment their pattern-recognition capability by teaching machines how to recognise patterns and correlate seemingly disparate data to gain new insights. "Specialised machine-learning algorithms are used to evaluate large quantities of data and derive and/or exploit relationships in the data," says IBM Watson Advanced Cognitive Technology and Solutions data scientist Stefan van der Stockt. "The basic idea behind machine learning is that we want to learn relationships and corelationships between the different elements of the data, whether it be recognising a face or identifying a potentially cancerous lesion on an X-ray image," says Council for Scientific and Industrial Research (CSIR) Mobile Intelligent Autonomous Systems unit principal researcher Dr Benjamin Rosman.

Are you a data analyst, data scientist, or a researcher looking for a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you! Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics.

For some, the spread of artificial intelligence and robotics poses a threat to our privacy, our jobs – even our safety, as more and more tasks are handed over to silicon-based brains. The 21st Century is continually throwing us new challenges and expecting us to adapt – but for every earth-shattering megatrend, there are dozens of genius solutions. Follow them all in BBC Future's special series, Grand Ideas. But even the most vocal critics highlight the potential good that AI and automated systems could do for humanity. As part of BBC Future Now's Grand Challenges, a panel of experts recently described how they saw our world changing as the machines we use grow smarter.

Today is World Malaria Day, a moment dedicated to raising awareness and improving access to tools to prevent malaria. The World Health Organization says nearly half of the world's population is at risk for malaria, and estimates that in 2015 there were 212 million malaria cases resulting in 429,000 deaths. DiSARM (Disease Surveillance and Risk Monitoring), a project led by the Malaria Elimination Initiative and supported by the Bill and Melinda Gates Foundation and Clinton Health Access Initiative, is fighting the spread of malaria by mapping the places where malaria could occur. With the help of Google Earth Engine, DiSARM creates high resolution "risk maps" that help malaria control programs identify the areas where they should direct resources for prevention and treatment. We sat down with Hugh Sturrock, who leads the DiSARM project and is an Assistant Professor of Epidemiology and Biostatistics in the University of California, San Francisco's Global Health Group, to learn more about DiSARM's fight against malaria, and how Google fits in.

With recent advances in science and technology, particularly in machine learning, organizations need to adopt more comprehensive analytics strategies rather than basic analytics as large volumes of data is needed to be analyzed. Predictive learning is a technique of machine learning which aims to build a model that makes predictions based on evidence in the presence of uncertainty. It can be called as an attempt to learn with a minimum of pre-existing mental structure. Predictive learning helps in solving real world's problems in economics, business and government sector. Apart from this, it can be applied to a range of businesses to influence the future strategies including BFSI companies on account of escalating incidences of defaulters and stringent regulations for ensuring compliance with banking principles.

A team of researchers at Wits University in Johannesburg, South Africa have made a major breakthrough in the field of biomedical engineering. According to a release published on Medical Express, for the first time ever, researchers have devised a way of connecting the human brain to the internet in real time. It's been dubbed the "Brainternet" project, and it essentially turns the brain "…into an Internet of Things (IoT) node on the World Wide Web." The project works by taking brainwave EEG signals gathered by an Emotiv EEG device connected to the user's head. The signals are then transmitted to a low cost Raspberry Pi computer, which live streams the data to an application programming interface and displays the data on an open website where anyone can view the activity.

In the last instalment of this blog series, we discussed objectives and accuracy in machine learning. And we described two crucial tests for the utility of a machine learning model: The model must be sufficiently accurate and we must be able to deploy the model so that it can produce actionable outputs from the available data. We then introduced a real-world scenario -- predicting train failures up to 36 hours in advance of their occurrence using sensor data -- to illustrate the application of those tests. But how did we decide which of the multitude of machine learning algorithms to use to train our model in the first place? To answer this question, we need to revisit the main classes of machine learning algorithms.