Do not go gentle into that good night, Old age should burn and rave at close of day; Rage, rage against the dying of the light. Welsh poet Dylan Thomas' famous lines are a passionate plea to fight against the inevitability of death. While the sentiment is poetic, the reality is far more prosaic. We are all going to die someday at a time and place that will likely remain a mystery to us until the very end. Researchers are now applying artificial intelligence, particularly machine learning and computer vision, to predict when someone may die.

It is impossible to accurately predict the jobs of the future, says Mark Scott, the secretary of the NSW Department of Education, but schools will need to prepare the next generations of students for a world that will be dominated by intelligent machines. "Children are now facing a more uncertain future than any child has faced since the Industrial Revolution," Mr Scott said. Mr Scott this week gave a speech to the Trans-Tasman Business Circle where he outlined publicly for the first time his vision for education of the future. It was the springboard for the launch of work being done within the department to prepare students for a fast-changing world. The department is commissioning research and papers from the world's leading experts and educators in the areas of artificial intelligence and education systems of the future.

Science and technology are essential tools for innovation. In the next few years, the impact of Digital Transformation will accelerate. Internet of Things (IoT aka M2M, IIOT, IoE – though each has its specific focus) is considered the most important trend in digitization. From connected homes, cars, cities to entire industries such as healthcare, insurance, manufacturing, public sector and utilities – connected and increasingly intelligent devices are transforming entire ecosystems, and one of the components are robots. Today, in our first Robotics Q&A, Mauro Fenzi, the CEO of Comau, discusses about different types of robots that already have a huge impact on manufacturing and other industries.

The revolution in customer services will come in the form of chatbots. These robots are able to converse with customers and, according to Cross, will increase the functionality of mobile banking exponentially: "If, for example, you want to know how much you have spent on coffee during the month, you can download an Excel sheet that will give you the amount. However, you can also ask a chatbot, "How much have I spent on coffee this month', and it will give you the answer." Artificial intelligence will also develop new functionalities in other areas of concern to banks such as money laundering, legal issues, and fraud detection. Artificial intelligence is in fact already being used for legal tasks.

LONDON (Reuters) - The world's leading drug companies are turning to artificial intelligence to improve the hit-and-miss business of finding new medicines, with GlaxoSmithKline unveiling a new $43 million deal in the field on Sunday. Other pharmaceutical giants including Merck & Co, Johnson & Johnson and Sanofi are also exploring the potential of artificial intelligence (AI) to help streamline the drug discovery process. The aim is to harness modern supercomputers and machine learning systems to predict how molecules will behave and how likely they are to make a useful drug, thereby saving time and money on unnecessary tests. AI systems already play a central role in other high-tech areas such as the development of driverless cars and facial recognition software. "Many large pharma companies are starting to realise the potential of this approach and how it can help improve efficiencies," said Andrew Hopkins, chief executive of privately owned Exscientia, which announced the new tie-up with GSK. Hopkins, who used to work at Pfizer, said Exscientia's AI system could deliver drug candidates in roughly one-quarter of the time and at one-quarter of the cost of traditional approaches.

Amaury Sport Organisation (A.S.O.), organizers of the Tour de France, and Dimension Data, the Official Technology Partner of the Tour de France, announced the introduction of machine learning technologies at this year's Tour de France to give cycling fans across the globe an unprecedented experience of this year's event. The race begins in Düsseldorf on Saturday and finishes at the Champs-Élysées in Paris on 23 July. This year, Dimension Data's data analytics platform, which was developed in partnership with A.S.O., incorporates machine learning and complex algorithms that combine live and historical race data to provide even deeper levels of insight as the race unfolds. Fans will also benefit from rider profiles to understand more about environments and circumstances in which riders perform best. As part of a new pilot this year, A.S.O. and Dimension Data are exploring the role of predictive analytics technologies to assess the likelihood of various race scenarios, such as whether the peloton will catch the breakaway riders at certain stages of the race.

The problem with regulating technology companies is that, faced with tough new rules, they can eventually innovate their way out, often by switching to newer, unregulated technologies. The risk of targeted regulation informed by little other than economic doctrines might even be fuelling a corporate quest for eternal disruption: instead of surrendering to the regulators, technology firms prefer to abandon their old business model. It's through this lens that we should interpret the likely fallout from the €2.4bn fine imposed on Alphabet, Google's parent company, by the European commission. It arrives after a lengthy, seven-year investigation into whether the company abused its dominance to promote its own online shopping service above search results. The commission's case seems sound; the sad fate of small online retailers, unable to compete with Alphabet over the past decade, suggests as much.

Volvo, like seemingly every other company, has been working on their autonomous vehicle technology and it has run into an interesting problem. While testing its cars in Australia, the company found that kangaroos were both a nuisance and very confusing to its cars. The vehicles' detection system has been exposed to large animals before -- it came across moose while being tested in Sweden and it can respond to deer, elk and caribou. But kangaroos move much differently than other animals and their hopping is throwing off the system. "When it's in the air it actually looks like it's further away, then it lands and it looks closer," Volvo Australia's technical manager David Pickett told the ABC.

LONDON & PARIS--(BUSINESS WIRE)--Amaury Sport Organisation (A.S.O.), organisers of the Tour de France, and Dimension Data, the Official Technology Partner of the Tour de France, today announced the introduction of machine learning technologies at this year's Tour de France to give cycling fans across the globe an unprecedented experience of this year's event. The race begins in Düsseldorf on Saturday and finishes at the Champs-Élysées in Paris on 23 July. This year, Dimension Data's data analytics platform, which was developed in partnership with A.S.O., incorporates machine learning and complex algorithms that combine live and historical race data to provide even deeper levels of insight as the race unfolds. Fans will also benefit from rider profiles to understand more about environments and circumstances in which riders perform best. As part of a new pilot this year, A.S.O. and Dimension Data are exploring the role of predictive analytics technologies to assess the likelihood of various race scenarios, such as whether the peloton will catch the breakaway riders at certain stages of the race.

Jürgen, it's a privilege to have you here as one of the pioneers of artificial intelligence and, more specifically, deep learning--its hottest field right now. Before you go into all of those fields, we would like to understand the person Jurgen Schmidhuber better. Perhaps you can tell us a few things that you're particularly proud of in your career. One of the things I'm proud of: I think I understand what it means to be curious and how to implement curiosity, which I think is essential to build agents that learn from experience through their own self-generated experiments. Agents who are motivated to invent, in a directed way, action sequences or experiments that lead to data that tell them something about how the world works that they didn't know yet.