At the OOP 2018 conference in Munich, I presented an updated version of my talk about building scalable, mission-critical microservices with the Apache Kafka ecosystem and deep learning frameworks like TensorFlow, DeepLearning4J, or H2O. I want to share the updated slide deck and discuss a few updates about newest trends, which I incorporated into the talk. The main story is the same as in my Confluent blog post about the Apache Kafka ecosystem and machine learning. But I focused more on deep learning/neural networks. I also discussed a few innovations in the ecosystem of Apache Kafka and trends in ML in the last months: KSQL, ONNX, AutoML, and ML platforms from Uber and Netflix.

A man who crashed his car in freezing night-time temperatures was saved from hypothermia when he was found by a police thermal-imaging drone. He was discovered in a deep ditch, 500ft (160m) away from his car on the A16 near Ludborough, Lincolnshire. Police were called at around 02:20 GMT by a member of the public who had found the overturned car and had talked to the driver who then wandered off. Sgt Mike Templeman said the man was found "unresponsive and hypothermic". He tweeted: "Great result & life possibly saved!" after a ground search had failed to find the man.

Sometimes, the tide of technological innovation seems unstoppable. But tech companies still have to abide by laws, rules and regulations, like the rest of us. Moreover, governments and other regulatory bodies are increasingly concerned with ensuring that basic rights and liberties don't get washed away as the digital future rushes in. The General Data Protection Regulation (GDPR) provides a great example of how regulations will shape the development of powerful new technologies like big data analytics, machine learning and artificial intelligence. GDPR is a new European Union regulation that will come into force in late May 2018.

Despite efforts to tackle corruption around the world, progress is still frustratingly slow, according to the latest report from Transparency International. Its annual Corruption Perception index reveals some alarming trends. It shows public service corruption is still a huge problem for two-thirds of the world's economies. The report uses a scale of zero to 100 to rank countries: zero is highly corrupt and 100 is very clean. New Zealand comes out on top but with a score of 89.

Imagine this: You're pulling out of your driveway, asking your phone to pull up navigation to your destination and just at that critical moment when you need to decide which way to turn, you look down at your phone and realize it's hanging. It can't find the destination, and it looks like it has no connection. This frustrating moment is an almost universal experience, but it's one Deutsche Telekom aims to eliminate for all of its customers in Europe and across the globe. As one of the world's top telecommunications providers, Deutsche Telekom and its subsidiaries, like T-Mobile in the United States, are using machine learning algorithms based on dynamic cloud infrastructure managed by Mesosphere DC/OS to dramatically improve the consumer experience when it comes to mobile connectivity. The Deutsche Telekom CONNECT app, now available on the Google Play and iOS App Store, launched this year to allow customers to optimize their connection based on cost or performance at any point in time.

"Five years from now, we will have a commercial quantum computer," says Holmdahl. A quantum computer hasn't yet been built, it is in the process of being prototyped. But Holmdahl, a 24-year Microsoft veteran who previously ran Microsoft's Xbox group, and Love, who has a PhD in quantum physics from Yale, and who has advised quantum startups and worked as a consultant at McKinsey, both believe Microsoft has come up with something very special. Microsoft's "qubit," the fundamental unit of computation in a quantum computer, is better than the competition, they say. "We believe we have the most stable qubit of anyone," Holmdahl tells me.

After initially performing routine cleaning tasks, the robot, using facial recognition, identifies the finance minister. It approaches her and detonates a concealed bomb. That's one of the scenarios sketched out in a new report called The Malicious Use of Artificial Intelligence. Produced by 26 researchers from universities, thinktanks and campaigning organisations in Europe and the US, it is the latest in a series of studies warning of the dangers of AI. "The development of full artificial intelligence", Stephen Hawking has claimed, "could spell the end of the human race." Elon Musk, the billionaire founder and chief executive of SpaceX and Tesla, has suggested that "we are summoning the demon".

LG today announced an updated version of the V30 smartphone and it comes with a supercharged camera powered by a third-party AI system. LG turned to EyeEm to add new features to the camera. Now, thanks to the EyeEm platform, the LG V30S ThinQ and soon the original V30 can automatically detect the subject of the picture -- being a hamburger or dachshund -- the camera will adjust the settings to produce the best results. EyeEm's AI platform runs locally on the device and does not need to pull AI information from the cloud to help with identification. According to a meeting with LG, even though this platform runs locally, it still has the ability to improve its capabilities as its used more.

DAVOS: "Artificial intelligence and robots will kill many jobs." It's a depressingly blunt statement for anyone to make, but even more so as it is the prediction of Jack Ma, CEO of the Chinese online sales giant Alibaba. The rise of AI -- its huge potential and fears over its potentially negative consequences -- is just one of the big issues discussed at the World Economic Forum in Davos, along with breaches of personal data and fake news. But it is probably artificial intelligence and the ability of machines to not only interact with, but manipulate human beings that raises the most suspicion. Aware of growing governmental and public distrust, the giants of tech are trying to address the issues.

Residual Networks (ResNets) have become state-of-the-art models in deep learning and several theoretical studies have been devoted to understanding why ResNet works so well. One attractive viewpoint on ResNet is that it is optimizing the risk in a functional space by combining an ensemble of effective features. In this paper, we adopt this viewpoint to construct a new gradient boosting method, which is known to be very powerful in data analysis. To do so, we formalize the gradient boosting perspective of ResNet mathematically using the notion of functional gradients and propose a new method called ResFGB for classification tasks by leveraging ResNet perception. Two types of generalization guarantees are provided from the optimization perspective: one is the margin bound and the other is the expected risk bound by the sample-splitting technique. Experimental results show superior performance of the proposed method over state-of-the-art methods such as LightGBM.