Bavaria


Limitations of Interpretable Machine Learning Methods

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This book explains limitations of current methods in interpretable machine learning. The methods include partial dependence plots (PDP), Accumulated Local Effects (ALE), permutation feature importance, leave-one-covariate out (LOCO) and local interpretable model-agnostic explanations (LIME). All of those methods can be used to explain the behavior and predictions of trained machine learning models. This book is the outcome of the seminar "Limitations of Interpretable Machine Learning" which took place in summer 2019 at the Department of Statistics, LMU Munich.


Wearable technology to disrupt aviation industry, says Amadeus

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Airport hubs increasingly are embracing technology in their operations. As part of a three-day Airport IT conference in Munich, Amadeus head of airport IT product management Holger Mattig outlined the future of airport management and said that aviation hubs will witness more use of wearables, internet of things (IoT) applications and predictive analysis in the future. Talking about how the IoT has impacted the aviation industry, Mattig said that computing devices are already exchanging data between each other. "If you look at the apron, all of the devices that go on there – the push back tractors, the de-icing elements, all of these are actually able to talk to each other and give data about every stage of activity," he said. "In terms of flight handling, we now have technologies from companies like Assaia who can make prediction through videos generated by machine learning, and technologies like geofencing, where you can manage drones and improve safety. "We have the same for indoor where there are a lot of initiatives that are used to engage with the mobile phones of passengers in events of potential disruptions." While aviation companies are increasingly using technologies such as IoT and machine learning, Mattig said that going forward, airport and airline companies will start using wearable technology to improve efficiency. He added that employees could start wearing devices such as "smart sunglasses" and "smart bracelets" to track passenger activity, and that monitoring how passengers prefer to shop, eat and spend their time in an airport could help authorities to understand consumer behaviour. "Airports must start to build what I would call airport-centric visible analytics by implementing CRM solutions with the aim to look at the profile of passengers.


Finding out how neural nets do what they do

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Now scientist from Italian research institute SISSA and the Technical University of Munich have found a light to shine inside – an approach for studying deep neural networks that reveals the processes that they are able to carry out – so long as they are image processing networks. "We have developed a method to systematically measure the level of complexity of the information encoded in the various layers of a deep network – the so-called intrinsic dimension of image representations," according to SISSA scientists Davide Zoccolan and Alessandro Laio. "Thanks to the collaboration of experts in physics, neurosciences and machine learning, we have exploited a tool originally developed in another area to study the functioning of deep neural networks". Working with Jakob Macke, of TUMunich, they applied the method to find out that, inside an image recognition deep neural network, representations of the image undergo a progressive transformation. Similar to what happens in the visual system, they analyse content progressively, through a chain of processing stages.


German data mining software provider Celonis valued at $2.5 bln after funding round

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German data mining software firm Celonis said on Thursday that it had raised $290 mln in a Series C funding round, putting a $2.5 billion valuation on the company that has been compared with enterprise application giant SAP . The funding round was led by Arena Holdings and investors included Ryan Smith, the founder of customer experience specialist Qualtrics that was bought by SAP for $8 billion a year ago. Celonis, based in Munich and New York, runs a cloud-based service that uses artificial intelligence to mine data and optimize business processes, serving customers including Siemens, 3M, Airbus and Vodafone. "We are in a market that shows enormous momentum," co-CEO and co-founder Bastian Nominacher told Reuters, adding that Celonis would invest the funds raised in its global sales and customer service and in enhancing its cloud platform. The funding round brings total investments into Celonis to $370 million.


How artificial intelligence, satellites, and drone tech could help fight climate change-driven wildfires - Richard van Hooijdonk Blog

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As climate change-driven wildfires continue to wreak havoc around the world, artificial intelligence, satellites, and drones are emerging as a potential solution to this problem. Despite repeated warnings from the scientific community about the gravity of the issue, our greenhouse gas emissions continue to rise year after year, resulting in deadly heatwaves, devastating wildfires, severe droughts, and powerful hurricanes. While these extreme weather events once used to be few and far between, they've become more frequent and lethal in recent years, a direct consequence of climate change. According to Munich Re, one of the world's leading insurance companies, weather and climate events killed more than 4,000 people worldwide and caused around $42 billion in insured losses in 2019. Wildfires in particular have become increasingly destructive in recent years.


Artificial intelligence: Towards a better understanding of the underlying mechanisms

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The automatic identification of complex features in images has already become a reality thanks to artificial neural networks. Some examples of software exploiting this technique are Facebook's automatic tagging system, Google's image search engine and the animal and plant recognition system used by iNaturalist. We know that these networks are inspired by the human brain, but their working mechanism is still mysterious. New research, conducted by SISSA in association with the Technical University of Munich and published for the 33rd Annual NeurIPS Conference, proposes a new approach for studying deep neural networks and sheds new light on the image elaboration processes that these networks are able to carry out. Similar to what happens in the visual system, neural networks used for automatic image recognition analyse the content progressively, through a chain of processing stages.


Artificial intelligence: Towards a better understanding of the underlying mechanisms

#artificialintelligence

The automatic identification of complex features in images has already become a reality thanks to artificial neural networks. Some examples of software exploiting this technique are Facebook's automatic tagging system, Google's image search engine and the animal and plant recognition system used by iNaturalist. We know that these networks are inspired by the human brain, but their working mechanism is still mysterious. New research, conducted by SISSA in association with the Technical University of Munich and published for the 33rd Annual NeurIPS Conference, proposes a new approach for studying deep neural networks and sheds new light on the image elaboration processes that these networks are able to carry out. Similar to what happens in the visual system, neural networks used for automatic image recognition analyse the content progressively, through a chain of processing stages.


STMicroelectronics and maxon Collaborate on Precision Motor Control for Robotics and Automation

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Geneva and Sachseln, Switzerland, November 25, 2019 – STMicroelectronics (NYSE: STM), a global semiconductor leader serving customers across the spectrum of electronics applications, is working with maxon, a leading precision-motor provider and a member of the ST Partner Program, to accelerate the design of robotics applications and industrial servo drives. The companies will demonstrate a jointly developed servo control kit at sps 2019 trade show in Nuremberg, November 26-28 (Booth 10.1/138). The EVALKIT-ROBOT-1 is a plug-and-play solution aimed to help users easily approach the world of precise positioning and high-end motion in servo drives and robotics. A maxon 100-Watt BLDC motor with built-on 1024-pulse incremental encoder is included in the kit, embodying the company's expertise in magnetic design in motors that ensures smoothness and balance to allow fine control even at low rotor speeds. The servo control board supplied with the kit contains ST's STSPIN32F0A intelligent 3-phase motor controller and a complete inverter stage built with ST power transistors ready to connect to the motor.


Siemens and IBM showcase an AI-based, CO2 friendly advisor

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Making the case for AI, or any nascent technology for that matter, can be a struggle for companies today. While large enterprises know they need to be fast, agile and innovation-obsessed to survive disruption, their age-old policies, antiquated systems, disconnected data and entrenched corporate habits can be serious blockers to adoption. With a century plus-long tradition of engineering excellence, we at Siemens knew we had a challenge to transform ourselves in order to continue to lead in the AI era. Adopting a mindset of risk-taking and innovation from the inside-out had to be a key part of that transformation journey. Thanks to our recent efforts with the IBM Data Science and AI Elite team, together with the IBM Garage at the Watson IoT Center in Munich, we recently made a critical breakthrough on our journey to AI. Working with partners such as IBM and others, we developed a proof of concept to showcase how we could harness AI and blockchain to drastically reduce our employees carbon output--not through mandates, but through incentivizing more eco-friendly behavior.


DataCareer: Your Career Platform for Data Science in the UK and Ireland

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Grade: G13/3 (net (basic) monthly salary* for this vacancy: EUR 12 435,12, which may be supplemented by various allowances depending on your personal circumstances) Duration of appointment: 5 years Career path: Managerial Location: Munich Application deadline: 17.11.2019 With almost 7 000 employees, the European Patent Office (EPO) is the second-largest public service institution in Europe. It supports innovation, competitiveness and economic growth across Europe through a commitment to high-quality and efficient services delivered under the European Patent Convention, its founding treaty. It has a yearly budget of EUR 2.3 billion, entirely financed by the fees paid by its users. As set out in its Strategic Plan 2023, the EPO is proud to deliver high-quality patents and efficient services that foster innovation, competitiveness and economic growth.