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7/6/18, 2)23 PMAutonomous Systems: Machine Learning - Digitalization & Software - Pictures of the Future - Innovation - Home - Siemens Global Website

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Pictures of the FutureThe Magazine for Research and Innovation

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Autonomous SystemsAutonomous Systems

Getting Machines to Mimic Intuition

The ability to learn is a precondition for autonomy. With this in mind, Siemens researchers are developing knowledgenetworks based on deep learning-related simulated neurons and connections. Such networks can be used to generalizeinformation by identifying associations between extraordinarily complex realms, such as the publicly accessible Internet and acompany’s internal information systems. Far-reaching and generic, this technology appears to hold the potential of mimickingwhat humans call intuition.

Biological systems that learn include everything from roundworms with approximately 300 nerve cells to adult elephants, whose brains contain 200billion neurons. But regardless of whether you’re dealing with a fruit fly, a cockroach, a chimpanzee or a dolphin, the neurons of all of these creaturesprocess and transmit information. Moreover, they do so for the same reasons: All organisms need to be able to discern and interpret their surroundingsand then react appropriately in order to avoid danger and ensure their survival, as well as their ability to reproduce. They also need to be able to recallstimuli that signal risk or reward. In other words, learning is the key to survival in the natural environment. Although reproduction and survival are hardlyof interest to machines, learning is – particularly when it comes to autonomous systems whose ability to learn translates into steadily improving ability toperform functions.

According to Dr. Volker Tresp, a Siemens expert for machine learning and a professor of computer science at Ludwig Maximilians University in Munich,there are three types of learning: learning through memory (e.g. recalling specific facts), through skill (e.g. throwing a ball), and through abstraction (e.g.deducing sets of rules through observation). Computers that are already true wizards in terms of the first type of learning are now catching up at anextraordinary pace as regards the other two types.

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Generating PredictionsWhat do these trends portend for the future? For one thing, as sensors become smaller, cheaper and capable of performing ever more functions, moreand more data will become available both locally and in networks. However, this flood of data needs to be analyzed intelligently by learning systems thatknow how associated machines and systems function and which sensors and measuring techniques need to be applied in order to access data that istruly useful. This “Internet of Things” is fundamentally changing not only industry but also entire infrastructures. Examples of this include traffic guidancesystems in which vehicles are linked with one another and with control centers, as well as autonomous industrial facilities and smart buildings.

“Machine learning is playing a key role in the development of new smart data applications,” Tresp explains. Unlike purely statistical procedures thatfocus on interpreting parameters, or data mining, which primarily seeks to recognize patterns in a sea of data, machine learning processes, such asthose that use artificial neural networks, generate predictions that can lead to automated decision-making.

For example, Siemens has developed a simulation environment for neural networks (SENN) that can be used to answer various questions. Amongother things, SENN can predict raw material prices. For example, in two out of three cases the software can forecast electricity prices for the next 20days — including the best day to purchase electricity. Siemens has been using this method since 2005 to purchase electricity at the most favorablepoints in time. The technique could also be used to predict the amount of renewable energy that will need to be fed into a grid or to precisely forecast airpollution levels in major cities several days in advance.

SENN predicts, for example, electricity prices for Siemens for the next 20 days and can correctly forecast the trend two outof three times.

Self-Optimizing TurbinesComputer systems that can learn from various types of data and draw their own conclusions are also being used in other areas at Siemens. Forexample, researchers at Corporate Technology (CT) are studying how machine learning techniques could be used to enable wind turbines toautomatically adjust to changing wind and weather conditions, thus boosting their electricity output. “The basis for self-optimizing wind turbines is theability to derive wind characteristics from the turbines’ own operating data,” says Volkmar Sterzing, an expert in this field at CT. The associatedparameters, which are measured by sensors in and outside wind power facilities, include wind direction and force, air temperature, current and voltage,and vibrations in large components such as generators and rotor blades. “Up until now, this type of data has been used exclusively for remotemonitoring and diagnosis,” Sterzing explains. “However, this same data can also be used to help improve the electricity output of wind turbines.”Sterzing is now working on ways to optimize gas turbine operation as well. Here, the goal is to create a self-learning system that not only analyzes orvisualizes turbine operating data but can also autonomously interpret it and then automatically calibrate associated turbine operations.

Creator of knowledge networks:Prof. Volker Tresp, Siemensexpert and chair for DatabaseSystems at the LMU Munich.

Siemens' SENN softwareensures precise predictionsbased on neural networks.

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Deep Learning and Simulated NeuronsDeep learning techniques are a new trend in machine learning. These techniques utilize up to 100,000 or more simulated neurons and ten millionsimulated connections —numbers that break all previous records in the field of artificial intelligence. Thanks to their many levels of artificial neurons,whereby each addresses a different level of abstraction of the material to be learned, deep learning techniques are expected, for instance, to enablenew applications for automated image recognition. Linking these levels with one another results in data that is much more detailed than was the casewith earlier types of artificial neural networks. Most of us already carry an artificial neural network around with us, as the voice command systems in ourAndroid smart phones. Tresp’s team is taking things a step further by modeling mathematical knowledge networks with as many as ten million objects.What’s more, the team can make as many as 1014 possible predictions about the relationships between these objects, which corresponds roughly to thenumber of synapses in an adult human brain.

A CT knowledge network can make up to 100 trillion predications. This corresponds to the total number of synapses in ahuman brain.

Such knowledge networks can be used in industry — for example, in the Smart Data Web project being carried out by the German Ministry forEconomic Affairs and Energy (BMWi). The goal of this project is to build a bridge between the publicly accessible Internet and the internal informationsystems at major companies. With the help of machine learning systems, this is expected to enable both realms to generalize information from oneanother, thus improving information extraction, which in turn would allow each realm to provide the other with new information and facts. Manufacturingcompanies could use the resulting information to significantly optimize planning and decision-making processes, such as those related to supply chainmanagement.

Knowledge networks could also be used to support medical decision-making processes at hospitals. To this end, Siemens is developing solutions withinthe framework of the Data Intelligence for Clinical Solutions project, which is being funded by the BMWi. These solutions are based on applications thecompany created with the Charité hospital in Berlin and Erlangen University Hospital. The goal here is to develop systems that can learn to makepredictions and “decision forecasts” (e.g. for prognoses as to the probability of success of different therapies) on the basis of available patient data.

Volkmar Sterzing’s team is optimizing the operation of wind turbineswith the help of adaptive software. Sterzing’s next goal: self-optimizinggas turbines.



AlphaGo: Machine vs. HumanThe capabilities of the latest machine learning systems are illustrated by AlphaGo, with which Google achieved a milestone in the development of self-learning machines and artificial intelligence in March 2016, when AlphaGo succeeded in defeating one of the world’s best Go players: Lee Sedol. Theamazing thing is that up until Google’s accomplishment, this Asian strategy game had been considered to be too complex for a computer. For example,the game has a nearly infinite number of possible positions, which means those who play it usually have to rely on intuition. AlphaGo was developed byGoogle DeepMind as a system for solving complex tasks. Like the system successfully used by Siemens to optimize its wind and gas turbines, AlphaGouses reinforcement learning. In this case, the system learned to utilize a value function that rates game positions by analyzing millions of past gamesand then playing against itself — and as it turned out, the system worked very successfully.

Sebastian Webel / Katrin Nikolaus / Arthur F. PeasePicture credits: from top: 1. u.5. Picture Google, 2. Christian Hass

In a contest of mental strength between man and computer, Google'sAlphaGo beat top player Lee Sedol (not pictured) in the Asian boardgame Go in five games. For computers, this game is significantly morecomplex than chess.

20 April 2016

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Digitalization & Software

Dossier: AutonomousSystems

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Undreamt of PossibilitiesAn overview of the trends driving the mostfundamental change ever witnessed inindustry: the birth of autonomous machines.

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Era of IndividualityAutonomous systems will make it possible toeconomically introduce individuality ineverything from buildings to consumer goods,says Prof. Roland Siegwart of ETH Zurich.

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Waves of Change for Tomorrow’s WorkersNew areas of opportunity for workers will be generated by autonomous systems.

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