These are qualities of people who have no advantage in medical diagnoses. Computers help people to make safer January 2019 Calendar Templates decisions of large amounts of data. They process information much faster, more reproducibly, and most of the time demonstrably better when it comes to simple cognitive tasks. In this sense, computers help doctors to bring the positive human qualities into everyday clinical practice. January 2019 Calendar Templates For example, by allowing the AI to do monotonous work and give the doctor time to listen to patients with empathy and orientation based on his broad medical knowledge and compassionate experience in a psychologically distressing situation "When it comes to life and death, AI has no business." Wrong: Especially where quick decisions are needed, KIverwertigen helps. Wherever the informational situation is complex, where there is a great deal of time pressure or where the general conditions are unfavorable, most human errors occur. An AI system that serves as a second pair of eyes and indicates that something has been overlooked is very useful. It sorts the information according to importance and supports the doctor. Www.mevis.fraunhofer.de 16COMPANTERCOMPUTER: DRAMATIC PROGRESS IN ARTIFICIAL INTELLIGENCE Quantum computers will soon be on everyone's lips. What is different from conventional computers? Bauckhage: In conventional digital computers, the minimum unit of information representation is the bit, which has either the value 0 or 1. In a quantum computer, on the other hand, individual ones become Electrons are manipulated-and the calculations are based on the principles of quantum mechanics. A so-called quantum bit is 0 and 1 at the same time. It does not commit itself until mapped. As long as no physical measurement is performed on a qubit system, one can only say with a certain probability whether it is 0 or 1. That sounds bizarre and in no way matches our everyday experience. But nature at the subatomic level works that way. We have to rely on mathematics, which is extremely complex in this case. But since the 1930s, humanity has been able to describe these processes mathematically. How far is research today? Are quantum computers being used? What sounds like science fiction may already be everyday in a few years. Initial companies have already developed quantum computer models and are working hard to bring them to market. Prof. Dr. Christian Bauckhage of the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS explains the background.FRAUNHOFER IAIS17Bauckhage: A Canadian company producing quantum computers has sold it to NASA, the NSA and Google for $ 15 million. VW Research leased these systems following the paradigm of adiabatic quantum computation. At the same time, IBM has its own quantum computers, which in turn follow the quantum gate computing paradigm. This is essentially the attempt to transfer logic circuits of digital computers - that is, "AND", "OR" and "NOT" - into quantum mechanics. January 2019 Calendar Templates Intel and Microsoft also work on such models. Google announced a quantum computer with 49 quantum bits for the end of 2017, which achieved the so-called quantum supremacy, so much faster than a classical computer.
In addition, systems in the field of autonomous driving, chat bots, are particularly visible to the public and user interfaces - for example, the greatly improved ability of computers to handle speech.What is the current state of research January 2019 Calendar Printable on AI? Wrobel: In recent years, we have been limited in the possibilities of very parameter rich, deep neural networks that can actually be trained We are far from the end of development. January 2019 Calendar Printable It will be important in the next few years to reconnect with other knowledge-based techniques in the field of AI. This is a focal point of research, which we are also focusing on at Fraunhofer. "In the next few years, it will be important to re-integrate other knowledge-based techniques in the area of AI." INTERVIEW13 In medicine, for example However, in the industry, a small business would not have millions of posts from a video or image service, but only 500 or 1000 individual classifications made in-house. This is going to be a big and important development. What do you think companies should do best in AI? Wrobel: Of course, companies should look at the current opportunities of AI and look at examples - and think about it: how can we take advantage of these opportunities? This must be done from the very top, because it affects the basic design of your own business model and your own positioning. I can not become a company using Artificial Intelligence unless I'm also a data-driven, data-driven, digitized company. If the data is the key resource I want to work with, then that data must be secure. If they are even an asset, a trump card I want to be in the market with, then I have to consider: Who can I share this data with, with whom do I build these business models? Which data can and may I collect? How is cybersecurity ordered? To that extent the simple message: "Start now". Invest now, build up your skills, find the right partner. Therefore, at Fraunhofer, we also support the support of KI in our advice on digitization or big data - because these questions are closely related. Www.iais.fraunhofer.de 14 »AI creates artificial Brains. "We do not build artificial brains or artificial people. Neither does an aircraft manufacturer try to build an artificial bird. He just wants to construct something that flies. We build machines that do elementary cognitive tasks that require intelligence. Such machines or mechanisms have already reached our everyday lives: there are devices that understand car steering, translate our language or translate it simultaneously. But even if we teach a machine some ability to be better at it than human beings - such as lip reading - one does not end up talking about an intelligent device. "AI system decisions are more neutral and objective." That's not right. AIs have no intrinsic motivation, no self-interest to be neutral or objective. They depend on the training material and the intention of the trainer. Finally, the machine is trained to process an input and deliver an output. An example from the medical field: If a machine is trained to detect malignant changes in the liver in CT scans, it is not capable of malignant changes in the spleen, kidney, or lung Find. We need people to review and take responsibility for machine-based decisions. ORDER: MYTHBUSTINGDr. Hans January 2019 Calendar Printable My and Dr. Markus Wenzel from the Fraunhofer Institute for Image-Assisted Medicine MEVIS puts myths and clichés to artificial intelligence to the test.FRAUNHOFER MEVIS15 »KI makes medicine cold and impersonal.« On the contrary. Nobody wants to lose the so-called "human factor" in medicine. But what is this human? For example, volatility, ignorance, misjudgment.
This is of course very useful for practical purposes. If you think of companies that want to use AI for customer contacts, for example, simple functionalities, simple first-level answers, can already be provided by machines. "Neuronal Networks" is January 2019 Calendar Australia another keyword currently used in the AI environment Mouth is. What can one imagine under this? Wrobel: Neuronal networks are an original biology-inspired method of machine learning. We understand a sequence of functions that calculate from a certain amount of input over multiple layers of output.
January 2019 Calendar AustraliaOver the past few years, we have acquired the ability to train such networks even if they have multiple levels of multiple parameters, which can reach hundreds of thousands, to millions. "Neuronal networks are an originally biologically inspired method of machine learning." Cerebrally, intermediate results from such a network can be detected, constructed, and represented in a very different way so that much greater performance is achievable. This is made possible by the algorithmic advances and massive training data we have today. The selection of this data is of immense importance, especially if one refrains from modeling knowledge and thereby also collecting certain guidelines for the system. Another important factor is the strong increase in computing power. INTERVIEW11 One often reads "strong" and "weak" CI. What exactly is the difference? Wrobel: This discussion has been going on for many decades, there is no universally accepted regulation. The discussion about "strong" and "weak" AI has to do with whether we classify AI only as "intelligently behaving" - what would rather describe a "weak" AI - or if AI ultimately works the way a human being works. This would be called "strong" AI. Linked to this is the question of whether we would grant an artificial intelligent system even a consciousness or personal rights - emotionally, philosophically or even legally. The question is always: what is intelligent in a deep sense, what is creative, how do we want to accept and treat artificial intelligent systems, and why is it worth discussing in detail? Is this the question of whether an AI has to behave ethically or morally? It would never be acceptable for an AI to behave less ethically, less morally, less correctly, less socially accepted than a human being. Of course, we need to apply at least the same standards to AI systems as to humans. We should even set higher standards, because KIs do not tire, are never unfocused or emotional. "It would never be acceptable for an AI to behave less ethically, less morally, less correctly, less socially accepted as a human being." Of course, what that means in a particular case becomes difficult to discuss. We all know the exemplary debate in autonomous vehicles. However, I believe that the discussion groundwork will be less complicated by the capabilities and reliability of machine systems. If AI systems do not anticipate dangerous situations from the outset, we must evaluate this positively. Basically, I want a committed, whole-of-society debate about what artificial intelligent systems should and should do, and what they can not do. How is AI being used January 2019 Calendar Australia in the business these days? Wrobel: In the field of image processing, for example, we have long intelligent solutions in the field of industrial operations, not learning Systems of machine vision, the so-called "machine vision", are already in use in the whole range of production, industry and visual inspection.
Deep learning refers to neural networks with a greatly increased number of levels that could be used to penetrate new classes of problems.Blackbox, Graybox, WhiteboxBlackbox, Graybox, Whitebox Models differ in whether and to what extent the algorithm knows the January 2019 Calendar Canada physical model of the problem to be learned and incorporates it into its learning process. Whitebox models know this as closely as possible, but black box approaches do not consider the model.
January 2019 Calendar CanadaGreybox refers to combination approaches between the two. Neuromorphic chips Neuromorphic chips are microchips that replicate nerve-cell characteristics and architecture at the hardware level. These neuron-like components simulate the learning and association ability of the brain, which can particularly accelerate the recognition of patterns in images or in big-data structures.78 What is "Artificial Intelligence?" Wrobel: Intelligence is a central feature of human beings, which we usually only accord humans. If machines are now able to do things we would classify as intelligent, we call them artificial intelligence. It currently includes machines that, for example, are capable of interpreting images, of responding to linguistic utterances, and even of supposedly simple things like the digital assistants on our mobile phones. What do you think about the definition of machine learning? Wrobel: It was already at the beginning of Artificial Intelligence It's clear to AI pioneer AlanTuring that it's possible to program intelligent computer space by hand to every detail. "That means that human labor will be less extensive. And that we can train intelligent systems." He wrote as early as 1950 that there had to be a faster method - the machine learning. With these »STILL LONG AT THE END OF DEVELOPMENT« An interview with Prof. dr. Stefan Wrobel, Head of the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS and Professor of Computer Science at the University of Bonn, on Chances, Challenges and the Acceptance of Artificial Intelligence.INTERVIEW9 methods, computers are able to learn from observations, existing data and examples and thereby improve their behavior. Can one compare these intelligent computer systems with human intelligence? Wrobel: You can always compare two things, even if they have different character. A plane does not fly like a bird, a computer does not think like a human. We need to look at the achievements and then judge whether we classify this as equally intelligent, less intelligent, or as intelligent than what a human would do. Computers have already made big breakthroughs, not to mention winning the gameshow Jeopardy or cracking the game GO, not to speak of chess. Could one say: In topics such as strategy or image recognition, the AI now works human again or even better, but with the creative human being still has that Staff in hand? Wrobel: I do not think it's that easy anymore to say that flat-rate. It depends on whether an action by the computer is actually learned from examples. Can the necessary knowledge be modeled in advance? Whether or not a creative component is included in the execution is a deeper philosophical question. In interactions, the question arises: How long do I need to identify an AI? When it comes to customer January 2019 Calendar Canada service and support inquiries, it's quite common nowadays to have a Chat-Bot.Prof. Dr. Stefan Wrobel10Wrobel: We can not, in a philosophical way, create an artificial intel authenticity, merely the indistinguishability between human-to-human and human-to-human interactions.
Deep learning refers to neural networks with a greatly increased number of levels that could be used to penetrate new classes of problems.Blackbox, Graybox, WhiteboxBlackbox, January 2019 Calendar UK Graybox, Whitebox Models differ in whether and to what extent the algorithm knows the physical model of the problem to be learned and incorporates it into its learning process. Whitebox models know this as closely as possible, but black box approaches do not consider the model.
January 2019 Calendar UKGreybox refers to combination approaches between the two. Neuromorphic chips Neuromorphic chips are microchips that replicate nerve-cell characteristics and architecture at the hardware level. These neuron-like components simulate the learning and association ability of the brain, which can particularly accelerate the recognition of patterns in images or in big-data structures.78 What is "Artificial Intelligence?" Wrobel: Intelligence is a central feature of human beings, which we usually only accord humans. If machines are now able to do things we would classify as intelligent, we call them artificial intelligence. It currently includes machines that, for example, are capable of interpreting images, of responding to linguistic utterances, and even of supposedly simple things like the digital assistants on our mobile phones. What do you think about the definition of machine learning? Wrobel: It was already at the beginning of Artificial Intelligence It's clear to AI pioneer AlanTuring that it's possible to program intelligent computer space by hand to every detail. "That means that human labor will be less extensive. And that we can train intelligent systems." He wrote as early as 1950 that there had to be a faster method - the machine learning. With these »STILL LONG AT THE END OF DEVELOPMENT« An interview with Prof. dr. Stefan Wrobel, Head of the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS and Professor of Computer Science at the University of Bonn, on Chances, Challenges and the Acceptance of Artificial Intelligence .INTERVIEW 9 methods, computers are able to learn from observations, existing data and examples and thereby improve their behavior. Can one compare these intelligent computer systems with human intelligence? Wrobel: You can always compare two things, even if they have different character. A plane does not fly like a bird, a computer does not think like a human. We need to look at the achievements and then judge whether we classify this as equally intelligent, less intelligent, or as intelligent than what a human would do. Computers have already made big breakthroughs, not to mention winning the gameshow Jeopardy or cracking the game GO, not to speak of chess. Could one say: In topics such as strategy or image recognition, the AI now works human again or even better, but with the creative human being still has that Staff in hand? Wrobel: I do not think it's that easy anymore to say that flat-rate. It depends on whether an action by the computer is actually learned from examples. Can the necessary knowledge be modeled in advance? Whether or not a creative component is included in the execution is a deeper philosophical question. In interactions, the question arises: January 2019 Calendar UK How long do I need to identify an AI? When it comes to customer service and support inquiries, it's quite common nowadays to have a Chat-Bot.Prof. Dr. Stefan Wrobel10Wrobel: We can not, in a philosophical way, create an artificial intel authenticity, merely the indistinguishability between human-to-human and human-to-human interactions.
The Industrial Data Space maintains the digital sovereignty of the owners of the data and at the same time forms the basis for smart services and innovative business processes. Artificial intelligence (AI), cognitive systems and learning machines play a crucial role in the future transformation of business and society. For the January 2019 Calendar USA international economy and industrial value chains, this means a fundamental structural change - because technical systems are adaptive and increasingly capable of transferring what they have learned to new situations.
January 2019 Calendar USAThey can plan processes, make predictions and even interact with people. The International Data Corporation expects $ 40 billion in global cognitive-solution spendouts by 2020. Using new technologies not only opens opportunities, it also challenges us all. For a lasting technological leadership in Germany in the field of AI not only technological solutions are in demand, but also the social dialogue. Fears and myths must be opposed to scientific knowledge. Machines will expand our circle of influence, but will not take over the leadership. The Fraunhofer-Gesellschaft develops key AI technologies and applications in fields such as robotics, image and speech processing, and process optimization. Machine learning processes for industry are as much part of this as the use of cognitive systems in cybersecurity and the need to explore artificial neuronal ones networks. Our research makes significant contributions to the theory and ethical design of AI, and is geared to "For 20 years, we have been regularly reading that the breakthrough of artificial intelligence is imminent. But now it really is true. "Sascha Lobo, author and blogger" The danger of artificial intelligence lies not in the fact that machines think more and more like humans, but that people are thinking more and more like machines. "Joseph Weizenbaum, computer scientist, cyberneticist and social critic» Evolution is more than the sum of all revolutions. "Gerald Dunkl, psychologist and aphorist." While they were researching, x-raying, filming, radiating, they themselves created the most delicious invention: the detour as the shortest link between two points. «Erich Kästner, writer» Artificial intelligence is always better than natural stupidity. «Hans Matthöfer, Former Federal Minister6The most important terms on the subject briefly explained: Cognitive Systems / MachinesCognitive systems / machines are technical systems that record digital information from sensor data and networks and turn it into B As a result of learning algorithms, conclusions, decisions and actions are derived and verified with their environment in dialogue and optimized. Machine LearningAs machine learning, processes are described in which an algorithm / machine learns by repetition of a task to perform it better with respect to a quality criterion. Artificial Intelligence Artificial Intelligence (Art. AI) is a branch of computer science that deals with equipping machines with skills that resemble intelligent (human) behavior. This can be achieved with preprogrammed rules or machine learning. Strong general AI refers to machines that can provide generalized intelligence and transfer services, and are thus not limited to very limited, predefined task areas. GLOSSARY7Neuronal Networks (Deep Learning) Artificial neural networks are a basis for machine learning based on neuronal networks in the brain. They consist January 2019 Calendar USA of data nodes and weighted connections between them. By changing various parameters in the network, machine learning techniques can be realized.
This raises the question of whether these now learned and conceptualized rules should not be used as a moral aspect in machines, "argues researcher Sütfeld." Personality recognition "for robotic people January 2019 Calendar With Holidays recognize gestures and interpret glances quickly and almost automatically. Computers and robots do not do that, so scientists around the world are working on ways to make human-computer collaboration more social, efficient and flexible.
January 2019 Calendar With HolidaysComputer scientists from Saarbrücken and Stuttgart have now reached an important milestone together with psychologists from Australia.The software system developed by them processes the eye movements of a person and calculates whether it is vulnerable, sociable, tolerable, conscientious or curious. "With our eyes, we not only capture the environment, they are also the window to our souls. Because they reveal who we are, how we feel and what we do, "explains Andreas Bulling, who heads the research group" Perceptual User Interfaces "in Saarbrücken at the Max Planck Institute for Informatics and the Cluster of Excellence of the Saarland University Bulling has trained scientists in Stuttgart and Australia to develop their own software system based on machine-learning algorithms so that they can evaluate eye movements and draw conclusions about their traits. To obtain the data for the training and the evaluation, the Flinders worked University in Australia took 50 students in. After arriving at the lab, the researchers equipped the students with a so-called "eye tracker," which filmed the subject's eye movements for about ten minutes people roamed the campus and bought coffee or other items in the campus store. Afterwards, the students were asked to take off their glasses and fill in special questionnaires to determine the personality and level of curiosity in a conventional way. "To analyze the recorded eye data, regardless of the duration of the recording, we have one slidable time frame, as it does not weaken any characteristics, "explains Bulling. From each of the resulting windows, the researchers gained 207 characteristics. These included statistics on gaze fixation as well as the blink rate. On the basis of this data and the information from the questionnaires, the researchers collected around 100 decision trees for each personality train and trained them for a classifier : The result: In the subsequent test with previously unused data, they were able to prove that the software system reliably recognizes traits such as emotional lability, sociability, compatibility and conscientiousness. "We also transfer this knowledge of nonverbal behavior to robots, so that they can how people behave. Such systems would then communicate with humans in a much more natural way and would therefore be more efficient and flexible in their use, "says researcher Bulling. International Data Spaces - Better data availability AI experts also see demand for data availability. . As already mentioned, in Germany there is a shortage of generally accessible, usable data worldwide. In order to create incentives to generate and share such data, professionals recommend that data creators retain control and sovereignty over their data, but share it for mutual benefit. Models such as the International Data Spaces January 2019 Calendar With Holidays and especially the Industrial Data Space are exemplary in this context. The Industrial Data Space is a virtual data room that enables the secure exchange of data and the simple linking of data in business ecosystems based on standards and with the help of collaborative governance Models supported.
The application potential of intelligent data analysis methods for monitoring and optimizing test data, ECUs and test benches in the automotive industry is, according to the partners, "extraordinary". For example, a state-of-the-art engine control unit has more than 50,000 setting parameters that govern performance, January 2019 Calendar PDF consumption, wear, and overall engine performance. Deep learning technologies, or more precisely the use of neural networks in the control unit, can autonomously "learn" how to optimally set the input variables.
January 2019 Calendar PDFThe use of such networks in the time series analysis of engine test data also enables new approaches for "predictive health monitoring" the prediction of wear and maintenance cases can be improved. Such procedures should be researched and developed in the new research laboratory. At the same time, the FLaP will also work on new visualization options for the diverse measurement data from the neural networks. It is planned to create a toolbox of AI tools that can be used intuitively by automotive engineers.Automotive: The PostBus plans its own routeAutonomous driving and electric driving are top priorities at the Deutsche Post DHL Group. For this purpose, the logistics group will set up a test fleet of autonomous and purely electric delivery vehicles. Partner is the automotive supplier ZF - because the "Postautos" are equipped with the control box ZF ProAI, which developed the ZF Friedrichshafen AG together with NVIDIA. The light, electrical and intelligent delivery vehicles can above all the future requirements on the "last mile" Customers who are currently very complex and costly due to the flexibility expectations in e-commerce and the requirements of the disposition. The Deutsche Post DHL Group currently has a fleet of 3400 street scooter delivery vehicles. These can be equipped with ZF sensor technology - camera, lidar and radar sensors - whose information is processed by the control box ZF ProAI. Thanks to AI, the vehicles can later "understand" their immediate surroundings, plan a safe route - or reschedule at short notice -, track the route and park the vehicle independently. This makes deliveries more accurate, safer and cheaper.Quo vadis Driver Assistance Systems? Autonomous DrivingQuo vadis Driver Assistance Systems? 15.05.18 - Autonomous driving is on everyone's lips. But the essential components required for the self-driving automobile are already part of modern driver assistance systems: they automate key aspects of driving. "The example of autonomous delivery vehicles shows how strongly AI and deep learning are affecting the commercial vehicle industry," said Jensen Huang, founder and CEO of NVIDIA , "As online shopping continues to grow, but the number of truck drivers is limited, KI-enabled autonomous vehicles are becoming a key player in future 'last mile' logistics. " To develop these AI delivery vehicles , the Deutsche Post DHL Group has already equipped its data center with the supercomputing chip NVIDIA DGX-1, thus training its artificial neural network. As the January 2019 Calendar PDF vehicle continues to develop, these deep learning algorithms are later transferred to the vehicle control boxes on the NVIDIA Drive PX platform. In a prototype presented at the NVIDIA developer conference "GPU Technology Conference" (GTC) in Munich, six cameras, one radar and two lidar systems provide the AI with data. Logistics
The young company helps buyers use artificial intelligence to find new suppliers and to optimize their supplier relationships. But what about Artificial Intelligence? What is currently being researched? What's in the pipeline? Machines can (but) act morallyMachines can soon mimic people's moral behavior. Scientists at the January 2019 Calendar Excel University of Osnabrück are convinced of this. The reason is autonomous driving, because self-driving automobiles are the first generation of intelligent robots that share everyday life with people.
January 2019 Calendar ExcelConsequently, it is essential to develop rules and expectations for autonomous systems that define how they should behave in critical situations. The Institute for Cognitive Science at the University of Osnabrück has now published a study in "Frontiers in Behavioral Neuroscience", i e shows that human-ethical decisions can be implemented in machines and autonomous vehicles will soon face moral dilemmas in traffic. Politically, the debate on the modeling of moral decisions is accompanied by an initiative of the Federal Ministry for Transport and Digital Infrastructure (BMVI). This has formulated 20 ethical principles. The Osnabrück study now provides the first empirical scientific data. "In order to be able to define rules or recommendations, two steps are necessary. First of all, you have to analyze and understand human moral decisions in critical situations. The second step is to statistically describe human behavior in order to derive rules that can then be used in machines, "explains Prof. Dr. med. Gordon Pipa, one of the leading scientists in the study. To realize both steps, the authors used a virtual reality to observe the behavior of subjects in simulated traffic situations. The participants of the study drove on a foggy day through the streets of a typical suburb. In the course of the experiments, there were inevitable and unexpected dilemma situations in which people, animals or objects stood as obstacles in the lanes. In order to avoid the obstacles in one of the two tracks, a moral balance was necessary. The observed decisions were then evaluated by a statistical analysis and translated into rules. The results indicate that, in the context of these unavoidable accidents, moral behavior can be explained by a simple valence of life, for every person, every animal, and every object. Leon Sutfeld, the principal author of the study, explains this: "The human moral Behavior can be explained or predicted with considerable precision by comparing the valence of life associated with each person, animal or object. This shows that human moral decisions can be described in principle with rules and that these rules could be used as a consequence of machines. "Basically, the findings of Osnabrück researchers contradict the eighth principle of the BMVI report (see above) based on the assumption that moral decisions can not be modeled But can this fundamental difference be explained? Algorithms can be described either by rules or by statistical models that relate several factors to each other. So laws are rule-based. By contrast, humans and modern artificial intelligent systems are more likely to use a complex statistical balance. This balance allows humans and the modern AI to assess new situations that January 2019 Calendar Excel humans and AI have not been exposed to. Sütfeld's scientific work has now used such data-like methods of human behavior. "Therefore, the rules do not have to be formulated abstractly at the desk by a human, but derived from human behavior and learned.
The algorithm replaces the dispatcher the sign of digitization fundamental change and always job profiles are under pressure to adapt. A good example of this is the dispatcher. His main task is traditionally in the January 2019 Calendar Word optimization of transport and in pricing. Both tasks are already supported by computers today. Digital forwarding companies such as FREIGHT ROOM immediately backfill the entire business model with self-learning algorithms.