Dimensions

17 Jun 19
Capgemini Norge
As we have shown in our previous blog posts, it is essential to align your Security Incident and Event Management (SIEM)/Security Operations Center (SOC) project across three dimensions: technical architecture use case definition and implementation as well as processes and organizational structure. This article is concerned with the latter. Often, the matters of processes or organizational structures are overlooked in SIEM/SOC implementations. While the use cases and the technical SIEM architecture make up the logic of every SIEM, it’s the processes that make it run in everyday operations. Processes and organizational structures are tightly linked to the SOC. We identified four areas that you should focus on when designing SOC-related processes: Incident management This is arguably the most important set of processes to align between your organization and the SOC. The main purpose of a SIEM/SOC is to detect and react to security incidents that are identified based on log information and the respective use cases. Hence, incident management stands at the core of every SOC. Identifying incidents is key, but it is just as important to get the incident information to the right people in your organization. One crucial element to consider is how to get the incident information into your standard ticket system. Whether you operate the SIEM system yourself or outsource it to a third party: The incident information must be wrapped into a ticket. This often requires the development of a non-standard interface between the SIEM and your default ticket system. You should not take this challenge lightly, as it can be a full-grown software development project that must be considered in change, release, and test management processes. Change management Change management is not only important for day-to-day operations but plays an important role in the project phase already. For the initial setup of the technical infrastructure you will be confronted with many necessary and time-critical changes. Here it is important to follow a standardized approach: Definition of changes Establishing changes in the non-production environment Testing changes Establishing changes in the production environment Monitoring system parameters to ensure that, for example, system load is within the expected range from the testing stage For day-to-day operations, it is critical to inform your SOC of upcoming changes. Consider your organization is planning the roll-out of a new software version which requires some of your servers to go down for maintenance. If the SOC was not informed of this change, unnecessary incidents would be created. As a preventive measure, the SOC must thus be informed of all planned infrastructure and application changes as well as any emergency changes. Asset management As in change management, asset management processes are relevant in both the project phase and the operations phase. Asset management revolves around the central repository for configuration items, the configuration management database (CMDB). In the project phase, a complete and accurate CMDB is crucial so that all assets are attached to the SIEM. Your organization must ensure that all relevant information is present – especially IP addresses, host names, device types, etc. This way, all assets can be efficiently onboarded with just a few change requests for the different asset types. If you do not have a full picture of your IT assets, the missing parts must be attached as soon as their absence becomes apparent which leads to: Incomplete monitoring until everything is onboarded Multiple unnecessary changes Possibly higher costs by the SOC and/or your IT service provider Increased workload on internal staff After all IT assets are attached to the SIEM, the CMDB serves as the foundation for the onboarding of new assets but also the removal of decommissioned assets. As mentioned above, unobserved IT assets pose a significant security risk because they offer attackers an opportunity to “fly under the radar.” If there are business or technical reasons for not onboarding a certain asset, the accountable business and IT owners must be presented with the associated risk and formally accept it. Furthermore, during operations the CMDB is used by the SOC to identify threats and decide if an event is really an incident. Capacity management Workloads can change substantially over a short period of time and capacity management aims to assure that all necessary resources are available. This includes but is not limited to human and technical resources such as network bandwidth or SIEM processing capabilities and licensing agreements. You must consider this from the get-go. For instance, the amount of log-file data volume per day has to be estimated during the vendor assessment and contract negotiation phase already. These are the most important core processes that must be aligned, although the list does not end there: Topics such as incident response during out of office hours or the coordination with the business continuity management and disaster recovery planning teams should be considered as well. If you would like to talk about the challenge of process alignment, feel free to get in touch with us.
17 Jun 19
DiannaOdom

In truth, it could be one purpose behind your hair fall. At the point when there’s any 1 thing we’ve all experience the ill effects of time to time, it is a horrible hair day. The second dimension has been disposed of. Hair fall possibly a natural marvel however there are huge amounts of answers […]

17 Jun 19
Capgemini Norge
By Pierre-Adrien Hanania and Michael Schimpke Applying AI to road traffic can lead to a better and safer society. On the one hand, security services would be able to better detect anomalies and fight danger. On the other hand, road infrastructures and their users would clearly benefit from a more energy-efficient and regulated use of what are the veins of a city. How AI enables a smarter road traffic But before start thinking about what kind of city we could live in, we need to determine how AI can help to make road traffic smarter. Machine learning algorithms require both high quantity and quality of data to efficiently gather information. But where to get it? Traffic neither keeps bills and receipts, nor does it generate sensor data as do production lines. The only source available is traffic cameras. Consequently, smart traffic management and surveillance rely mainly on advanced computer vision systems. As traffic surveillance videos are highly complex data sources comprising a plethora of information, a model that is more sophisticated than simple image recognition must be built. A two-step model is needed, where all relevant shapes have to be identified first. This is important to filter crucial picture components and reduce computational efforts. Quelle: pexels.com In a second step, each identified shape is classified. This procedure has already been implemented successfully based on convolutional neural networks showing high accuracies, such as Mask-R-CNN[1] by Facebook AI Research and R-FCN[2] by Microsoft Research. Quelle: pexels.com Nevertheless, these networks are precise but rather slow. Therefore,  they are not applicable to real-time analysis of traffic camera input. New algorithms, including single shot detectors (SSDs), provide a solution to this problem. They substantially increase the processing speed of pictures, such that videos with frame rates over 30 fps can be analyzed instantly. Although the speed boost trades off with decreasing recognition accuracy, state-of-the-art techniques[3],[4],[5] manage to maintain high frame rates while keeping a decent precision level. Hence, these thoughts are clearly not a vision of the future but already a reality that is applied in several fields. AI identifies, searches, and tracks specific elements The model focus needs to be more detailed in order to influence security and surveillance matters. It might be not enough to just identify cars as cars or persons as persons. To find persons of interest or cars, the detection algorithms also need to disclose unique specifics. This implies detecting license plates or faces, capturing relevant information, and identifying it by matching it to databases. This procedure highlights three features of what AI can do in road traffic: Identifying and recognizing wanted objects, such as stolen cars or missing persons An accurate working identification system enables searching for characteristics or details All classes of the classified model can be searched for in a connected traffic surveillance database, for example persons with red shirts, green cars, or blue bikes. This search function can be extended into a tracking process using an integrated system where all traffic cameras share information on a central platform. A certain person is identified by several characteristics, his profile gets enriched with detailed data describing clothes and other features. The profile is sent to the platform, where other traffic cameras can access and search for it. As the next camera identifies the person, a movement profile can be recorded. From security cases to the bigger picture of a smart city The huge increase in computational power as well as the continuous elaboration of data-driven AI systems has made the vision of smart roads a realistic one. Traffic cameras were first installed decades ago, but today their benefit no longer depends on human control capacities. Single objects can be recognized, analyzed, and matched with databases in real time. Automatic decision-making based on this gathered information opens up many opportunities. For security cases, applying AI could help fight organized crime, which often uses roads as channels for transporting drugs or weapons. These criminal activities tend to follow common schemes regarding road behavior and suspicious car components, among other things. The identification of punctual dangers, such as ghost drivers or objects lying on high-speed roads, might be quicker and more efficient with AI. What serves security can obviously also serve other purposes; security being only one element of the bigger picture that is a smart city. Monitoring parking, for instance, is a good example of how AI can improve road traffic. By assessing the overall situation or recognizing free parking spots and communicating these to the driver, AI-based insights might help the driver decide whether he wants to drive or take public transportation, or it can direct that driver to the nearest available parking spot.[6] AI could also use cameras on street crossroads to connect with traffic lights in order to enable intelligent traffic flow.[7] By detecting license plates, AI-based cases can identify the need for toll charges and initiate the payment process. This is actually done in London with the Congestion Charge and has led to 30% less traffic.[8] Furthermore, replacing old streetlamps with smart lighting systems significantly lowers energy consumption while increasing the subjective sense of security.[9] Potential use cases for AI in road traffic AI ethics will need to be at the heart of it all By dealing with roads as playground, AI developers will need to think about core elements of what has been conceptualized as AI ethics. Four dimensions must be at the center of the use cases mentioned above: Reliability of AI How “right” is AI – does it recognize false positive, such as a fake weapon, or will I be asked to pull over by the police when I take my child to a costume party dressed as knight with a sword? Beyond the recognition of factitious objects also lies the question of the neutrality of AI – how can one guarantee that the AI will be able to identify the independent variable and avoid jumping to conclusions based on skin color or ethnicity? Data privacy and processing By dealing with very personal data, AI applications in road traffic will be subject to the EU’s General Data Protection Regulation, which regulates the processing of personal data. Many of the use cases mentioned will therefore need to address data privacy – with one of the possible solutions being anonymization. Explainability of AI When it comes to security services, we have to legitimize the use of AI in making decisions that affect our day-to-day lives. While the black-box issue unavoidable, substantial insights on how AI reaches its conclusions will be a crucial asset. The role of the human Who is accountable for AI-based decisions? Where does the human step in and how can misled AI be contradicted? Many studies have shown that the human tends to stop thinking when AI takes over – this is a danger that cannot happen in use cases involving road security. As in many fields, we have to think of AI as something that augments the human, rather than replacing him. Do all roads lead to artificial intelligence? In our ever-expanding cities, AI can help organize and optimize the way we use roads safely. The benefits of AI could have a valuable impact on better organizing our movements, mastering the urban veins, and making decisive environmental and security-related advances. The industry will need to embrace this potential in a comprehensive way that respects privacy and builds on a robust technology that is able to deal with the huge amount of data needed. One key component of it will be to involve the human in all processes in which AI should only add time-saving and accuracy-helping features. Only then, the trip toward AI will be a safe and successful one.   [1] Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick 2017, Mask-R-CNN, arXiv:1703.06870 [cs.CV]. [2] Jifeng Dai, Yi Li, Kaiming He, Jian Sun 2016, R-FCN: Object Detection via Region-based Fully Convolutional Networks, arXiv:1605.06409 [cs.CV]. [3] Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi 2016, You Only Look Once: Unified, Real-Time Object Detection, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p. 779–788. [4] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár, Focal Loss for Dense Object Recognition, arXiv:1708.02002 [cs.CV]. [5] Wei Liu , Dragomir Anguelov , Dumitru Erhan , Christian Szegedy , Scott Reed , Cheng-Yang F , Alexander C. Berg 2016, SSD: Single Shot MultiBox Detector, arXiv:1512.02325v5 [cs.CV]. [6] See https://www.youtube.com/watch?v=eydYEEhPRkg. [7] See https://www.youtube.com/watch?v=j79offP5evc. [8] Garcia, Irene 2019, $15 a Day to Drive? Londoners Say ‘Thanks, I’ll take the train,’ on https://www.bloomberg.com/news/articles/2019-02-28/london-s-congestion-charge-has-cut-traffic-by-30-percent. [9] See https://www.youtube.com/watch?v=pL4QbP_Y9rM.
17 Jun 19
Capgemini Norge
The possibilities that intelligent systems hold and the disruption that will be driven by them will sooner rather than later require a change in the mindset, concerning social and economic factors. While the emergence of Artificial Intelligence (AI) has started a political and economic struggle between countries over gaining AI supremacy. It has also caused a technological shift that gradually affects almost every industry. Especially, the healthcare sector is said to be influenced the most by this change. If applied correctly this technology may lead to an aspired improvement in patient care. What sounds like a spell in the first place can be explained by two main factors. First, the revolution of AI is primarily being driven by the rapid progress in computational power as well as increased access to a huge amount of data. Secondly, when this is combined with the machine learning methods, it can be used to discover patterns and develop relationships in large data sets in a manner that humans are hardly capable of – but which is imitating human intelligence. AI unleashing new potential The opportunities that AI presents to the healthcare sector can be described through the following five levels and it could give birth to the concept of augmented doctors.  Monitor – First level includes AI being able to monitor patient data. MIT researchers recently came up with the idea of a wireless smart-home system that can possibly improve elderly care. Within the framework of their latest project called “RF-Pose”[1], the CSAIL developed an AI system that is able to detect radio signals behind walls caused by human bodies in order to sense their activities. According to the team, their way of using neural networks to analyze human movements and postures allows for monitoring certain diseases such as Parkinson or multiple sclerosis. This means, as a corollary, that physicians can use these insights to adjust medications for an optimal patient treatment.[2] Preventing – The second dimension of AI in healthcare enables preventing critical health events and danger. The Berlin Start-Up xbird claims that it will “save one million lives by 2020”[3]. How? It identifies symptoms of diseases in early stages by using a combination of data collection through smartphone sensors and pattern recognition. This project, which is financed by the EU, enables physicians to improve diagnoses and decision-making in order to personalize patient treatment. Identifying – On a third and more advanced level, AI is capable of identifying patterns and diseases. While xbird’s solution focuses on behavior and environmental impacts, KI elements [4] makes use of speech recognition to detect and diagnose cognitive disorders such as dementia. Their app records voice data and then automatically extracts and evaluates scientific metrics which helps clinicians to make more informed and professional decisions. Another idea originating from the UK improves the classification of stroke or neurological impairments and hence supports recommendations for best treatments. The NVIDIA Corporation and the King’s College have recently announced their intentions on planning and deploying the rationalization of AI within the areas of radiology and pathology. This new partnership combines the college’s expertise in medical imaging and health records as well as NVIDIA’s AI technology that will eventually provide an infrastructure for developing AI applications in a way that will promise better and faster patient care. [5] Guide and communicate – The fourth level includes how AI can be used in terms of guiding and communicating. Processing and identifying patterns are one thing, instantaneously communicating results to the human is another. While chatbots are one typical case for communicating AI, other forms exist too, such as AiServe. Once hooked up to a patient’s glasses, AiServe’s small gadget is designed to assist blind and visually impaired people and help improve their quality of life. A combination of computer vision and AI enables the technology to identify obstacles and guide users through streets independently by giving them voice instructions.[6] Furthermore, one could easily imagine a use case involving a chatbot communicating with its patient about basic healthcare processes, for example, a guide during a treatment. Act – Finally, while robotic process automation in healthcare is just in theory, for now, we are seeing more use cases with AI playing an active role in surgery thus emphasizing the capability of AI in assisting or improving the overall performance during surgery. In fact, a Florida-based hospital just launched a new AI technology to prevent maternal deaths and postpartum health complications such as undetected hemorrhage that are specifically related to cesarean deliveries. The technology behind it is similar to facial recognition. With the help of an iPad, AI is now able to provide a real-time estimation of blood loss during surgery to the doctor, by processing images of surgical towels and canisters through machine learning.[7] [8] By fulfilling this task, which was initially based on a doctor’s estimation, AI does not only provide a more reliable parameter but also actively makes an important contribution during childbirth.   What can AI do with the collected & analyzed data? What AI does Monitor Prevent Identify Communicate Act Example RF-Pose xbird Ki elements AiServe’s Florida Hospital Use Case Detection and analysis of radio signals triggered by people’s movements & postures Prevention of critical health events through data that was obtained via smartphone sensors Evaluation of scientific metrics via speech recognition to detect & diagnose dementia Navigation of blind and visually impaired people Blood loss estimation during cesarean deliveries AI and the patient – trust and ethics must be established first AI, the technology is based on large data sets and (un)supervised learning methods. AI will bring healthcare to a whole new level. It will offer opportunities in terms of prediction, diagnosis and decision-making tools. This new dimension of health is already on its way – from the smartphone in the pocket through to the operation bloc. At this point, it is primordial to keep in mind that AI isn’t supposed to replace doctors – but rather augment their capabilities. While building a sustainable relationship between AI and the current health structures, one might also underline the need to address AI ethics in a field that is subject to the highest caution. First, the patient’s (personal) data should be treated carefully and will have to be processed within the frame of GDPR. This involves a debate that is still to be deliberated upon, about automated decisions made by algorithms such as the one AI employs. Second, one very important effort must be made in order to avoid biases that might influence AI’s work in healthcare, in the wrong way. We must remember that AI can be easily misled if fed with bad data. The danger of bad data is not only linked to its accuracy, but also to its consistency, availability and fairness. Beyond poor data quality, AI is also able to be just as badly intentioned as its developer. Imagine an algorithm reproducing racist patterns in its decision-making process (such as organ donation). The idea of algorithm audits, and the quality assurance that goes with it seems to be much needed here. Third and beyond these technical features, a disruptive phenomenon like AI must win another race of great importance – the trust race that can not be won without the relevant actors embracing these new possibilities. This involves a trust-building discussion with the patient, the hospital and also the society as a whole. Just like autonomous vehicles, AI will have to nurture a relationship with the society that is based on trust and reliability. By Lisa Neumann and Pierre-Adrien Hanania [1] Conner-Simons, Adam ; Gordon, Rachel, on: MIT News (2018): AI senses people through walls, on: http://news.mit.edu/2018/artificial-intelligence-senses-people-through-walls-0612. [2] See El País Retina, on: www.retina.elpais.com (2018): Un dispositivo inalámbrico analiza tus signos vitales mientras te paseas por casa. [3] See http://www.xbird.io/. [4] Michael Mast, on: EIT Digital (2018): Neue App „Delta“ unterstützt Demenzdiagnose, on: https://www.eitdigital.eu/newsroom/news/article/neue-app-delta-unterstuetzt-demenzdiagnose. [5] King’s College London (2018), on: [1] https://www.kcl.ac.uk/lsm/research/divisions/imaging/newsevents/newsrecords/2018/king’s-college-london-nvidia-build-gold-standard-for-ai-infrastructure-in-the-clinic.aspx. [2] https://blogs.nvidia.com/blog/2018/10/10/kings-college-london-nvidia-clara/?ncid=so-twi-nrcmtghrtglsx3-60739. [6] See https://www.aiserve.co/. [7] Winnie Palmer Hospital for Women & Babies (2018), on: https://www.winniepalmerhospital.com/news-and-events/news/winnie-palmer-hospital-launches-invests-in-new-ai-technology-to. [8] Atlanta Journal Constitution (2018): Florida hospital uses artificial intelligence to save lives in the delivery room, on: https://www.ajc.com/news/national/florida-hospital-uses-artificial-intelligence-save-lives-the-delivery-room/d64mAgHWC3TiomiPadi8vK/?_lrsc=67e57dfc-48a4-4c46-976e-38daacca8543.
17 Jun 19

Fanny pack is the ultimate accessory for people on the go. And this waist bag has everything—the right size, a small inside pocket, and adjustable straps—to become your favorite fashion item if you’re going to a festival, getting ready for a vacation, or just like to keep your hands free. • 100% polyester • Fabric […]

17 Jun 19

Fanny pack is the ultimate accessory for people on the go. And this waist bag has everything—the right size, a small inside pocket, and adjustable straps—to become your favorite fashion item if you’re going to a festival, getting ready for a vacation, or just like to keep your hands free. • 100% polyester • Fabric […]

17 Jun 19
The B-Trader

Agencies, are you set up for ongoing Google Tag Manager success? GTM isn’t the easiest tool in the world to work with, but if you know how to use it, it can make your life much easier. Make your future self happier and more productive by setting up your GTM containers the right way today. Dana DiTomaso shares…

17 Jun 19
Chindogu

Friday we worked on the overall shape and dimensions of the sourdough machine. We made calculations on the volume of both the flour and water containers, based on travel mode, as this mode sets the highest demands on the flour and water amount. Both of them can be shortened quite a bit which makes the […]

17 Jun 19
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17 Jun 19
Capgemini Sweden
The possibilities that intelligent systems hold and the disruption that will be driven by them will sooner rather than later require a change in the mindset, concerning social and economic factors. While the emergence of Artificial Intelligence (AI) has started a political and economic struggle between countries over gaining AI supremacy. It has also caused a technological shift that gradually affects almost every industry. Especially, the healthcare sector is said to be influenced the most by this change. If applied correctly this technology may lead to an aspired improvement in patient care. What sounds like a spell in the first place can be explained by two main factors. First, the revolution of AI is primarily being driven by the rapid progress in computational power as well as increased access to a huge amount of data. Secondly, when this is combined with the machine learning methods, it can be used to discover patterns and develop relationships in large data sets in a manner that humans are hardly capable of – but which is imitating human intelligence. AI unleashing new potential The opportunities that AI presents to the healthcare sector can be described through the following five levels and it could give birth to the concept of augmented doctors.  Monitor – First level includes AI being able to monitor patient data. MIT researchers recently came up with the idea of a wireless smart-home system that can possibly improve elderly care. Within the framework of their latest project called “RF-Pose”[1], the CSAIL developed an AI system that is able to detect radio signals behind walls caused by human bodies in order to sense their activities. According to the team, their way of using neural networks to analyze human movements and postures allows for monitoring certain diseases such as Parkinson or multiple sclerosis. This means, as a corollary, that physicians can use these insights to adjust medications for an optimal patient treatment.[2] Preventing – The second dimension of AI in healthcare enables preventing critical health events and danger. The Berlin Start-Up xbird claims that it will “save one million lives by 2020”[3]. How? It identifies symptoms of diseases in early stages by using a combination of data collection through smartphone sensors and pattern recognition. This project, which is financed by the EU, enables physicians to improve diagnoses and decision-making in order to personalize patient treatment. Identifying – On a third and more advanced level, AI is capable of identifying patterns and diseases. While xbird’s solution focuses on behavior and environmental impacts, KI elements [4] makes use of speech recognition to detect and diagnose cognitive disorders such as dementia. Their app records voice data and then automatically extracts and evaluates scientific metrics which helps clinicians to make more informed and professional decisions. Another idea originating from the UK improves the classification of stroke or neurological impairments and hence supports recommendations for best treatments. The NVIDIA Corporation and the King’s College have recently announced their intentions on planning and deploying the rationalization of AI within the areas of radiology and pathology. This new partnership combines the college’s expertise in medical imaging and health records as well as NVIDIA’s AI technology that will eventually provide an infrastructure for developing AI applications in a way that will promise better and faster patient care. [5] Guide and communicate – The fourth level includes how AI can be used in terms of guiding and communicating. Processing and identifying patterns are one thing, instantaneously communicating results to the human is another. While chatbots are one typical case for communicating AI, other forms exist too, such as AiServe. Once hooked up to a patient’s glasses, AiServe’s small gadget is designed to assist blind and visually impaired people and help improve their quality of life. A combination of computer vision and AI enables the technology to identify obstacles and guide users through streets independently by giving them voice instructions.[6] Furthermore, one could easily imagine a use case involving a chatbot communicating with its patient about basic healthcare processes, for example, a guide during a treatment. Act – Finally, while robotic process automation in healthcare is just in theory, for now, we are seeing more use cases with AI playing an active role in surgery thus emphasizing the capability of AI in assisting or improving the overall performance during surgery. In fact, a Florida-based hospital just launched a new AI technology to prevent maternal deaths and postpartum health complications such as undetected hemorrhage that are specifically related to cesarean deliveries. The technology behind it is similar to facial recognition. With the help of an iPad, AI is now able to provide a real-time estimation of blood loss during surgery to the doctor, by processing images of surgical towels and canisters through machine learning.[7] [8] By fulfilling this task, which was initially based on a doctor’s estimation, AI does not only provide a more reliable parameter but also actively makes an important contribution during childbirth.   What can AI do with the collected & analyzed data? What AI does Monitor Prevent Identify Communicate Act Example RF-Pose xbird Ki elements AiServe’s Florida Hospital Use Case Detection and analysis of radio signals triggered by people’s movements & postures Prevention of critical health events through data that was obtained via smartphone sensors Evaluation of scientific metrics via speech recognition to detect & diagnose dementia Navigation of blind and visually impaired people Blood loss estimation during cesarean deliveries AI and the patient – trust and ethics must be established first AI, the technology is based on large data sets and (un)supervised learning methods. AI will bring healthcare to a whole new level. It will offer opportunities in terms of prediction, diagnosis and decision-making tools. This new dimension of health is already on its way – from the smartphone in the pocket through to the operation bloc. At this point, it is primordial to keep in mind that AI isn’t supposed to replace doctors – but rather augment their capabilities. While building a sustainable relationship between AI and the current health structures, one might also underline the need to address AI ethics in a field that is subject to the highest caution. First, the patient’s (personal) data should be treated carefully and will have to be processed within the frame of GDPR. This involves a debate that is still to be deliberated upon, about automated decisions made by algorithms such as the one AI employs. Second, one very important effort must be made in order to avoid biases that might influence AI’s work in healthcare, in the wrong way. We must remember that AI can be easily misled if fed with bad data. The danger of bad data is not only linked to its accuracy, but also to its consistency, availability and fairness. Beyond poor data quality, AI is also able to be just as badly intentioned as its developer. Imagine an algorithm reproducing racist patterns in its decision-making process (such as organ donation). The idea of algorithm audits, and the quality assurance that goes with it seems to be much needed here. Third and beyond these technical features, a disruptive phenomenon like AI must win another race of great importance – the trust race that can not be won without the relevant actors embracing these new possibilities. This involves a trust-building discussion with the patient, the hospital and also the society as a whole. Just like autonomous vehicles, AI will have to nurture a relationship with the society that is based on trust and reliability. By Lisa Neumann and Pierre-Adrien Hanania [1] Conner-Simons, Adam ; Gordon, Rachel, on: MIT News (2018): AI senses people through walls, on: http://news.mit.edu/2018/artificial-intelligence-senses-people-through-walls-0612. [2] See El País Retina, on: www.retina.elpais.com (2018): Un dispositivo inalámbrico analiza tus signos vitales mientras te paseas por casa. [3] See http://www.xbird.io/. [4] Michael Mast, on: EIT Digital (2018): Neue App „Delta“ unterstützt Demenzdiagnose, on: https://www.eitdigital.eu/newsroom/news/article/neue-app-delta-unterstuetzt-demenzdiagnose. [5] King’s College London (2018), on: [1] https://www.kcl.ac.uk/lsm/research/divisions/imaging/newsevents/newsrecords/2018/king’s-college-london-nvidia-build-gold-standard-for-ai-infrastructure-in-the-clinic.aspx. [2] https://blogs.nvidia.com/blog/2018/10/10/kings-college-london-nvidia-clara/?ncid=so-twi-nrcmtghrtglsx3-60739. [6] See https://www.aiserve.co/. [7] Winnie Palmer Hospital for Women & Babies (2018), on: https://www.winniepalmerhospital.com/news-and-events/news/winnie-palmer-hospital-launches-invests-in-new-ai-technology-to. [8] Atlanta Journal Constitution (2018): Florida hospital uses artificial intelligence to save lives in the delivery room, on: https://www.ajc.com/news/national/florida-hospital-uses-artificial-intelligence-save-lives-the-delivery-room/d64mAgHWC3TiomiPadi8vK/?_lrsc=67e57dfc-48a4-4c46-976e-38daacca8543.
17 Jun 19
Hottest Casino Bonus

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17 Jun 19
Priceanalyses.com

Cryptocurrency market data provider Stock Price Reports Recently released its March 2019 Exchange Review, surveying trading data across major trading Archives. Over the previous year, the digital money market took a progression of overwhelming punches from the Chinese government. The market endured the shots like a warrior, however the combos have incurred significant damage in […]

17 Jun 19
Travel

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17 Jun 19
Travel

Leather Provides A Comfortable Feeling And Protection From Dust And Abrasions.
Lightweight Slim Design, Adding No Unnecessary Bulk Or Weight.
Provides Comfortable Touch And Durable Protection Of Your Travel Essentials During Trip.