A Perfectly Imperfect World
The second edition of the first European event on artificial intelligence, AI Convention Europe, will be held the 3rd of October 2019 at Event Lounge in Brussels in a more ambitious formula and larger capacity with a conference and networking area rising from 500m2 to 1500m2. TIMGlobal Media – publisher of IEN Europe – jointly with its partner Mark-Com Event, will bring together B2B professionals, industrial players and AI experts in a one-day conference dedicated to Artificial Intelligence.
The last edition of AI Convention Europe saw the participation of more than 300 visitors and 18 qualified speakers, from companies such as SAS, IBM, Amazon, Microsoft, Robovision, Moonoia, ThinkNext, TIMi, AI Lab (VUB), ML6, Faktion, NLP Town or Excellium.
The rich lineup will introduce the latest developments, product innovations and most interesting applications of AI. Technology issues and opportunities will be discussed during the day, with the aim of creating the best conditions for training, learning and networking.
The program will put a spotlight on AI from a contemporary and wide point of view and will provide participant with a better understanding of the AI issues at different scales and will also provide an excellent opportunity for peer-to-peer meetings and exchange of views between participants that, through their ties and interest in AI, share common values and issues.
AI use cases will be the fil rouge of the convention. Dealing with different topics such as Ethics, Education, Business, Financial Services and Industrial Automation, the specialists invited to take floor will discuss challenges and opportunities coming from the responsible adoption of AI for the improvement of our lives at every level.
Among the speakers: Patrice Latinne, EMEIA Financial Services at Ernst & Young Partner and Paul Peeters, Lead Expert at Agoria
Resulted from the cooperation between TIMGlobal Media and Mark-Com Event, IoT Mobility Europe will take place parallel to AI Convention Europe. The event addresses both public and private players and will be entirely dedicated to the Internet of Things and mobile business applications.
Date: October 3, 2019
Location: Event Lounge, Brussels
Opening hours: From 8:30 to 18:00
Business, Finance, Enterprise, Big Data, Industry, Automation, Health, Education, Society
Decision-makers from the public and private sectors, professional users, B2B players
François Vajda, Managing Director at Mark-Com Event: f.vajda@mark-com
Orhan Erenberk, President at TIMGlobal Media: email@example.com
Sharelynne Paras, Marketing & Communication at Mark-Com Event: firstname.lastname@example.org
TIMGlobal Media - publisher of IEN Europe - and Mark-Com Event are renewing their cooperation for the first ever edition of “IoT & Mobility Europe” that will take place at Event Lounge Brussels, the 3rd of October 2019.
Following a format combining quality conferences and exhibition space for Sponsors, the day will be entirely dedicated to the Internet of Things and mobile business applications.
It is addressed to both public and private players willing to learn about state-of-the-art solutions, challenges and potential developments of these new industries.
A few years ago, the economic turn that the IoT was bound to take was regarded with skepticism, but these times are now over. Recent estimates forecast for 2020 that no less than 50 billion objects will be connected or/and have some level of intelligence. According to the McKinsey Global Institute, the potential economic impact of IoT is set to reach US $ 11 trillion per year in 2025.
Numerous enablers turned this trend into a genuine strategic imperative. First, there was the boom in wireless networking technology and the rapid adoption of cloud platforms. Then there was the upgrade and uptake of both advanced Data Analytics and Data processing, and finally a global decrease in the cost of connected devices. In addition to that, the end-users’ sentiment towards these data-collecting sensors has grown warmer with a slight increase in demand while the move to professionalisation within the supply side overtook the somewhat hands-off approach that was prevalent until now.
However quick the rise of IoT so far, its course might suffer a setback arising from the considerably slower-paced evolution of the necessary infrastructure. The swelling volume of data, the surge in bandwidth demand and the multiplication of connections necessitate a variety of adjustments and updates such as moving to +5G and seven-core glass fibre or a speedup in converting to IPv6 systems.
On top of that, the deployment of these technologies is further impeded by interoperability issues that the lack of standards is only reinforcing. By the same token, the sector, falls into the trap of pending issues surrounding the legal framework for the protection of personal data.
The event draws on its past regional edition of IoT mobility organised at Namur Expo in 2018 and 2017 but aligns with the wider European scale in order to be in tune with the geographic scope of investments and development. In IIoT (Industrial IoT), a market expected to weigh close to US $ 123.8 billion by 2021, Europe should in fact, in the future, occupy a leading position, or so is the claim of Industry ARC’s latest study. These forecasts appear to be consistent with the peak in demand of European patents; the EPO (European Patent Office) reported a jump of 54% over three years of demands related to the Fourth Industrial revolution compared to a general increase of 7.65% only.
This essentially B2B conference day is the chance to strengthen the growing IoT and Mobility ecosystem. It is also a unique occasion to learn more about the latest applications and business models and to seek information on legal aspects that could prove decisive for the future of these sectors. All in all, one can expect to come out with a precise picture of the issues at hand and of the key actors that form the European IoT landscape (governments, agencies, corporate groups, incubators, research centres, universities ...).
As far as business organisation is concerned, and in particular regarding digital transformation, both the IoT and mobility for enterprise have been assisting each other in their expansion: it is undoubtedly the ever-growing number of devices coupled with the massive data analysis capacity set up by mobile enterprises that enabled the IoT to exist. On the other hand, a constant rise in connected devices will make it vital for its users to possess an « Enterprise Mobile Management » solution (EEM).
Used jointly, and efficiently run, they make for a non-negligible competitive advantage by boosting productivity (stronger reactivity, more precise predictions etc...) and by reducing costs simply by exploiting their synergies and positive externalities.
Among the speakers: David Bol, Assistant professor at Catholic University of Louvain, Frederic Vander Sande, Strategy & Transformation Expert at Capgemini.
Resulted from the cooperation between TIMGlobal Media and Mark-Com Event, AI Convention Europe will take place in parallel to IoT Mobility Europe. The event addresses both public and private players and will be entirely dedicated to Artificial Intelligence.
Date: October 3, 2019
Location: Event Lounge, Brussels
Opening hours: From 9:00 to 16:00
M2M, Domotics, Telematics, Smart Cities, Smart Building, Smart Cars, Healthcare, Energy, MDM, ERP, MEAPS
Decision-makers from the public and private sectors, professional users, B2B players
François Vajda, Managing Director at Mark-Com Event: f.vajda@mark-com
Orhan Erenberk, President at TIMGlobal Media: email@example.com
Sharelynne Paras, Marketing & Communication at Mark-Com Event: firstname.lastname@example.org
It was 2016 when Oracle and Mitsubishi Electric started their common journey towards the launch of disruptive Industry 4.0 solutions with the aim of changing existing business models and digitalize industrial processes. The idea was to invest in the future of Industry 4.0 by combining the IT expertise of Oracle – global leader in the design of database software and cloud engineered systems – with the OT skills of Mitsubishi Electric – one of the world’s main manufacturers of technology for factory automation.
The two companies recently introduced to the market a robot with integrated predictive maintenance features, an enhancement made possible thanks to the integration of artificial intelligence and the connection to the cloud built up by Oracle.
This was just the beginning of their exciting adventure. The challenge, now, is to identify a series of scalable and valuable use cases that will make the disruptive change envisaged and the transition towards Industry 4.0 possible for many industrial companies. IEN Europe interviewed Eric Prevost, Vice President Industry 4.0 and Advanced Technologies at Oracle, and Klaus Petersen, Marketing Director, Factory Automation EMEA, Mitsubishi Electric Europe B.V, to go into detail of this groundbreaking partnership.
E. Prevost: Three years ago, Oracle decided to invest in Industry 4.0. We asked ourselves how we could change the business models that are currently leveraging the manufacturing industry to create a flexible and agile digital model that would be simple to adopt and validate in shop floors. I made a first screening of the most innovative potential partners and went to Japan to meet with the Head of Worldwide Factory Automation at Mitsubishi Electric. We decided to test the water to see the reaction of the market – at the time, a partnership between a digital solution provider and an industrial manufacturer was unusual. This was the first test and then we started to undertake bigger projects, including different technologies designed by Mitsubishi Electric – from Automation to Robotics. The purpose of the partnership was to create a link between the collection of data and the use of this data by different business divisions.
K. Petersen: Already in 2003, Mitsubishi Electric had established the e-F@ctory Alliance. It is an integral part of our e-F@ctory approach to the increasing digital transformation affecting business. The global network includes manufacturers of industrial components as well as specialised system integrators and software providers. It became obvious to us that the IT layer was gaining much more importance when the industry 4.0 initiative was launched. We immediately understood that if IT and OT are not talking to each other, there are no benefits for manufacturing companies. In order to take full advantage of digitalization, it’s fundamental to connect all the different departments – Logistics, Production, Accounting, Finance, Sales, Marketing, R&D. This way, they can have access to all the valuable information available. In the past, this process meant exchanging papers at meetings. The purpose of digitalization is to put all this information in a central data source so everyone can take full advantage of it. This is exactly where we saw that the collaboration with Oracle would be beneficial to the complete value chain.
E. Prevost: There are two side effects behind this centralized system. The first side effect is what we can call the ‘’human real-time’’, which is a neologism. We are looking at real-time on the automation side and, historically, decisions are not taken quickly in business processes. With this connection, we are not creating real-time decisions but a ‘’human real-time’’: humans make almost instant decisions about what’s happening in the real world. This was impossible in the past.
Being able to apply AI to data, this is the second side effect. The purpose of AI is to be predictive, by analyzing weak signals that couldn’t be analyzed in the past. Right now, we can identify weak signals and abnormal conditions, and we can use this information across business processes and technologies to identify correlations and be proactive in specific situations.
This creates a new definition of collaborative robots where robots are working on specific business processes in which we have humans making decisions. This is changing the way we do business today, as the robot can collaborate not only with people but also with processes.
K. Petersen: Imagine a factory that works on periodical maintenance. A factory where every week you do maintenance, no matter if there is a need for it. Imagine how many maintenance engineers are needed and how much time companies spend on it. How often is production down just because you are doing unnecessary maintenance? These are all costs absorbed by consumers or by the company itself. So, if companies go for a predictive maintenance model, in many cases the savings are huge in terms of uptime and manpower. This way, you can reduce your manufacturing cost. This is just one of many possible examples of how our technology can contribute to improving the competitive position of manufacturers.
E. Prevost: We are at the beginning of our collaboration. The next step is to find use cases and help different industries undertake and assimilate the digital transition, supporting them in the decision-making process without concerns. Changes in business and in the shop floor need to be digested first and it is pivotal to be well prepared to anticipate any potential side effect, For this reason, we need to collect and analyze data, learn from it and identify new use cases.
E. Prevost: The main challenge today is really identifying new use cases. Thanks to the technology that we have now, we know how to process a large diversity of data. Finding the valuable and scalable use cases for the company to change its business model in a sustainable way; helping them create a new stream to make the innovation possible – that’s our biggest challenge. Why do we think this is a good time? Because now there is a big market pressure from customers to reduce costs while improving product diversification.
Klaus Petersen: I can confirm that we feel this disruptive wave in our market as well. Today, a successful company needs to differentiate itself and provide added value by adopting and integrating new technologies to keep customers satisfied.
The partnership between Oracle and Mitsubishi Electric is very important because together we will be able to deliver completely new and disruptive solutions that can help keep a competitive advantage. We are just at the beginning of that journey.
K. Petersen: Changing people’s mindset is hard: we have habits even in management processes. It surely takes time, but it is already happening now.
E. Prevost: It will take time until some disruption in the market comes and changes the game for everybody. At that point, everything will go very fast!
The Mustang-V100 Series from ICP Deutschland is a flexibly scalable solution for implementing Deep Learning Inference at the Edge, which is energy saving (low power consumption of <15 Watt TDP. 2.5 Watt per VPU) and has a low latency time. The Mustang V100-MX4 PCIe KI AI accelerator card is a variant equipped with four Intel Movidius Myriad X MA2485 Vision Processing Units. The PCI Express bus-based card can be integrated into various embedded systems. Its multi-channel capability enables each VPU to be assigned a different DL topology for simultaneous computing, such as AlexNet, GoogleNet, Yolo Tiny, SSD300, ResNet, SqueezeNet or MobileNet.
Moreover, its compatibility with the OpenVINO Toolkit from Intel optimizes the performance of the training model and scales it to the target system at the edge, for an optimized integration without tedious trail and error. The Mustang-V100 Series is compatible with operating systems such as Ubuntu 16.04, CentosOS 7.4 and Windows 10 IoT. Furthermore, it is actively cooled and the operating temperature ranges from 5°C to 55°C. ICP Deutschland also offers a PCIe variant with 8 VPU units as well as variants based on the Mini-PCIe and M.2 bus.
Nowadays, the availability of powerful data center (physical or virtualized in the Cloud) allows extreme data processing and storage capabilities to train neural networks and AI algorithms. However, stringent timing and computational requirements, especially in the Industrial market, led to the need for mission-critical applications to bring the computing power at the Edge, close to the source of data. As the amount of data collected by sensors is increasing, this generates bandwidth and latency issues that cannot be tolerated in this kind of applications.
To answer this problematic, Eurotech, which can deliver products that move the paradigm of the data center processing capabilities from the Cloud to the Edge, develops rugged and high-performance computers specifically engineered to be deployed in embedded applications. Its BoltCOR 30-17 family of powerful and rugged Edge servers fits the needs of vertical markets like transportation, Industry 4.0, Smart Energy or Retail. The BoltCOR 30-17 is a fanless EN50155 certified server with numerous configuration options, made to exceed the requirements of embedded and IoT applications.
In order to match real-world automation challenges, Yamaha’s Factory Automation Section introduced a full robot lineup designed to make the robots work together in a fully integrated assembly cell. The range includes cartesian, Scara, single axis and multi-axis articulated robots, that benefit from one-stop technical support and a common environment for programming and control. Moreover, the portfolio comprises machine sizes for payloads from 5kg to 50kg, with special options such as high-speed pick-and-place robots, dust-/drip-proof, and clean room models.
These robots can deliver superior speed and reliability thanks to their contamination-resistant position resolvers and innovative vector-control drives. Furthermore, the LCM100 robotic linear conveyor module enables bidirectional flexibility, independent module speed control, and easy reprogramming. Consequently, the restrictions of conventional belt-and-roller transport are overcome.
Commonly known among the general public as the co-creator of Siri, but also the creator of CHIC (Computer Human Interaction Center), co-creator of Nuance Communications (now World leader in speech recognition), Luc Julia has been a researcher at the French CNRS and is nowadays at the head of the Paris based AI Lab of Samsung. He has spent nearly 25 years in Silicon Valley and has released a book: "There is no such thing as Artificial Intelligence."
As a French researcher in AI, but also as a scientist working for an industrial player like Samsung, he answered the questions IEN Europe asked him with regard to the developments of AI, its relation with IoT, its application in the world of industry, the commitments made at a French level, and his projects at Samsung.
Luc Julia: The title of my book is "There is no such thing as Artificial intelligence". Artificial intelligence that does not exist is the one that we talk too often these ten last years in the media or cinema. It does not exist because it is a fantasy. True artificial intelligence is characterized by the everyday work of researchers who know the dangers, the limits. My definition is that it is a mathematical and statistical tool to increase the capacity of humans.
L. Julia: The term “augmented intelligence” keeps the acronym "AI". Augmented Intelligence is a tool that increases us, to make tasks faster, more pleasant, whether by robots that assist us or by calculating machines. Artificial intelligence is the practical part, it's just a tool.
L. Julia: The progress of AI has been fluctuating since the first time it was mentioned in 1956 during the Dartmouth conference. Later, we quickly fell into the "winter of AI" as funding dried up. Then the funding went better with the expert systems and dried up again, then the expert systems continued to progress linearly and last, we had a renewal of neural networks thanks to Big Data, which exists globally on the Internet. Today the progression is more exponential in the sense that there has been an acceleration in the application fields that allows to make image recognition, speech recognition, DNA recognition, and more. We can apply the statistical method to what one wants since one brings computational capacities, capacities of memory, and data. The progression has been rather exponential since 2007 until we reach new limits. Since human interest is in the realm of the unlimited, then there is no limit to this progression.
L. Julia: Image recognition has improved a lot in the last 10 years. This can be applied to autonomous cars, to the medical field for the recognition of radio images for example. There has been wonderful progress and further progress is yet to come. Besides, the recognition of motives in the DNA, which we do not see, that we do not necessarily understand. As such, there is still some progress to be made since we discover DNA. Moreover, about speech recognition and natural language: we have a lot of data, a lot of conversations that are available and that can make much better progress than what existed a few years ago.
L. Julia: There are several challenges. The first concerns the current methods which are statistical and mathematical tools, the second is the energy challenge. Today, data centers use a lot of data, thus, use a lot of energy, and we need to understand why. Is a machine that needs 440kW / h to play Go useful? Is it worth it to save someone's life with a tool using 440 kW / h? Here the answer is obvious. Our energy is not unlimited and the AI that uses deep learning can be a problem because it utilizes too much energy. We use too much data: Big Data, Big Energy, it's a big problem. The second problem is that, since we use statistics and mathematics, we will never get to true Artificial Intelligence if it is a complete imitation of us. We will never reach AGI (Artificial General Intelligence) with these methods. The challenge would then be to look elsewhere, at other methods and areas of reflection, for example towards the organic artificial intelligence that is the ally of biology, physics and other fields of knowledge. We already know how to do many things that can help people in their daily lives, but there are limits, including energy.
L. Julia: The internet of things consists of connected objects that are connected to their app. If we have one hundred objects connected at the factory, we will need a hundred different apps, and this is not an optimized process. We need to move beyond other IoT caps to achieve the "interoperability of things", to make these objects communicate with each other. Thus, they can join forces to have strategies to deliver useful services that will then be intelligent. This is how we get to the intelligence of things.
L. Julia: Not much because we do not have a platform that brings together objects of any type and that can make them work together. Some people like me can make the objects operate together. For example, at home I have 212 connected objects that communicate together and work for me. I have hardly created a platform that allows this interoperability but today there is no alliance or way to use the objects together and make them communicate with each other, so no ideal platform today.
L. Julia: More than a data bank! The data bank is necessary to know what the objects do, in addition to that, it would be necessary to find the "glue" which connects the objects between them but that does not exist, unfortunately.
L. Julia: Once we have managed to connect all these objects, will be a network of objects with specific capabilities, and the AI will be able to take advantage by bringing together the right objects at the right time to provide the best service. The AI can manage the communication between the objects but also the best way to make them interact with each other, also from the energy point of view.
L. Julia: IoT has a problem with the fact that it is "I": in general, the data cannot leave the factory for data security reasons. Factory workers are very suspicious regarding their data. The IIoT is still very local which implies that it takes time to implement. In addition, the machines in the factory are very specific. There is tremendous hope, but we do not dare to centralize this data in order to compare them, to obtain other information. These are therefore specific systems for each installation. It's a pity, however, it evolves. We already take advantage of the models available on the Internet to apply them to particular installation systems. What is inherent in the IIoT is energy management, the machine management, to know when they will break down so to do predictive maintenance. Some surveys suggest that in factories, 20% of the machines are "down" at any time, even with preventive maintenance. With predictive maintenance, this number could be reduced to 5%, so plant activity would increase by 15%.
L. Julia: The house of the future is mine! This is the ideal home but, of course, it depends on people. Repetitive and uninteresting tasks are done automatically and in context. When I arrive near my home, the garage opens automatically, I park my car and the garage closes automatically. Then I get out of the car and the door between the garage and the house is unlocked automatically. If no one is at home, the alarm is also automatically removed. Once home, if it's late, the lights come on. If it is 7 pm, shortly before the meal, the fireplace will light automatically, with music and lights that correspond to the context. I am often asked what my favorite object in this automatic house is, it is the blinds. I have 53 windows in the house. If I closed them manually it would take me about 12 minutes, so doing it morning and evening would take double. I can use those 24 minutes doing something else. Likewise, the management of opening and closing of these blinds are made in a better way than I could do with regard to such parameters as the orientation of the sun, or if the TV is turned on.
L. Julia: Efficiency and productivity gains. If we are able to model what the human could do to optimize the supply, it is obvious that it gives an advantage over what is done manually. Anyway, a machine will do it faster.
L. Julia: Regarding the use of robots in the shop floors, there are fewer errors, fewer accidents. The products are therefore of better quality. Robots must be able to capture the know-how, but when it is done well, it will implement the technique much better than humans. The final product, except accidents, will be better than the one made by humans. Robots have an efficiency close to 100% while humans is 90%.
L. Julia: Nowadays we prefer to reproduce the process in an unlimited way rather than take advantage of the models available on the Internet. Consequently, we need to change mindsets, this is a recurring problem in the industry. In the chains of production and supply, the use of AI is a plus which diminishes errors. We must not be afraid of these robots that according to me increase the human, allow him to focus on tasks that are more interesting, less repetitive, thus cognitively more pleasant for him while making fewer mistakes.
L. Julia: Some countries are well placed. France is well placed thanks to the good level in mathematics. We are competing with the United States in terms of the number of Fields medals. AI means mathematics, statistics, and logic, then the benefits will come from education. If we keep the value of mathematics education, we will be able to keep our AI value. The AI plan that was set up last year by the French government and led by Cédric Villani presents interesting opportunities. Since 2012-2013, France has highlighted the French Tech to bring innovation back to France and to reveal young talents. 52% of students leaving the best schools now want to create start-ups, it's amazing! In my time it was rather 0.52%. Students were more thinking about being hired by big companies.
L. Julia: I owe a lot to France since I did practically all my education there. I praise the French mathematical know-how, and that's why I opened a center in Paris [Samsung AI Laboratory] to show that the talent is there.
L. Julia: I can tell you about some work, especially in what I also call AI: advanced innovation. We are interested in three areas that make it possible to improve the lives of humans with technology. The first area is health and well-being. We need to use technology to help people live longer. The second is transportation in general, transportation of people, goods, whether on the ground, in the air ... Transport in general should benefit from technologies related to AI. The third area is IoT, to make these objects cooperate to provide services.
L. Julia: Educate yourself! AI can be dangerous if it is used in a bad way. But when researchers create innovations, they are meant for the people, because the service brings something useful. We need to understand how it works which services are provided. Why do you give your data? It must be understood that it is always an exchange. What do free services like some social networks bring me? What do I give in exchange? Let's educate ourselves, understand and make good choices in good conscience. Technology can be dangerous if the user is malicious, so regulation is useful and may have an educational value.
On early July, imec, a world-leading research and innovation hub in nanoelectronics and digital technologies presented TEMPO (Technology & hardware for nEuromorphic coMPuting), a European innovation project and a cross-border collaboration between 19 research and industrial partners. Funded by ECSEL Joint Undertaking (JU), this three-year program aims to develop process technology and hardware platforms leveraging emerging memory technologies for neuromorphic computing for future applications in mobile devices which need complex machine-learning algorithms. Moreover, this one-of-a-kind collaboration aims at enabling applications that need cloud-based server racks, to be executed within battery-powered mobile devices, for instance cars and smartphones.
The ultimate edge artificial intelligence applications require intelligent energy-efficient local processing. To answer the increasing demand of applications including complex computational algorithms such as smart home assistants, face-recognition-based security systems or autonomous vehicles, TEMPO leverages the process technology platforms being developed by the European research technology organizations and cooperating foundries in the project. These organizations’ skills are combined with the application and hardware knowledge from further partners. The project will evaluate the current solutions at device, architecture and application level, and build and expand the technology roadmap for European AI hardware platforms. It will also leverage MRAM (imec), FeRAM (Fraunhofer) and RRAM (CEA-Leti) memory to implement both spiking neural network (SNN) and deep neural network (DNN) accelerators for 8 different use cases, from consumer to automotive and medical applications.
Emmanuel Sabonnadiere, CEO at CEA-Leti: “It is our aim to sweep technology options, covering emerging memories, and attempt to pair them with contemporary (DNN) and exploratory (SNN) neuromorphic computing paradigms. The process- and design-compatibility of each technology option will be assessed with respect to established integration practices and meet our industrial partner roadmaps and needs to prepare the future market of Edge IA where Europe is well positioned with multiple disruptive technologies.”
Prof. Hubert Lakner, Director of the Fraunhofer Institute for Photonic Microsystems (IPMS) and Chairman of the Board of Directors of the Fraunhofer Group Microelectronics: “A key enabler for machine learning and pattern recognition is the capability of the algorithms to browse through large datasets. Which, in terms of hardware, means having rapid access to large memory blocks. Therefore, one of the key focal areas of TEMPO are energy efficient nonvolatile emerging memory technologies and novel ways to design and process memory and processing blocks on chip.”
Luc Van den hove, CEO at imec: “We are delighted to enter in such broad European collaboration effort on Edge Artificial Intelligence, gathering the relevant stakeholders in Europe, including CEA-Leti and Fraunhofer, two of our most renowned colleague research centers in Europe. Thanks to our combined expertise, we can scan more potential routes forward than what would be possible by each of us individually, and as such, position Europe in the driver seat for R&D on AI. Imec looks forward to the progress we can make together in the TEMPO project and hopes this will lead to more similar collaborations in the future. Behind the scenes, we are already defining more public and bilateral agreements with several of the partners involved.”
Kicked off on the 1st of April 2019, the TEMPO consortium includes nineteen members, with imec as leader as the sole Belgian consortium partner. The other consortium members are, for France: CEA-LETI, ST-Microelectronics Crolles, ST-Microelectronics Grenoble, Thales Alenia Space and Valeo. For Germany: Bosch, Fraunhofer EMFT, Fraunhofer IIS, Fraunhofer IPMS, Infineon, Innosent, TU Dresden and Videantis. For the Netherlands: imec the Netherlands, Philips Electronics and Philips Medical Systems. Last, for Switzerland: aiCTX and the University of Zürich.
Different from standard robots, collaborative robots, or cobots, aren’t designed to fulfil tasks in a fully automated, autonomous fashion. What makes them unique and what is simultaneously their biggest strength is their ability to physically interact with humans in a shared workspace. Human input is essential for a cobot to perform its tasks and this truly collaborative approach produces a better end result than either a human or a robot could have achieved on their own.
Cobots greatly increase efficiency, can handle complex and dangerous tasks and offer much greater flexibility than traditional robots. We’ve compiled an overview of the top 10 industries that can and do benefit from using cobots:
Perhaps the industry with the largest amount of possible cobot applications, manufacturing is at the forefront of the collaborative robotics movement. Due to their lower cost, small and medium sized companies can introduce collaborative robots without investing in the large industrial robots that big manufacturing businesses use as workhorses. Using cobots to perform a large variety of tasks such as working on delicate circuit boards, inserting parts into machinery or lifting heavy loads, the workspace of the factory becomes increasingly shared by humans and cobots.
Warehousing, logistics and fulfilment are fields that are constantly racing towards deadlines. Innovative solutions in these sectors are largely focused on ensuring that products get from point A to point B as efficiently as possible. Crucially, cobots massively cut down on travel time in warehouses and, in a pick to cart environment, more than double the rate of units picked, increasing it from 90 to 200 per hour.
Ranging from lab automation to neurosurgery to medical device packaging and beyond, the use of cobots in the healthcare industry has progressed significantly within the last few years. With the elimination of risk being a vital part of the industry, cobots need to be approved by governing bodies before they can be used. Increasingly, the benefits of collaborative robots are being seen in healthcare as they are being approved.
Construction is one of the most dangerous industries for its workers – in the US, one in five worker deaths occurs in the construction industry. Collaborative robots can come to the rescue and change the face of the industry by significantly improving worker safety, taking on some of the more hazardous tasks like pouring cement.
Oil and gas exploration has traditionally been high risk, high reward. Rigs are not pleasant working environments and thus increased automation as a form of process optimisation is not a new concept at extraction and heavy industry sites. Cobots are one way to address the isolation and danger faced by the sector’s workers.
The agricultural industry benefits from cobots over traditional robots for two main reasons. Firstly, their force limiting capabilities come in handy when harvesting crops. Traditional robots are generally not used in harvesting in fear of damaging produce, however cobots’ more delicate touch can get the job done easily. Secondly, they are leagues ahead of robots when it comes to safety and the ability to work alongside humans and animals.
In the automotive industry, flexibility is a major requirement – with cars becoming more and more customised, regular industrial robots that are often bolted down in cages and can only focus on one task are increasingly being switched out for cobots, whose applications can be changed with minimum effort.
Law enforcement has some of the most flashy examples of cobot applications, and though collaborative robots in no way replace law enforcement employees, they almost act as assistant police officers. For one, remote controlled bomb disposal cobots take on risky work requiring high precision.
Requiring a strong focus on hygiene, the food industry can rely on cobots to fulfil its specific needs. They can work in surroundings between 0 to 50 degrees Celsius and in low-oxygen environments and support human workers with repetitive tasks that require careful handling, like stacking eggs. Enabling smaller and medium sized companies to keep up with the fierce competition through increasing productivity, they are levelling the playing field and simultaneously providing a more interesting working environment for the workers that they collaborate with.
The textile industry benefits from cobots in the same way that the manufacturing industry does, and their high precision makes them ideally suited for textile production and tasks like cutting materials or moving fabric parts without folding or wrinkling them. They are responsible for a significant reduction of human error, which in turn improves efficiency, and have truly made their way to working safely side by side with humans.
Already adopted in a plethora of industries, it has become clear that the future of robotics is veering towards collaborative robots for all the reasons that lead to them being embraced in the first place: They are incredibly efficient, increase productivity, flexibility and safety, can be adopted for a relatively low capital investment and open up new working environments. Their popularity will only continue to grow as they work alongside humans, and their unlimited uses will be discovered and exploited across all industries before we know it.
Perfection is a controversial concept in our physical world. Certainty is far away from being our daily bread, and the more we strive to build a certain existence – made of guarantees, wellness, a healthy life style – the more we understand that we will never be sure of what can happen tomorrow. Only mathematics gives us certainty, but as soon as you go to the physical world things get immediately different and unsure. ‘’Many people think that the world is digital, but it’s not. The world will never become digital. It’s a physical thing not a mathematical thing, so you are never 100% sure,’’ claimed Dr. Tim Foreman, European R&D Manager at Omron Europe, just a few minutes after our introductory handshake. I met him at the last edition of Hannover Messe and we started a philosophical conversation about life, uncertainty and the role of the technological progress. The conclusion: You can buy whichever insurance you want, but you will never be 100% sure to have things under control!
‘’We can decline the same concept in the industrial world. With vision cameras for quality control, you can detect if, for instance, something is missing. But with vision camera you can also get false negative or false positive: You are never 100% sure of the result that you get,’’ said Dr. Foreman, who also mentioned Artificial Intelligence as a powerful ‘’remedy’’ that the world is trying to exploit to respond to uncertainty: ‘’With AI, we try to predict or classify physical processes with the best possible accuracy, but we should always bear in mind that neither AI can give 100% certainty: it’s impossible. Only mathematics has such a degree of certitude.’’
When we speak about Artificial Intelligence, we think that we are just at the beginning, as many consequences – from ethics to long-term sustainability – are still unknown, but that’s not true, as the mathematics for AI was invented 70 years ago. What’s new today is that machine technology is becoming powerful enough to really calculate mathematics. ‘’Thanks to this, there has been a huge technology push in the last years to generate more powerful devices that everybody wants to buy. The result is that prices are going down,’’ explained Dr. Foreman.
In other words, today customers can get smart functionalities, that makes it easier to do mathematics in a reasonably fast time, at the old same price. ‘’In ten years we will be able to add other functionalities to the machine and be even faster, reaching 99.99% certainty of the results that we get. 100% is never possible,’’ added Dr. Foreman.
That’s bad news for all the people who are not ready to accept uncertainty. Certainty is fake: In real life we always have surprises.
Based on its reservoir of advanced technologies and comprehensive range of devices, Omron set forth a strategic concept called ''i-Automation'' or "innovative-Automation" consisting of three innovations or «i's». ‘’’i-Automation’ means that we want to help customers use all equipment easily and in an integrated way. Therefore, the first «I» stands for ‘Integration’,’’ said Dr. Foreman.
The second «i» means ‘’interactive’’, since Omron is committed to offering solutions where machines and people can interact in the best possible way and in harmony. ‘’People and machines are working closely together and are supporting each other. This can sound a little bit fake, but with our tennis table robot trainer, for instance, we have a machine which is actually helping operators improve their skills,’’ illustrated Dr. Foreman. We saw the robot trainer at Hannover Messe and the principle is very simple: The machine starts at a speed that the operator can follow. When the operator improves, the machine gets a little bit faster.
The goal here is not only to make beautiful machines but also to interact with the operator in a way that the machine understands what it can learn from the operator and how it can be a better tool for the operator. ‘’The robot is learning from the human and the human is learning from the robot. Our tennis robot trainer can also give advice to the operator like anyone else more experienced would do in the same situation, refining its skills by learning from users who had a very good result. Machines can really help increase knowledge,’’ said Dr. Foreman.
And what about the third «i»? The third «i» stands for ‘’intelligent’’. And that’s where AI comes into play.
AI is a new way of thinking and people need to be guided to adapt to it. At Hannover Messe, Dr. Tim Foreman was responsible to tell the ‘’Artificial Intelligence story’’ at Omron’s booth. Some of the members of Dr. Foreman’s team developed the products showcased and were there to help customers understand how to use this intelligent technology. ‘’I’ve never seen a perfect machine. Machines are always predictable in 95% of cases but there is always something unexpected. Now that people are used to the fact that machines are not perfect, they know that if they want to improve, they have to use new technologies based on digitalization and AI,’’ he explained. Smart technologies are able to control the machine and to measure and analyze data at the same time. This way, the machine builder can identify a broad number of signals and monitor them in real time.
Omron’s upgraded demonstration of a bottle filling machine with AI controller, which adds real-time AI functionality to monitor multiple signals simultaneously to detect potential faults and to determine the appropriate response. ‘’Every time we fill a bottle, for every signal that we are monitoring, we are able to get 5000 measurements. Thanks to these measurements, we can see what’s happening and if there is any abnormal activity,’’ illustrated Dr. Foreman.
If in 95% of the time a machine is predictable, there is a 5% possibility that something goes wrong. Predictive maintenance acts in that 5%. With technology such as the Bottle Filling Machine presented by Omron, you can see if something is going wrong and react within a timeframe that very short compared to getting the data from the machine, sending it to the cloud and analyzing it. It’s a real-time process. ''Questions of milliseconds,'' as Dr. Foreman said.
This means that as soon as the machine recognizes the problem, it reacts by skipping one or two bottles, instead of creating a quality problem and lose the entire production. The information collected by the machine can even be stored into a local database in order to have full traceability and access data even after years.
Data really seems to be at the heart of our contemporary technological revolution. Will this last forever? ''If the machine is designed in the wrong way, data won’t help,'' affirmed Dr. Foreman. A proper design with good combination between mechanics, electronics and software is essential. And, finally, Man created Data! But not the opposite: Never forget it!
The Adlink Edge is an IoT smart gateway solution from Adlink that helps identify the operational data required to achieve real business results before full-scale commitment. It connects previously unconnected equipment and sensors at the Edge and streams data securely in real time to Google Cloud, allowing analysis and easy visualization to optimize decisions and operations.
Benefits from this data streamer occur as accessing valuable data instantly, continuously and securely to monitor operations, and viewing and understanding which data have to be used to make decisions. By using Google Cloud tools such as advanced analytics, AI and machine learning, it is possible to improve existing customer databases and ERP systems. This allows to minimize downtime, improve quality and do predictive maintenance.
Varvel introduced Dadistel, a new digital solution for production process automation in the supply chain, in order to improve the management, fasten delivery times and grant greater control over every step of manufacturing through the computerization of production processes. Consequently, it provides real-time information and accurate estimates on delivery times, the availability of resources and an analysis of production processes and trends.
The system is passive, so adding modifications to existing plants is no longer necessary. Moreover, as it is an artificial intelligence system, it learns from past experiences and continually improves its analysis. The main added value lies in the control of timings. Therewith, the information is made available in various formulas related to preparation, retooling, machining, downtime/pauses, production resumption and changeover, and the counting of parts as well.
The Retail Deep Learning Application is a platform made by Basler in partnership with Congatec and NXP Semiconductors. It is a proof-of-concept platform utilizing Artificial Intelligence to fully automate the retail checkout process. It shows the possibilities of vision for embedded applications, simplifying the everyday life. It works similarly as a face recognition tool.
The user can select what to put in the basket, then the trained neural network detects the product and finally the total pricing will be displayed. This allows to easily add new products to a trained neural network and then to the sales portfolio. Consequently, retail stores can benefit from less labor costs and an improved customer experience through instant checkouts, minimized queues and 100% checkout capacity at all times.
The BionicSoftHand from Festo is pneumatically operated, so that it can interact safely and directly with people. Unlike the human hand, the BionicSoftHand has no bones. Its fingers consist of flexible bellows structures with air chambers. The bellows are enclosed in the fingers by a special 3D textile coat knitted from both, elastic and high-strength threads. With the help of the textile, it is possible to determine exactly where the structure expands and generates power, and where it is prevented from expanding.
This makes it light, flexible, adaptable and sensitive, yet capable of exerting strong forces.
The learning methods of machines are comparable to those of humans: either in a positive or a negative way - they require a feedback following their actions in order to classify and learn from them. BionicSoftHand uses the method of reinforcement learning.
This means: Instead of imitating a specific action, the hand is merely given a goal. It uses the trial and error method, to achieve its goal. Based on received feedback, it gradually optimises its actions until the task is finally solved successfully.
Specifically, the BionicSoftHand should rotate a 12-sided cube so that a previously defined side points upwards at the end. The necessary movement strategy is taught in a virtual environment with the aid of a digital twin, which is created with the help of data from a depth-sensing camera via computer vision and the algorithms of artificial intelligence.
In order to keep the effort of tubing the BionicSoftHand as low as possible, the developers have specially designed a small, digitally controlled valve terminal, which is mounted directly on the hand. This means that the tubes for controlling the gripper fingers do not have to be pulled through the entire robot arm. Thus, the BionicSoftHand can be quickly and easily connected and operated with only one tube each for supply air and exhaust air. With the proportional piezo valves used, the movements of the fingers can be precisely controlled.
The strict separation between the manual work of the factory worker and the automated actions of the robot is being increasingly set aside. Their work ranges are overlapping and merging into a collaborative working space. In this way, human and machine will be able to simultaneously work together on the same workpiece or component in the future – without having to be shielded from each other for safety reasons.
The BionicSoftArm is a compact further development of Festo's BionicMotionRobot, whose range of applications has been significantly expanded. This is made possible by its modular design: It can be combined with up to seven pneumatic bellows segments and rotary drives. This guarantees maximum flexibility in terms of reach and mobility, thus enables it to work around obstacles even in the tightest of spaces if necessary. At the same time, it is completely flexible and can work safely with people. Direct human-robot collaboration is possible with the BionicSoftArm, as well as its use in classic SCARA applications, such as pick-and-place tasks.
The modular robot arm can be used for a wide variety of applications, depending on the design and mounted gripper. Thanks to its flexible kinematics, the BionicSoftArm can interact directly and safely with humans. At the same time, the kinematics make it easier for it to adapt to different tasks at various locations in production environments.
Nature teaches us impressively, how optimal drive systems for certain swimming movements should look like. To move forward, the marine planarian and sepia create a continuous wave with their fins, which advances along their entire length. For the BionicFinWave, the bionics team was inspired by this undulating fin movement. The undulation pushes the water backwards, creating a forward thrust. This principle allows the BionicFinWave to maneuver forwards or backwards through an acrylic tube system.
Its two side fins are completely cast out of silicone and do not require struts or other supporting elements. The two fins are attached to the left and right of nine small lever arms, which in turn are powered by two servo motors. Two adjacent crankshafts transmit the force to the levers so that the two fins can be moved individually to generate different shaft patterns. They are particularly suitable for slow and precise locomotion and whirl up less water than, for example, a screw drive. A cardan joint is located between each lever segment to ensure that the crankshafts are flexible. For this purpose, the crankshafts including the joints and the connecting rod are made of plastic in one piece using the 3D printing process.
With the bionic technology carrier, our Bionic Learning Network once again provides an impulse for future work with autonomous robots and new drivetrain technologies in liquid media. It would be conceivable to further develop concepts such as the BionicFinWave for tasks such as inspections, measurement series or data collections - for example for water and wastewater technology or other areas of the process industry.
Based on neural networks, the SIMATIC S7-1500 TM NPU by Siemens is an intelligent unit that helps solve complex machine-related automation tasks using Artificial Intelligence. The neural networks permit efficient processing and easy integration of the solution in terms of quantity structure as modules can be plugged in one behind the other. Besides, the TM NPU module allows human expert knowledge, for example in complex pattern recognition, to flow into automation by processing input data via neural networks.
The sensor technology compatible with Gigabit Ethernet / USB C interfaces and production data transferred from the CPU (USP) can be used as input data. The unit adapts fast and easy to changing circumstances as there is no need to program complex algorithms for each product.
IEN Europe participated for the first time in LiveWorx, the event to enhance the digital transformation experience of smart, connected enterprises. The show closed its doors last week in Boston, after four days of intense breakout sessions, technology trainings, live exhibitions and demonstrations, professional exchange, networking, parties, ideas, inspiring moments. The next appointment of this ''high-energy'' event is already scheduled for June 8-11, 2020.
''Energy is the capacity to do work and cannot be created or destroyed but it can change form,’’ said Jim Happelmann, CEO at PTC, during his opening speech. LiveWorx, organized by the computer software and technology company PTC – headquartered in Boston – is a way to increase our potential energy and get digital transformation on the next level. The idea is: ‘’Come to LiveWorx, get the most out of it, and transform your potential energy into new energy when you go back to your company.’’
What does it exactly mean? ‘’In business, there is always a lot of work to do and the levels to get the work done are tipically three: The employees level, the machines level, and the computers level. Computers and machines are there to free us up so we can focus our capacity to do works in areas that are usually a human prerogative: Strategizing, Planning, Innovating.’’ said Jim Happelmann.
The objective at Liveworx is to take this abstract concept of digital transformation and make it tangible and actionable, by giving specific and powerful examples that people can take and apply immediately to their business. PTC has been focusing for years in cutting-edge fields like IoT, Artificial Intelligence, Computer Vision, Virtual and Augmented Reality, Generative Design, Real-Time Simulation, enabled by High-Performance Computing, Cloud and 5G. All of this is tied together thanks to critical concepts like Digital Thread and Digital Twin. ‘’PTC CAD and PLM has proven to be the company’s new technology breakthroughs. Technologies like IoT and AI and AR and High-performance computing are actually transforming CAD and PLM too, and this is evident in our Vuforia app.’’ added Mr. Happelmann.
For Jim Happelmann, CAD and PLM will never go out of style, and they are truly going to transform the industrial panorama with the key endorsement of a boosted digital and human world.
With the IoT, smart connected machines are more productive, and the workload can be raised without impacting the workflow, with the advantage that we can control, monitor and optimize the digital chain. The potential energy is therefore increased across these three dimensions of the physical, digital, and human world.
Innovation happens in the intersections of different ideas and forces. When Virtual and Augmented Reality come into play and we marry digital with human, ‘’we get widely more productive workers thanks to the ability of the digital world to monitor and optimize the work of humans. That’s what Smart Connected People are about.’’ explained Jim Happelmann. Intersecting the ideas of smart connected people and smart connected products we get smart connected processes.
Use cases were at the center of LiveWorx. PTC aims to build a community of historical clients willing to share roadmaps and visions that can leverage the industry to transform technology and people. At the show, PTC demonstrated real examples of how the integration of AR, AI and IoT-based solutions can empower existing technologies, taking them to the next level. An example of what PTC calls ''the digital thread’’ can be found in its collaboration with Volvo. Volvo began collaborating with PTC back in 2016, when the company decided to test PTC’s Thingworx and Vuforia in France. Together with PTC, Volvo reimagined the design for state-of-the-art trucks to reduce the weight of the engine without compromising performance. This means to generate an optimized design thanks to PTC generative design platform and to perform quality control checks easily and reliably.
For Beneteau, well-known French sail and motor boat manufacturer, PTC has been an important partner to meet client needs for customized products. ‘’We needed a powerful tool, like the PLM, to handle different customized configurations and options for our clients. A tool allowing us to reflect immediately and with accuracy all the modifications in our design projects. Thanks to PTC’s PLM software Windchill, this is now possible.’’ declared Bertrand Dutilleul, DSI Global, Groupe Beneteau. With the 3D digital twin model of the boat, the client can have an internal vision of how his product will look like.
To know more: https://www.liveworx.com/
It might be a common term that is integral to most modern technology, but the phrase artificial intelligence (AI) itself was only coined little over six decades ago. Nowadays, according to numbers reported in March 2018 by Voicebot.ai, one in five adults in the US have access to a smart speaker and artificial intelligence (AI) powered virtual assistant. AI assistants have similar popularity elsewhere in the world; China is predicted to have 85.5 thousand smart speaker users by the end of 2019, and there is soon to be 22 million smart homes across Europe.
These numbers show that society is becoming increasingly accustomed to the use of AI and automation to make everyday processes more efficient, effortless and convenient. This isn’t just a domestic shift. We’re seeing industrial businesses investing more in automation systems and AI to provide the same efficiency in complex industrial environments.
Of course, AI in industry means more than a plant manager simply installing a smart speaker in their office — though this is something that could be done for hands-free status updates if connected to a SCADA system. More often though, incorporating AI into industrial systems involves the integration of machine learning (ML) algorithms into industrial internet of things (IIoT) platforms to manage, monitor and control systems.
In recent years, the IIoT has become an unavoidable topic of conversation for most engineers and plant managers. Data has become increasingly valuable, in line with the development of technology able to collect new operational and performance metrics and the growing adoption of cloud-based infrastructure, giving managers and supervisors the tools to remotely monitor systems in near real-time.
According to statistics provided by Riello UPS, the typical smart factory produces approximately five petabytes, or five million gigabytes, of data every week. Not only is this volume of data more than a human can efficiently analyse manually, but it’s also an ample amount of information for a ML algorithm to learn from.
This is one of the core applications of AI in the industrial sector. With a complex network of systems and equipment in a typical plant, AI can provide extensive, effective insight into operations in a fraction of the time that it would take a human. For plant managers, this means many of the more analytical management tasks can be automated to improve efficiency.
Maintenance is a prime example of this. The constant stream of operational data from smart systems can be analysed in real time by AI, which can highlight any consistently outlier data readings to maintenance staff through an alarm system. This supports a culture of proactive maintenance rather than reactive, which in turn will help minimise downtime — both scheduled and unplanned.
Crucially, ML means that the system can learn to identify the normal operating range for data from individual pieces of equipment, reducing the number of incorrectly identified issues. It can also quickly spot correlations between different data sets, which an engineer may not be able to identify in a timely manner.
A single machine can contain dozens of sensors or other health signals. To get a clear picture of all the things that affect reliability, that data should be evaluated alongside things like maintenance records and a history of what the machine was running. Even ambient conditions and crew data can help spot the issues that can crop up.
The only effective way to navigate the abundance of variables is with an IoT platform with machine learning, such as GE Digital’s Predix platform. Predix’s machine learning algorithms can analyse equipment with advanced analytics, providing businesses with valuable insight. Rather than just identifying what a problem is, the AI can present a reason why that problem is occurring.
The reason why ML is specifically valuable when integrated into an IIoT platform is because it allows detailed simulations of possible operational scenarios based on collected data. GE Digital’s Predix, as an example, can use this data to create a digital twin of a plant, in which the plant manager can use the AI to visualise how actions — or inaction — might affect systems.
With this, predictive maintenance becomes a reality. The AI can run simulations and predict when a piece of machinery will require maintenance, which means maintenance engineers can avoid non-value-added time by only attending to equipment at the optimal time.
In fact, there are certain industrial applications where the algorithms could directly reconfigure a machine with the right settings, in instances where it identifies an issue. And as machine algorithms learn, this will become an increasingly viable way of improving efficiency.
We have learned a lot in the last six decades since the term AI was coined, and both we and modern ML systems will learn much more in the decades to come. One thing that remains apparent is that AI systems are helping humans achieve more, efficiently and effectively, and providing the potential for greater productivity.
Advantech, AMD, and Mentor, a Siemens business, announced their partnership to make AI technology more accessible and easier to implement, which is expected to create more AI-based business opportunities. AI technology will take embedded systems to the next level with higher efficiency and smarter systems designed to improve people’s lives. For instance, diagnostic errors among the 300M diagnostic radiology images that are captured in the US contain errors of up to 4%.
AI-infused image recognition using machine learning can see far more detail in MRI and X-ray images than human eyes, so it can improve diagnostic accuracy and help prioritize treatment. AI technology in embedded system is often integrated in service model innovation rather than the manufactured product and it is highly sophisticated in the numerous tasks it performs including data collection, data analysis, pre-trained models, and inference. In terms of machine learning, programmed algorithms rely on powerful and reliable computing units for big data consumption. That’s why edge computing plays an important medium to satisfy the connection between the cloud and sensor devices.
With the partnership of Advantech, AMD, and Mentor, the companies aim to help customers accelerate AI implementation by integrating each party’s products and services. The partners are each devoting resources to make edge computing easier to apply so customers can concentrate on AI application development for their hardware and middleware. Advantech offers an embedded platform (SOM-5871/AIMB-228) equipped with the latest AMD Ryzen™ Embedded V1000 processers, in combination with the Mentor® Embedded Linux operating system.
The AMD Ryzen Embedded V1000 processor supports frameworks, libraries, tools, and compilers for machine vison applications that leverage the full power of the Ryzen processor with Radeon™ Vega GPU technology. More importantly, the three companies embrace open standards such as OpenVX, OpenCL API supported on the Linux® kernel, so users can migrate machine learning across diverse hardware architectures for a variety of AI applications.
EFCO announced the partnership with congatec to support industrial computer modules carrier board series design service for all congatec’s COM Express® Type 6, Type 7, SMARC and Qseven® modules. EFCO also provides a system-level customization solution with a combination of carrier boards assembled with design-in computer modules, thermal solution and necessary peripherals to fulfill customers’ needs for various vertical applications, including factory automation, machine vision, in-vehicle, surveillance, GPU computing, IOT and edge computing.
The carrier board houses the application-specific connectivity and interfaces such as USB 3.0, multiple Ethernet ports (optional PoE), UART, Display port, miniPCI expansion etc. The new carrier boards will be also equipped with the company’s eKit software package, based on its propriety artificial intelligence algorithms, providing all systems with monitoring and system analysis.
“Being a strategic partner with congatec, EFCO will provide standard carrier board amount all popular industrial computer modules platform, reducing the barriers for embedded system integration, bringing extreme reductions in time and expenditures for embedded customers,” stated Bryan Lin, General Manger of EFCO. “The partnership between EFCO and congatec brings unique value to facilitate embedded system customers can address a broader range of applications with time-to-market solution. With 20 years of experience, EFCO has a wide range of ready-to-use sustainable and scalable solutions that are compatible with the latest technologies and hardware vendors.”
“congatec’s modules have been deployed into in a variety of industries and applications, such as industrial automation, medical, entertainment, transportation, tele-communication, test & measurement and point-of-sale,” said Fred Barden, congatec Vice President Worldwide Sales. “Together, we will partner to offer consolidated end-to-end solutions, which can ensure the product quality as well as empower customers’ applications in an efficient way.”
B. Maisonnier: AI applied to industry is only getting started. The major break to adoption is that the currently dominating AI technique is Deep Learning (DL). As it happens, DL is simply not particularly suited for industrial applications for at least 3 reasons:
1. DL needs large samples of data – This is a real problem for industry which has used Total Quality Management (TQM) to reduce its error levels considerably (think of 1 wrong part in 100,000 manufactured) and therefore finds it difficult to provide the statistically relevant samples which DL is after
2. DL is not explainable – By training, industry engineers like to understand whatever they implement. On the contrary DL models are inherently black box, so engineers don't like it. More importantly, methods or regulators prohibit the implementation of technologies which cannot be audited, so DL’s lack of explainability is a real problem for industry
3. Finally, in practice DL is difficult to deploy in industry. AI is currently hard to implement in real-life, factory environments
Of course, given the terrific momentum behind DL, lots of efforts are going into making it work for industry. The feeling however is that we will need to see the emergence of new approaches of AI, which requires less data, can be explained and is easy to deploy in factory environments for mainstream adoption of AI by industry.
B. Maisonnier: AnotherBrain takes a radically different approach to AI. Rather than using a statistical approach which requires large data samples and iterative retraining, we use the model of the human brain, i.e. an intelligence which is learning truly on the fly. A key word here is “emerging”: Nature is good at creating very powerful emerging results from simple rules carried by huge number of elementary “entities”.
AnotherBrain’s unique approach is based on the replication of the workings of the human brain, and this is the main reason of our success. Where most have started by replicating a neurone to get to the brain (which is mission impossible in the short term), we concern ourselves with imitating (in software and eventually silicon) the key functions of the brain. This is a much simpler (relatively speaking) endeavour.
B. Maisonnier: In our view, there are not many very innovative AI companies. They are a lot of companies helping their customers to innovate thanks to “mainstream deep learning AI”, which is different. Given that they make money from selling data, they want to see the world that way. They know very well all AI 3.0 approaches, but they have no interest in finding new ways.
As a result, only a few startups dare to think outside the box. And of course, it means that the direct competitors to alternative AI are giants like Google, etc. The challenge is not only on technology, but more importantly is having the means and power to build an eco-system which can eventually rival Google’s, Alibaba’s and the other world’s largest technology companies.
B. Maisonnier: There is an old IT principle which says be wary of GIGO: garbage in, garbage out. Given DL works on the basis that it programs itself (in a completely opaque way) using the examples you have fed it, it is of course open to sample bias. There are more and more examples of DL systems just not working, e.g. Amazon’s AI-powered CV sorting system. Recent studies showed that autonomous vehicles would more often run over black than white people.
What we need first is an AI that you can audit. Second, we need an AI which learns from new situations, all the time, in real-time, not just because it has received a given (and therefore biased) data set. This is the promise of AI 3.0.
B. Maisonnier: To be honest, the conflict of interest among technology giants is not only conditioning the industry but also the entire humanity. Are we really going to allow a few companies to monopolize our personal data? More importantly, are we going to accept a system which demands that everybody is always connected, which is prone to privacy, security and state abuse? Internet giants are trying to convince us, all of us, that there is no other way: big data or no progress. Well, the result is going to be, and already is to some extent, Big Brother. The debate is growing, with Surveillance Capitalism on one hand, and the social obedience index that China is building for all its citizens.
AnotherBrain argues there are other ways, which don’t require big data. New, decentralized ways which don’t generate monopolies and have low requirements in terms of memory and computing power, and are inexpensive to install and economical to run. All this makes those AI 3.0 human-friendly. This is AnotherBrain’s project: Organic AI™, with ethics as its core, by design.
Digitalization, Machine Learning, Predictive Maintenance: In order to really drive a change, all technical developments require willingness in the first place, especially if the change is disruptive, as it is happening now with this recent wave of technological innovation. ‘’We find ourselves at the dawn of a new era. The decisions that we take now will shape our future and how production will be designed.’’ commented Andreas Hamm, Country Director Germany at Rockwell Automation, during our conversation about how to tackle the problems that many companies are facing during their ‘’digital journey’’. Today, users are demanding more flexibility, an element which new technologies have enabled at higher levels of complexity than before.
Predictive Maintenance is a key advancement for productivity and quality control, and it’s based on AI. But to which extent it can be considered reliable and refined? ‘’Quite advanced and reliable and continuously evolving.’’ said Mr. Hamm. ‘’Creating diagnostic analytics solutions in industrial operations has long required expert data scientists with a deep understanding of the specific application to be analyzed.’’ he continued.
This is not all, since experts also need weeks, months or even years to understand and conceive the system, and this reality led Rockwell Automation to invest in the development a solution to deal with analytics in a continuous and automated fashion. It is the Sherlock Artificial Intelligence Module, that can seamlessly watch operations and look for anomalies.
The data-driven analytics algorithm of the system is delivered inside a module that fits directly into the controller chassis. Once installed, the module leverages novel physics-based modeling to “learn” the application that controller manages. ‘’The solution scours controller tags to identify the application or allows users to choose what they would like modeled by selecting inputs and outputs via an add-on-instruction (AOI). Project Sherlock AI will then quickly learn from the stream of data passing through the controller to build a model. A process that can be accomplished in just a few minutes.’’ explained Mr. Hamm with further details. If the system spots a problem, it can trigger an alarm on an HMI screen or dashboard.
Andreas Hamm also brings our attention to the fact that future iterations will even go beyond diagnostics, and it will be possible to remedy the issue or to automatically adjust system parameters without human intervention.
At the last edition of SPS IPC Drives, Rockwell Automation presented an expanded version of the PowerFlex® 755T drives. These drives were designed to help reduce energy costs, add flexibility while increasing productivity, and include interesting predictive maintenance features. The new version presented at SPS guarantees predictive diagnostics and maintenance for system health analysis. ‘’The drives now offer an expanded power range, helping engineers with applications from 10 to 6,000 horsepower (7.5 to 4,500 kW), improve productivity and reduce their lifecycle costs. The expansion brings harmonic mitigation, regeneration and common bus-system configurations to a wider range of high-demand applications.’’ illustrated Mr. Hamm.
In this latest addition, we find an enhanced version of the TotalFORCE technology – a patented technology which basically deploys a series of exclusive features developed for system optimization. ‘’TotalFORCE technology now includes more powerful adaptive control capabilities, which allow the drives to monitor machine characteristics that can change over time and automatically compensate for the changes that occur. An adaptive tuning feature uses up to four automatic tracking notch filters to block resonance and vibration that can impact quality, waste energy and prematurely wear out a machine.’’ added Mr. Hamm.
In addition to that, predictive maintenance features provide real-time information about the health of the drive. For example, the drives are able to calculate the remaining life of critical components – by monitoring operational characteristics such as temperature, voltage and current – and notify users, who can act in time and prevent unplanned downtime.
Besides that, at SPS IPC Drives 2018 Rockwell Automation also announced its partnership with PTC, a software and service company that provides technologies for the IoT at a global scale. The partnership was sealed for the launch of the new FactoryTalk InnovationSuite and the acceleration of Digital Transformation. ‘’The newly launched FactoryTalk InnovationSuite, powered by PTC is a software suite that enables companies to optimize their industrial operations and enhance productivity by providing decision makers with improved data and insights. It delivers complete visibility of operations and systems status from a single source of information.’’ said Mr. Hamm.
The software suite not only enables digital transformation but also the delivering of tools for end-to-end digitization. ‘’Furthermore, it improves the connectivity to operational technology (OT) devices on the plant floor, natively supporting the rapid, scalable, and secure connection of the most commonly used industrial equipment.’’ added Mr. Hamm.
The combination of data from IT and OT will give decision makers a complete digital overview of their industrial equipment, lines, and facilities anywhere in the world, predisposing the digital transition.
Two is better than one. We’ve heard this motto many times, on the television, on ads and promotional spots. Usually this is a commercial way to tell you: ‘’Hey, buy this one and get two instead’’, which can have a powerful impact on our consumerist souls, even though we don’t know exactly why we want the second thing when we were only needing the first one to begin with.
In industry integration, where you get two or even more products in one, things are different. It’s a serious matter with major cost benefits. Take LMI Technologies, specialist of 3D scanning solutions and manufacturer of 3D sensors for automation, inspection, and optimization: At SPS IPC Drives 2018, the company announced the integration of its Gocator® Snapshot sensor into applications using Universal Robots (UR) devices.
We were there when LMI gave this exciting announcement, with our camera and microphone ready to film the results of this integration. Now, we want to learn more about it and about the collaboration between LMI and UR, with the help of Christian Benderoth, Regional Development Manager, EMEAR at LMI Technologies.
For those who think that integration just means fitting one device onto another, I have bad news: Don’t try it at home as you will be disappointed. For LMI to receive an official certification from Universal Robots required a lot of time and effort: ‘’UR is very rigorous in its approach to certification in order to ensure high-quality and performance, so our engineers went through many development steps to get Gocator® on the level it needed to be at. It has been a great experience, we’ve learned a lot, and we’re happy to finally be at this stage where the integration process has reached fruition.’’ said Christian Benderoth during our conversation.
LMI is very proud to now be on the list of respected providers with UR plus certification. The job is not over yet, as the company will keep working with UR to refine and further improve the solution.
Going into more detail, we asked Mr. Benderoth to enlighten us on the characteristics and the advantages of this integration, which is made possible thanks to the implementation of the Gocator® URCap plugin. ‘’The plugin allows engineers to connect to a robot controller or PC application to perform sensor hand-eye calibration and implement pick-and-place movement. Setting up robotic systems for factory automation applications it’s now easier, as there is no need for additional programming.’’ explained Mr. Benderoth.
With UR integration now in place, engineers can use snapshot sensors in a wide variety of factory automation applications. ‘’Gocator snapshot sensors use stereo structured light that generates high-density 3D data with a single scan trigger. Each sensor offers onboard data processing, built-in 3D measurement tools, and decision-making logic to scan and inspect any part feature with stop/go motion at speeds up to 10 kHz, achieved with our GoMax® solution for vision acceleration.’’ illustrated Mr. Benderoth. This translates into high-performance 3D results with the guarantee of a complete robot vision-guidance solution that can deliver inspection for quality control and smart pick-and-place for automated assembly.
As in every relationship, the two parties must be sure that they are the best fit for each other. How is this achieved? Take a blank piece of paper, draw a line, and write down all the pros and cons… and then count! But let’s not be too cynical… The use of UR cobots with an integrated snapshot sensor enables greater flexibility and control and creates a more customizable tool: ‘’Since you don’t have to write any robot programs or calibration routines, it doesn’t take any expertise in order to set up your robotic systems for factory automation applications. Any engineer can do it.’’ explained Christian Benderoth.
With the Gocator® URCap plugin, which consists of an application that is installed on the UR robot, it is possible to trigger scans on the sensor and retrieve the positional information of the calibration target in the sensor’s field of view. ‘’After you’ve performed your hand-eye cali-bration, you can add programming nodes in the UR robot’s interface to tell the robot to connect to the sensor, load a job on the sensor, trigger a scan, and return positional measurements in the X, Y, and Z axes.’’ added Mr. Benderoth. The magic is done.
How do technologies based on integration fit into today’s global market? The competition among companies is high, as is the desire to gain market share by transforming the business model from system provider to solution provider. This is a general trend among industrial companies. So, what is the future of a model based on integration? If flexibility and user-friendliness are guaranteed, the potential for the integration market is immense: ‘’Engineers using UR robots can now easily integrate Gocator® smart 3D snapshot sensors into their applications. Gocator® is able to provide the UR robot not only with the vision it needs in order to “see”, but also with the smart functions required in order to take action and make decisions. This presents a huge global market opportunity to provide customers with a complete robotic solution.’’ said Mr. Benderoth. Automation of repetitive tasks can deliver many benefits, and when it comes to increased flexibility, accuracy, waste minimization, and efficiency… Bob's your uncle!
Thinkin for Industry 4.0 is the innovative platform that combines high-precision real-time locating systems, big data tools and edge artificial intelligence to improve the efficiency of industrial processes and to reduce safety-related costs.
ThinkIN technology tracks assets (e.g., pallets, forklifts, working tools) and workers in real-time and with sub-meter accuracy throughout the entire shop floor. The collected data is processed using scalable IoT analytics and cutting edge AI in order to extract real-time knowledge and KPIs on the execution of industrial workflows.
This information provides ThinkIn customers with the ideal tools to:
DA-500 from Gas Dna detects combustible or toxic gas leakage and displays the gas density on the built-in LCD. The 420mA standard current output signal can be connected into the various controllers such as gas leakage warning devices (GMS-1000/2000), PLC, DDC, or MMR for individual or integrated gas monitoring system.
The various functions from artificial intelligence by built-in micro-processor offers more convenient & comprehensive gas monitoring environment with the easiest operation as well as the most extensive applications.
By built-in micro processor, various artificial intelligence functions enables more convenient, more accurate, and more efficient operation & maintenance.
By magnetic switch with digital processing auto-calibration, you can avoid a troublesome work of opening the sensor cover for operation or maintenance. This function is especially effective for the repair work in the explosion-proof area.
By real-time displaying the detection density on LCD, instant confirmation of density is possible. Also, auto back lighting enforces the readability of LCD even in dark area.
User programming menus for alarm, calibration, scale and so on.
Standard electric current output signal (4-20mA) allows stable long distance (up to 2.5km) transmission.
Alarm Output : SPST relay contact (ALARM-1 & ALARM-2).
Communication Output : serial data output(option).
Explosion-Proof Approval : Ex d IIC T5
Mitsubishi Electric’s new industrial robot series combines reduced cycle times with scalable safety features and offers versatile connectivity options for easy integration into production systems. Companies who want to achieve a more flexible and productive working environment will also benefit from new capabilities created by the implementation of artificial intelligence.
Mitsubishi Electric’s AI technology is adding efficiency to almost every phase of customer process, from design and start-up through to operation and maintenance. For example, unscheduled downtime can be avoided by implementing flexible servicing schedules: Live usage data is analysed by a preventive maintenance feature which compares the data to a dynamic model of the robot’s component parts. Service life and maintenance requirements can then be predicted in real-time. In addition, the new MELFA FR-robot series offers functions that assist with automatic calibration, which enables greater accuracy and shorter start-up times.
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