production

The future of production systems, between customization and sustainable development

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Editorial

 

[dropcap]W[/dropcap]hat will the production lines of tomorrow look like? Over the past decades, machines have played an increasingly important role in factories. We all have an image in our minds of robotic arms moving at lightning speed and with truly superhuman precision, carrying parts that are undoubtedly too heavy for our arms. Faced with such a demonstration of physical superiority, it is hard to imagine how anything organic can compete. When it comes to production rate one thing is certain: we are beaten by machines. And we’re already imagining humans being excluded from production lines, or at least reassigned to different tasks—complex programming of robots, overseeing machine networks, data analysis etc. All of these “new careers” are exclusively high-skilled positions and require profound changes in training and in companies.

But being so quick to eliminate humans and replace them with robots may be going a step too far. When we talk about production, we’re talking first and foremost about meeting a demand. What is produced is that which is desired, bought and consumed by end users. And what today’s customers want more than anything is a customized product. They want a car that aligns with their own needs, desires and values. They do not want to buy one of the 500,000 diesel cars with options they won’t use. They want the same model, only electric, without air conditioning because it’s bad for the environment, but with a sun roof because they love pulling over in the countryside and looking up at the stars.

But entirely-automated production lines have a hard time adapting to such specific demands. It is amusing to learn that researchers studying the issues involved in this new commercial paradigm are reasserting the importance of humans in production systems. Yes, we are slower, weaker and less precise, but we are also more flexible, versatile and better able to adapt to the typically human demand for diversity. At Mines Saint-Étienne, Xavier Delorme is one such researcher. His work has shown that it is important not to dehumanize production in order to respond to new demands from customers.

Production line flexibility: human operators to the rescue!

This does not mean adopting a primarily anti-technology stance, but rather emphasizing the strength of human-machine cooperation. At IMT Mines Albi, Élise Varielles is working on software tools that do precisely that by helping teams understand customers’ needs. The tools developed by the Albi-based researcher tackle the task of breaking down a demand, understanding it in great detail to determine whether it is feasible, then determining how it can be met as effectively as possible.

Breaking products down for customization

But growing demand for tailor-made products is just one of many new demands. Having a customized product is not enough. Customers also need to have it right away—or at least, as soon as possible. For this reason, new production systems cannot be considered in isolation from the transportation and distribution networks further downstream. The reality is that the entire supply chain is undergoing a transformation. It must transport goods more quickly, but must also meet sustainable development requirements. The environmental footprint is no longer a mere detail. Trucks can no longer travel half-empty and must progressively be replaced by trains. For this to happen, companies will have to learn how to communicate and collaborate with one another. The logistics network is undergoing profound changes.

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Synchronizing future transportation: from trucks to drones

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What is the physical internet?

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This series takes a look at some of the new issues facing industry, for which researchers are trying to find solutions. It was created following the IMT symposium on production systems of the future. As such, it focuses less on political and social aspects—training for new careers, disappearance of low-skilled jobs—than on technical subjects involving major scientific challenges. Against a backdrop of artificial intelligence, ecological and energy transition and human-machine interaction, it presents some interesting examples of research for the benefit of society and the industry of the future.

physical internet

What is the physical internet?

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The physical internet is a strange concept. It borrows its name from the best-known computer network, yet it bears little connection with it, other than being an inspiration for bringing together economic stakeholders and causing them to work together. The physical internet is in fact a new way of organizing the logistics network. In light of the urgent climate challenges facing our planet and the economic challenges of companies, we must rethink logistics from a more sustainable perspective. Shenle Pan, a researcher in management science at Mines ParisTech and specialist in logistics and transport, explains this concept and its benefits.

This article is part of our series on “The future of production systems, between customization and sustainable development.”

 

What does the physical internet refer to?

Shenle Pan: It’s the metaphor of the internet applied to supply chain networks and related services. When we talk about the physical internet, the objective is to interconnect distribution networks, storage centers, suppliers, etc. Today, each contributor to the supply chain system is on their own. Companies are independent and have their own network. The idea of the physical internet is to introduce interoperability between stakeholders. The internet is a good analogy for guiding the ideas and structuring new organizational methods.

What is the benefit of this subject?

SP: Above all, it is a way of making logistics more sustainable. For example, when each stakeholder works on its own, a delivery truck leaves without being full. The delivery must be on time, and the truck leaves even if it is only half full. By connecting stakeholders, a truck can be filled with more goods for another supplier. If enough companies share transport resources, they can even reach a flow of goods significant enough to use rail freight. Since one full truck emits less CO2 than two half-filled trucks, and the train runs on electricity, the environmental impact would be greatly reduced for the same flow of goods. Companies also save due to the scale effect. The benefits are also related to other logistics departments, such as storage, packaging and handling.

How will this impact the logistics markets?

SP: By interconnecting stakeholders, competing companies will be connected. Yet today, these stakeholders do not share their information and logistical means. New rules and protocols must therefore be established to control stakeholders’ access to components in the supply chain, using the networks, transporting goods, etc. This is what protocols do, which in the case of the internet include TCP/IP. New intermediaries must also be introduced on the markets. Some are already beginning to appear. Start-ups offer to mutualize transport to maximize the trucks’ capacity. Others sell storage areas for one pallet for a short period of time to adapt to the demand, whereas stakeholders are generally used to buying entire warehouses they do not always fill. The physical internet therefore leads us toward a new logistics model called Logistics as a Service. This new model is more flexible, efficient, interoperable and sustainable.

What makes the physical internet a field of study?

SP: Real interdisciplinary research is needed to make all these changes. It is not easy, for example, to design standardized means for promoting interoperability. We must determine which mechanisms are the best suited and why. Then, in the area of management science, we must ask which intermediaries should be introduced into the network to manage the openness and the new business models this would involve. From a computer science perspective: how can the services of the various stakeholders be connected? Personally, I am working on the mathematical aspect, modelling new types of organization for the network, for example for assessing gains.

What are the tangible gains of the physical internet in terms of logistics?

SP: We took two major supply chains from mass distribution in France and we integrated the data into our new organizational models to simulate the gains. Depending on the scenarios, we improved the filling of trucks by 65% to 85%. Greenhouse gases decreased 60% for CO2 emissions due to multi-modality. In our simulations, these significant results were directly linked to interoperability and the creation of the network. Our models allow us to determine the strategic locations where shared storage centers should be established for several companies, optimize transport times, reduce supply times and storage volumes… We also had gains of over 20% in stock sizes.

Does the logistics sector already use the principles of the physical internet?

SP: The physical internet is a fairly recent concept. The first scientific publication on the topic dates to 2009, and companies have only been interested in the subject for approximately three years. They are adopting the concept very quickly, but they still need time. This is why we have a research chair on the physical internet at Mines ParisTech, with French and European companies; they submit their questions and use cases to help develop the potential of this concept. They recognize that we need a new form of organization to make logistics more sustainable, but the market has not yet reached a point where the major players are restructuring based on the physical internet model. We are currently seeing start-ups beginning to emerge and offer new intermediary services.

When will we experience the benefits of the physical internet?

SP: In Europe, the physical internet has established a solid roadmap, developed in particular by the ALICE alliance, which connects the most significant logistics platforms on the continent. This alliance regularly issues recommendations that are used by European H2020 research programs. Five focus areas have been proposed for integrating the physical internet principles in European logistics by 2030. This is one of the largest initiatives worldwide. In Europe, we therefore hope to quickly see the physical internet comprehensively redefine logistics and offer its benefits, particularly in terms of environmental impacts.

 

transportation

Synchronizing future transportation: from trucks to drones

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With the development of delivery services, the proliferation of various means of transportation, saturated cities and mutualized goods, optimizing logistics networks is becoming so complex that humans can no longer find solutions without using intelligent software. Olivier Péton, specialized in operational research for optimizing transportation at IMT Atlantique, is seeking to answer this question: how can deliveries be made to thousands of customers under good conditions? He presented his research at the IMT Symposium in October on production systems of the future. 

This article is part of our series on “The future of production systems, between customization and sustainable development.”

 

Have you ever thought about the future of the book, pair of jeans or alarm clock you buy with just one click? Moved, transferred, stored, redistributed, these objects made their way from one strategic place to the next, across the entire country to your city. Several trucks, vans and bikes are used in the delivery. You receive your order thanks to the careful organization of a logistics network that is becoming increasingly complex.

At IMT Atlantique, Fabien Lehuédé and Olivier Péton are carrying out operational research on how to optimize transportation solutions and logistics networks. “A logistics network must take into account the location of the factories and warehouses, decide which production site will serve a given customer, etc. Our job is to establish a network and develop it over time using recent optimization methods,” explains Olivier Péton.

This job is in high demand. Changes in legislation to limit the access of certain vehicles during given timeframes in city centers has required companies to rethink their distribution methods. At the same time, like these new requirements in the city, the development of new technology and new distribution methods offer opportunities for re-optimizing transportation.

What are the challenges facing the industry of the future?

“Most of the work from the past 10 years pertains to logistic systems and the synchronization of vehicles,” remarks Olivier Péton. “In other words, several vehicles must manage to arrive at practically the same time at the same place.” This is the case, for example, in projects involving the pooling of transportation means, in which goods are grouped together at a logistics platform before being sent to the final customer. “This is also the case for multimodal transportation, in which high-capacity vehicles transfer their contents to several smaller-capacity vehicles for the last mile,” the researcher explains. These concepts of mutualization and multimodal transport are at the heart of industry of the future.

In the path from the supplier to the customer, the network sometimes transitions from the national level to that of a city. On the one hand, national transport relies on a network of logistic hubs that handle large volumes of goods. On the other hand, urban networks, particularly for e-commerce, focus on last-mile delivery. “The two networks involve different constraints. For a national network, the delivery forecast can be limited to one week. The trucks often only visit three or four places per day. In the city, we can visit many more customers in one day, and replenish supplies at a warehouse. We must take into account delays, congestion, and the possibility of adjusting the itinerary along the way,” Olivier Péton explains.

Good tools make good networks

A network’s complexity depends on the amount of combinations that can be made with the elements it contains. The higher the number of sites, customer orders and stops, the more difficult it becomes to optimize the network. There could be billions of solutions, but it is impossible to list them all to find the best one. This is where the researchers’ algorithms come into play. They rely on the development of heuristic methods, in other words, coming as close as possible to an optimal solution within a reasonable calculation time of a few seconds or a few minutes. To accomplish this, it is vital to have reliable data: transport costs, delivery time schedules, etc.

There are also specific constraints related to each company. “In some cases, transport companies require truck itineraries in straight lines, with as few detours as possible to make deliveries to intermediate customers,” explains Olivier Péton. Other constraints include the maximum number of customers on one route, fair working times for drivers, etc. These types of constraints are modeled as equations. “To resolve these optimization problems, we start with an initial transport plan and we try to improve it iteratively. Each time we change the transport plan, we make sure it still meets all the constraints”. The ultimate result is based on the quality of service: ensuring that the customer is served within the time slot and in only one delivery.

Growing demand

Today, this research is primarily used prior to delivery in national networks. It helps design transport plans, determine how many trucks must be chartered and create the drivers’ schedules. Olivier Péton adds, “it also helps develop simulations that show the savings a company can hope to make by changing its logistics practices. To accomplish this, we work with 4S Network, a company that supports its customers throughout their entire transport mutualization projects.” This work can also be of interest to the major decision-makers managing a fleet with transport that can vary greatly on a daily basis. If the requests are very different from one day to the next, the software solution can develop a transport plan in a few minutes.

Read more on I’MTech: What is the Physical Internet?

What is the major challenge facing researchers? The tool’s robustness. In other words, its ability to react to unforeseeable incidents: congestion, technical problems… It must allow for small variations without having to re-optimize the entire solution. This is especially the case as new issues arise. Which exchange zone should be used in a city to transfer goods: a parking lot or vacant area? For what tonnage is it best to invest in electrical trucks? There are many different points to consider before real-time optimization can be achieved.

Another challenge involves developing technologically viable solutions with a sustainable business model that are acceptable from a societal and environmental perspective. As part of the ANR Franco-German project OPUSS, Fabien Lehuédé and Olivier Péton are working to optimize complex distribution systems. These systems combine urban trucks and transport with fleets of smaller, autonomous vehicles for last mile deliveries. That is, until drones come on the scene…

 

Article written by Anaïs Gall, for I’MTech.

human

Production line flexibility: human operators to the rescue!

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Changing customer needs have cast a veil of uncertainty over the future of industrial production. To respond to these demands, production systems must be flexible. Although industry is becoming increasingly automated, a good way to provide flexibility is to reintroduce human operators. An observation that goes against current trends, presented by Xavier Delorme, an industrial management researcher at Mines Saint-Étienne at the IMT symposium on “Production Systems of the Future”.

This article is part of our series on “The future of production systems, between customization and sustainable development.”

 

Automation, digitization and robotization are concepts associated with our ideas about the industry of the future. With a history marked by technological and technical advances, industry is counting on autonomous machines that make it possible to produce more, faster. Yet, this sector is now facing another change: customer needs. A new focus on product customization is upending how production systems are organized. The automotive industry is a good example of this new problem. Until now, it has invested in production lines that would be used for ten to twenty years. But today, the industry has zero visibility on the models it will produce over such a period of time. A production system that remains unchanged for so long is no longer acceptable.

In order to meet a wide range of customer demands that impact many steps of their production, companies must have flexible manufacturing systems. “That means setting up a system that can evolve to respond to demands that have not yet been identified – flexibility – so that it can be adjusted by physically reconfiguring the system more or less extensively,” explains Xavier Delorme, a researcher at Mines Saint-Étienne. Flexibility can be provided through digital controls or reprogramming a machine, for example.

But in this increasingly machine-dominated environment, “another good way to provide flexibility is to reintroduce versatile human operators, who have an ability to adapt,” says the researcher. The primary aim of his work is to leverage each side’s strengths, while attempting to limit the weaknesses of the other side. He proposes software solutions to help design production lines and ensure that they run smoothly.

Versatility of human operators

This conclusion is based on field observations, in particular through collaboration with MBtech Group, in which the manufacturer drew attention to this problem. The advanced automation of its production lines was reducing versatility. The solution proposed by researchers: reintroduce human operators. “We realized that some French companies had conserved this valuable resource, although they were often behind in terms of automation. There’s a balance to be found between these two elements,” says Xavier Delorme. It appears that the best way to create value, in terms of economic efficiency and flexibility, is to combine robots and humans in a complementary manner.

A system that produces engines manufactures different models but does not need to be modified for each variant. It adapts to the product, switching almost instantaneously from one to another. However, the workload for different stations varies according to the model. This classic situation requires versatility. “A well-trained, versatile human operator reorganizes his work by himself. He repositions himself as needed at a given moment; this degree of autonomy doesn’t exist in current automated systems, which cannot be moved quickly from one part of the production line to another,” says Xavier Delorme.

This flexibility presents a twofold problem for companies. Treating an operator like a machine reduces his range of abilities which does result in efficiency. It is therefore in companies’ interest to enhance operators’ versatility through training and assigning them various tasks in different parts of the production system. But the risk of turnover and the loss of skills associated with short contracts and frequent changes in staff still remain.

The arduous working conditions of multifunctional employees must also not be overlooked. This issue is usually considered too late in the design process, leading to serious health problems and malfunctions in production systems. “That’s why we also focus on workstation ergonomics starting in the design stage,” explains Xavier Delorme. The biggest health risks are primarily physical: fatigue due to a poor position, repetitiveness of tasks etc. The versatility of human operators can reduce these risks, but it can also contribute to them. Indeed, the risks increase if an employee lacks experience and finds it difficult to carry out tasks at different workstations. Once again, the best solution is to find the right balance.

Educating SMEs about the industry of the future

“Large corporations are already on their way to the industry of the future, but it’s more difficult for SMEs,“ says Xavier Delorme. In June 2018, Mines Saint-Étienne researchers launched IT’mFactory, an educational platform developed in partnership with the Union des industries et métiers de la métallurgie (Union of Metallurgy Industries). This demonstration tool makes it possible to connect with SMEs to discuss the challenges and possibilities presented by the industry of the future. It also provides an opportunity to address problems facing SMEs and direct them towards appropriate innovations.

Such interactions are precious in a time in which production methods are undergoing considerable changes (cloud manufacturing, additive manufacturing, etc.) The business models involved provide researchers with new challenges. And flexibility alone will not respond to the needs of customization — or how to produce by the unit and on demand. Servicization, which sells a service associated with a product, is also radically changing the ways in which companies have to be organized.

 

Article written by Anaïs Culot, for I’MTech.

 

personalizing, customization

Breaking products down for customization

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Customers’ desire to take ownership of products is driving companies to develop more customized products. Élise Vareilles, a researcher in industrial engineering at IMT Mines Albi, works to develop interactive decision support tools. Her goal: help companies mass-produce customized goods while controlling the risks related to production. This research was presented at the IMT symposium on “Production Systems of the Future”.

This article is part of our series on “The future of production systems, between customization and sustainable development.”

 

Mr. Martin wants a red 5-door car with a sunroof. Mrs. Martin wants a hybrid with a rearview camera and leather seats. The salesperson wants to sell them a model combining all these features, but the “hybrid” and “sunroof” options are incompatible. More and more companies are beginning to offer customized services (loans, credit, etc.) and goods (cars, furniture, etc.). Yet they are facing a challenge: how can they mass-produce a product that will meet the customer’s specific request? To find a solution to this aspect, companies must customize their production. But how is this possible?

To configure their products, companies must, in a sense, cut them down into pieces. For example, for a car, they must separate the engine from the wheels and the bodywork. Identifying all these elements plays a major role in customizing a product.

“The goal is to develop computer tools that allow us to model each of these elements, like Lego bricks we put together. I have different color bricks, called “variants” and several shapes that represent options. We model their compatibility. Companies’ goal is to ensure the customer’s request can be met using all the options they offer,” explains Élise Vareilles, a researcher at IMT Mines Albi.

What catalog of products or services can I offer my customers using the options I have and their compatibility? To address this growing concern, Élise Vareilles’ team turned to artificial intelligence.

Identifying and controlling risks

The research team works on developing interactive configurators that enable a dialogue with the user. These configurators allow the user to enter various customization criteria for a product or service and view the result. For example, the options for your next car. To accomplish this, the artificial intelligence is fueled by the company’s knowledge. “We make computerized records of explicit knowledge (behavior laws, weight of components, etc.) and implicit knowledge related to the trade (manufacturing processes, best practices, etc.). All this information allows us to create modules, or building blocks, that make up the software’s knowledge base,” Élise Vareilles explains.

Yet not all a company’s knowledge is needed to manufacture each product. Therefore, the tool activates the relevant knowledge base according to the context specified by the user. Targeting this pertinent information allows the system to accompany the user by making suitable suggestions. Élise Vareilles adds, “with some configurators, I enter all my needs and they indicate, without explanation, that none of the products match my request. I do not know which options are causing this. Our tool guides the user by specifying the incompatibility of certain criteria. For example, it can tell the user that the size of the engine affects the size of the wheels and vice versa.”

Challenges 4.0 for the factory of the future

Researchers have developed a generic tool that can be applied to a variety of contexts. It has especially helped architects in configuring new insulation systems for the facades of 110 social housing units in the Landes area of France. “The best way to arrange the panels was to install them symmetrically, but the architects told us it didn’t look nice! An attractive appearance is not a parameter we can program with an equation. We had to find a compromise from among the software’s proposals by assessing all the assembly options and the constraints that only a human can evaluate,” the researcher recalls. The tool’s interactive aspect helped remedy this problem. It proposed assembly configurations for the insulation panels that the architects could adjust. It could also intervene to add to the architects’ proposals based on constraints related to the facades (windows, shutters, etc.) and the geometric nature of the panels.

In the context of the industry of the future, this type of tool could offer a competitive advantage by taking into account 80% of customers’ needs. It also helps control design costs. Breaking the knowledge and possible associations down into bricks means that the tool can help design increasingly adaptable products, which can be modified according to customers’ whims. It also increases the control of production risks by preventing the salesperson from selling a product that is too difficult or even impossible to manufacture. In addition, data mining techniques access the company’s memory to offer recommendations. However, if the knowledge is not constantly updated, the model faces the risk of becoming obsolete. The company’s experts must therefore determine the best time to update their tool.

Humans take on new roles

The two major risks involved in the manufacturing processes have therefore been reduced thanks to this tool from IMT Mines Albi. First, it reduces the risk of designing an object that does not match the customer’s request. By integrating knowledge from the company’s experts (risks, marketing, etc.) into the software, the company guarantees the feasibility of long projects. For example, the tool reduces risks linked to staff turnover, which could result in a loss of skills due to an engineer leaving the company.

However, humans are not being replaced. Instead, they are taking on new roles. “With this tool, 40% of an employee’s activities will be redirected to more complex tasks in which the added value of humans is undeniable. Keep in mind that our tool offers decision support and must rely on the previous work of experts,” Élise Vareilles adds. Yet implementing this type of solution is a long process—lasting approximately 2 years. This conflicts with the short-term investment mentality advocated by industrial culture. It’s now up to stakeholders to recognize these long-term benefits before their competition.

 

Article by Anaïs Culot, for I’MTech.

algorithms

Restricting algorithms to limit their powers of discrimination

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From music suggestions to help with medical diagnoses, population surveillance, university selection and professional recruitment, algorithms are everywhere, and transform our everyday lives. Sometimes, they lead us astray. At fault are the statistical, economic and cognitive biases inherent to the very nature of the current algorithms, which are supplied with massive data that may be incomplete or incorrect. However, there are solutions for reducing and correcting these biases. Stéphan Clémençon and David Bounie, Télécom ParisTech researchers in machine learning and economics, respectively, recently published a report on the current approaches and those which are under exploration.

 

Ethics and equity in algorithms are increasingly important issues for the scientific community. Algorithms are supplied with the data we give them including texts, images, videos and sounds, and they learn from these data through reinforcement. Their decisions are therefore based on subjective criteria: ours, and those of the data supplied. Some biases can thus be learned and accentuated by automated learning. This results in the algorithm deviating from what should be a neutral result, leading to potential discrimination based on origin, gender, age, financial situation, etc. In their report “Algorithms: bias, discrimination and fairness”, a cross-disciplinary team[1] of researchers at Télécom ParisTech and the University of Paris Nanterre investigated these biases. They asked the following basic questions: Why are algorithms likely to be distorted? Can these biases be avoided? If yes, how can we minimize them?

The authors of the report are categorical: algorithms are not neutral. On the one hand, because they are designed by humans. On the other hand, because “these biases partly occur because the learning data lacks representativity” explains David Bounie, researcher in economics at Télécom ParisTech and co-author of the report. For example: the recruitment algorithm for the giant Amazon was heavily criticized in 2015 for having discriminated against female applicants. At fault, was an imbalance in the history of the pre-existing data. The people recruited in the previous ten years were primarily men. The algorithm had therefore been trained by a gender-biased learning corpus. As the saying goes, “garbage in, garbage out”. In other words, if the input data is of poor quality, the output will be poor too.

Also read Algorithmic bias, discrimination and fairness

Stéphan Clémençon is a researcher in machine learning at Télécom Paristech and co-author of the report. For him, “this is one of the growing accusations made of artificial intelligence: the absence of control over the data acquisition process.” For the researchers, one way of introducing equity into algorithms is to contradict them. An analogy can be drawn with surveys: “In surveys, we ensure that the data are representative by using a controlled sample based on the known distribution of the general population” says Stéphan Clémençon.

Using statistics to make up for missing data

From employability to criminality or solvency, learning algorithms have a growing impact on decisions and human lives. These biases could be overcome by calculating the probability that an individual with certain characteristics is included in the sample. “We essentially need to understand why some groups of people are under-represented in the database” the researchers explain. Coming back to the example of Amazon, the algorithm favored applications from men because the recruitments made over the last ten years were primarily men. This bias could have been avoided by realizing that the likelihood of finding a woman in the data sample used was significantly lower than the distribution of women in the population.

“While this probability is not known, we need to be able to explain why an individual is in the database or not, according to additional characteristics” adds Stéphan Clémençon. For example, when assessing banking risk, algorithms use data on the people eligible for a loan at a particular bank to determine the borrower’s risk category. These algorithms do not look at applications by people who were refused a loan, who have not needed to borrow money or who obtained a loan in another bank. In particular, young people under 35 years old are systematically assessed as carrying a higher level of risk than their elders. Identifying these associated criteria would make it possible to correct the biases.

Controlling data also means looking at what researchers call “time drift”. By analyzing data over very short periods of time, an algorithm may not account for certain characteristics of the phenomenon being studied. It may also miss long-term trends. By limiting the duration of the study, it will not pick up on seasonal effects or breaks. However, some data must be analyzed on the fly as they are collected. In this case, when the time scale cannot be extended, it is essential to integrate equations describing potential developments in the phenomena analyzed, to compensate for the lack of data.

The difficult issue of equity in algorithms

Other than the possibility of using statistics, researchers are also looking at developing algorithmic equity. This means developing algorithms which meet equity criteria according to attributes protected under law such as ethnicity, gender or sexual orientation. As for statistical solutions, this means integrating constraints into the learning program. For example, it is possible to impose that the probability of a particular algorithmic result will be equal for all individuals belonging to a particular group. It is also possible to integrate independence between the result and a type of data, such as gender, income level, geographical location, etc.

But which equity rules should be adopted? For the controversial Parcoursup algorithm for higher education applications, several incompatibilities were raised. “Take the example of individual equity and group equity. If we consider only the criterion of individual equity, each student should have an equal chance at success. But this is incompatible with the criterion of group equity, which stipulates that admission rates should be equal for certain protected attributes, such as gender” says David Bounie. In other words, we cannot give an equal chance to all individuals regardless of their gender and, at the same time, apply criteria of gender equity. This example illustrates a concept familiar to researchers: the rules of equity contradict each other and are not universal. They depend on ethical and political values that are specific to individuals and societies.

There are complex, considerable challenges facing social acceptance of algorithms and AI. But it is essential to be able to look back through the algorithm’s decision chain in order to explain its results. “While this is perhaps not so important for film or music recommendations, it is an entirely different story for biometrics or medicine. Medical experts must be able to understand the results of an algorithm and refute them where necessary” says David Bounie. This has raised hopes of transparency in recent years, but is no more than wishful thinking. “The idea is to make algorithms public or restrict them in order to audit them for any potential difficulties” the researchers explain. However, these recommendations are likely to come up against trade secret and personal data ownership laws. Algorithms, like their data sets, remain fairly inaccessible. However, the need for transparency is fundamentally linked with that of responsibility. Algorithms amplify the biases that already exist in our societies. New approaches are required in order to track, identify and moderate them.

[1] The report (in French) Algorithms: bias, discrimination and equity was written by Patrice Bertail (University of Paris Nanterre), David Bounie, Stephan Clémençon and Patrick Waelbroeck (Télécom ParisTech), with the support of Fondation Abeona.

Article written for I’MTech by Anne-Sophie Boutaud

To learn more about this topic:

Ethics, an overlooked aspect of algorithms?

Ethical algorithms in health: a technological and societal challenge

cave paintings

The hidden secrets of the colors of cave paintings at prehistoric sites

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The colors of cave paintings are of great interest because they provide information about the techniques and materials used. Studying them also allows fewer sample to be taken from ancient paleolithic works. Research in colorimetry by Dominique Lafon-Pham at IMT Mines Alès provides a better definition of the colors used in paintings by our ancestors.

 

Mammoths, steppe lions and woolly rhinoceroses have been extinct for thousands of years, but they have by no means disappeared from paleolithic caves. Paintings of these animals still remain on the walls of the caves that our ancestors once lived in or travelled to. For archeologists, cave art specialists and paleo-anthropologists, these paintings are a valuable source of information. Cave art, found at various sites in different regions and dating from a long period that covers several tens of thousands of years, reflects the distribution and evolution of prehistoric wildlife. Analysis of the complex scenes sometimes depicted — such as hunting — and study of the artistic techniques used also bear valuable witness to paleolithic social practices. They are an expression of the symbolic world of our ancestors.

Scientists examine and handle these works with minute care. “Permission to take samples of the painted works is only granted after a strict application process and remains exceptional. Decorated caves can contain a wealth of information but are also be extremely restrictive due to the fragility of the information itself,” explains Dominique Lafon-Pham. The researcher at IMT Mines Alès is developing measurement methods that do not require contact with the color and which help characterize rock paintings. She has been carrying out her work for several years in close collaboration with the French National Center for Prehistory (CNP). She alternates field work and lab experiments in partnership with Stéphane Konik, geoarcheologist at the CNP attached to the PACEA[1] laboratory.

“Colorimetric analysis isn’t a replacement for chemical and mineralogical methods of analysis”, Dominique Lafon-Pham stresses. In certain cases it does, however, provide initial information on the nature of the colorant material. The color alone is not enough to accurately trace the constituents of the mixes, but it does provide a clue. Comparing the colors in different works is a way to avoid taking samples of the pictorial layer from the walls of prehistoric caves. The researcher’s work helps contribute to a “detective investigation” led by archaeologists at scenes dating from several tens of thousands of years ago, where even the smallest piece of evidence merits examination.

The color and, more generally, the appearance of the drawings observed by teams of scientists in caves such as Chauvet and Cussac tell us some of the history of the chosen colorant material that was prepared and applied and has been exposed to the passing of time. It is a way of entering into the work through analysis of the ancient material used. Data produced from this analysis may allow parietal archaeologists to approach the work from the perspective of its creation and even its purpose, whereas conservation specialists are more interested in its evolution over time.

Our visual ability does not allow us to compare subtle differences in color that do not fall within our visual range. We do not have perfect color memory. In addition, the impressions created by an area of color are influenced by the surrounding chromatic environment. “When we can measure the color of a mark without the problem of deterioration due to aging, we will be able to establish similarities between works of the same color, whether they are on the same rock wall or not,” indicates the researcher at IMT Mines Alès.

Objectifying the perception of colors

This comparative method may seem a simple one, but it is important not to underestimate the complexity of the site. Lighting — very often artificial — alters the perception of the human eye. A colored surface will not appear the same when lit in two different ways. The aging of the rock also has an impact. The calcite that forms in the caves sometimes covers the paintings and alters the optical performance of the material, dulling and modifying the color of the depictions. In addition, moisture conditions vary with the seasons and between different sectors at a single site, leading to reversible variation in the colors perceived and measured. All these different impacts require set procedures to be put in place to identify, in the most objective way possible, the color produced by the interaction between light and material.

Measuring the color of cave paintings is not an easy task. Researchers use spectroradiometry and a whole set of associated procedures to keep the lighting constant for each measurement, as seen here in the cave of Chauvet.

 

Researchers use a spectroradiometer, an instrument that measures the spectral power distribution of a luminous radiance in the range of visible light, which is a physical scale that has no correlation with the color perceived by the eye. “The advantage of working at an underground site is that we can control the lighting of rock paintings,” explains Dominique Lafon-Pham. “We always try and light the work in the same way.” The situation becomes more complex when the scientists need to work outside. “We are currently taking measurements at the site of the Cro-magnon rock shelter,” explains the researcher. This site, listed as UNESCO World Heritage, is located in Dordogne in France and was a shelter for Cro-magnon men approximately 30,000 years ago. “The analysis of potentially decorated rock walls which are exposed to the open air is much more complex due to changes in the natural light. It is a real challenge in this situation to try and distinguish between very similar colors using measurements.”

Towards virtual caves?

The use of mixed reality (part-way between augmented reality and virtual reality) at cultural sites is increasingly common. This technology opens up new possibilities for transmitting knowledge such as through the creation of remote guided tours in an entirely virtual environment. The quality of the cultural mediation depends on the realism and exactitude of the features and objects in the virtual world. Taking objectified measurements allows standardization of data collection on the optical characteristics of the parietal art at prehistoric sites. Data collected in this way can be processed using modelling and realistic simulation tools. It provides some of the information required for the construction of virtual facsimile.

The scientific community is also keeping a close eye on such devices which capitalize on new media technology. Highly accurate virtual replicas of prehistoric sites could offer considerable research opportunities by enabling researchers to access sites regardless of how easily accessible they are or not. For conservation and safety reasons — such as the presence of high levels of CO2 in the air at certain times of the year — it is only possible to access caves for very short periods of time and under strict control of movement. Although Dominique Lafon-Pham agrees that these are particularly promising prospects, she nevertheless tempers expectations: “For the moment, the image generators we have tested are a long way off being able to render the subtlety of light and color variations that we see in reality.”

It will be a little longer before it is possible to create identical virtual replicas of paleolithic caves and their art with sufficient realism to allow quality cultural and scientific mediation. Nevertheless, this doesn’t stop the researchers at Mines Alès continuing to study the colors of rock paintings and, in particular, the way they looked at the time of our ancestors. 30,000 years ago, our predecessors painted and viewed their art by firelight, which has been replaced in caves today by very different electric lighting. “The light cast by fire flickers: what does that mean for the way in which the painted or engraved work is seen and perceived?” wonders Dominique Lafon-Pham. Another question: if researchers today are able to detect multiple shades of red in a single drawing using these systems of measurement, were these different shades seen by our Homo sapiens ancestors? If so, were they accidental or deliberate and did they serve a purpose for the artist?

[1] “From Prehistory to Today: Culture, Environment and Anthropology” (PACEA) laboratory. A mixed research unit attached to the CNRS, the University of Bordeaux and the French Ministry of Culture and Communication.

Digital twins in the health sector: mirage or reality?

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Digital twins, which are already well established in industry, are becoming increasingly present in the health sector. There is a wide range of potential applications for both diagnosis and treatment, but the technology is mostly still in the research phase.

 

The health sector is currently undergoing digital transition with a view to developing “4P” treatment: personalized, predictive, preventive and participative. Digital simulation is a key tool in these changes. It consists in creating a model of an object, process or physical process on which different hypotheses can be tested. Today, patients are treated when they fall sick based on clinical studies of tens or, at best, thousands of other people, with no real consideration of the individual’s personal characteristics. Will each person one day have their own digital twin to allow prediction of the development of acute or chronic diseases based on their genetic profile and environmental factors, and anticipation of their response to different treatments?

“There are a lots of hopes and dreams built on this idea,” admits Stéphane Avril, Researcher at Mines Saint-Étienne. “The profession of doctor or surgeon is currently practiced as an art on the strength of experience acquired over time. The idea is that a program could combine the experience of a thousand doctors, but in reality there is a huge amount of work still to do to create equations for and integrate non-mathematical knowledge and skills in very different fields such as immunology and the cardio-vascular system.”  We are still a long way from simulating an entire human being. “But in certain fields, digital twins provide excellent predictions that are even better than those of a practitioner,” adds Stéphane Avril.

From imaging to the operating room

Biomechanics, for example, is a field of research that lends itself very well to digital simulation. Stéphane Avril’s research addresses aortic aneurysms. 3D digital twins of the affected area are developed based on medical images. The approach has led to the creation of a start-up called Predisurge, whose software allows the creation of individual endoprostheses. “The FDA [American Food and Drug Administration] is encouraging the use of digital simulation for validating the market entry of prostheses, both orthopedic and vascular,” explains the researcher from St Etienne.

Digital simulation also helps surgeons prepare for operations because the software provides predictions on the insertion and effects of these endoprostheses once in place, as well as simulating any pre-surgical complications that could arise. “This technique is currently still in the testing and validation phase, but it could have a very promising impact on reducing surgery time and complications,” stresses Stéphane Avril. The team at Mines Saint-Étienne is currently working on improving our understanding of the properties of the aortic wall using four-dimensional MRI and mechanical tests on aneurysm tissue removed during the insertion of prostheses. The idea is to validate a digital twin designed using 4D MRI images, which could predict the future rupture or stability of an aneurysm and indicate the need for surgery or not.

Read more on I’MTech: A digital twin of the aorta to prevent Aneurysm rupture

Catalin Fetita, a researcher at Télécom SudParis, also uses digital simulation in the field of imaging, but this time in the case of air-borne transmission alongside pulmonary parenchyma analysis. The aim of her work is to obtain biomarkers from medical images for a more precise definition of pathological phenomena in respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD) and idiopathic interstitial pneumonia (IIP). The model allows assessment of an organ’s functioning based on its morphology. “Digital simulation is used to identify the type of dysfunction and its exact location, quantify it, predict changes in the disease and optimize the treatment process,” explains Catalin Fetita.

Ethical and technical barriers

The problem of data security and anonymity is currently at the heart of ethical and legal debates. For the moment, researchers are having great difficulty accessing databases to “feed” their programs. “To obtain medical images, we have to establish a relationship of trust with a hospital radiologist, get them interested in our work and involve them in the project. We need images to be precisely labeled for the model to be relevant.” Especially since the analysis of medical images can vary from one radiologist to another. “Ideally, we would like to have access to a database of images that have been analyzed by a panel of experts with a consensus on their interpretation,” Catalin Fetita affirms.

The researcher also points to the lack of technical staff. Models are generally developed in the framework of a thesis, and rarely lead to a finished product. “We need a team of research or development engineers to preserve the skills acquired, ensure technology transfer and carry out monitoring and updates.” Imaging techniques are evolving and algorithms can encounter difficulties in processing new images that sometimes have different characteristics.

For Stéphane Avril, a new specialization in engineering and health with mixed skills is needed. “These tools will transform doctors’ and surgeons’ professions, but it’s still a bit like science fiction to practitioners at the moment. The transformation will take place tentatively, with restraint, because full medical training takes more than 10 years.” The researcher thinks that it will be another ten years or so before the tools to integrate the systemic aspect of physiopathology will be operational: “like for self-driving vehicles, the technology exists but there are still quite a few stages to go before it actually arrives in hospitals.”

 

Article written by Sarah Balfagon for I’MTech.

 

The TeraLab data machines.

TeraLab: data specialists serving companies

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Belles histoires, bouton, CarnotTeraLab is a Big Data and artificial intelligence platform that grants companies access to a whole ecosystem of specialists in these fields. The aim is to remove the scientific and technological barriers facing organizations that want to make use of their data. Hosted by IMT, TeraLab is one of the technology platforms proposed by the Carnot Télécom & Société Numérique. Anne-Sophie Taillandier, Director of TeraLab, presents the platform.

 

What is the role of the TeraLab platform?

Anne-Sophie Taillandier: We offer companies access to researchers, students and innovative enterprises to remove technological barriers in the use of their data. We provide technical resources, infrastructure, tools and skills in a controlled, secure and neutral workspace. Companies can prototype products or services in realistic environments with a view to technology transfer as fast as possible.

In what ways do you work with companies?

AST: First of all, we help them formalize the use case. Companies often come to us with a vague outline of the use case, so we help them with that and can provide specialist contributions if necessary. This is a crucial stage because our aim is also for companies to be able to assess the return on investment at the end of the research or innovation work. It helps them estimate the investment required to launch production, so the need must be clearly defined. We then help them understand what they have the right to do with the data. There again we can call upon expert legal advice if necessary. Lastly, we support them in the specification of the technical architecture.

How do you stand out from other Big Data and artificial intelligence service platforms?

AST: Firstly, by the ecosystem we benefit from. TeraLab is associated with IMT, so we have a number of specialist researchers in these fields as well as students we can mobilize to resolve technological challenges posed by companies. Secondly, TeraLab is a pre-competitive platform. We can also define a framework that brings together legal and technical aspects to meet companies’ needs in an individual way. We can strike a fairly fine balance between safety and flexibility to reassure the organizations who come to us and at the same time give researchers enough space to find solutions to the problems posed.

What level of technical security can you provide?

AST: We can reach an extremely high level of technical security, where the user of the data supplied, such as the researcher, can see it but never extract it. Generally speaking, a validation process involving the data supplier and the Teralab team must be followed in order to extract a piece of data from the workspace. During a project, data security is guaranteed by a combination of technical and legal factors. Moreover, we work in a neutral and controlled space which also provides a form of independence that reassures companies.

What does neutrality mean for you?

AST: The technical components we propose are open source. We have nothing against products under license, but if a company wants to use a specific tool, it must provide the license itself. Our technical team has excellent knowledge of the different libraries and APIs as well as the components required to set up a workspace. They adapt the tools to the company’s needs. We do not host the service beyond the end of the experimentation phase. Instead, we enter a new phase of technology transfer to allow the products or services to be integrated at the client’s end. We therefore have nothing to “sell” except our expertise. This also guarantees our neutrality.

What use cases do you work on?

AST: Since we started TeraLab, more than 60 projects have come through the platform, and there are currently 20 on the go. They can last between 3 months and 3 years. We have had projects in logistics, insurance, public services, energy, mobility, agriculture etc. At the moment, we are focusing on three sectors. The first is cybersecurity: we are interested in seeing what data access barriers there are, how to make a workspace compliant, and how to guarantee respect of personal data. We also work a lot in the health sector and industry. Geographically speaking, we are increasingly working at a European level in the framework of H2020 projects. The platform also benefits from growing recognition among European institutions with, in particular, the “Silver i-space” label awarded by the BDVA.

Physically, what does TeraLab look like?

AST: TeraLab comprises machines at Douai, a technical team in Rennes and a business team in Paris. The platform is accessible remotely, so there is no need to be physically close to it, making it different to other service platforms. We have recently also been able to secure client machines directly on site if the client has specific restrictions with regard to the movement of data.

 

User immersion, between 360° video and virtual reality

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I’MTech is dedicating a series of success stories to research partnerships supported by the Télécom & Société Numérique (TSN) Carnot Institute, which the IMT schools are a part of.

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To better understand how users interact in immersive environments, designers and researchers are comparing the advantages of 360° video and full-immersion virtual reality. This is also the aim of the TroisCentSoixante inter-Carnot project uniting the Télécom & Société Numérique and the M.I.N.E.S. Carnot Institutes. Strate Research, the research department at Strate School of Design which is a member of the Carnot TSN, is studying this comparison in particular in the case of museography mediation.  

 

When it comes to designing immersive environments, designers have a large selection of tools available to them. Mixed reality, in which the user is plunged into a more or less interactive environment, covers everything from augmented video to fully synthetic 3D images. To determine which is the best option, researchers from members of the TSN Carnot Institute (Strate School of Design) and the M.I.N.E.S Carnot Institute (Mines ParisTech and IMT Mines Alès) have joined forces. They have compared, for different use cases, the differences in user engagement between 360° video and full 3D modeling, i.e. virtual reality.

“At the TSN Carnot Institute we have been working on the case of a museum prototype alongside engineers from Softbank Robotics, who are interested in the project,” explains Ioana Ocnarescu, researcher at Strate. A room containing exhibits such as a Minitel, tools linked to the development of the internet, photos of famous researchers in robotics and robots has been created at Softbank Robotics to create mediation on science and technology. Once the object is in place, a 3D copy is made and a visit route is laid out between the different exhibits. This base scenario is used to film a 360° video guided by a mediator and to create a virtual guide in the form of a robot called Pepper, which travels around the 3D scene with the viewer. In both cases, the user is immersed in the environment using a mixed reality headset.

Freedom or realism: a choice to be made

Besides the graphics, which are naturally different between video and 3D modelling, the two technologies have one fundamental difference: freedom of action in the scenario. “In 360° video the viewer is passive,” explains Ioana Ocnarescu. “They follow the guide and can zoom in on objects, but cannot move around freely as they wish.” Their movement is limited to turning their head and deciding to spend longer on certain objects than others. To allow this, the video is cut in several places allowing a decision tree to be made that leads to specific sequences depending on the user’s choices.

Like the 3D mediation, the 360°-video trial mediation is guided by a robot called Pepper.

Like the 3D mediation, the 360°-video trial mediation is guided by a robot called Pepper.

 

3D modeling, on the other hand, grants a large amount of freedom to the viewer. They can move around freely in the scene, choose whether to follow the guide or not, walk around the exhibits and look at them from any angle, which is where 360° video is limited by the position of the camera. “User feedback shows that certain content is better suited to one device or the other,” the Strate researcher reports. For a painting or a photo, for example, there is little use in being able to travel around the object, and the viewer prefers to be in front of the exhibit in it its surroundings with as much realism as possible. “360° video is therefore better adapted for museums with corridors and paintings on the walls,” she points out. On the other hand, 3D modeling is particularly adapted to looking at and examining 3D artefacts such as statues.

These experiments are extremely useful to researchers in design, in particular because they involve real users. “Knowing what people do with the devices available is at the heart of our reflection,” emphasizes Ioana Ocnarescu. Strate has been studying user-machine interaction for over 5 years to develop more effective interfaces. In this project, the people in immersion can give their feedback directly to the Strate team. “It is the most valuable thing in our work. When everything is controlled in a laboratory environment, the information we collect is less meaningful.”

The tests must continue to incorporate a maximum amount of feedback from as many different types of audience as possible. Once finished, the results will be compared with those of other use cases explored by the M.I.N.E.S Carnot Institute. “Mines ParisTech and IMT Mines Alès are comparing the same two devices but in the case of self-driving cars and exploration of the Chauvet cave,” explains the researcher.

 

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Carnot TSN, a guarantee of excellence in partnership-based research since 2006

The Télécom & Société numérique (TSN) Carnot Institute has partnered companies in their research to develop digital innovations since 2006. On the strength of over 1,700 researchers and 50 technology platforms, it offers cutting-edge research to resolve complex technological challenges produced by digital, energy and environmental and industrial transformations within the French production fabric. It addresses the following themes: Industry of the Future, networks and smart objects, sustainable cities, mobility, health and security.

The TSN Carnot Institute is composed of Télécom ParisTech, IMT Atlantique, Télécom SudParis, Institut Mines-Télécom Business School, Eurecom, Télécom Physique Strasbourg, Télécom Saint-Étienne, École Polytechnique (Lix and CMAP laboratories), Strate School of Design and Femto Engineering.

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