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Product design gets an AI makeover

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Product design gets an AI makeover


It’s a tall order, but one that Zapf says artificial intelligence (AI) technology can support by capturing the right data and guiding engineers through product design and development.

No wonder a November 2020 McKinsey survey reveals that more than half of organizations have adopted AI in at least one function, and 22% of respondents report at least 5% of their companywide earnings are attributable to AI. And in manufacturing, 71% of respondents have seen a 5% or more increase in revenue with AI adoption.

But that wasn’t always the case. Once “rarely used in product development,” AI has experienced an evolution over the past few years, Zapf says. Today, tech giants known for their innovations in AI, such as Google, IBM, and Amazon, “have set new standards for the use of AI in other processes,” such as engineering.

“AI is a promising and exploratory area that can significantly improve user experience for designing engineers, as well as gather relevant data in the development process for specific applications,” says Katrien Wyckaert, director of industry solutions for Siemens Industry Software.

The result is a growing appreciation for a technology that promises to simplify complex systems, get products to market faster, and drive product innovation.

Simplifying complex systems

A perfect example of AI’s power to overhaul product development is Renault. In response to increasing consumer demand, the French automaker is equipping a growing number of new vehicle models with an automated manual transmission (AMT)—a system that behaves like an automatic transmission but allows drivers to shift gears electronically using a push-button command.

AMTs are popular among consumers, but designing them can present formidable challenges. That’s because an AMT’s performance depends on the operation of three distinct subsystems: an electro-mechanical actuator that shifts the gears, electronic sensors that monitor vehicle status, and software embedded in the transmission control unit, which controls the engine. Because of this complexity, it can take up to a year of extensive trial and error to define the system’s functional requirements, design the actuator mechanics, develop the necessary software, and validate the overall system.

In an effort to streamline its AMT development process, Renault turned to Simcenter Amesim software from Siemens Digital Industries Software. The simulation technology relies on artificial neural networks, AI “learning” systems loosely modeled on the human brain. Engineers simply drag, drop, and connect icons to graphically create a model. When displayed on a screen as a sketch, the model illustrates the relationship between all the various elements of an AMT system. In turn, engineers can predict the behavior and performance of the AMT and make any necessary refinements early in the development cycle, avoiding late-stage problems and delays. In fact, by using a virtual engine and transmissions as stand-ins while developing hardware, Renault has managed to cut its AMT development time almost in half.

Speed without sacrificing quality

So, too, are emerging environmental standards prompting Renault to rely more heavily on AI. To comply with emerging carbon dioxide emissions standards, Renault has been working on the design and development of hybrid vehicles. But hybrid engines are far more complex to develop than those found in vehicles with a single energy source, such as a conventional car. That’s because hybrid engines require engineers to perform complex feats like balancing the power required from multiple energy sources, choosing from a multitude of architectures, and examining the impact of transmissions and cooling systems on a vehicle’s energy performance.

“To meet new environmental standards for a hybrid engine, we must completely rethink the architecture of gasoline engines,” says Vincent Talon, head of simulation at Renault. The problem, he adds, is that carefully examining “the dozens of different actuators that can influence the final results of fuel consumption and pollutant emissions” is a lengthy and complex process, made by more difficult by rigid timelines.

“Today, we clearly don’t have the time to painstakingly evaluate various hybrid powertrain architectures,” says Talon. “Rather, we needed to use an advanced methodology to manage this new complexity.”

For more on AI in industrial applications, visit www.siemens.com/artificialintelligence.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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Decarbonizing industries with connectivity and 5G

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Decarbonizing industries with connectivity and 5G


The United Nations Intergovernmental Panel on Climate Change’s sixth climate change report—an aggregated assessment of scientific research prepared by some 300 scientists across 66 countries—has served as the loudest and clearest wake-up call to date on the global warming crisis. The panel unequivocally attributes the increase in the earth’s temperature—it has risen by 1.1 °C since the Industrial Revolution—to human activity. Without substantial and immediate reductions in carbon dioxide and other greenhouse gas emissions, temperatures will rise between 1.5 °C and 2 °C before the end of the century. That, the panel posits, will lead all of humanity to a “greater risk of passing through ‘tipping points,’ thresholds beyond which certain impacts can no longer be avoided even if temperatures are brought back down later on.”

Corporations and industries must therefore redouble their greenhouse gas emissions reduction and removal efforts with speed and precision—but to do this, they must also commit to deep operational and organizational transformation. Cellular infrastructure, particularly 5G, is one of the many digital tools and technology-enabled processes organizations have at their disposal to accelerate decarbonization efforts.  

5G and other cellular technology can enable increasingly interconnected supply chains and networks, improve data sharing, optimize systems, and increase operational efficiency. These capabilities could soon contribute to an exponential acceleration of global efforts to reduce carbon emissions.

Industries such as energy, manufacturing, and transportation could have the biggest impact on decarbonization efforts through the use of 5G, as they are some of the biggest greenhouse-gas-emitting industries, and all rely on connectivity to link to one another through communications network infrastructure.

The higher performance and improved efficiency of 5G—which delivers higher multi-gigabit peak data speeds, ultra-low latency, increased reliability, and increased network capacity—could help businesses and public infrastructure providers focus on business transformation and reduction of harmful emissions. This requires effective digital management and monitoring of distributed operations with resilience and analytic insight. 5G will help factories, logistics networks, power companies, and others operate more efficiently, more consciously, and more purposely in line with their explicit sustainability objectives through better insight and more powerful network configurations.

This report, “Decarbonizing industries with connectivity & 5G,” argues that the capabilities enabled by broadband cellular connectivity primarily, though not exclusively, through 5G network infrastructure are a unique, powerful, and immediate enabler of carbon reduction efforts. They have the potential to create a transformational acceleration of decarbonization efforts, as increasingly interconnected supply chains, transportation, and energy networks share data to increase efficiency and productivity, hence optimizing systems for lower carbon emissions.

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Surgeons have successfully tested a pig’s kidney in a human patient

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Surgeons have successfully tested a pig’s kidney in a human patient


The reception: The research was conducted last month and is yet to be peer reviewed or published in a journal, but external experts say it represents a major advance. “There is no doubt that this is a highly significant breakthrough,” says Darren K. Griffin, a professor of genetics at the University of Kent, UK. “The research team were cautious, using a patient who had suffered brain death, attaching the kidney to the outside of the body, and closely monitoring for only a limited amount of time. There is thus a long way to go and much to discover,” he added. 

“This is a huge breakthrough. It’s a big, big deal,” Dorry Segev, a professor of transplant surgery at Johns Hopkins School of Medicine who was not involved in the research, told the New York Times. However, he added, “we need to know more about the longevity of the organ.”

The background: In recent years, research has increasingly zeroed in on pigs as the most promising avenue to help address the shortage of organs for transplant, but it has faced a number of obstacles, most prominently the fact that a sugar in pig cells triggers an aggressive rejection response in humans.

The researchers got around this by genetically altering the donor pig to knock out the gene encoding the sugar molecule that causes the rejection response. The pig was genetically engineered by Revivicor, one of several biotech companies working to develop pig organs to transplant into humans. 

The big prize: There is a dire need for more kidneys. More than 100,000 people in the US are currently waiting for a kidney transplant, and 13 die of them every day, according to the National Kidney Foundation. Genetically engineered pigs could offer a crucial lifeline for these people, if the approach tested at NYU Langone can work for much longer periods.

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Getting value from your data shouldn’t be this hard

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Getting value from your data shouldn’t be this hard


The potential impact of the ongoing worldwide data explosion continues to excite the imagination. A 2018 report estimated that every second of every day, every person produces 1.7 MB of data on average—and annual data creation has more than doubled since then and is projected to more than double again by 2025. A report from McKinsey Global Institute estimates that skillful uses of big data could generate an additional $3 trillion in economic activity, enabling applications as diverse as self-driving cars, personalized health care, and traceable food supply chains.

But adding all this data to the system is also creating confusion about how to find it, use it, manage it, and legally, securely, and efficiently share it. Where did a certain dataset come from? Who owns what? Who’s allowed to see certain things? Where does it reside? Can it be shared? Can it be sold? Can people see how it was used?

As data’s applications grow and become more ubiquitous, producers, consumers, and owners and stewards of data are finding that they don’t have a playbook to follow. Consumers want to connect to data they trust so they can make the best possible decisions. Producers need tools to share their data safely with those who need it. But technology platforms fall short, and there are no real common sources of truth to connect both sides.

How do we find data? When should we move it?

In a perfect world, data would flow freely like a utility accessible to all. It could be packaged up and sold like raw materials. It could be viewed easily, without complications, by anyone authorized to see it. Its origins and movements could be tracked, removing any concerns about nefarious uses somewhere along the line.

Today’s world, of course, does not operate this way. The massive data explosion has created a long list of issues and opportunities that make it tricky to share chunks of information.

With data being created nearly everywhere within and outside of an organization, the first challenge is identifying what is being gathered and how to organize it so it can be found.

A lack of transparency and sovereignty over stored and processed data and infrastructure opens up trust issues. Today, moving data to centralized locations from multiple technology stacks is expensive and inefficient. The absence of open metadata standards and widely accessible application programming interfaces can make it hard to access and consume data. The presence of sector-specific data ontologies can make it hard for people outside the sector to benefit from new sources of data. Multiple stakeholders and difficulty accessing existing data services can make it hard to share without a governance model.

Europe is taking the lead

Despite the issues, data-sharing projects are being undertaken on a grand scale. One that’s backed by the European Union and a nonprofit group is creating an interoperable data exchange called Gaia-X, where businesses can share data under the protection of strict European data privacy laws. The exchange is envisioned as a vessel to share data across industries and a repository for information about data services around artificial intelligence (AI), analytics, and the internet of things.

Hewlett Packard Enterprise recently announced a solution framework to support companies, service providers, and public organizations’ participation in Gaia-X. The dataspaces platform, which is currently in development and based on open standards and cloud native, democratizes access to data, data analytics, and AI by making them more accessible to domain experts and common users. It provides a place where experts from domain areas can more easily identify trustworthy datasets and securely perform analytics on operational data—without always requiring the costly movement of data to centralized locations.

By using this framework to integrate complex data sources across IT landscapes, enterprises will be able to provide data transparency at scale, so everyone—whether a data scientist or not—knows what data they have, how to access it, and how to use it in real time.

Data-sharing initiatives are also on the top of enterprises’ agendas. One important priority enterprises face is the vetting of data that’s being used to train internal AI and machine learning models. AI and machine learning are already being used widely in enterprises and industry to drive ongoing improvements in everything from product development to recruiting to manufacturing. And we’re just getting started. IDC projects the global AI market will grow from $328 billion in 2021 to $554 billion in 2025.

To unlock AI’s true potential, governments and enterprises need to better understand the collective legacy of all the data that is driving these models. How do AI models make their decisions? Do they have bias? Are they trustworthy? Have untrustworthy individuals been able to access or change the data that an enterprise has trained its model against? Connecting data producers to data consumers more transparently and with greater efficiency can help answer some of these questions.

Building data maturity

Enterprises aren’t going to solve how to unlock all of their data overnight. But they can prepare themselves to take advantage of technologies and management concepts that help to create a data-sharing mentality. They can ensure that they’re developing the maturity to consume or share data strategically and effectively rather than doing it on an ad hoc basis.

Data producers can prepare for wider distribution of data by taking a series of steps. They need to understand where their data is and understand how they’re collecting it. Then, they need to make sure the people who consume the data have the ability to access the right sets of data at the right times. That’s the starting point.

Then comes the harder part. If a data producer has consumers—which can be inside or outside the organization—they have to connect to the data. That’s both an organizational and a technology challenge. Many organizations want governance over data sharing with other organizations. The democratization of data—at least being able to find it across organizations—is an organizational maturity issue. How do they handle that?

Companies that contribute to the auto industry actively share data with vendors, partners, and subcontractors. It takes a lot of parts—and a lot of coordination—to assemble a car. Partners readily share information on everything from engines to tires to web-enabled repair channels. Automotive dataspaces can serve upwards of 10,000 vendors. But in other industries, it might be more insular. Some large companies might not want to share sensitive information even within their own network of business units.

Creating a data mentality

Companies on either side of the consumer-producer continuum can advance their data-sharing mentality by asking themselves these strategic questions:

  • If enterprises are building AI and machine learning solutions, where are the teams getting their data? How are they connecting to that data? And how do they track that history to ensure trustworthiness and provenance of data?
  • If data has value to others, what is the monetization path the team is taking today to expand on that value, and how will it be governed?
  • If a company is already exchanging or monetizing data, can it authorize a broader set of services on multiple platforms—on premises and in the cloud?
  • For organizations that need to share data with vendors, how is the coordination of those vendors to the same datasets and updates getting done today?
  • Do producers want to replicate their data or force people to bring models to them? Datasets might be so large that they can’t be replicated. Should a company host software developers on its platform where its data is and move the models in and out?
  • How can workers in a department that consumes data influence the practices of the upstream data producers within their organization?

Taking action

The data revolution is creating business opportunities—along with plenty of confusion about how to search for, collect, manage, and gain insights from that data in a strategic way. Data producers and data consumers are becoming more disconnected with each other. HPE is building a platform supporting both on-premises and public cloud, using open source as the foundation and solutions like HPE Ezmeral Software Platform to provide the common ground both sides need to make the data revolution work for them.

Read the original article on Enterprise.nxt.

This content was produced by Hewlett Packard Enterprise. It was not written by MIT Technology Review’s editorial staff.

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