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Darktrace Reports 50% Increase In AI-driven Responses To Cyber Attacks

Darktrace Reports 50% Increase In AI-driven Responses To Cyber Attacks

Darktrace has announced that its Antigena ‘machine fights back’ technology has autonomously responded to an increasing number of attacks on cloud and collaboration applications this year. The company reports a 50% increase in AI actions taken during the first half of 2021 compared to the latter half of 2020, to thwart attacks on business-critical Software-as-a-Service (SaaS), collaboration and video conferencing applications hosted in the cloud.

Powered by Self-Learning AI, Darktrace’s Antigena technology is capable of taking precise and proportionate actions at machine speed to mitigate in-progress attacks across any SaaS application, without disrupting the business. With an in-depth understanding of the behavior of users across multiple environments, including Microsoft 365, Teams, Salesforce, Zoom, Slack, and Google Workspace, the technology protects users from both known and novel attacks, such as account takeovers, insider threat, data theft, and impersonation attacks.

In addition, Darktrace for SaaS now incorporates a context gathering capability for Microsoft 365 users, displaying contextual information about the user including their groups, assigned roles and licenses. This data on user’s roles within SaaS environments is fed back into Self-Learning AI for enhanced threat detection, and can also be used to escalate autonomous responses.

“AI has been profound in highlighting threats that we may not have seen otherwise, and Autonomous Response acts across all of our data and applications,” said Jenny Moshea, CIO at Sellen Construction, a US Pacific Northwest construction company. “It buys us back time we wouldn’t have had otherwise, and helps us sleep better at night knowing all of our infrastructure across our cloud, SaaS, and Microsoft Suite is being kept safe.”

“The adoption of virtual collaboration tools and SaaS applications continues to rise, and is one of the key trends in Enterprise technology still,” says Dave Palmer, Chief Product Officer, Darktrace. “These platforms not only allow quick team calls or simple ways of sharing data, but are used by everyone from governments to global banks, and now house confidential conversations and critical data. This sharp rise in attacks demonstrates why organisations need Self-Learning AI to autonomously detect, investigate and respond to threats everywhere to secure the modern workforce.

 

ZEISS Applies Artificial Intelligence To 3D X-ray Microscope Reconstruction Technologies

ZEISS Applies Artificial Intelligence to 3D X-ray Microscope Reconstruction Technologies

Two new reconstruction technologies that was introduced by ZEISS use Artificial Intelligence (AI) to improve data collection and analysis, and speed up decision-making. ZEISS DeepRecon Pro and ZEISS PhaseEvolve modules increase throughput by up to 10x while producing better than ever image quality. They’re designed for research fields including geosciences, pharmaceuticals, electronics, battery and engineering materials as well as for semiconductor failure analysis.

ZEISS PhaseEvolve is a post-processing reconstruction algorithm that enhances the image contrast by revealing the material contrast uniquely inherent to X-ray microscopy, often overprinted by phase effects in low-medium density samples or in high-resolution imaging.

The Advanced Reconstruction Toolkit (ART) on ZEISS Xradia 3D X-ray platforms gives researchers continuous access to the latest technologies for reconstruction, providing flexible workflow strategies as their imaging needs evolve. The toolbox is based on advancing reconstruction technologies beyond the typical “filtered back projection” or Feldkamp-Davis-Kress (FDK) algorithms. It enables acquisition and analysis with even fewer projections, reducing scan times by up to 10x, depending on the sample type and size, thus saving a considerable amount of time. These developments offer real benefits to professionals in academic R&D, high technology industry R&D, and industrial quality control & failure analysis laboratories as well as to university shared (core) facilities and mining/oil exploration (economic GEO) test labs.

ZEISS DeepRecon Pro

The DeepRecon AI-based technology is in two forms: ZEISS DeepRecon Pro and ZEISS DeepRecon Custom. Both modules provide improved image quality at high speed. DeepRecon Pro is a reconstruction technology that provides up to 10x higher throughput along with image quality benefits across a diverse set of sample types. It is applicable to unique samples as well as to semi-repetitive and repetitive workflows. Users can now train new machine learning network models themselves, working on-site with an easy-to-use interface.

ZEISS DeepRecon Custom is targeted specifically at repetitive workflow applications. ZEISS collaborates closely with users to develop custom-created network models that fit their repetitive application needs precisely.

ZEISS PhaseEvolve

The PhaseEvolve is a post-processing reconstruction algorithm that enhances the image contrast by revealing the material contrast uniquely inherent to X-ray microscopy, often overprinted by phase effects in low-medium density samples or in high-resolution imaging. This new module allows users to conduct more accurate quantitative analyses with improved contrast and segmentation of results.

Dr. Markus Ohl, Department of Earth Sciences, Utrecht University (The Netherlands) was one of the first users of ART. He says: “ZEISS DeepRecon Pro provides a straightforward, uncomplicated and powerful application of AI and deep neural network technology for enhancing X-ray tomography results without prior knowledge on deep learning technology.”

Both ZEISS DeepRecon Pro and ZEISS PhaseEvolve modules offer greatly improved image quality for many applications – typically 3D non-destructive sub-µm resolution imaging and 4D in situ studies. The traditional challenge of choosing between either image quality or sample throughput has been resolved with these new capabilities.

ZEISS ART with the optional DeepRecon and PhaseEvolve modules is available for immediate upgrade on existing ZEISS Xradia Versa and Context microscopes, enhancing the capability of installed systems as well as on new ZEISS Xradia X-ray microscopes.

 

 

Google Cloud And Siemens To Cooperate On AI-Based Solutions In Manufacturing

Google Cloud And Siemens To Cooperate On AI-Based Solutions In Manufacturing

Google Cloud and Siemens has announced a new cooperation to optimise factory processes and improve productivity on the shop floor. Siemens intends to integrate Google Cloud’s leading data cloud and artificial intelligence/machine learning (AI/ML) technologies with its factory automation solutions to help manufacturers innovate for the future.

Data drives today’s industrial processes, but many manufacturers continue to use legacy software and multiple systems to analyse plant information, which is resource-intensive and requires frequent manual updates to ensure accuracy. In addition, while AI projects have been deployed by many companies in “islands” across the plant floor, manufacturers have struggled to implement AI at scale across their global operations.

By combining Google Cloud’s data cloud and AI/MLmachi capabilities with Siemens’ Digital Industries Factory Automation portfolio, manufacturers will be able to harmonise their factory data, run cloud-based AI/ML models on top of that data, and deploy algorithms at the network edge. This enables applications such as visual inspection of products or predicting the wear-and-tear of machines on the assembly line.

Deploying AI to the shop floor and integrating it into automation and the network is a complex task, requiring highly specialised expertise and innovative products such as Siemens Industrial Edge. The goal of the cooperation between Google Cloud and Siemens is to make the deployment of AI in connection with the Industrial Edge—and its management at scale— easier, empowering employees as they work on the plant floor, automating mundane tasks, and improving overall quality.

“The potential for artificial intelligence to radically transform the plant floor is far from being exhausted. Many manufacturers are still stuck in AI ‘pilot projects’ today – we want to change that,” said Axel Lorenz, VP of Control at Factory Automation of Siemens Digital Industries. “Combining AI/ML technology from Google Cloud with Siemens’ solutions for Industrial Edge and industrial operation will be a game changer for the manufacturing industry.”

 

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Toyota Industries Corporation And Siemens Partner On Digital Transformation For Die Casting

Toyota Industries Corporation And Siemens Partner On Digital Transformation For Die Casting

To support their goal of manufacturing quality parts, Toyota Industries Corporation and Siemens have cooperated to develop artificial intelligence (AI) that can predict product abnormalities in aluminium die casting, a key process in automotive air conditioning compressor production.

The development is one of the world’s first to use defect prediction AI for die casting. It improves quality and productivity by utilising the AI application in Industrial Edge, the Siemens edge computing platform for industry. The initiative is an innovative example of digital transformation in manufacturing, and Toyota Industries Corporation aims to use it to further evolve their technology and incorporate it into their production plants in Japan and overseas. Siemens hopes that more businesses in the manufacturing industry will adopt their digitalisation and automation solutions such as Industrial Edge.

Aluminium die casting is a high-speed moulding process in which molten aluminium is shot into a die at high pressure. It is ideal for the accurate manufacture of metal cast parts that demand high dimensional precision, and therefore is often used for automotive parts that require high quality and reliability.  The die casting process is challenging to manage due to a range of constantly changing production conditions such as variations in the molten aluminium temperature or the injection rate. Success relies on the judgement of experienced workers, and sometimes the parts require secondary processing to handle abnormalities and maintain high quality standards.

During development, the two companies used a Siemens Simatic S7-1500 controller to gather big data totaling approximately 40,000 data points per die casting shot at the model line and then analysed the data using AI technology. They succeeded in preventing defects and improving quality by monitoring the production status in real time and automatically predicting equipment abnormalities that lead to quality issues. The production data is processed by the defect prediction AI on Industrial Edge, enabling instant analysis of the data on production conditions at the time of a shot and assessment of the part quality immediately after the casting. This series of AI technologies boosts productivity, improves quality, and transforms how operators work

“Digital transformation is a game changer. I am delighted to have the opportunity to partner with Toyota Industries Corporation in this revolutionary endeavor and to work together to forge the future,” says Rainer Brehm, CEO of Factory Automation, Siemens AG. “We will continue to develop and provide solutions for industries incorporating the latest technologies and to contribute to optimised and sustainable manufacturing.”

 

 

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A.I. Engineering Pioneer Hyperganic Raises $7.8m Funding For R&D Expansion In Singapore

A.I. Engineering Pioneer Hyperganic Raises $7.8m Funding For R&D Expansion In Singapore

Hyperganic has announced the closing of $7.8m in funding, with a focus on significantly expanding their presence in Singapore. The round is led by German funds HV Capital and VSquared Ventures. Co-investors are US-based tech fund Converge, industrial partner Swarovski and PC pioneer Hermann Hauser, co-founder of ARM.

Hyperganic was founded in 2017 to radically accelerate innovation in design, engineering and production of physical objects. The company drives a paradigm shift, where complex products are created by computer algorithms and Artificial Intelligence. The resulting objects are traded digitally and manufactured in digital factories based on industrial 3D printing (Additive Manufacturing).

The investment will drive a significant expansion of the existing Hyperganic team and the establishment of R&D centers in both Singapore and China.

“Humanity’s biggest challenges can only be solved through a giant leap in technology. We’ve created Hyperganic to fundamentally change how we design and build the things around us. Now we are ready to shift gears. We are happy to have the support of such a diverse team of investors on this exciting journey,” said Lin Kayser, co-founder and CEO, Hyperganic.

Hyperganic will use Singapore as a key pillar of the company’s development strategy. This decision was driven by the country’s investment in a vibrant deep technology ecosystem, specifically for advanced manufacturing technologies such as A.I., robotics and industrial 3D printing. As part of Singapore’s five-year RIE (Research, Innovation, Enterprise) plans, NAMIC, the National Additive Manufacturing Innovation Cluster, was incepted to orchestrate and implement strategies for the future of production.

“Singapore is one of only a few countries which have recognised the seismic shift happening through digital factories based on Additive Manufacturing. The products designed by our Singapore A.I. engineers will be game changers for many industries that are highly relevant to the region. With NAMIC, Singapore has a unique organisation that demonstrates the country’s strategic commitment to transforming an entire industry sector,” said Kayser.

“We have engaged Hyperganic as early as 2018 when it was in stealth mode, after Lin and I met at a conference. Back then, it was extraordinary to me what the company had envisaged — a paradigm shift in the way people design, using algorithms to create functional products with biomimicry designs. We are delighted to be partnering with Hyperganic on their growth journey, and excited by its plans to rapidly expand its footprint in Singapore and Asia,” said Dr. Chaw Sing Ho, Director, NAMIC Singapore.

 

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Would You Trust The Algorithm?

Would You Trust The Algorithm?

Imagine if you could automate some of the day-to-day operational decisions in your organization, so that your employees could focus on strategic projects, like developing new product lines or expanding the business. How good would an artificial intelligence (AI) model need to be, before you give it control? Would it, for example, need to equal the performance of human engineers, or demonstrate better performance? What if an error could cause significant financial losses or even human injury, how would this change your response?

A survey put scenarios like this to 515 senior leaders from the industrial world (including the energy, manufacturing, heavy industry, infrastructure and transport sectors) as part of a research into the uses, benefits, barriers and attitudes towards AI. Their responses offer a unique insight into the future of AI in industrial enterprises.

Heavy industry and heavy consequences

In these industries, many use cases for AI are expected to help avoid disasters and make workplaces safer. This is important because while AI methodologies are similar across industries, the consequences of failure are not. In many industrial organizations, bad decisions can leave thousands of people without a train to work; millions of dollars can be lost if machinery overheats; slight changes in pressure can lead to an environmental catastrophe; and innumerable scenarios can lead to loss of life.

It is therefore significant that a large set of respondents (44%) believe that, over the course of the next five years, an AI system will autonomously control machines that could potentially cause injury or death. Even greater numbers (54%) believe that AI will, within the same period, autonomously control some of their organization’s high-value assets.

To give AI such responsibility, industrial AI will need to become more sophisticated, and often this will be driven by new approaches to the way data is managed, generated, represented, and shared. For example:

  • Contextual data and simulations: Already today we see AI applied to data sets created and organized in new ways to enhance insights and understanding. Examples include knowledge graphs, which capture the meaning of – and relationships between – items in diverse data sets, and digital twins, which provide detailed digital representations and simulations of real systems, assets, or processes.
  • Embedded AI and big picture insights: Internet of Things (IoT) and Edge technologies are giving rise to diverse machine-generated data sets which can support new levels of situational awareness and real-time insights in the cloud or directly in the field.
  • Data from beyond the walls: Improved protocols and technologies for sharing data between organizations could support the development of AI models that simultaneously draw from the data of suppliers, partners, regulators, customers, and perhaps even competitors.

Context changes meaning

To take one example from the above, there is enormous potential in using industrial knowledge graphs to enhance AI models by combining different datasets. “Knowledge graphs add context to the data you’re analyzing,” explains Norbert Gaus, Head of R&D in Digitalization and Automation at Siemens. “For example, machine data can be analyzed in the context of design data, including the tasks the machine is made for, the temperatures it should operate at, the key thresholds built into the parts, and so forth. To this we could add the service history of similar machines, including faults, recalls and expected inspection outcomes throughout the machine’s operational life. Knowledge graphs make it much easier to augment the machine data we use to train AI models, adding valuable contextual information.”

The survey explored the kinds of contextual data that leaders believe would be most useful today. Data from equipment manufacturers came out on top, with 71% rating this as a major or minor benefit. This was followed by internal data from other divisions, regions or departments (70%), data from suppliers (70%) and performance data from sold products in use with customers (68%).

A company that uses knowledge graphs to bring different kinds of data together – such as product history, operational performance, environmental conditions – would be able to create a single AI model that drives better predictions, useful ideas, new efficiencies, and more powerful automation.

Building faith in algorithms

Ever more powerful applications will no doubt raise new challenges. It will require trusting AI with responsibilities that were only ever given to humans. In these cases, AI applications will need to win the confidence of decision-makers, while organizations will need to develop new risk and governance frameworks.

To explore these issues, the survey asked respondents to imagine several scenarios like the one at the start of this article. For example, 56% decided to accept the decision of an impressive AI model over an experienced employee (44%), where the decision would have major financial consequences. Is 56% high or low? One might think it is low considering respondents were told that the AI model had outperformed the organization’s most experienced employees in a year-long pilot. It suggests that the other 44% could have a bias towards human decisions, even when the evidence favors AI. You can read more about these and other important issues in the next-gen industrial AI research report.

Challenges aside, the research suggests an optimistic outlook for AI. As AI grows more sophisticated, leaders expect fewer harmful cyberattacks, easier risk management, more innovation, higher margins, and safer workplaces. Overall, with the promise of such a diverse and important range of positive impacts potentially on the horizon, there will be no shortage of motivation to overcome all challenges on the path to next-gen industrial AI.

 

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TRUMPF AI Assistant Optimises Sorting Process

TRUMPF AI Assistant Optimises Sorting Process

TRUMPF has developed a solution that makes life easier for machine operators by helping them sort sheet metal parts from a laser cutting machine. The Sorting Guide uses an AI solution to identify which part the operator has removed from the machine and displays all the required information on screen. This includes information such as the next step in the process chain, for example. The Sorting Guide improves efficiency at the interface between the laser cutting machine and intralogistics, especially when dealing with sheets containing parts from multiple different orders.

It helps machine operators optimise their workflows, for example by highlighting all the parts from one order in the same color on screen. At the same time, it enables the operator to keep tabs on the machine’s status when working at the pallet changer – and, as an option, even to keep an eye on the interior of the machine. That makes it easier for the operator to react quickly to any problems that might arise. The Sorting Guide automatically registers each part in the system as it is removed.

This eliminates the need to re-check the number of parts and manually update the part count in the control system. The digitally recorded data in the control system thus represent the real production progress in real time. The operator can see at a glance which parts are ready for further processing and where he may need to initiate post-production. The result is higher productivity on a day-to-day basis and more efficient overall use of the laser cutting machine.

Image Processing That Learns From Data

The Sorting Guide works using artificial intelligence. It consists of a camera and an industrial PC equipped with intelligent image processing software. Its neural network detects which parts the machine operator has already removed from the sheet. Unlike conventional image processing techniques, this network can be trained and improved on the basis of the data that is collected continuously. In addition, the database serves to open up further optimisation potential for users in the future. The Sorting Guide is currently available on systems running TruTops Fab or TruTops Boost software.

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TRUMPF And Fraunhofer IPA Research Alliance Ramps Up AI For Industrial Use

TRUMPF And Fraunhofer IPA Research Alliance Ramps Up AI For Industrial Use

TRUMPF and the Stuttgart-based Fraunhofer Institute for Manufacturing Engineering and Automation IPA have formed a research alliance that is set to to introduce artificial intelligence (AI) solutions for connected manufacturing on an industrial scale. Running until 2025, ten employees from TRUMPF and Fraunhofer IPA are involved in the project, which will receive some two million euros of funding spread over the next five years.

“TRUMPF’s mission is to further extend its AI leadership in sheet-metal fabrication. To that end, we have already started investing in the kind of future technologies that will drive major efficiency gains within our company and boost our competitiveness,” says Thomas Schneider, managing director of development at TRUMPF Machine Tools.

Five years have passed since TRUMPF and Fraunhofer IPA joined forces to work on smart factory topics, and the two partners will be continuing to pursue these existing projects within the framework of their new research alliance.

“TRUMPF has been working with us on connected manufacturing for years because they share our view that Industry 4.0 developments represent a major opportunity. Everything depends on what happens over the next few years – so these are exciting times! We expect the coronavirus pandemic to act as a kind of catalyst: those who are well prepared will be perfectly placed to exploit the huge opportunities that lie ahead. Soon we’ll see whether we have laid the right foundations for the future in our joint projects,” says Professor Thomas Bauernhansl, director of Fraunhofer IPA.

Future projects aim to make AI explainable

One of the goals TRUMPF and Fraunhofer IPA hope to achieve over the next five years is to develop solutions for better data quality in manufacturing. This reflects the crucial importance of high-quality data when it comes to achieving efficiency gains with AI. To address this need, the two partners will be increasing their research activities in the field of explainable artificial intelligence, or XAI. Their goal is to make the operation of neural networks interpretable. New findings in this area are of great benefit to sheet-metal fabricators. The results of this kind of data analysis can boost the quality of manufacturing, save time and cut costs.

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Siemens Revolutionizes CAD Sketching With AI Technology

Siemens Revolutionizes CAD Sketching With AI Technology

Siemens Digital Industries Software has launched a new solution for capturing concepts in 2D. The new NX Sketch software tool revolutionizes sketching in CAD, which is an essential part of the design process. By changing the underlying technology, users are now able to sketch without pre-defining parameters, design intent and relationships.

Using Artificial Intelligence (AI) to infer relationships on the fly, users can move away from a paper hand sketch and truly create concept designs within NX software. This technology offers significant flexibility in concept design sketching, and makes it easy to work with imported data, allowing rapid design iteration on legacy data, and to work with tens of thousands of curves within a single sketch. With these latest enhancements to NX, Siemens’ Xcelerator portfolio continues to bring together advanced technology, even within the core of modelling techniques, helping remove the traditional barriers users have experienced to dramatically improve productivity.

“The ability to make intelligent changes to 2D entities that one imports into the new sketcher is astounding,” said Steve Samuels, CEO of Design Visionaries Inc.

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With these latest enhancements to NX, Siemens’ Xcelerator portfolio continues to bring together advanced technology, even within the core of modelling techniques.

With these latest enhancements to NX, Siemens’ Xcelerator portfolio continues to bring together advanced technology, even within the core of modelling techniques.

Analysis has shown that in an average day or workflow, around 10% of a typical user’s day is spent sketching. In addition, within current design environments most concept sketching is happening outside of the CAD software due to the level of rules and relationships that must be decided on and built into the sketch by the user up front. Often designers in concept design stage do not necessarily know what the final product may be, which requires a sketching environment that is flexible and can evolve with the design. NX offers the flexibility of 2D paper concept design within the 3D CAD environment, as the first in the industry to eliminate upfront constraints on the design. Instead of defining and being limited by constraints such as size or relationships, NX can recognize tangents and other design relationships to adjust on the fly.

“Sketching is at the heart of CAD and is critical to capturing the intent of the digital twin,” said Bob Haubrock, Senior Vice President, Product Engineering Software at Siemens Digital Industries Software. “Even though this is an essential part of the process, sketching hasn’t changed much in the last 40 years. Using technology and innovations from multiple past acquisitions, Siemens is able to take a fresh look at this crucial design step and modernize it in a way that will help our customers achieve significant gains in productivity and innovation.”

 

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Top 21 Global Risks That Threaten The Next Decade

Top 21 Global Risks that Threaten the Next Decade

Frost & Sullivan has released a report providing a compelling analysis that will help stakeholders understand the impact of future risks through the short, medium and long terms, and equip them to act on clear growth opportunities. The study, Global Future Risks—Future-proofing Your Strategies, 2030, presents over 80 growth opportunities arising from 21 risks, which will help policymakers, businesses and individuals take immediate tactical and strategic actions and draw plans to lower impact.

Unpreparedness to address these challenges, which are looming across the globe, will adversely impact businesses, societies, economies, cultures, and personal lives.

“Risks are now increasingly interconnected, which, if amplified, can trigger a ripple effect across industries, regions, and diverse stakeholder groups. Therefore, mitigating them will involve measures to quantify relevant risks, estimate the probability of occurrence and the adoption of a cohesive, interdisciplinary, and multipronged approach,” said Murali Krishnan, Visionary Innovation Group Senior Industry Analyst at Frost & Sullivan.

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Krishnan added, “With an estimated 200 billion connected devices by 2030, the risk of sophisticated cyberattacks will escalate. Therefore, companies should invest in future-casting tools that help identify the impact of such risks to their business, thereby detecting vulnerabilities and avoiding disruptions.”

“Over 40 million Americans are at risk of flooding from rivers, nearly 300,000 have lost their lives to the COVID pandemic, and close to $1 billion in costs will be borne annually by economies as they become victims to cyberattacks. The stakes are high, claiming both lives and resources. Businesses must look to invest in early warning systems that help future-proof them against the known and the unknown. Understanding risks and its levers can become a growth opportunity if used as a tool to create value,” noted Archana Vidyasekar, Visionary Innovation Group Research Director at Frost & Sullivan.

To tap into growth opportunities emerging from the short-, mid- and long-term risks, organisations can focus on the following areas:

Short-term risks

  • Privacy and disinformation risks: Organisations must adopt a “privacy-by-design” approach, which goes beyond accepted privacy standards and assumes global regulatory compliance.
  • Rise of infectious diseases:Advanced technologies such as next-generation sequencing (NGS) and CRISPR-based diagnostics will enable new approaches in the treatment of infectious diseases.
  • Water crisis:Innovation in agricultural practices such as vertical farming and optimised crop selection can play an important role in reducing the ongoing water crisis.

Mid-term risks

  • Urbanisation: Urbanisation stress will trigger the demand for smart solutions such as intelligent grid control and electrification, smart buildings, and smart storage solutions.
  • Climate change risks: Organisations will emphasise investments in buildings with net-zero energy consumption, and greenhouse gas emissions will increase minimally.

Long-term risks

  • Artificial intelligence (AI) as a threat: AI can be a strong reason for the polarisation of jobs across the world, but investing in collaborative computational capabilities can help allocate an ideal division of tasks between humans and robots based on their distinct capabilities and deployment costs.
  • National identity crisis:Great business opportunities exist in providing digital authentication of eGovernment services such as digitisation of birth, marriage, and death certificates, passports, and driving licenses.

 

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