AI-42, Giacomo Bonavera, London, June 14h 2019
AI-42 Market Intelligence Ltd. is the leading research, advise and market intelligence provider for financial institutions in the area of Artificial Intelligence. As an independent authority AI-42 classifies the AI market and constantly monitors 11.000+ companies in the field of Artificial Intelligence. AI-42 issues Market Intelligence Special Reports to different topics about the AI industry.
NVIDIA (NASDAQ: NVDA) share price slumped to half the value from its last year highs. While the top executives try to give reasons for the depression, we look deeper and find the real reasons that caused revenues to crash in the recent quarters.
NVIDIA reported revenue declines of 31% from a year ago for the first quarter ended April 28, 2019, to $2.22 billion compared with $3.21 billion from a year ago and $2.21 billion in the previous quarter.
Founder & CEO of NVIDIA, Jensen Huang had this to say, “NVIDIA is back on an upward trajectory, we’ve returned to growth in gaming, with nearly 100 new GeForce Max-Q laptops shipping. And NVIDIA RTX has gained broad industry support, making ray tracing the standard for next-generation gaming.
“Despite the near-term pause in demand from hyperscale customers, the application of AI continues to accelerate. AI adoption is accelerating in the world’s largest industries, moving beyond the cloud to the edge where AI processing has to be instantaneous. We’re excited about our pending acquisition of Mellanox, which will help us drive data center architecture for high performance computing and AI from the cloud to the edge,” he said.
Let us unpack that by looking at the revenue by reportable segments of NVIDIA:
GPU Business. GPU business revenue decreased by 27% in the first quarter of fiscal year 2020 compared to the first quarter of fiscal year 2019, which reflects declines in gaming GPU and data center revenue, as well as the absence of $ 289 million of revenue from cryptocurrency mining processors.
GeForce GPU product sales for gaming decreased 28%. Data center revenue, including Tesla (not to be confused with the electric car manufacturer), GRID and DGX, decreased 10%, primarily reflecting a slowdown in certain hyperscale and enterprise customer purchases, partially offset by growth in inference sales.
Revenue from Quadro GPUs for professional visualization increased 6% due primarily to higher sales across desktop and mobile workstation products. The PC OEM revenue decreased by 78% primarily driven by the absence of cryptocurrency mining processor sales.
Tegra Processor Business. Tegra Processor business revenue decreased by 55% for the first quarter of fiscal year 2020 compared to the first quarter of fiscal year 2019 . This was driven by a decline in shipments of SOC modules for gaming platforms, which was only partially offset by an increase of 14% in automotive revenue, primarily from growth in AI cockpit modules.
There is quite a big gap between saying “NVIDIA is back on an upward trajectory” and “the application of AI continues to accelerate” when revenues in all reportable segments plunged between a quarter and three quarters.
To say “we’ve returned to growth in gaming, with nearly 100 new GeForce Max-Q laptop shipping” must mean they returned to growth from a very low base because a laptop with the best GeForce Max-Q graphics chip in it retails on Amazon for between $1800 and $2900. If that was the bullwhip Kevin Cassidy of Stifel was referring to in a written report that “Nvidia likely cleared out channel inventories in the quarter for its graphics processing units for gaming”, then the slack in the logistical pipeline couldn’t have been too big. But let’s not forget, Kevin Cassidy reiterated a ‘Hold’ rating on Nvidia last year which must mean he was holding some of the stock in his portfolio.
To see the problems that Nvidia’s stock plunge since early October is causing for its shareholders, look no further than Softbank.
Softbank has drawn headlines during the past year for its ambitious Vision Fund, a massive $100 billion fund engineered by CEO Masayoshi Son. The fund has invested in a number of tech companies—including Nvidia, WeWork, Uber and Slack. The idea behind it was to get in early on companies that will be reshaping technology for the next 100 years.
According to Bloomberg, Softbank is considering selling off some or even all of its stake in Nvidia early next year—more than 98 years short of its 100-year vision. Softbank reported that the valuation of its Vision Fund in the September quarter had increased by ¥504 billion ($4.5 billion) year over year, with much of that increase due to its investment in chipmaker Nvidia. Since Oct. 1, however, Nvidia has lost 50% of its value, potentially delivering a blow to the Vision Fund that its biggest investors, notably sovereign funds in the Middle East, may not welcome.
All of the top AI ETFs including BOTZ, ROBO, ROBT, IRBO and UBOT have weightings in NVIDIA and all of those ETFs have slumped. So can it be said that the AI ETFs that financial market investors are using as benchmarks are a true reflection of the AI market?
VCs invested more than $1.5B in chip startups in 2017, nearly doubling the investments made two years ago. There are at least 45 startup chip companies focused on NLP, speech recognition, and self-driving cars. Silicon Valley startup Cerebras and UK’s Graphcore are quietly working on bots that can carry on conversations and systems that can automatically generate video and virtual reality images. Not only do these newcomers have strong backing by leading VCs, but they have also been on an active hiring spree, cherry picking key executives from many of the older and established chipmakers. Cerebras has hired dozens of engineers from Intel, notably bringing in its CTO from Intel’s Datacenter group. Graphcore was founded by semiconductor veterans who have founded multiple startups in the past, including Icera, a mobile chip company, which was sold to Nvidia in 2011 for $376M. Another promising startup, SambaNova, which was funded by Google Ventures and co-founded by an Oracle veteran and professors from Stanford, is working on solutions to integrate hardware and software to maximize the performance and efficiency of AI chips.
With such a crowd of innovators focused on a single target, new innovations are expected to continue to accelerate at a rapid clip. Perhaps the biggest differentiator on deck at the moment is the development of key software that is tightly incorporated into single solution sets. So far, Nvidia appears to have the clear advantage, and its equally weighted software and hardware development teams reflect the importance of software integration in the next generation AI chip sets. But with each new advancement comes the opportunity for new leaders of the pack, and the specialization of AI chips for different segments is already evolving faster than most analysts had expected. As each of these companies fights for its share of this $35B chip market opportunity, watch for accelerating M&A activity—and more opportunities for investors in every area of the space.
PR NEWSWIRE, London, May 10h 2019
The expert members of the AI-42 Index Rebalancing Committee Meeting got together and conducted the quarterly review of the AI-42 INDEX™ constituents, discussed the latest Artificial Intelligence trends and shared market insights from their delegation visit to China.
The rise of AI companies globally is continuing, with the AI-42 INDEX™ Q1/2019 closing at 10.677,55 points, an increase of 2.465,24 points (+30,02%) since the beginning of this year. The index, which is expressing the global growth of the 42 top public companies in the field of Artificial Intelligence, grew +127,53% since January 3rd 2017.
The cyber-security sector continues to lead the most promising growth class. More and more devices are being connected with each other due to the trend of IoT (“Internet of Things”), it is becoming more and more challenging to provide the necessary security systems, especially for private individuals. The race against cyber threads is fuelled by upcoming new technologies, especially with Quantum computing coming soon.
China is continuing to put massive efforts into becoming their self-declared goal of being the “AI Superpower”. Our expert team visited some of the most promising AI companies in China to get more insights into the status quo. One of the biggest advantages of China is the availability of data. With almost no privacy restrictions in place, the local AI companies are able to obtain millions of visual datasets, collected from CCTV systems all around the cities. This allows them to create unparalleled computer vision related machine learning applications like facial and emotion recognition.
“China’s AI presence in Western media is generating both alarm and acquiescence. The danger is that this can lead us to over and/or underestimate what is happening on the ground across China. Sticking to the facts, it was only in May 2017 when China’s Ministry of Science and Technology announced its decision to add AI 2.0 to the line-up of the planned Science and Technology Innovation 2030 Megaprojects. The overall initiative was launched as part of the 13th Five-Year National Science and Technology Innovation Plan, including (amongst others) robotics, big data and intelligent manufacturing.“ says Mike Halsall, Expert Member of the Advisory Board of 42.
Another trend, the notion of a Digital Twin, is now being widely adopted. It is rapidly becoming the technology of choice for virtualizing the physical and digital worlds. Similar to the evolution of AI, the notion of Digital Twin began around 60 years ago, in the pioneering era of the space exploration. At that time, the US National Aeronautics and Space Administration (NASA) was grappling with the challenge of designing objects that could travel so far away they would be beyond the human ability to see, monitor or modify them directly. NASA's innovation was the “digital twin” of a physical system – a comprehensive digital double which people could use to operate, simulate and analyse an underlying system led by physics.
„Digital Twins strengthen the “human front-end” of all we do, making it more dynamic, faster learning, and also highly interactive. Furthermore, they offer excellent opportunities to investigate the unexpected and discover the very best solutions – true to the motto “It is not the technology that changes the world but the way people use it”.", explains Jivka Ovtcharova, Expert Member of the Advisory Board of 42.
As simulations of – especially – real live environments is an extremely complex task, AI is empowering this trend.
Expert Advisor Opinion, Mike Halsall, May 7th 2019
In February 2018, the FT published an article outlining ‘Why we are in danger of overestimating AI’, arguing that ‘considerably more work is needed before we can reach the long-dreamt-of moment when machine intelligence matches the human variety’. By 2030, just 11 years from now, China has vehemently expressed its ambition to ‘lead the world in AI’. Is this a realistic ambition, or has China become entirely conceited in the midst of its new-found wealth and growing economic muscle?
It seems, as with any emerging technology in the fast-paced era of today, challenging if not impossible, to predict how AI might evolve in the coming years. In China’s case, this uncertainty is heightened by the nature of its own government and politics. We mostly rely on data verified by the CCP (Chinese Communist Party) where China stands in the global race for AI supremacy; our first assumption is to assume what they say is accurate.
China’s AI presence in Western media is generating both alarm and acquiescence. The danger is that this can lead us to over and/or underestimate what is happening on the ground across China. Sticking to the facts, it was only in May 2017 when China’s Ministry of Science and Technology announced its decision to add AI 2.0 to the line-up of the planned Science and Technology Innovation 2030 Megaprojects. The overall initiative was launched as part of the 13th Five-Year National Science and Technology Innovation Plan, including (amongst others) robotics, big data and intelligent manufacturing.
How is this transpiring into daily life in China? Rather well. AI is everywhere; it is used in classrooms, hospitals, offices, on the streets and in homes. Thanks to the ubiquity of WeChat, China’s own hybrid version of Facebook, that includes a wallet, home page, public services, utilities features and more; urban Chinese society was almost instantly digitalized, giving rise to an explosion of data to help them develop AI. Whether or not China’s AI capabilities are really as advanced as they allude to, Chinese society is standing arms wide open, ready to embrace AI in all forms without having much idea as to the consequences.
Alongside China’s national efforts to promote AI, decentralised efforts have also begun; cities such as Beijing, Shanghai, Hangzhou, Zhejiang, and Tianjin now have their own plans for AI. Beijing, home to Haidian Science Park, alternatively known as China's Silicon Valley, which sits within a stone’s throw of both Tsinghua and Peking University, plans to build a 13.8 billion RMB AI development park, potentially hosting up to 400 uniquely AI enterprises. 42 is looking forward to assessing these companies to ensure they really are ‘AI’.
Many budding entrepreneurs aspire to repeat the success of incumbent Chinese AI giants, including Ubtech Robotics, a Shenzhen based robotics company, SenseTime a Chinese government supplier of face-recognition technology, and DJI, which has a 70% share in the global drone market and alleges AI inside. With financial backing from Baidu, Alibaba, and Tencent, China’s AI ambitions will be well funded.
China’s AI could benefit its citizens through improve quality of life, particularly in terms of healthcare and education. However, the transformative nature of AI could likewise bring adverse effects should unsuitable practices be applied. Whilst China’s AI agenda becomes increasingly more pronounced, competing against all other nations for market-share and ultimately supremacy, 42 and its partners will establish the facts to ensure the global AI debate remains open and transparent.
March 29th 2019
The expert members of the Index Rebalancing Committee Meeting got together and conducted the quarterly review of the AI-42X INDEX and the AI-42 INDEX constitutients to ensure all companies meeting the eligibility requirements are included and continue to represent the A.I. growth asset class. Ratings for 150+ companies have been reviewed and updated by the expert gremium and changes to the AI market environment, new gamechanging topics and break-trough technologies have been discussed. We will soon provide additional information about our main findings, stay tuned and watch for our update!
PR NEWSWIRE, London, March 29th 2019
LONDON, March 29, 2019 /PRNewswire/ -- Today, AI-42 Market Intelligence Ltd., the leading Artificial Intelligence Market Intelligence provider, announced the results of its latest research about the AI competence levels and financial health of Artificial Intelligence related companies worldwide. The staggering result: Only 5.45% of all AI companies analysed have a high degree of AI competence, 41.35% are totally lacking AI expertise. The average financial health of AI companies is low, only 4.59% are in strong financial condition.
To get an "x-ray" view of the market, 42 conducted its own research of the AI market: 10,861 companies have been evaluated by applying a proprietary quantitative & qualitative analytics approach, where metrics like market environment, financial health, customer-, partnership and social media momentum have been quantitatively analysed and expert scoring of metrics like AI competence, AI focus, AI growth potential or AI sector potential has been added. Data points like scientist employment growth rates, number of patent filings, publications and research papers of these companies have been taken into account.
Only 592 (5.45%) of all the AI companies analysed have an AI score of more than 500 (on a scale from 0-1,000, a weighted metric based on AI competence, AI focus, AI growth potential and revenue contribution). Only 6,371 (58.65%) of them have a score of more than 250, which is considered a minimal AI expertise. Also, the financial health of most of the companies is questionable. Only 499 (4.59%) are in strong financial condition.
Also, a recent survey from London venture capital firm MMC found that 40 percent of European start-ups that are classified as AI companies don't actually use Artificial Intelligence. They studied some 2,830 AI start-ups in 13 EU countries to come to its conclusion. On the flipside, have the artificial intelligence market forecasts been constantly revised upwards, Tractica currently assumes a growth from $5.4 billion in 2017 to $105.8 billion by 2025, a prediction which was $80 billion just a few months ago. This creates an environment of possibilities but also the challenge to identify the real pearls.
It is clearly confirmed that Artificial intelligence is one of the most misused terms in tech today, it has become a hyped technology. It is important to identify the real quality AI players in the market, which has become a more and more challenging task. Only by rigorously analysing massive data about these companies, combined with a high level of AI expertise, a sufficient opinion can be built.
Expert Advisor Opinion, Gabriele Zedlmayer, March 2019
So there seems to be trouble in Paradise. The globe is facing massive challenges like climate change, cyber-crime, shortage of available resources, political instability and the potential of massive job loss because everything that can be automated will be. At the same time, we are seeing unprecedented opportunities like we have never before. Algorithms help us to better diagnose, predict and prevent diseases, they revolutionize the education sector by treating each student as an individual, and they free us from many repetitive and often cumbersome tasks.
These new AI technologies disrupt our personal and our professional lives. Platform based services address more and more of our needs as they deliver tailor made personalized experiences to us like shopping recommendations, driving assistance, even recognizing our mood just to provide us with music that matches our state of mind. Simply amazing. It is happening all around us and yet it seems that many of us do not understand that we are transforming from an old world with established rules to a new world that lacks definitions and witnesses constantly evolving business models. This new world is changing at exponential speed and no stone remains unturned.
Industry experts argue that we are currently in a two-horse race between China and the US when it comes to AI and how it is changing the world. Europe is a distant third and yet so far behind that it does not even matter who is actually in third or fourth place. This begs the question how we can better prepare the people in Europe and anywhere else in the world to successfully compete in a space that requires new skills and competencies, new forms of collaborations and most importantly new mindsets?
Education would seem a natural answer. But not in the traditional sense. When we hear the word education we tend to think about primary, secondary and tertiary learning institutions like elementary school, high school, college or university. We have to rethink the concept however. Education in the future will be all about lifelong learning – about constantly assessing what skills we need for future jobs and identifying the best learning platforms to get them. So what are these skills and how can we get trained? In their report Skill Shift Automation and the Future of the Workforce consultancy company McKinsey reports that the biggest change will take place in technological skills, both in advanced skills such as programming and advanced data analysis, and also in more basic digital skills relating to the increasing presence of digital technologies in all workplaces.
While not everybody is capable of programming algorithms or advanced data analytics, the broader population should be exposed to the basics of Artificial Intelligence and its impact on our daily lives as well as the Future of Work. The Finish government has recognized this need and set itself an ambitious goal. In collaboration with the University of Helsinki it designed a course to introduce 1% of its population to the basics of Artificial Intelligence. Citizens and interested individuals anywhere can go online and take the course which offers in total six modules; (1) what is AI, (2) solving problems with AI, (3) real world AI, (4) machine learning, (5) neural networks, and (6) implications. The course provides an introductory level overview of the technology and enables the students to engage in the discussion about a future shaped by Artificial Intelligence. Also, it encourages them to get deeper into the subject matter if they are interested in more technical careers.
Interested stakeholders can also turn to the more established Massive Open Online Courses (MOOCs) like edX, Udacity, Coursera, or Udemy to gain a more in-depth understanding of Artificial Intelligence and Machine Learning. Access to Education has been democratized and the opportunities to learn are endless. It takes however great willpower and motivation for people to sign up and more importantly finish these courses on their own. A good way to improve the odds of completing a course is by creating a study group with like minded friends or colleagues. Setting a common goal and helping each other to achieve the mutual objective is a recipe for success. To get started I recommend the following website: DIYGENIUS. Here you will find plenty of relevant information about the future of work and how to best prepare for it.
The future is not what it used to be. Let´s make the most of it and help shape this exciting era that is ahead of us. We need to engage in the discussion about Man versus Machine now and raise our voice to help actively shape the ethical principles that will guide the future development of this technology. To get this right we need to involve the broad population, get them trained and agree on a set of values and principles that will be the foundation on which future developments in AI will be built.Gabriele Zedlmayer
Expert Advisor Opinion, Anne Gfrerer, March 2019
In the last years, technology has gradually become invisible and digital ubiquity is arising in our daily work and private lives. This is also true for artificial intelligence and its impact for companies to succeed in the digital age. Organizations must foster their digital readiness, which decides about their future business model and which encompasses a range of aspects: it comprises the capability of companies to obtain the benefits which arise from information technology, including their successful transformation into truly digital enterprises and using innovative technologies like artificial intelligence, and people’s individual preparedness to embrace and use the new digital technologies for their jobs. Although there is substantial evidence that digital readiness positively influences company outcome in the digital world and employees have to be ready, studies show, that the associated capabilities are only rarely in place. This goes along with a perceived lack of digitally skilled and experienced managers, who do not have enough knowledge about innovation and major digital trends and who are not yet able to fasten reaction speed and to foster an innovation culture of their firms, which is seen as one of the most important innovation barriers by many employees.
In times, where innovation decides about a company´s wealth and autonomous innovation is a future outlook, this matters even more: Possibly in the future roboters and machines will support innovation processes in fields like scouting, idea generation, but also in areas like innovation team set ups in form of performance predictions and emotion tracking. There might be people-roboter innovation teams collaborating to succeed. This implies even more, that companies have to be prepared and have to enlarge their digital readiness by far. Managers do play a major role in the course of this and new leadership skills are becoming a necessary precondition in the digital world. So what should managers do? In a nutshell, the author proposes three core measures to be taken to succeed: First, the advancing of the own digital readiness of managers matters, as leaders are perceived as role models for the employees.
Having digital knowledge – e.g. on important digital trend topics like blockchain, artificial intelligence or digital ubiquity – and digital skills on their own, and not leaving this over to digital experts, enables managers to better develop a digital strategy, rate digital innovation ideas, define the right problems to be solved and bring business forward in this respect or act as game changers in their industry branch. Dealing with ambidextry - exploiting core business and exploring new business models at the same time - requires digitally enabled mangers on a broader scale. Second, managers should foster life long learning of their employees and enable employees to become more digitally mature, according to the motto from “knowing it all” to “learning it all”. This needs a growth mindset, that empowers people to outgrow themselves. Third, on an organizational level, managers should foster shared beliefs and an open innovation culture. With this goes along, that management communication therefore has to be adjusted accordingly.
To sum up, artificial intelligence can be seen as a great chance for individuals and organizations as well, and managers do play an important role to make the best use out of it for their business to succeed. Nevertheless, in times of artificial intelligence also humans and their skills, that machines can´t learn – like creativity at the first place – will get relevant even more.Anne Gfrerer
Expert Advisor Opinion, Jivka Ovtcharova, February 2019
We live in exciting times. Artificial Intelligence (AI) is fundamentally transforming the world with all its facets. Even if it has been around for a while in science and technology, aiming to create machines with some form of human intelligence, over time AI has become an inseparable part of our daily lives, personal as well as professional. Online searching engines, financial transactions, digital personal assistants, image and voice recognition are just a small selection of the most popular applications of AI today.
Unlike the current mainstream, i.e. in the media, communication and financial sectors just dealing with data, the AI of tomorrow will be more about perceptible presence, based on the fusion of contents. Based on current market research and own insights the main trends for 2019 clearly indicate the transition of data analytics towards augmented analytics, called also an analytics of behavior.
Due to the rapid development of human-centered platforms, social networks and AI applications in daily live the AI refocuses the way of consideration from the "object" (machine, computer) to the "subject" (human being). People's roles are getting more and more important. Furthermore, people are developing an "AI mindset" in direct communication with other people, but also with machines and computers. The term still sounds like a buzzword, but we're already linking our personal attitude to AI, the way we think, act or feel. It is also about our human skills, competences and experiences in dealing with AI, which belong together and are reciprocal.
To understand the matter of which a human being, a physical or an artificial thing consists and how they behave requires the analysis in context. A mash up of AI and business technologies, such as Internet of Things (IoT), Cyber-Physical Systems and Automation will begin to make a significant impact on AI market in 2019. Edge and Fog Computing, Mesh Wi-Fi, Beacons, Blockchain, 5G, Immersive Experience and Quantum Computing - the list of technological trendsetters for future AI is long. However, it is not the technologies that drive changes, but the acceptance, the way people perceive and use technology. Thus, it is already possible today to recognize relationships and interactions that can be symbolically captured in a four-dimensional "all-in-one" behavior space characterized by intelligence, immersion, infrastructure and real-time capability.
The boundary between "online and offline" disappears influencing the way people imagine reality in space and in time. Material and immaterial worlds merge. Real-time applications, supported by realistic visualization technologies make invisible phenomena visible and understandable to humans in order, for example, to realize new product features and functions. Numerous examples of the use of AI, such as intelligent voice assistants and in-store beacons, show that the mass markets will rely on technologies that are available, affordable and intuitive to use, enhancing experiences of customers and evaluating their data in context. At the same time, the immersion level of people's perceptions is increasing as both Bluetooth headsets and speakers and other peripherals such as VR/AR headsets, smart watches and glasses are becoming more prevalent.
Furthermore, it is also about digital business, education and qualification. As a result, prejudice, traditions and physical constraints (including availability and local presence) become less important. Networked thinking skills, with a sense for the big picture, are in demand as never before. Life is analogue, but communication is getting more and more digital. This trend offers unimaginable potential for new professions, qualifications and business models.
Today, companies from a very wide range of industries express their extreme commitment to bringing AI into the core of their business and plans for growth. The most important thing is to get rid of the stereotypes that are outdated and to look forward with the courage to disrupt. Due to the transition of AI to more behavioral analytics not just Silicon Valley companies are on the trend anymore. Indeed, companies driven by engineering innovations, i.e. Digital Twins, take the chances to create new AI markets making analytics to come alive, driving actions and delivering value.
The notion of a Digital Twin is now being widely adopted. It is rapidly becoming the technology of choice for virtualizing the physical and digital worlds. As versatile and powerful as AI may be the original purpose of the Digital Twin remains unchanged: to enable people to perceive realities, study problems more easily, get to the point, understand, decide and proceed pragmatically and rapidly. Digital Twins strengthen the “human front-end” of all we do, making it more dynamic, faster learning, and also highly interactive. Furthermore, they offer excellent opportunities to investigate the unexpected and discover the very best solutions – true to the motto “It is not the technology that changes the world but the way people use it”.
But, what is the Digital Twin exactly? Similar to the evolution of AI, the notion of Digital Twin began around 60 years ago, in the pioneering era of the space exploration. At that time, the US National Aeronautics and Space Administration (NASA) was grappling with the challenge of designing objects that could travel so far away they would be beyond the human ability to see, monitor or modify them directly. NASA's innovation was the “digital twin” of a physical system – a comprehensive digital double which people could use to operate, simulate and analyze an underlying system led by physics.
Fundamentally, the Digital Twin is a virtual representation, an embodiment of an asset of any type, material or non-material – including everything from power turbines, plants and buildings to services and maintenance. The Digital Twin is described by the structure and behavior of connected “things” generating real-time data. That data is stored usually in the cloud or edge and analyzed with relation to the running environment around it. It is then presented to users from different perspectives and in a variety of roles, so they can remotely understand its status, its history, its needs, and interact with it to do their jobs.
The interfaces to external systems and validation environments with consideration of all relevant resources and processes ensure high-level connectivity and are the key for success. For example, using the Digital Twin, it is possible to validate operational concepts for production systems in real-time, for manual as well as automatic operations, and for configuration via intuitive man-machine interfaces (e.g. web surface, haptic interaction devices). This makes it easier to take decisions based on up-to-date, transparent information. Thus, by merging real and virtual environments, intelligent commissioning of production can be used to generate forecasts based on real-time data from the shop floor.
How Digital Twins work? A ready-to-use solution at the Industry 4.0 Collaboration Lab at the Karlsruhe Institute of Technology (KIT), Germany, offers a good example. The use case of a Digital Twin of a milling machine used for process optimization and networking in virtual reality, while taking account of resource flows, demonstrates the practical advantages of the proposed solution by increasing productivity more than 20 percent. Systems which make it easier to gain a unique, deep knowledge of assets and their behaviors throughout the life cycle will pave the road to achieving new levels of optimization and business transformation. For example, we want the physical build to return data to its Digital Twin through sensors so the Digital Twin contains all the behavioral information we would have if we inspected the physical build itself.
According to the German Association for Information Technology, Telecommunications and New Media (BITKOM), Digital Twins in manufacturing industry will have a combined economic potential of more than € 78 bn by 2025. However, this potential can only be achieved if Digital Twins are implemented in a comprehensive and self-optimizing manner which enables them to adapt to future changes. Current studies show that mature and widespread implementation has not yet taken place. There are three main weak points to be discussed: model semantics of Digital Twins is mostly geometry driven, data analytics is aligned but not embedded into the model and simulation and user-interaction take place offline, due to the huge model complexity and the lack of computational power. To overcome current limitations, the following three conditions must be fulfilled: model semantics must be usage driven and adaptive for changes, analytics should be embedded into behavior and working in runtime, and the implementation should go along with experiments and experiences.
Implementing Digital Twins demands that we put real problems “into the sandpit“ of business units, think, try out, create “all-in-one“, apply emerging AI technologies playfully and quickly, test new solutions in runtime to gain experience fast and transform knowledge into actions and skills. The time to act is now! We invite you to join us in establishing “AI Twinning” as a trademark.Jivka Ovtcharova