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Generative AI / The New Era of Super-Exponential Growth
« Last post by Imrul Hasan Tusher on March 13, 2024, 04:17:46 PM »
The New Era of Super-Exponential Growth

KEY POINTS

1. "Stacked AI innovation" drives super-exponential technological growth and industry transformation.
2. AI evolves to solve problems autonomously and intuitively, marking a significant leap in capability.
3. Progress relies on adaptation to AI's transformative potential, shaping innovation and opportunities.


Just when you caught your breath, a new era is emerging—one that transcends the familiar bounds of exponential growth to venture into the breathless territories of super-exponential acceleration. This seismic shift is largely propelled by the profound and relentless force of artificial intelligence (AI), a catalyst setting the stage for unprecedented transformations across the spectrum of human endeavor. It's crucial to delve into what I term "stacked AI innovation" and the broader implications for the future of technology, society, and global economic growth.

The Path of Stacked AI Innovation

At the heart of this technological renaissance is the idea of stacked AI innovation, a concept that may need redefining in the context of artificial intelligence. Gone are the days when AI was merely a tool for processing and computation; today, AI stands as the architect of a new intellectual landscape, layering knowledge upon knowledge, insight upon insight. This is not merely AI assisting AI; it is AI exponentially enhancing its own capabilities, akin to a virtuoso musician who, having mastered one instrument, proceeds to orchestrate an entire symphony of innovation.

The trajectory of AI today sets a new benchmark, far surpassing the linear progression dictated by Moore's Law. We are witnessing the emergence of systems that are not just faster but inherently smarter and more adaptable, capable of learning from each iteration and evolving in real-time. This dynamic innovation propels us from a realm of predictable advancements to one of continuous, self-driven transformation.

The Far-Reaching Impact of AI's Evolving Mastery

The ramifications of stacked AI innovation are vast and varied, promising to be a potent catalyst across myriad industries. In finance, AI systems that commence with basic market analyses can evolve to predict intricate economic trends, thereby informing more strategic investment decisions. In transportation, AI can transition from optimizing individual routes to overhauling entire traffic management ecosystems, enhancing efficiency and safety in urban mobility.

This evolution of AI transcends mere data accumulation or algorithmic efficiency; it embodies AI systems developing an almost intuitive understanding of the tasks at hand. They are not simply problem-solvers; they are pioneers, identifying new challenges and devising innovative solutions. This leap from traditional machine learning to a self-evolving, insightful, and autonomous problem-solving dynamic marks the dawn of a new era in technological and intellectual advancement.

The Exponential Multiplier Effect

The true essence of stacked AI innovation lies in its exponential multiplier effect. Each layer of learning and adaptation does not merely add to the AI's capabilities; it magnifies them, enabling quantum leaps in understanding and application. This signifies a fundamental shift in our approach to addressing challenges and seizing opportunities across various sectors, positioning AI as the central driver of our collective quest for progress and innovation.

Collaborative Adaptation: The Path Forward

The key to unlocking AI's full potential lies in our collective ability to collaborate and adapt. This journey with AI is not a solitary venture but a collaborative expedition that demands the convergence of diverse minds from academia, industry, and government. Such a collaborative ecosystem ensures the development of AI in a manner that is both beneficial and ethical, catering to societal needs while promoting sustainable progress.

Adapting to an AI-enhanced world necessitates a cognitive shift—a willingness to embrace continual learning and change. As AI reshapes various facets of our existence, from our professional lives to our approach to global challenges, maintaining an informed and flexible mindset is paramount. Embracing AI entails recognizing it as more than a technological breakthrough; it is a transformative force that, when wielded responsibly, can harmonize with and amplify our shared aspirations and values.

In this new era of super-exponential growth, we are not merely spectators of technological advancement; we are active participants, shaping a future that mirrors our collective vision of progress and innovation. As we forge ahead, it will serve us well to embrace the transformative power of AI with both foresight and responsibility, steering the course of this extraordinary journey towards a future breathless with limitless possibilities.

Source: https://www.psychologytoday.com/us/blog/the-digital-self/202403/the-new-era-of-super-exponential-growth




92
Generative AI / Using generative AI to improve software testing
« Last post by Imrul Hasan Tusher on March 13, 2024, 02:41:42 PM »
Using generative AI to improve software testing


DataCebo offers a generative software system called the Synthetic Data Vault to help organization

Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a person interacts with a software application.

Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms—especially in scenarios where real-world data are limited or sensitive.

For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.

The Synthetic Data Vault, or SDV, has been downloaded more than 1 million times, with more than 10,000 data scientists using the open-source library for generating synthetic tabular data. The founders—Principal Research Scientist Kalyan Veeramachaneni and alumna Neha Patki '15, SM '16—believe the company's success is due to SDV's ability to revolutionize software testing.

SDV goes viral

In 2016, Veeramachaneni's group in the Data to AI Lab unveiled a suite of open-source generative AI tools to help organizations create synthetic data that matched the statistical properties of real data.

Companies can use synthetic data instead of sensitive information in programs while still preserving the statistical relationships between datapoints. Companies can also use synthetic data to run new software through simulations to see how it performs before releasing it to the public.

Veeramachaneni's group came across the problem because it was working with companies that wanted to share their data for research.

"MIT helps you see all these different use cases," Patki explains. "You work with finance companies and health care companies, and all those projects are useful to formulate solutions across industries."

In 2020, the researchers founded DataCebo to build more SDV features for larger organizations. Since then, the use cases have been as impressive as they've been varied.

With DataCebo's new flight simulator, for instance, airlines can plan for rare weather events in a way that would be impossible using only historic data. In another application, SDV users synthesized medical records to predict health outcomes for patients with cystic fibrosis. A team from Norway recently used SDV to create synthetic student data to evaluate whether various admissions policies were meritocratic and free from bias.

In 2021, the data science platform Kaggle hosted a competition for data scientists that used SDV to create synthetic data sets to avoid using proprietary data. Roughly 30,000 data scientists participated, building solutions and predicting outcomes based on the company's realistic data.

And as DataCebo has grown, it's stayed true to its MIT roots: All of the company's current employees are MIT alumni.

Supercharging software testing

Although their open-source tools are being used for a variety of use cases, the company is focused on growing its traction in software testing.

"You need data to test these software applications," Veeramachaneni says. "Traditionally, developers manually write scripts to create synthetic data. With generative models, created using SDV, you can learn from a sample of data collected and then sample a large volume of synthetic data (which has the same properties as real data), or create specific scenarios and edge cases, and use the data to test your application."

For example, if a bank wanted to test a program designed to reject transfers from accounts with no money in them, it would have to simulate many accounts simultaneously transacting. Doing that with data created manually would take a lot of time. With DataCebo's generative models, customers can create any edge case they want to test.

"It's common for industries to have data that is sensitive in some capacity," Patki says. "Often when you're in a domain with sensitive data you're dealing with regulations, and even if there aren't legal regulations, it's in companies' best interest to be diligent about who gets access to what at which time. So, synthetic data is always better from a privacy perspective."

Scaling synthetic data

Veeramachaneni believes DataCebo is advancing the field of what it calls synthetic enterprise data, or data generated from user behavior on large companies' software applications.

"Enterprise data of this kind is complex, and there is no universal availability of it, unlike language data," Veeramachaneni says. "When folks use our publicly available software and report back if works on a certain pattern, we learn a lot of these unique patterns, and it allows us to improve our algorithms. From one perspective, we are building a corpus of these complex patterns, which for language and images is readily available. "

DataCebo also recently released features to improve SDV's usefulness, including tools to assess the "realism" of the generated data, called the SDMetrics library as well as a way to compare models' performances called SDGym.

"It's about ensuring organizations trust this new data," Veeramachaneni says. "[Our tools offer] programmable synthetic data, which means we allow enterprises to insert their specific insight and intuition to build more transparent models."

As companies in every industry rush to adopt AI and other data science tools, DataCebo is ultimately helping them do so in a way that is more transparent and responsible.

"In the next few years, synthetic data from generative models will transform all data work," Veeramachaneni says. "We believe 90% of enterprise operations can be done with synthetic data."

Source: https://techxplore.com/news/2024-03-generative-ai-software.html

93
How the A.I. That Drives ChatGPT Will Move Into the Physical World

Companies like OpenAI and Midjourney build chatbots, image generators and other artificial intelligence tools that operate in the digital world.

Now, a start-up founded by three former OpenAI researchers is using the technology development methods behind chatbots to build A.I. technology that can navigate the physical world.

Covariant, a robotics company headquartered in Emeryville, Calif., is creating ways for robots to pick up, move and sort items as they are shuttled through warehouses and distribution centers. Its goal is to help robots gain an understanding of what is going on around them and decide what they should do next.

The technology also gives robots a broad understanding of the English language, letting people chat with them as if they were chatting with ChatGPT.

The technology, still under development, is not perfect. But it is a clear sign that the artificial intelligence systems that drive online chatbots and image generators will also power machines in warehouses, on roadways and in homes.

Like chatbots and image generators, this robotics technology learns its skills by analyzing enormous amounts of digital data. That means engineers can improve the technology by feeding it more and more data.

Covariant, backed by $222 million in funding, does not build robots. It builds the software that powers robots. The company aims to deploy its new technology with warehouse robots, providing a road map for others to do much the same in manufacturing plants and perhaps even on roadways with driverless cars.


Covariant’s headquarters in Emeryville, Calif. From left, Andrew Sohn, product manager; Daniel Adelberg, senior software engineer; and Anusha Nagabandi, a research scientist.

The A.I. systems that drive chatbots and image generators are called neural networks, named for the web of neurons in the brain.

By pinpointing patterns in vast amounts of data, these systems can learn to recognize words, sounds and images — or even generate them on their own. This is how OpenAI built ChatGPT, giving it the power to instantly answer questions, write term papers and generate computer programs. It learned these skills from text culled from across the internet. (Several media outlets, including The New York Times, have sued OpenAI for copyright infringement.)

Companies are now building systems that can learn from different kinds of data at the same time. By analyzing both a collection of photos and the captions that describe those photos, for example, a system can grasp the relationships between the two. It can learn that the word “banana” describes a curved yellow fruit.

OpenAI employed that system to build Sora, its new video generator. By analyzing thousands of captioned videos, the system learned to generate videos when given a short description of a scene, like “a gorgeously rendered papercraft world of a coral reef, rife with colorful fish and sea creatures.”

Covariant, founded by Pieter Abbeel, a professor at the University of California, Berkeley, and three of his former students, Peter Chen, Rocky Duan and Tianhao Zhang, used similar techniques in building a system that drives warehouse robots.

The company helps operate sorting robots in warehouses across the globe. It has spent years gathering data — from cameras and other sensors — that shows how these robots operate.

“It ingests all kinds of data that matter to robots — that can help them understand the physical world and interact with it,” Dr. Chen said.


Brandon Swaby, an engineering technician

By combining that data with the huge amounts of text used to train chatbots like ChatGPT, the company has built A.I. technology that gives its robots a much broader understanding of the world around it.

After identifying patterns in this stew of images, sensory data and text, the technology gives a robot the power to handle unexpected situations in the physical world. The robot knows how to pick up a banana, even if it has never seen a banana before.

It can also respond to plain English, much like a chatbot. If you tell it to “pick up a banana,” it knows what that means. If you tell it to “pick up a yellow fruit,” it understands that, too.

It can even generate videos that predict what is likely to happen as it tries to pick up a banana. These videos have no practical use in a warehouse, but they show the robot’s understanding of what’s around it.

The technology, called R.F.M., for robotics foundational model, makes mistakes, much like chatbots do. Though it often understands what people ask of it, there is always a chance that it will not. It drops objects from time to time.

Gary Marcus, an A.I. entrepreneur and an emeritus professor of psychology and neural science at New York University, said the technology could be useful in warehouses and other situations where mistakes are acceptable. But he said it would be more difficult and riskier to deploy in manufacturing plants and other potentially dangerous situations.

“It comes down to the cost of error,” he said. “If you have a 150-pound robot that can do something harmful, that cost can be high.”



Covariant’s A.I.-powered Robotic Putwall system

As companies train this kind of system on increasingly large and varied collections of data, researchers believe it will rapidly improve.

That is very different from the way robots operated in the past. Typically, engineers programmed robots to perform the same precise motion again and again — like pick up a box of a certain size or attach a rivet in a particular spot on the rear bumper of a car. But robots could not deal with unexpected or random situations.


As companies train robots on increasingly large and varied collections of data, researchers believe the technology will rapidly improve.

y learning from digital data — hundreds of thousands of examples of what happens in the physical world — robots can begin to handle the unexpected. And when those examples are paired with language, robots can also respond to text and voice suggestions, as a chatbot would.

This means that like chatbots and image generators, robots will become more nimble.

“What is in the digital data can transfer into the real world,” Dr. Chen said.

Source: https://www.nytimes.com/2024/03/11/technology/ai-robots-technology.html


94
Generative AI / Competition on Virtual Worlds and Generative AI
« Last post by Imrul Hasan Tusher on March 13, 2024, 02:22:53 PM »
Comments to the European Commission’s Directorate General for Competition on Virtual Worlds and Generative AI

The Information Technology and Innovation Foundation (ITIF; Transparency Register #: 923915716105-08), the world’s leading think tank for science and technology, appreciates the opportunity to contribute to the European Commission’s consultation on “Competition in Virtual Worlds and Generative AI.” This submission represents feedback from the Center for Data Innovation (Transparency Register #: 367682319221-26) and ITIF’s Schumpeter Project on Competition Policy.

SUMMARY

This submission considers competition issues impacting two important emerging technologies.

First, the market for augmented reality and virtual reality (AR/VR) technologies (i.e., the virtual worlds market), though nascent, presents a multifaceted competitive landscape spanning hardware, software, and distribution. While the potential for mutual benefit exists within the ecosystem, natural market concentration could occur in the future, potentially streamlining operations and benefiting users. However, complex and inconsistent regulations, particularly regarding data protection, can significantly hinder the growth of virtual world platforms, especially for startups. As the technology is still evolving, it is too early to predict which companies will dominate the market, and existing market power in other sectors does not necessarily translate to dominance in the metaverse. The debate between open standards and proprietary technology is ongoing. Some advocate interoperability to avoid siloed experiences, while others see value in closed ecosystems for specific needs like security. Regardless of the chosen model, fostering competition between them will likely benefit consumers and drive innovation. Currently, paid downloads and subscriptions are the primary revenue models, but alternative models like targeted advertising and blockchain-based peer-to-peer transactions are also emerging. The market is fragmented, so it is hard to predict the future, but potential antitrust concerns similar to those in traditional markets might arise in the future. However, existing legal frameworks can adapt to address these issues without requiring fundamental changes.

Second, the generative AI market is experiencing early-stage growth with no significant entry barriers evident, particularly concerning data, computational resources, and talent. Companies like OpenAI, Anthropic, and Mistral AI have succeeded in developing leading models despite not having the large user data sets of large platforms. Competition exists at multiple levels, including among cloud providers like Google, Microsoft, and Amazon, and chip manufacturers such as Nvidia, AMD, and Intel. And while firms compete for talent, Mistral AI has demonstrated it is possible to be successful with a small team. Competition among firms making generative AI models centers on performance, price, speed, interface, licenses, customization, and specialization. Varying degrees of vertical integration among large tech companies, such as Meta, Amazon, and Google, as well as different approaches to producing open-source versus proprietary generative AI models, indicate firms are pursuing different strategies which provides users with more choice. Finally, acquisitions and partnerships between established firms and emerging ones fuel growth and innovation and foster competition.

VIRTUAL WORLDS

1. What entry barriers or obstacles to growth do you observe or expect to materialise in Virtual World markets? Do they differ based on the maturity of the various markets?

Regulatory barriers pose a significant challenge, notably in raising the cost of entry to markets and making it more difficult to scale up businesses, particularly for startups. Non-uniform regulations across jurisdictions can exacerbate this challenge, hindering the expansion of virtual world platforms. For example, complex, contradictory, and duplicative data protection regulations, particularly concerning biometric data and bystander privacy, can create significant hurdles. Compliance with stringent data protection measures may not only require substantial financial investments but also implementing technical adaptations. Similarly, restrictions on targeted advertising could impede growth, constraining how companies can reach potential customers and monetize their services. Additionally, regulations governing the provision of services to youth impose restrictions that may limit the scope of virtual world platforms, curtailing their potential user base and revenue streams. Finally, laws and regulations designed for existing, centralized services might not work well for newer, decentralized ones, creating even more challenges.

2. What are the main drivers of competition for Virtual World platforms, enabling technologies of Virtual Worlds and/or services based on Virtual Worlds (e.g., access to data, own hardware or infrastructure, IP rights, control over connectivity, vertical integration, platform and payment fees)? Do you expect that to change and, if so, how?

Competition within the metaverse ecosystem is multifaceted, spanning various layers of the AR/VR stack, from hardware components to distribution platforms and applications. This includes components like displays, cameras, and controllers, as well as devices such as headsets and handheld devices. Additionally, competition extends to 3D capture, audio and video tools, software development kits, and distribution platforms, among others.1 One notable aspect of competition in this space is the potential for mutual benefit. For instance, the success of a VR game can attract more users to the platform, thereby creating opportunities for other gaming companies to sell their rival games. Similarly, the success of a VR headset can broaden the appeal of the metaverse to users, ultimately lowering barriers to adoption for other players in the market. To be sure, by virtue of these positive network externalities, as AR/VR technologies mature there may be a shift towards natural market concentration. While this may seem counterintuitive to competition, it can actually streamline the ecosystem and benefit stakeholders. For example, fewer competing development platforms can make it easier to deploy applications across multiple devices, enhancing interoperability and user experience, as well as incentivizing follow-on incremental innovations that benefit consumers.

3. What are the current key players for Virtual World platforms, enabling technologies of Virtual Worlds and/or services based on Virtual Worlds, which you consider or expect to have significant influence on the competitive dynamics of these markets?

The market for metaverse platforms is still nascent. Between 2016 and 2021, the three leading VR headset makers—Meta, Sony, and HTC—sold 20.3 million units globally, with the Quest 2 accounting for three-fourths of those sales.2 To put this in comparison, Apple sold 232 million iPhones in 2023 alone.3 Similarly, Meta’s Quest platform reportedly had 6.4 million monthly active users as of October 2022 whereas Sony’s PlayStation gaming platform has 112 million monthly active users during the same period.4 Given the early stage of this technology, it is too soon to determine if any of the initial key players will have a significant influence on the competitive dynamics of the market. For example, it remains to be seen whether consumers will adopt more premium headsets, such as the Apple Vision Pro or Microsoft HoloLens, or more affordable headsets, such as the Meta Quest 2 or HTC Vive Cosmos. Given the relatively small size and unpredictability of the market for metaverse platforms, no company has sufficient market power to distort competition.

4. Do you expect existing market power to be translated into market power in Virtual World markets?

Nobody can predict the future, and competition authorities should not base policy decisions today on any such unprovable, speculative claims about tomorrow. Some businesses with existing market power in current digital technologies are investing their resources in developing next-generation technologies and it is possible that they will emerge as leaders in immersive technologies. However, their success in future technologies is not a foregone conclusion. History shows that competition often comes from disrupters who challenge dominant firms with new technologies (what Austrian economist Joseph Schumpeter famously termed “creative destruction”), such as online streaming companies displacing video rental stores. Indeed, the threat of disrupters challenging today’s leading platforms is one reason some existing technology companies are investing in research and development (R&D) for new products and services related to the metaverse.

5. Do you expect potential new entrants in any Virtual World platforms, enabling technologies of Virtual Worlds and/or services based on Virtual Worlds in the next five to ten years and if yes, what products and services do you expect to be launched?

As noted above, disruptive innovations are invariably accompanied by new firms challenging old incumbents, and AR/VR markets are no different. Additionally, ITIF has co-organized the AR/VR Policy Conference every year since 2021 with the XR Association.5 Over these three years, there has been a steady influx of new ideas, products, and businesses working in the immersive technology sector. In particular, there is a growing set of products and services that go beyond gaming, which is the primary use of the technology today, to include applications focusing on education, business, entertainment, and fitness. As these application areas mature, they will need supporting technologies, including additional apps, services, and peripherals. In addition, advances in generative AI offer new opportunities for creating content more easily for virtual spaces.

6. Do you expect the technology incorporated into Virtual World platforms, enabling technologies of Virtual Worlds and services based on Virtual Worlds to be based mostly on open standards and/or protocols agreed through standard-setting organisations, industry associations or groups of companies, or rather the use of proprietary technology?

Many of the early proponents of the metaverse have committed to a long-term vision of interoperability and do not want to see these platforms develop into walled gardens.6 Indeed, the idea of the metaverse—a shared, immersive virtual space where people can interact online—as a successor to today’s Internet depends on it being built using open and interoperable standards and protocols. However, while many companies will pursue that vision, others may prefer to build closed ecosystems to address specific customer needs, such as privacy, security, safety, cost, or performance. Allowing competition between different models—both open and closed—will give consumers more options and allow businesses to innovate more quickly than forcing the industry to adhere to a single set of standards.

7. Which data monetisation models do you expect to be most relevant for the development of Virtual World markets in the next five to ten years?

Paid downloads are the primary revenue model for many leading apps on VR platforms, such as the Quest Store. For example, over half of the apps (52 percent) in Apple’s App Store for the Vision Pro are paid downloads, compared to only 5 percent of mobile apps in the App Store.7 Others earn money through subscriptions. As of March 2023, 40 titles in the Quest Store generated more than $10 million in revenue.8 However, as the number of apps grows on these platforms, they may use alternative business models, including targeted online advertising, to provide free or low-cost apps. The relative number of apps on these platforms is still small, especially compared to the mobile app market, which numbers in the millions. In contrast, Meta announced that there were more than 500 apps available on the Quest Store in March 2023, and Apple announced in February 2024 that it had more than 600 apps available for its Vision Pro headset.9

Moreover, some metaverse platforms may enable an ecosystem of peer-to-peer transactions built on blockchain technology. For example, creators may buy and sell virtual land, objects, clothing, and more in these immersive 3D worlds. These transactions, especially if they are on the blockchain—a digital public ledger of transactions—could create new data streams that businesses may analyze or monetize. In addition, consumers may choose to use blockchain technology to directly monetize their own data, such as creating smart contracts that allow advertisers to access their data for a given price or enforce data usage policies when they provide access to their personal data.

8. What potential competition issues are most likely to emerge in Virtual World markets?

Currently, the virtual world market is dynamic and fragmented; no company has sufficient market power to distort competition. Moreover, there is competition in virtual worlds from both Web 2 (e.g., user-generated content and social media) and Web 3 companies (e.g., blockchain and other decentralized services with new forms of digital content ownership)—in other words, a mix of incumbents and new players.

While predicting future competition in virtual worlds with certainty is impossible, potential antitrust issues—such as those on the physical and digital markets—may occur in certain circumstances just as they can in any market. For example, firms with market power might leverage their power in ancillary markets to obtain power in virtual world markets as well; however, their success in other markets does not at all guarantee their future success in immersive technologies, and leveraging strategies can not only fail for a variety of reasons but also lead to procompetitive outcomes. In particular, companies investing in next-generation virtual world technologies may engage in self-preferencing to bolster innovation and user experience, potentially leading to a more robust and competitive virtual world ecosystem in the long run. There are also pro-competitive aspects of limiting interoperability, as closed ecosystems might be able to better address specific customer needs than interoperable competitors. Although virtual worlds might become siloed, with users unable to move their digital assets or avatars between different platforms without interoperability, allowing competition between different models—both open and closed—will often give consumers more options in practice and encourage businesses to invest in innovation.

9. Do you expect the emergence of new business models and technologies to trigger the need to adapt certain EU legal antitrust concepts? & 10) Do you expect the emergence of new business models and technologies to trigger the need to adapt EU antitrust investigation tools and practices?

The rise of decentralized services may seem to push the boundaries of traditional antitrust concepts. For example, identifying dominant undertakings—operating without a central entity and relying on distributed networks—may present difficult facts that make it harder to identify the undertakings with market power. In addition, in a decentralized system, decision-making and control might be spread across a network, resulting in difficulties and liability issues within the network. However, the emergence of new business models and technologies should not by itself trigger the need for new EU or other legal antitrust concepts. Consider the rise of the Internet. The Internet created a new environment for communication, information sharing, and commerce. It brought new technologies such as web browsers, search engines, email, and e-commerce platforms that emerged to facilitate online activities and serve users. Along the way, new business models emerged. Companies like Amazon, eBay, and Google adopted innovative business models like online retail marketplaces, auction platforms, and search advertising, which thrived in the new digital landscape. These models revolutionized traditional brick-and-mortar retail and advertising industries, communication, and the way of life in general—and up until recently, these could fit into the traditional frames of antitrust laws and enforcement. And, while the EU enforced its antitrust laws vigorously, it did not change its core competition law framework. As long as emerging business models and technologies can be evaluated within the existing legal framework, legislation should rest. The same is true here: The European Union and its member states have a well-structured legal and judicial system to answer interpretation questions and provide remedies. Antitrust enforcers and judges will develop expertise in the virtual worlds and better understand their technical aspects and economic dynamics to assess competition issues effectively.

GENERATIVE AI

1. What are the main components (i.e., inputs) necessary to build, train, deploy and distribute generative AI systems? Please explain the importance of these components

Data, compute, and talent are the three main components necessary to build, train, deploy, and distribute generative AI systems. Data is essential to train generative AI models.10 AI models, like BERT, Claude, GPT, Llama, and Gemini, use a transformer-based architecture where increasing the amount of training data, especially high-quality data, results in improvements in performance.11

Compute refers to the computational resources, such as high-performance processors and specialized hardware, required to train and run AI models.12 Training a generative AI model requires more computing power as the amount of training data and size of the model increases.13 GPT 4, one of the most advanced AI models, required training that used 25,000 A100 chips for about 2,300 hours.14 These chips are available for less than a dollar per hour, so the cost of training the model at that rate would have been under fifty million dollars.15 This amount is a large sum for an individual, but not an entry barrier for a firm competing in this sector.

Talent refers to professionals who possess the requisite skills and knowledge to develop and deploy AI models. However, there is no commonly agreed-upon definition of what constitutes “AI expertise” or the “AI workforce,” which means there is no common definition of a skills gap problem despite broad consensus that there is one. Indeed, existing literature on the AI labor market vastly disagrees on the pervasiveness, scale, and concentration of skill misalignments. There are many types of AI expertise one can include in a measure of the AI workforce, ranging from a top computer scientist who can lead an AI R&D team, to an entry-level engineer who is not an AI specialist but has sufficient skills to execute coding tasks.16 There are also many different domains of expertise, including experts in hardware, software, and data.

2. What are the main barriers to entry and expansion for the provision, distribution or integration of generative AI systems and/or components, including AI models? Please indicate to which components they relate.

The generative AI market is still in its early stages, and as of now, there is no evidence of significant entry barriers. Concerns about data being an entry barrier in AI are speculative and unsubstantiated. Firms seeking to create generative AI models can use data from various sources, including publicly available data on the Internet, government and open-source datasets, datasets licensed from rightsholders, data from workers, and data shared by users. They also have the option to generate synthetic data to train their models.17 Some firms, such as OpenAI, Anthropic, and Mistral AI, have succeeded in creating leading generative AI models despite not having access to the large corpus of user data held by social media companies such as Meta or X.com. Additionally, companies with internal data can leverage it to build specialized models tailored to specific tasks or fields, such as financial services or healthcare.

Similarly, compute resources required for training generative AI models have not proven to be an entry barrier. There are numerous players in the cloud server market that provide the necessary infrastructure for training and running AI models. For example, Anthropic used Google Cloud to train its Claude AI models.18 In terms of chips, Nvidia’s graphics processing units (GPUs) are popular, but face meaningful potential competition from firms like AMD and Intel.19 Other firms are also investing in chip design and manufacturing, fostering competition in the market.20 For example, Google has invested heavily in Tensor Processing Units (TPU), specialized chips designed to train and run AI models.

Talent, another essential input in building AI models, is not a barrier either. For example, Mistral AI, a French startup that makes open-source and closed AI models, has demonstrated that it is possible to build industry-leading models with a team of fewer than 50 employees.21

3. What are the main drivers of competition (i.e., the elements that make a company a successful player) for the provision, distribution or integration of generative AI systems and/or components, including AI models?

Generative AI firms compete on various dimensions to differentiate themselves in the market. The main drivers of competition include performance, price, speed, interface, licenses, customization, and task specificity. In terms of pricing, many generative AI systems providers, such as OpenAI, Gemini, and Claude, offer chat interface access to their models at around $20 per month.22 However, the prices for API access may vary between these providers.23 Speed is another crucial factor, with models like GPT Turbo and Claude Instant focusing on delivering faster inferences to meet the demands of real-time applications.24 Firms also compete on the licensing structure. Meta's Llama model, along with Mistral's Mixtral-8x7B and Mistral 7B models, and the BLOOM model created by independent researchers, use an open-source license that allows users to use, reproduce, distribute, and modify the original model. This freedom allows developers to use and adapt the model to a diverse set of applications, including some of which the original model developers might not have accounted for. Other generative AI models, including GPT-3 from OpenAI and Claude from Anthropic, have built proprietary models. From a consumer’s point of view, having different licensing models increases the options in the market. From the supply side, an open-source model maker does not bear the full cost of creating and sharing the models. For example, Mistral shared its models via a torrent file on a peer-to-peer file-sharing network. Hardware advancements are also driving competition in both the provision and distribution of models. As the AI industry matures, there is a growing demand for specialized AI systems. Companies like Bloomberg have recognized this need and have created its own models tailored to specific applications of AI in finance.25

Looking ahead, there will be increasing opportunities for AI systems to specialize in niche areas while also providing general capabilities. Successful players in the AI market will be those who can effectively balance these various dimensions of competition and deliver value to their customers through innovative solutions.

4. Which competition issues will likely emerge for the provision, distribution or integration of generative AI systems and/or components, including AI models? Please indicate to which components they relate.

The rapid development of generative AI systems has generated concerns from some regarding potential anticompetitive practices by large, vertically integrated firms that control the entire AI stack, from cloud infrastructure to applications, and may use tactics that stifle competition and hinder innovation, such as excluding downstream rivals. This could involve restricting access to essential cloud resources or copying and integrating features from competitors, which results in effectively squeezing them out of the market due to their own scale and reach. Additionally, these firms might prefer their own AI products and services within their ecosystem, further limiting market access for new entrants.

Enforcement should not be based on theoretical concerns but on specific evidence of anticompetitive behavior that harms consumers. Moreover, this behavior can also result in procompetitive benefits that outweigh any possible anticompetitive harms. Indeed, vertical integration often results in a number of procompetitive benefits relating to increased scale and scope that can drive innovation and consumer benefits. Concerns about speculative competitive harms should not outweigh real benefits to innovation and consumers. The competitive dynamics in the AI hardware and cloud markets suggest these concerns have not materialized. While firms like Google are heavily vertically integrated, Nvidia, Microsoft, and Amazon have partnered with multiple generative AI startups, such as OpenAI, Mistral, and Anthropic, as well as larger generative AI companies, such as Meta. In other words, competition between large firms at the hardware and cloud levels is facilitating, rather than stifling, competition at the model and applications level.

5. How will generative AI systems and/or components, including AI models likely be monetised, and which components will likely capture most of this monetization?

Firms have various opportunities to monetize generative AI systems and components. On the components side, compute providers can monetize by renting compute resources through cloud platforms. Many of the compute providers are engaged in model making as well, where they use up these resources themselves. These compute providers can either build the chips in-house or purchase them from chip manufacturers. Firms, such as news agencies and social media platforms, can monetize data they own or control, while firms can also create and sell synthetic data.

Some components are not always monetized. For example, firms may use open-source datasets, such as Pile, which have been instrumental in training generative AI models.26 Some firms monetize model access via chat interfaces or APIs, including OpenAI with ChatGPT, Google with Gemini, and Anthropic with Claude. These firms charge for the use of these models through chatbots or charge per API call. Aggregators, like poe.com, provide access to multiple models for a single subscription.

In a market economy, each component will be compensated according to its marginal contribution. High returns on one component will attract investments and increase its availability, eventually driving down its return. Furthermore, in an innovative and rapidly evolving industry like AI, new use cases and monetization methods will continually emerge.

6. Do open-source generative AI systems and/or components, including AI models compete effectively with proprietary AI generative systems and/or components? Please elaborate on your answer.

Open-source generative AI systems and components effectively compete with proprietary AI systems in the market. Many of the AI systems making significant technological advances are based on the publicly available "Attention is All You Need" paper from the Google DeepMind research team.27 Recognizing the importance of open-source models, industry leaders like Meta and Google have released open-source AI models such as Llama and Gemma, respectively. In addition, open-source models like Mistral 7B, Vicuna, and Zephyr 7B have made remarkable progress and score well on many benchmarks.28 Open-source generative AI models offer the advantage of being adaptable and modifiable by firms, enabling their use in a wide range of applications that model makers might not have initially anticipated. This flexibility is a major selling point for innovative downstream applications as well as for firms that create new models based on existing open-source ones.29

Open-source data sources allow new firms to enter the market with fewer resources, which stimulates procompetitive follow-on innovations. Talented individuals can also contribute to the knowledge base by participating in open-source projects. As a result, open-source plays a crucial role in ensuring competition between AI firms, helping outsiders enter the market, and contributing to the growth of the scientific research base and knowledge sharing. Indeed, the continuing success of open-source AI systems and components confirms their ability to compete effectively with proprietary alternatives.

7. What is the role of data and what are its relevant characteristics for the provision of generative AI systems and/or components, including AI models?

Please see prior responses.

8. What is the role of interoperability in the provision of generative AI systems and/or components, including AI models? Is the lack of interoperability between components a risk to effective competition?

Please see prior responses.

9. Do the vertically integrated companies, which provide several components along the value chain of generative AI systems (including user facing applications and plug-ins), enjoy an advantage compared to other companies? Please elaborate on your answer.

Vertically integrated companies that provide multiple components along the generative AI value chain, including user-facing applications and plug-ins, do not necessarily enjoy an advantage over other companies. Each firm chooses its level of integration based on what it believes will be most profitable. For example, Meta has decided to build large language models (LLMs) and make them available via an open-source license. And while Meta has access to compute infrastructure, it does not provide cloud services or allow others to run its models on its servers. On the other hand, Amazon provides cloud services but it is not building LLMs. Google provides cloud services and has built LLMs that run on its compute resources. The presence of varying degrees of vertical integration among large tech companies suggests that each approach has its own tradeoffs and not all firms will adopt the same integration strategies.

Microsoft, for example, integrates a generative AI system (Copilot) with its operating system, web browser, and office productivity suite to enhance the user experience. However, this does not imply an inherent advantage over standalone AI systems like those developed by Mistral AI. Microsoft must work within specific constraints to ensure compatibility and usability within the Windows environment, which differs significantly from creating an open-source AI model like Mistral does.

Companies pursue vertical integration to achieve efficiency gains, which benefit consumers and competition, which can be essential to recoup fixed costs. For an AI firm entering the chip manufacturing market, high initial investments are required without any guarantee that their chips will outperform available alternatives. Moreover, even if the vertically integrated firm successfully manufactures superior chips, the competitive advantage may be short-lived if rivals can achieve greater efficiency gains or other innovations.

10. What is the rationale of the investments and/or acquisitions of large companies in small providers of generative AI systems and/or components, including AI models? How will they affect competition?

Large companies, both in the tech industry and beyond, are investing in and acquiring smaller generative AI firms to integrate this technology into their existing offerings. AI has diverse applications in many different fields, and companies recognize the potential benefits it can bring to their products and services. For example, Google could use AI to summarize search results, while McDonald’s could employ AI systems to optimize drive-thru operations.30

Tech companies have been particularly active in acquiring AI firms due to the greater synergy between their existing products and AI technologies. These acquisitions and partnerships provide smaller AI firms with access to more funding and resources, enabling them to scale and innovate faster. The possibility of a lucrative buyout also incentivizes venture capitalists and entrepreneurs to invest in new AI startups, fueling further growth and innovation in the sector.31

In the rapidly evolving LLMs market, incumbent tech firms are making significant investments and engaging in direct competition. Microsoft has invested in OpenAI, while Amazon has partnered with Anthropic. Alphabet has entered the fray with its Gemini models, and Meta has contributed to the development of Llama models.32 This increased involvement from major players has intensified competition and accelerated innovation in the LLM space. OpenAI’s GPT-3 was a breakthrough made possible by funding from Microsoft. Anthropic later released its Claude models and increased the context window to two hundred thousand tokens to be a differentiator. To match that move, OpenAI released GPT-4 Turbo with a context window of 128,000 tokens.33 Gemini models from Google have a token window of up to a million tokens.34 These examples showcase the competition within the market for LLMs on a single dimension. Further, even as the models have gotten better the prices have stayed at $20 per month for the chat version.

11. Do you expect the emergence of generative AI systems and/or components, including AI models to trigger the need to adapt EU legal antitrust concepts?

In lieu of legal adaptations, the EU should prioritize enforcing its existing competition laws in a way that strikes the balance between fostering innovation and combatting harmful anticompetitive behavior if it occurs.

The rise of generative AI is a new wave of Schumpeterian competition—a dynamic form of competition driven by innovation, creative destruction, and the constant flux of market leadership positions—that will result in tremendous benefits for consumers. The nascent and dynamic nature of the market, the growth of both open and proprietary AI models, and the potential benefits of procompetitive practices by leading companies (e.g., sharing anonymized data sets, open-source collaboration), all weigh heavily against new laws or regulations to address generative AI.

Laws are ultimately designed to serve people; they do not exist for their own sake. Therefore, policymakers should be vigilant in adapting the law when necessary to protect consumers and ensure a healthy and competitive market, but not before a necessity for this type of ex-ante regulation to address market failure arises.

12. Do you expect the emergence of generative AI systems to trigger the need to adapt EU antitrust investigation tools and practices?

Competition authorities should develop expertise in generative AI systems to effectively assess potential issues using existing frameworks. Traditional investigation methods—often focusing on tangible assets and market share—might require adjustments to handle the unique characteristics of this evolving landscape.

Source: https://itif.org/publications/2024/03/08/comments-to-dg-comp-on-virtual-worlds-and-generative-ai/?utm_source=ITIF+Newsletter+Subscribers&utm_campaign=e99adffb39-EMAIL_CAMPAIGN_2023_05_20_06_01_COPY_01&utm_medium=email&utm_term=0_-2a5bf84f26-%5BLIST_EMAIL_ID%5D&mc_cid=e99adffb39&mc_eid=b61832c89d
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Robotics / How Innovative Is China in the Robotics Industry?
« Last post by Imrul Hasan Tusher on March 13, 2024, 02:10:48 PM »
How Innovative Is China in the Robotics Industry?

KEY TAKEAWAYS

1. Robotics is one of the most important advanced technology industries of our time and will play an increasing role in the world economy.
2. China’s goals are to first become self-sufficient in robotics and to then lead the industry globally.
3. China is already the largest industrial robot market in the world. It accounted for 52 percent of robot installations worldwide in 2022, giving Chinese robot producers a key advantage in their home market.
4. Chinese robotic firms are also gaining global market share, but by and large they are “fast followers,” competing principally on lower costs while still relying on more advanced Western companies for key inputs.
5. The Chinese robotic innovation ecosystem is dynamic, and the Chinese government is making massive investments to develop the industry domestically.
6. Given the recent development of other industries in China, it is likely that China will become as innovative as foreign robotic producers in the midterm at the latest, while also enjoying a significant cost advantage.
7. Absent coherent policy responses by Western nations, China’s share of robotics production will likely fall significantly.

INTRODUCTION

A robot is a machine that can perform complex actions automatically. With advances in both hardware and software, including artificial intelligence (AI), MEMS (micro-electromechanical system), and vision recognition, robots are becoming ever more capable and versatile. As such, they are likely to be one of the most important technologies of the next few decades. Robots are already used in a wide range of applications and industries, including manufacturing, logistics, hospitality, healthcare, construction and many other areas—and hold great promise for reversing the current global productivity slowdown.

Yet, robotics is the Rodney Dangerfield of the technology field: It gets no respect. That is a mistake because, going forward, robots increasingly will be used in most areas involving shaping, making, or moving physical items. In addition, humanoid robots will likely be adopted to help humans with a wide array of tasks. And they are an important defense and dual-use technology.

While the United States invented robotics, it is now an also-ran—at least in production—with the leading robotics companies located in engineering powerhouses of Germany, Japan, and Switzerland. However, by volume, both of production and use, China leads the world. And as with so many technologies, China has significant cost advantages. But can Chinese robotics firms innovate and reach the same level of quality as that of the world leaders? This report assesses this question.

BACKGROUND AND METHODOLOGY

The common narrative is that China is a copier and the United States the innovator. That narrative often supports a lackadaisical attitude toward technology and industrial policy. After all, we lead in innovation, so there is nothing to worry about. First, this assumption is misguided because it is possible for innovators to lose leadership to copiers with lower cost structures, as we have seen in many U.S. industries, including consumer electronics, semiconductors, solar panels, telecom equipment, and machine tools. Second, it’s not clear that China is a sluggish copier and always destined to be a follower.

To assess how innovative Chinese industries are, the Smith Richardson Foundation provided support to the Information Technology and Innovation Foundation (ITIF) to research the question. As part of this research, we are focusing on particular sectors, including robotics.

To be sure, it is difficult to assess the innovation capabilities of any country’s industries, but it is especially difficult for Chinese industries. In part, this is because, under President Xi, China discloses much less information to the world than it used to, especially about its industrial and technological capabilities. Notwithstanding this, ITIF relied on three methods to assess Chinese innovation in robotics. First, we conducted in-depth case study evaluations of three Chinese robotics companies randomly selected from robotics companies listed on the EU R&D 2000 list. Second, we conducted interviews and held a focus group roundtable with global experts on the Chinese robotics industry. And third, we assessed global data on robotics innovation, including scientific articles and patents.

IMPORTANCE OF ROBOTICS AND THE U.S. ROLE

The United States invented robotics, but like so many other industries, it lost leadership to foreign competitors, in part due to a lack of patient capital; companies in other nations were willing to invest for the long haul. Today, the leading robotics producers are in Germany, Japan, and Switzerland, with China working vigorously to catch up. According to one study, Japan accounted for 46 percent of global robotics output and 36 percent of global exports in 2022.1 In contrast, the United States accounted for just 5.4 percent of global exports, despite having a gross domestic product (GDP) that was more than three times larger. In other words, Japan had a robot export intensity 20 times greater than America’s.

Today, there are no foundries making industrial robots in the United States. While major robotics firms such as ABB and Fanuc have a presence in the United States, most of their research and development (R&D) and advanced production takes place in their home countries. Moreover, few component suppliers are in the United States. This is why, in 2022, the United States ran a $1.26 billion trade deficit in robotics, with exports being just 28 percent of the value of imports.2

Notwithstanding the overall U.S. lag in robotics production, the United States is home to such companies. For example, Productive Robotics, a California-based robotics company that manufactures 95 percent of its parts in America, creates multi-axis collaborative robots that aid in automating the machining process.3 In addition, Ingersoll Machine Tools, headquartered in Illinois, developed the Master Print Robotic, which effectively combines 3D printing and computer numerically controlled (CNC) milling into one machine. The United States also has many innovative robotics start-ups, in part because of strong software capabilities; and U.S. companies such as Rockwell Automation (an ITIF supporter) are strong on the services side of this business. Nonetheless, innovation does not always translate into production and sales leadership, especially if fast followers in other nations can quickly and effectively copy, and have a price premium.

CHINA’S ROBOTICS INDUSTRY AND MARKET

According to data from the International Federation of Robotics (IFR), China is the world’s largest consumer of industrial robots. In 2021, China had installed 18 percent more robots per manufacturing worker than did the United States. And when controlling for the fact that Chinese manufacturing wages were significantly lower than U.S. wages, in 2021, China had 12 times the rate of robot use in manufacturing than did the United States. The reason for this was not market forces, but rather government policy. The Chinese Communist Party (CCP) has made manufacturing robot adoption a top priority, backing it up with generous subsidies.

IFR provides data on robot use in manufacturing by various nations. Korea was the world’s largest adopter of industrial robots, with over 1,000 robots per 10,000 manufacturing workers, while Singapore was second with 730, followed by Japan and Germany with close to 400 each. The United States had 285 robots per 10,000 workers, while China had 392. (See figure 2.)

But the decision to install and run a robot is usually based on the cost savings that can be achieved when a robot can perform a task instead of a human worker—and those cost savings are directly related to the compensation levels of manufacturing workers. It should therefore come as no surprise that high-wage Germany has a higher penetration rate of robots than does low-wage India. But the interesting question is how national economies perform in robot adoption when controlling for wage levels, given that the payback time for a robot gets shorter as manufacturing labor costs increase. For a full description of ITIF’s methodology on this question, see the 2018 report “Which Nations Really Lead in Industrial Robot Adoption?”5

In 2022, 52 percent of all industrial robots installed in the world were installed in China, up from 14 percent a decade earlier.

Comparing robot adoption rates as a share of the adoption rates that would be expected based on countries’ manufacturing wage levels, we can see that China leads the world with an astounding 12.5 times more robots adopted than would be expected, up from 1.6 times more in 2017.6 (See figure 3.) The United States had just 70 percent of expected robots adoption given its manufacturing wages.

Indeed, when it comes to robot adoption, China appears to be in a class of its own, with its national and provincial governments committing massive amounts of money to subsidize adoption of robots and other automation technology. This is one reason why, according to IFR, China has been the world’s largest market for industrial robots for eight consecutive years.8 In 2022, 52 percent of all industrial robots in the world were installed in China, up from 14 percent a decade earlier.9 The fact that the Chinese automobile industry is now the largest in the world is also a boon to Chinese robotic adoption, as the auto industry is a major purchaser of industrial robots.

Assessing Chinese Robotics Innovation

This enormous and rapidly growing demand for robotics in China means that most of the major Western robot manufacturers have set up production operations there, existing Chinese companies have expanded, and many new start-ups have been created.

In Shanghai, ABB and Fanuc have built the largest robot production factories in the world, with facilities even more advanced than what Fanuc has in Japan. Japan’s Yaskawa Electric Corporation has built three factories in China that can produce 18,000 units of robots annually. And if recent history is any guide, China is using this foreign investment to capture knowledge and force technology transfer to Chinese robot makers. Even when foreign investors put tight controls on intellectual property theft, there are spillover effects in terms of industrial knowledge that help Chinese firms close the innovation gap.10

China has many domestic robotics companies, such as Geek Robotics, Hikvision, and Blue Sword (robots for China’s military). In fact, since 2017, there have been over 3,400 robotics start-ups in China—not just industrial robots, but also autonomous mobile robots (AMRs). This was part of China’s “100 Million Robot Program.” Moreover, China has made very rapid progress in the development of robotics companies in the last year. For example, Tracxn lists 188 Chinese robotics start-ups.11 Of the top 10, 8 have venture investors from outside China, indicating their innovative potential.12

Many of these start-ups are from Songshan Lake, an industrial development zone south of Dongguan, China, that has hundreds of robotics companies, both start-ups and established.13 While some of this may be hype, one Hong Kong professor stated that “people here [at Dongguan] can develop a new tech product 5 to 10 times faster than in Silicon Valley or Europe, at one-fifth or one-fourth the cost.”14

Notwithstanding this growing domestic production, China is still the largest importer of industrial robots, which suggests that it is still relying heavily on foreign technologies.15 In 2019, 71 percent of new robots in China were sourced from overseas, including from Japan, South Korea, Europe, and the United States. Core components are dominated by Japan and other firms.16 For example, Chinese firms hold just 25 percent of the market for harmonic gear reducers. Indeed, China is dependent on many imported components. As one Chinese analyst stated, “The value of imported parts is still very high in the robots exported by China.”17 In 2022, China exported just 36 percent of the value of robotics that it imported.18 Another study looked at three key upstream systems going into industrial robots, robot gear reducers, robot controllers, and robot servo systems.19 These three key inputs account for almost 70 percent of production costs of industrial robots. In 2020, these were predominantly made by foreign companies, particularly Japan, Germany, and Switzerland. The study suggests that most of China’s industrial robot firms are system integrators, performing lower-value-added work.

However, China lags behind in at least two areas. The first is software. About 80 percent of the value of today’s robotics is software, and one differentiator of robot quality and versatility is software. And China still lags behind in industrial software capabilities. As one expert we interviewed noted, “We see a lot of copycat hardware, but most of what differentiates vehicle warehouse robots, especially in terms of throughput capacity, is driven by the software capabilities, and China is behind there.” The second is integrated systems development and robotics as a service as a business model, where China is weaker than Western companies.

Moreover, many Chinese robotics companies are copiers. One expert reported that Japanese robot producer Fanuc found its foundry cast mark on a Chinese competitor’s robot. As another example, after Boston Robotics developed its dog-like walkable robot, several years later, Chinese companies copied it.

While Chinese robots generally do not match the quality of the best Western companies, they usually have a price advantage, and for many companies, particularly not in high-income countries, such a cost-quality trade-off is one worth making. For these customers who are less demanding, the low price is attractive. As one expert told us, many Chinese robots are 80 percent as good as the best foreign ones, but are much cheaper. This price point drives sales. According to Dr. Anwar Majeed, associate professor at the School of Robotics, XJTLU Entrepreneur College, “It is worth noting that its products are estimated to be 30 percent cheaper than their European and Japanese counterparts allowing them to be more attractive to rising economies.”20 For example, Chinese firm Humanoid’s price point is around $90,000, five times less than Western firms. As one article states, Chinese robot producers are currently competing on cost.21 Indeed, the strategy appears to be to trade quality for price to achieve scale. Gain sales at the low and medium end of the market (from the leading players) and then reinvest (along with help from the government) in higher-end, more innovative offerings.

China is, however, innovating in certain markets. For example, experts have argued that Chinese firms such as Geek and HAI are innovators in the materials handling space. Leader Drive is strong in components. Unitree is a robotics start-up moving fast to close to the gap. Their robots, such as BD, are not quite as good, but they are much cheaper, so they are adopted by universities and other organizations that do not need the same level of quality. China is also making progress in emerging areas of robotics, especially humanoid robots. China’s Ministry of Industry and Information Technology (MIIT) has announced its plan to dominate this by 2027 and is providing significant state funding to companies for this.22 Indeed, leaders say it has a plan to, within two years, mass produce humanoid robots that can “reshape the world.”23

China has also used foreign acquisition to gain capabilities. Most notably, in 2016, Midea Group announced its acquisition of Germany robot maker KUKA.24 Likewise, EFORT bought or invested in three robotics companies in Italy, including CMA, Evolut, Robox, and WFC Group.25 Chinese industrial robot maker Estun acquired or invested in several foreign robot companies, including BARRETT (a U.S. exoskeleton drive system company) and fMAi (Germany), while partnering with a leading European robot producer, CLOOS.26

On the whole, it appears that China and Chinese robotic companies recognize that they need to pivot from being fast followers and copying to being innovators. One way they are doing this is by focusing on many projects that are cutting edge. Moreover, the government is forcing robotics researchers at universities to rub shoulders with companies. As such, while China is still largely a follower in robotics, it is becoming a fast follower.

At the same time, China has followed this path in other technologies to become innovation leaders. A case in point is DJI, the world-leading drone maker. DJI dominated drones by using thousands of engineers and a scale of manufacturing and R&D that was well beyond anything any other company was doing elsewhere. Similarly, as one study of Chinese robotics argues:

Upgrading trajectory of industrial robots … is similar to the development of the mobile phone sector in China: at first, the domestic firms provided slightly lower quality but much cheaper alternatives to foreign produced high-end phones; and later on, when the domestic firms accumulated enough resources, they could make significant technological breakthroughs and become internationally competitive.27

As such, there are different views of China’s innovative capabilities. One expert told ITIF that because of this fast progress, “China is at least on-par, and possibly ahead, of the United States and Europe in robotics. China’s firms are strong on the hardware side of robots, especially for automotive.”

One participant stated, “The Chinese companies are innovators in the materials handling space.” They’ve launched products we haven’t in the United States. HAI Robotics would be a good example. But it appears the domestic Chinese firms are getting the most penetration in the second- and third-tier markets, at least on the AMRs and robots for retail applications.”

However, another expert told us that in the field of AMRs, “China is deploying a fast follower strategy, but they are rapidly catching up. Right now, their robots aren’t as good as ours, but are much cheaper, so are being adopted by educational institutions or the less serious commercial customers. But that gap is closing.”

Another expert said that “China will be able to close the gap, just a question of how long it takes.”

One reflection of how China is not leading in terms of innovation (and quality) is the fact that foreign firms probably account for 75 percent of the Chinese robotic market today, with domestic firms about 25 percent. Core components are dominated by Japanese firms.

Innovation Data

Innovation data is useful to assess innovation capabilities but still has significant limitations. Academic publications don’t necessarily translate into commercial innovation capabilities performance. Similarly, patents don’t distinguish between high-value patents and low. Nonetheless, they can provide insights, especially with regard to trends.

China leads the world in robotics patents, accounting for 35 percent of the world’s total between 2005 and 2019.28 By comparison, the United States accounted for about 13 percent of total robotics patents. Interestingly, a report on robotic patenting by CSET finds that 92 percent of the robotic patents filed in China are from universities, while that rate is only 8 percent in the United States.29 In contrast, just 4 percent of robotic patents in China are filed from companies, versus 82 percent in the United States. But China files almost three times more robotic patents than does the United States. Of the top 20 organizations filing robotic patents between 2005 and 2019, none were American and 7 were Chinese.30 However, patents filed in China are not equivalent to those in the United States, with many being lower quality. As the CSET study suggests, there is “reason to believe that for high-quality, high-quantity Chinese assignees, granted robotics patents are indeed reflective of significant robotics research/work.”31

China also leads in academic publishing in the field, especially in sensors/sensing. A study by the Australian Strategic Policy Institute looked at the most cited robotics research articles and found that China accounted for 27.9 percent and the United States 24.6 percent.32 Interestingly, the United States produces slightly more robotics papers, but a greater share of the Chinese papers is among the most cited.

However, in terms of innovative products, China appears to lag behind. One publication, The Robot Report, issues an annual innovation award to the most innovative 50 robotics products globally. In 2022, just 3 were from China, while 35 were from the United States.33 In 2023, just one was from China.34 Some of this difference may be because the publication is based in the United States, but still, the difference is significant.

Company Case Studies

While, overall, most Chinese robotics companies seem to be copiers, there are a number of Chinese companies exhibiting innovative features and potentials. Cleaning-robots maker, Narwal, for example, was the first team to come to a Chinese robotics start-up incubator in 2015, and closed two rounds of fundraising within two months, with investors including Sequoia, Source Code Capital, Hillhouse, and ByteDance.35 Similarly, Dongguan-based AgileX, a five-year-old start-up that manufactures robot chassis, raised over $15 million in a round of investment from top venture capitalists such as Sequoia Capital, 5Y Capital, and Vertex Ventures, a subsidiary of Singapore sovereign wealth fund Temasek.36 Another example is SIASUN, a robotic enterprise that belongs to the Chinese Academy of Sciences. As a leading enterprise in Automatic Guided Vehicles (AGVs), SIASUN was among the first enterprises to apply AGV in real-life situations such as automobile assembling. Another Chinese research team won an international award for a robot that mimics the way various animals walk. The “robot, named Origaker, can change its shape to simulate the movement of various types of reptiles, mammals and even arthropods within a single mechanical structure.”37 Chinese Mech-Mind Robotics, an industrial robot company, meanwhile, has formed partnerships with leading Japanese companies including Yaskawa Electric, Denso Corporation, and Kawasaki.38

In 2020, Chinese industrial robot maker Estun had an R&D intensity (R&D relative to revenue) of 5.9 percent, higher than ABB’s, a leading Swedish-Swiss automation corporation, whose R&D intensity was 4.2 percent.39 Estun has also been implementing a number of overseas mergers and acquisition transactions dedicated mainly to R&D. In 2017, it acquired TRIO in the United Kingdom (motion controller), and 20 percent of Euclid Labs in Italy (visionics). Meanwhile, Leaderdrive has been lauded by the Chinese Institute of Electronics for its success in achieving breakthroughs in “bottleneck” technologies and developing robotics core technologies.40 The company has developed long-term collaborative partnerships with several universities and was involved in four National Key R&D programs between 2017 and 2019.

ITIF examined four Chinese robotics companies in depth: Ecovacs Robotics, Beijing Roborock, Estun Automatic, and Saisun.

Ecovacs Robotics

Founded in 1998 and headquartered in Suzhou, Jiangsu Province of China, Ecovacs Robotics specializes in developing and manufacturing robotic home appliances, primarily robotic vacuum cleaners, floor cleaning robots, and window cleaning robots. Ecovacs strives to invest in technology basics for the long term—in home robotics’ R&D and its value chain of technologies from integration of chips and sensors to managing data and AI applications. Its vision is of “creating a service robot with all scenes of life, production and ecology, and bringing a brand-new experience of wisdom, convenience and humanization to all mankind.”41

Ecovacs has sold over 22 million robots in 145 countries around the world. It has expanded from its home market in China to establish strong sales subsidiaries in Japan, Germany, and the United States.42 In terms of Ecovacs’s automated technology innovation, its “Smart Navi” mapping system, for example, uses lasers to map out a room and create an efficient cleaning path for its robots.43

Ecovacs has been investing heavily in developing its R&D capacity and strategy. Since 2006, the company has invested to develop more than 20 new robotic products each year.44 Ecovacs employs around 1,000 engineers and holds over 1,200 patents in China and overseas.45 In 2022, the company invested about $78 million in R&D, which accounted for 6.1 percent of expenses. With an operating income in 2021 of $18.9 billion, the company ranked first in the global sweeping robotics industry.46 But just 36 percent of its revenue came from foreign sales in 2021.47

Ecovacs has also established its own research institutions dedicated to R&D and innovation. In 2018, it established the Robotics Artificial Intelligence Research Institute in Nanjing.48 In 2022, Ecovacs launched its Innovation Model Research Institute (IMRI), the predecessor of which was the first robot-themed museum in China established in 2016.49 IMRI is a differentiated business model for entrepreneurs and internal and external senior management groups that “aims to balance the inheritance and development of the innovative spirit and help more innovative practices to be implemented in reality.”50 In 2023, Ecovacs signed a cooperation agreement with Huazhong University of Science and Technology to establish the Huazhong University of Science and Technology-Ecovacs Joint Research Center to conduct research in photoelectric sensors, intelligent algorithms, robot platforms, and other technical fields; promote the deep integration of scientific research and industry; and serve the national innovation-driven development strategy.51 The two parties will jointly train doctoral (master’s) graduate students with professional degrees and jointly build postdoctoral workstations in related technical fields; meanwhile, Ecovacs will build an off-campus internship practice platform for Huazhong University of Science and Technology, developing special recruitment and talent reservation programs for students at the university.52

Looking at the global market of the robotics sweeping industry, leading U.S. global home service robot company iRobot dominates while Ecovacs Robotics and Stone Technology are catching up. iRobot’s share in the main consumer markets of sweeping robots in the world, such as North America, Japan, and Europe, the Middle East, and Africa, has reached 75 percent, 76 percent, and 50 percent respectively.53 In comparison, Ecovacs Robotics’ global market share outside China is only 6 percent, suggesting that its products may lag behind the global leaders in innovation and quality.

However, the company’s products have won international innovation awards. For example, its DEEBOT X1 OMNI robot won the Consumer Electronic Show Innovation Award in 2022.54 Ecovacs has also partnered with companies outside China in developing their products. For example, Ecovacs Robotics cooperated with Jacob Jensen Design Studio, a Nordic design firm headquartered in Denmark and with additional studios in Bangkok and Shanghai that partners with companies worldwide to develop products.55

Moreover, in May 2020, Ecovacs Robotics reached a cooperation agreement with iRobot Corporation.56 According to Securities Daily, the cooperation aims to “improve the performance and comprehensive competitiveness of their respective brand products in the global market and improve the penetration rate of sweeping robots with many years of technology accumulation, engineering capabilities and patent layout in the field of product research and development.”57 The agreement stipulates that iRobot will exclusively purchase a sweeping robot product based on Ecovacs Robotics’ design. At the same time, iRobot will license its exclusive Aeroforce technology and related intellectual property to Ecovacs Robotics.58

Ecovacs has reported receiving subsidies from both local and central governments, for example, $1.8 million in 2020 to support 32 projects for the purpose of stimulating technological innovation and alleviating talent shortage.59

Beijing Roborock Technology Co., Ltd.

Founded in 2014, Roborock also specializes in robotic sweeping and mopping devices.60 Roborock is backed by the tech giant Xiaomi, which invested in the company a few months after its establishment; Roborock since became part of the ecological chain of Xiaomi, for which it makes automatic vacuum cleaners.61 The company’s main products are Xiaomi’s custom-made brands Mi Home Intelligent Cleaning Robot and Mi Home Handheld Wireless Vacuum Cleaner, and its own brands Roborock Intelligent Cleaning Robot and Xiaowa Intelligent Cleaning Robot.62

In 2023, Roborock launched its new S7 model, S7 Max Ultra, which, according to The Verge, is a mopping and vacuuming robot with a charging base that also empties the vacuum’s bin, refills the water tank, and washes and dries the mop with warm air. The main difference aside from the warm air is that S7 MaxV uses ReactiveAI 2.0 technology to avoid clutter in homes, whereas the new S7 Max uses Roborock’s Reactive Tech. Largely, AI-powered object avoidance generally means the robot can learn specific objects to avoid, such as pet waste and socks, whereas reactive tech simply avoids any object in its path.63

In 2020, the company had about 9 percent share of the global robotic vacuum cleaner industry, up from 10 percent in 2017. In contrast, iRobot’s share fell from around 63 percent in 2017 to 46 percent in 2020.64

The core technical employees are experts with R&D experience, having worked with tech companies such as Microsoft, Huawei, Intel, Advanced Robotics Manufacturing (ARM), and Nokia.65 In terms of R&D spending, Roborock invested $68 million in 2022, which witnessed around an 11 percent year-on-year increase. Regarding R&D personnel, over half of the company employees engage in R&D; specifically, 493 of its employees were on the R&D team as of the end of 2022, accounting for 54.5 percent of the company’s total employees.66 As shown in its 2022 annual report, Roborock had filed 3,306 patent applications in total by the end of 2022, with 1,664 of them being granted. Among the granted patents, 451 were invention patents.67 By late 2023, “Roborock,” had filed 730 patent applications with the World Intellectual Property Organization (WIPO), and 81 with the United States Patent and Trademark Office (USPTO).68

Roborock has some unique advantages in China; for example, Chinese privacy regulations and enforcement are not as strict as in most developed nations. Enterprises can use data collected by networked devices to continuously optimize AI algorithms, thus making technological progress at a faster rate.

Throughout 2022, the company’s domestic revenue accounted for 47.5 percent, and the overseas market revenue 52.5 percent. The company established divisions in the United States, Japan, the Netherlands, Poland, Germany, and South Korea, among others, opened online brand stores on Amazon, Home Depot, Target, Best Buy, Wal-Mart, and other online platforms in the United States, and gained wide consumer recognition through continuous brand investment.69 In 2020, the company had about 17 percent of the global robotic vacuum cleaner company in the world, up from 0 percent in 2017. In contrast, iRobot’s share fell from around 63 percent in 702017 to 46 percent in 2020.71

The company’s products have won a number of international innovation awards. Time magazine had Roborock’s S7 MaxV Ultra robotic vacuum in its list of Best Inventions of 2022.72 Its products have also been awarded the “International IF Design Award” and the “Taiwan Golden Pin Award.”73 Roborock’s S8 Pro Ultra was named a Global Honoree of the IHA Global Innovation Awards in the Household + Home Electronics category for its innovative product design.74 Since launching at CES 2023, the Roborock S8 Pro Ultra has won awards and accolades including “Best of CES” recognitions such as Digital Trends’ Top Tech CES 2023, TechHive’s Best of CES 2023, TWICE Picks Award, Best Products’ Best of CES, among others.75 In March 2022, Roborock S7+ was recognized by Innovation & Tech Today, a Denver-based company specializing in commemorative publications,as a Top 50 Most Innovative Product.76

Estun Automatic

Estun was founded in 1993 in Nanjing and has evolved from focusing on CNC systems for machine tools to becoming a leading force in China’s industrial robot market. In 2022, Estun’s revenue was approximately $540 million, with the industrial robot business generating about 66 percent of it. The business predominantly operates in China, making up about two-thirds of its operations, while the remaining third is from international activities, mainly in Europe and Southeast Asia.

Estun offers 64 types of industrial robot products, including general-purpose six-axis robots, four-axis palletizing robots, SCARA robots, and customized robots for specific industries, with working loads ranging from 3 kg to 600 kg. The company provides over 20 categories of standardized robotic work units.

The company has undertaken several major projects from the Ministry of Science and Technology, MIIT R&D projects, as well as provincial science and technology achievement transformation projects. In 2022, Estun was designated by MIIT as a national-level specialized and new “Little Giant” enterprise.

Estun has acquired a number of foreign companies in order to gain technological capabilities. In 2016, for instance, it acquired a 20 percent stake in the Italian company Euclid, a global leader in 3D vision technology. This acquisition provided Estun with 3D vision technology for robots, steering its robot products toward intelligent robotics. In 2017, Estun acquired U.K. firm TRIO, one of the top ten global brands in motion controllers, for 15 million GBP. This acquisition gave Estun advanced multi-axis motion control technology capabilities for simultaneously managing multiple servos, vision systems, and mechanical units, allowing for the control of numerous axes, such as 64 or 128 axes. Also in 2017, Estun also invested $9 million in Barrett, a U.S. company, enhancing its robots’ human-machine collaboration capabilities and enabling intelligent functions such as learning, perception, and feedback. It also acquired the German company M.A.i for €8.87 million, leveraging its product and technology platform to transform integrated robot applications from the mid-low-end to the mid-high-end market. In 2020, Estun completed the acquisition of a 32.5 percent stake in German CLOOS, a leader in welding robots, quickly becoming a frontrunner in China’s welding robot sector through CLOOS’s technology.

Beyond its capital activities overseas, Estun has established an R&D center and manufacturing plant in Milan, Italy, while collaborating with local academic institutions on scientific research projects.

In 2022, world leading robot maker Fanuc, Estun’s Japanese peer, reported revenue of $5.7 billion and a net profit of $1.14 billion. Compared with Estun, Fanuc’s revenue was more than tenfold, with a gross profit margin in its robotics business exceeding 42 percent, significantly higher than Estun’s approximately 20 percent. Fanuc’s R&D-to-sales ratio reached 7.2 percent, 9.6 percent, and 10.3 percent of its revenue in 2020, 2021, and 2022, respectively—the same rate as Estun’s in 2022. While Fanuc’s absolute R&D expenditure is more than ten times that of Estun, its 2,100 R&D personnel is only about double that of Estun, showing the purchasing power of low R&D wages in China.

By the end of 2023, Fanuc had applied for 9,820 patents and received 4,874 authorizations. From 2014 to 2020, Fanuc’s patent applications were evenly distributed across Japan, China, the United States, and Europe. Among Estun’s patents, 371 were registered in Mainland China, while the remaining 190 were international patents, primarily in Germany and Southeast Asia. A search in the USPTO database reveals that Estun has 12 patents registered in the United States, showcasing the company’s strong emphasis on innovation and its strategic approach to safeguarding its technological advancements globally. Compared with Estun’s 561 patent authorizations, Fanuc demonstrates a more substantial patent foundation, reflecting its extensive R&D and innovation efforts across global markets.

SIASUN

Established in 2000 and headquartered in Liaoning Province, SIARSUN Robot & Automation Co., Ltd. is a listed high-tech company belonging to the Chinese Academy of Science (a government research body), mainly engaging in robotics technology and intelligent manufacturing solutions. SIASUN’s main products include industrial robots, mobile robots, and special robots. SIASUN has expanded to the global market and established overseas subsidiaries in Singapore, Thailand, Malaysia, and Germany, among other places. Overseas sales accounted for 18.6 percent of operating income in 2022.77

SIASUN’s main products and technologies include industrial robot, mobile robot, special robot, welding automation, assembly automation, and logistics automation. Its recent technological developments in intelligent and automated manufacturing leverage emerging technologies including robotics, 5G, AI, big data, cloud computing, and the Internet of Things.

The company invested approximately 351 million RMD ($49 million) in its R&D activities in 2022, which is around a 53 percent year-on-year increase, accounting for around 10 percent of sales. Surprisingly, in 2022, the company had 2,537 employees working in R&D, accounting for approximately 65 percent of the company’s total employees—an amazingly high number. But it only pays its R&D workers an average of $19,314 per year, which is one reason it can afford so many. However, it is likely that the company overstates the number of R&D workers it employs, as 61 percent of its R&D workers have just a bachelor’s degree, and are likely technicians rather than R&D workers. Chinese companies appear to overstate the number of R&D workers they employ, as well as the amount of R&D they conduct, in order to please the central government.

China’s Robotics Strategy

Unlike the United States, where policy generally either ignores or disparages robotics, Chinese governments have made global leadership in robotics development, production, and use a top priority. it is a top industrial priority. China understands that it is behind in robotics and still runs a trade deficit, which is why it has set a goal of moving into higher-end robotics, including humanoid robots, robots to replace workers in dangerous conditions, and high-precision industrial robots. China’s Robotics Industry Development Plan (2016–2020) set a goal for China to become the source of innovation for global robot technology and the center of a high-end manufacturing cluster with integrated applications by the year 2025—and the country’s comprehensive strength of the robotics industry is to reach the international leading level where robots become an important component of economic development, people’s lives, and social governance.78 The Plan outlines tasks such as improving the innovation capability of the robotics industry, consolidating the industrial development foundation, increasing tax and financial support, enhancing the protection of intellectual property, strengthening the talent training systems, and deepening international exchanges and cooperation.79

The government has also set national goals for the use of robotics, laying out 11 key areas where it would like more robotic innovation and adoption, including in healthcare, education, and energy.80 Overall, it set a goal of expanding robot use tenfold by 2025. As a result, many provincial governments have provided generous subsidies for firms to buy robots—although the accuracy of reported figures is perhaps dubious, as their size defies comprehension. For example, in 2018, Guangdong province planned to invest 943 billion yuan (approximately $135 billion) to help firms carry out “machine substitution.”81 Likewise, the provincial government of Anhui stated it will be investing 600 billion yuan (approximately $86 billion) to subsidize industrial upgrading of manufacturers in its province, including through robotics.82 To put this in perspective, it is the equivalent, on a per-GDP basis, of the United States investing $4 trillion. However, these numbers maybe wildly inflated, as the Boston Consulting Group reported that figure to be only around $6 billion in subsidies, a mere fraction of what was reported.83 China also provides tax incentives for equipment investment.84 And it has established its second five-year plan for the robotics industry.85 Either way, China appears to provide greater subsidies for robot adoption than does any other nation, both in absolute terms and per robot.

Like it has done in so many other industries and technologies China is using its domestic market advantage, particularly with state-owned enterprises, to try to force more domestic consumption from domestic manufacturers over time. Indeed, there’s a broad push to localize robot production in China to securitize their economy against external shocks and geopolitical competition with the United States.86

The Chinese government has also established regional innovation hubs and research institutes focused on robotics that receive support from both national and provincial governments. It also incentivizes manufacturers to locate near research centers to help adopt new technology. In particular, China has copied the Manufacturing USA system and set up a number of public-private research institutes, including one in robotics. But one difference is that, in China, activities—institutes, companies, R&D players—are co-located in one place. One of these is the Dongguan robot city, where there is a government-supported robotics research institute at the core and a host of Chinese robotics companies located around it. (See figure 4.)

China has also established the Shenyang robotics and smart manufacturing cluster. As the Merics institute reports, the anchor tenant is “Siasun, a rising robotics company that was spun out of the Shenyang Institute of Automation (SIA), a branch of the Chinese Academy of Sciences (CAS) with a long history in robotics research.”88 The goal of the company and the cluster is to eliminate China’s reliance on imports.

China has one other advantage: The media, academics, and government officials don’t constantly whine about robots taking jobs as they do in the United States. In China, robots are seen as critical to the future development of the nation. In the United States they are either seen as immiseration of the proletariat or “Terminator” machines. In the long run, innovation is easier in a society that welcomes robots than in one that demonizes them.

WHAT SHOULD AMERICA DO?

As noted, the United States appears to perform well in robotic innovation robotics, but very poorly at robotic production. This is not a sustainable strategy, and in fact resembles the history of many advanced technologies where the U.S. innovates and other countries produce, eventually significantly reducing U.S. innovation capabilities.

The U.S. Department of Commerce should convene a robotics industry advisory group to advise the government on industry needs to be able to rebuild the U.S. robotics industry. Some of that will surely be focused on the need to rebuild our electrical and mechanical engineering university programs, especially their ability to graduate Americans. Congress and the administration need to expand funding for robotics research, especially at ARM which has completed over 120 advanced technology projects, including for new tooling, sensors, and software.

At the same time, we need companies in the United States that can scale robotics. Although some companies, such as Boston Dynamics, are trying to do that, we still need more. One key step will be to enable large American companies to buy smaller U.S. robotics firms in order to provide the patient capital needed to match China’s companies. As such, it was a major error by the EU antitrust authorities to reject Amazon’s proposed purchase of U.S. firm I-Robot. Amazon doesn’t compete in this space, so there is no competitive impact. But Amazon does have the capital to support I-Robot in its intense global battle with Chinese cleaner robots. In addition, Congress should institute a robot factory tax credit, akin to the semiconductor tax credit established in 2022, to encourage domestic and foreign companies to establish robot-producing factories in America. And the United States and allies should ban all Chinese investments in or purchases of their domestic robot companies.

But America will not restore its robotics industry if most of the demand for robotics is outside the United States. As such, Congress should increase funding for the National Institute of Standards and Technology’s (NIST’s) Manufacturing Extension Partnership (MEP) program specifically targeted to help small manufacturers adopt robotics. It should establish a tax code that rewards investment in capital equipment, ideally by instituting an investment tax credit on new machinery and equipment; and absent that, by restoring first-year expensing on capital goods investment. In addition, a higher minimum wage and less low-skill migration would provide companies with more incentives to install robots rather than hire workers at rock-bottom wages.

Finally, policymakers need to reject the anti-robot swarm that constantly complains about robots, and instead paint a robot-intensive vision for America wherein robotics plays a key role in boosting productivity, increasing safety, and enhancing quality of life.


Source: https://itif.org/publications/2024/03/11/how-innovative-is-china-in-the-robotics-industry/ utm_source=ITIF+Newsletter+Subscribers&utm_campaign=e99adffb39-EMAIL_CAMPAIGN_2023_05_20_06_01_COPY_01&utm_medium=email&utm_term=0_-2a5bf84f26-%5BLIST_EMAIL_ID%5D&mc_cid=e99adffb39&mc_eid=b61832c89d

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রমজানের সিয়ামের বিশেষ অনুষঙ্গ তারাবির সালাত। রমজান মাসে আল্লাহর নৈকট্য অর্জনের বাড়তি নেয়ামত হলো নামাজে তারাবি।

পবিত্র রমজান মাসে সারাদিন রোজা রেখে রাতে তারাবির নামাজ পড়া অত্যন্ত ফজিলতপূর্ণ ইবাদত, যা আল্লাহর কাছে অতি পছন্দনীয়।

তারাবি শব্দটি একটি আরবি পরিভাষা। এর আভিধানিক অর্থ হলো আরাম করা, বিশ্রাম করা, ধীরে ধীরে স্বাচ্ছন্দ্যবোধ করা ইত্যাদি।

তারাবির নামাজের ফাঁকে ফাঁকে যেহেতু কিছুক্ষণ বসে বিশ্রাম নেওয়া হয় এবং নামাজের সময় প্রলম্বিত করে ইবাদতের মাত্রা বৃদ্ধি করা হয়, এ জন্য একে তারাবির নামাজ বলা হয়ে থাকে।

পবিত্র মাহে রমজানের বিশেষ ইবাদত হলো তারাবির নামাজ। এই নামাজ প্রতিদিন এশার ফরজ ও সুন্নত নামাজের পর এবং বিতরের আগে আদায় করা হয়।

২০ রাকাত তারাবির নামাজ আদায় করা সুন্নাতে মুয়াক্কাদাহ, গুরুত্বের দিক থেকে ওয়াজিবের কাছাকাছি। ওজর বা অপারগতা ছাড়া তারাবির নামাজ পরিত্যাগ করা বড় গুনাহ।

পবিত্র কুরআন নাজিলের মাসে নামাজের মাধ্যমে কুরআন খতম অশেষ সওয়াবের। এটা জামাত সহকারে আদায় করতে পারলে অপরিসীম ফজিলত রয়েছে।

আমাদের দেশে দুই ধরণের তারাবি প্রচলিত। একটি হলো সুরা তারাবি এবং অন্যটি হলো খতম তারাবি। সুরা তারাবি হলো পবিত্র কুরআনের যে কোন সুরা দিয়ে ২০ রাকাত নামাজ আদায় করা।

খতম তারাবি হলো রমজান মাসে সম্পূর্ণ কুরআন সহকারে তারাবি আদায় করা। উভয় পদ্বতিই ইসলাম অনুমোদন করে। তবে খতমে তারাবিতে সওয়াব বেশি। সুরা তারাবির মাধ্যমে নামাজ আদায় করলেও নামাজ আদায় হবে।

তারাবির নামাজ পড়ার নিয়ম:

এশার নামাজের চার রাকাত ফরজ ও দুই রাকাত সুন্নতের পর এবং বিতর নামাজের আগে দুই রাকাত করে ১০ সালামে যে ২০ রাকাত নামাজ আদায় করা হয়। আর এ নামজকেই ‘তারাবির নামাজ’ বলা হয়।

তারাবি নামাজের নিয়ত:

আরবি এবং বাংলা উভয়ভাবে নিয়ত করা যাবে। আরবি নিয়ত হচ্ছে,

نَوَيْتُ اَنْ اُصَلِّىَ للهِ تَعَالَى رَكْعَتَى صَلَوةِ التَّرَاوِيْحِ سُنَّةُ رَسُوْلِ اللهِ تَعَالَى مُتَوَجِّهًا اِلَى جِهَةِ الْكَعْبَةِ الشَّرِيْفَةِ اللهُ اَكْبَرْ

 নাওয়াাইতু আন উসাল্লিয়া লিল্লাহি তাআলা, রকাআতাই সালাতিত তারাবিহ, সুন্নাতু রাসুলিল্লাহি তাআলা, মুতাওয়াজ্জিহান ইলা জিহাতিল কাবাতিশ শারিফাতি, আল্লাহু আকবার।

বাংলায় নিয়ত হচ্ছে, আমি কেবলামুখি হয়ে দুই রাকাআত তারাবির সুন্নতে মুয়াাক্কাদাহ নামাজের নিয়ত করছি। আল্লাহু আকবার। (জামাআত হলে যোগ করতে হবে এ ইমামের পেছনে পড়ছি)।

তারাবি নামাজের নিয়ত আরবিতে করা আবশ্যক বা বাধ্যতামূলক নয়। বাংলাতেও এভাবে নিয়ত করা যাবে যে, ‘আল্লাহর সন্তুষ্টির জন্য তারাবি -এর দুই রাকাত নামাজ কেবলামুখী হয়ে পড়ছি।

নামাজ, সেহরি ও ইফতারের সময়সূচি
 

তারাবির নামাজ কিভাবে পড়বেন:

দুই রাকাত নামাজ আদায় করে সালাম ফিরিয়ে নামাজ শেষ করা। আবার দুই রাকাত নামাজ পড়া। এভাবে ৪ রাকাত আদায় করার পর একটু বিশ্রাম নেয়া।

বিশ্রামের সময় তাসবিহ তাহলিল পড়া, দোয়া-দরূদ ও জিকির আজকার করা। তারপর আবার দুই দুই রাকাত করে আলাদা আলাদা নিয়তে তারাবি আদায় করা।

তারাবি নামাজের দোয়া

তারাবি নামাজে প্রতি চার রাকাত পর বিশ্রাম নেওয়া হয়। এ সময় একটি দোয়া পড়ার প্রচলন রয়েছে আমাদের দেশে। প্রায় সব মসজিদের মুসল্লিরা এই দোয়াটি পড়ে থাকেন। দোয়াটি হলো-

سُبْحانَ ذِي الْمُلْكِ وَالْمَلَكُوتِ سُبْحانَ ذِي الْعِزَّةِ وَالْعَظْمَةِ وَالْهَيْبَةِ وَالْقُدْرَةِ وَالْكِبْرِيَاءِ وَالْجَبَرُوْتِ سُبْحَانَ الْمَلِكِ الْحَيِّ الَّذِيْ لَا يَنَامُ وَلَا يَمُوْتُ اَبَدًا اَبَدَ سُبُّوْحٌ قُدُّوْسٌ رَبُّنا وَرَبُّ المْلائِكَةِ وَالرُّوْحِ

উচ্চারণ : ‘সুবহানা জিল মুলকি ওয়াল মালাকুতি, সুবহানা জিল ইয্যাতি ওয়াল আঝমাতি ওয়াল হায়বাতি ওয়াল কুদরাতি ওয়াল কিব্রিয়ায়ি ওয়াল ঝাবারুতি। সুবহানাল মালিকিল হাইয়্যিল্লাজি লা ইয়ানামু ওয়া লা ইয়ামুত আবাদান আবাদ; সুব্বুহুন কুদ্দুসুন রাব্বুনা ওয়া রাব্বুল মালায়িকাতি ওয়ার রূহ।’

তবে মনে রাখতে হবে, তারাবি নামাজ বিশুদ্ধ হওয়া বা না হওয়ার সঙ্গে এই দোয়ার কোনো সম্পর্ক নেই। এই দোয়া না পড়লে তারাবি নামাজ হবে না, কোনোভাবেই এমন মনে করা যাবে না। মূলত এ দোয়ার সঙ্গে তারাবি নামাজ হওয়া কিংবা না হওয়ার কোনো সম্পর্ক নেই।

এ সময় কুরআন-হাদিসে বর্ণিত যেকোনো দোয়াই পড়া যাবে। আলেমদের মতে, তারাবি নামাজে চার রাকাত পর বিশ্রামের সময়টিতে কুরআন-হাদিসে বর্ণিত দোয়া, তওবা,-ইসতেগফারগুলো পড়াই উত্তম।

একটি বিষয় মনে রাখতে হবে যে, কোনো কারণবশত যদি একদিন তারাবির নামাজ পড়তে না পারেন তাহলে রোজার কোনো ক্ষতি হবে না। নামাজ না পড়ার শাস্তির ভোগ করতে হবে। সমাজের অনেকেই মনে করেন তারাবির নামাজ আদায় না করলে রোজা হবে না, অথচ এমন কোন কথা কুরআন-হাদিসে নেই। এটা সম্পূর্ণ একটি ভুল ধারণা।
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In age of AI, companies put focus on tech & soft skills and ‘internal mobility’


Organisations are placing a lot of emphasis on improving the skillsets of employees as the nature of work is changing. Companies are taking care to embed a culture of learning so that the process is continuous.

According to LinkedIn’s recent Workplace Learning Report, 94% of companies that took part in the survey said it plans to enhance employees’ skills in 2024. The survey was conducted by Censuswide with a sample of about 4,323 hiring managers (middle management) aged 18-77 years in countries such as the UK, Ireland, France, Germany, Italy, Spain, the USA, India, Australia, Singapore, Japan, Indonesia, China, the Netherlands, Sweden, MENA and Brazil. The data was collected between the middle of December and early January 2024.

The key insights of the report were that along with upskilling, aligning the learning programmes to business goals and creating a learning culture remain the focus areas for professions in the learning and development (L&D) industry.

The underlying reasons are clear: in the face of ongoing AI and automation, 98% of the employers have felt the need for significant shifts in the skills of the employees. Meanwhile, about half of the hiring managers said they are prioritising internal mobility to enable better career advancement opportunities for employees.

What does this mean in terms of skills?

Apart from core technology skills, the report said that 91% of the L&D professionals identify soft skills as critical. Communication was the most in-demand skill across APAC countries, including in India. As with other studies, problem-solving and critical thinking are other high-demand skills as people try to make sense of the current transformational shifts in work. These are considered the most important in the era of AI.

Clarifying this trend, Ruchee Anand, Senior Director-Talent, Learning and Engagement Solutions, LinkedIn India, says, “Last year, we saw a 21x surge in job postings mentioning ChatGPT or GPT on LinkedIn, reflecting the growing demand for tech skills as businesses explored AI. This year, we are seeing a pronounced shift towards skills — both technical and soft skills — to thrive in the era of AI.”

Further, Anand added, “With skills for jobs globally expected to change 68% by 2030, we are seeing a greater emphasis on learning both technical and soft skills with a majority of employers surveyed agreeing that this balance will be critical for organisations to succeed in the age of AI.”

Building a culture of continuous learning

In terms of specific actions, companies are looking at online training and development programmes (53%) as well as hands-on experimentation with generat ..

This ongoing focus can also help show outcomes. As many as 96% of L&D professionals in India said that they believe they can show business value by helping employees gain skills to move into different internal roles; 48% of the hiring managers said that they believe “helping employees build the skills needed for the future of work” (38%) and “providing competitive salary and benefits” (31%) are key to retaining top talent.

They also said that highlighting career advancement opportunities (59%), and “increasing internal mobility” (51%) are crucial to attract top talent.

Source:https://economictimes.indiatimes.com/jobs/hr-policies-trends/in-age-of-ai-companies-put-focus-on-tech-soft-skills-and-internal-mobility/articleshow/108317883.cms?from=mdr
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