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Understanding the World of Artificial Intelligence: A Comprehensive … – Medium

Welcome to the fascinating world of Artificial Intelligence (AI). As technology continues to evolve at an unprecedented pace, AI stands at the forefront, reshaping our lives and industries. Lets dive deep into the core concepts that make AI the marvel it is today.

Algorithms are the unsung heroes of the digital age. Think of them as a chefs recipe, detailing step-by-step instructions for a computer to whip up a delightful dish. From ancient Babylonian clay tablets to todays sophisticated computer systems, algorithms have been guiding processes and decisions. For instance, the age-old Euclidean algorithm for division is still very much in use. Even our daily activities, like brushing our teeth, can be broken down into a series of algorithmic steps.

Machine Learning (ML) is like giving computers a brain of their own. Instead of spoon-feeding them every piece of information, we let them learn from patterns and data. Imagine showing a computer millions of pictures of cats and dogs. Over time, it starts recognizing the subtle differences and can classify new images with remarkable accuracy. However, while theyre pattern recognition champions, they might stumble when faced with tasks requiring intricate reasoning.

Natural Language Processing (NLP) is the art and science of making machines understand and respond to human language. If youve ever chatted with Siri or Alexa, youve experienced NLP in action. Todays advanced NLP systems can even discern the context of words. For instance, they can figure out whether club refers to a sandwich, a golf game, or a nightlife venue based on surrounding text.

Neural Networks take inspiration from the human brain. Just as our brain has neurons that transmit signals, AI has artificial neurons or nodes that communicate. These networks continuously learn and adapt. For instance, platforms like Pinterest use neural networks to curate content that resonates with users preferences.

Deep Learning is like Neural Networks on steroids. The deep signifies the multiple layers of artificial neurons. These layers enable the system to process information in a more intricate manner, making them adept at handling complex tasks.

Large Language Models (LLMs) are the maestros of text. They can summarize, create, and even predict text. These models are trained on vast amounts of data, making them incredibly versatile. They owe their efficiency to the transformer model, a groundbreaking development by Google in 2017.

Generative AI can craft content, be it text, images, or even audio. By feeding specific prompts into foundation models, we get outputs tailored to our needs. These models have given birth to innovations like OpenAIs ChatGPT and Google Bard.

Chatbots are our digital conversationalists. Powered by Generative AI, they can engage in meaningful dialogues, answer queries, and even generate content in the style of famous personalities. ChatGPT, for instance, can discuss topics ranging from history to music and offer insights on a plethora of subjects.

Hallucination in AI is when a model produces outputs that might sound plausible but arent rooted in reality. Its essential to differentiate between hallucinations and biases, as the former is an output error, while the latter stems from skewed training data.

Artificial General Intelligence (AGI) is the zenith of AI development. Its the dream of creating machines that can think, learn, and adapt just like humans. While were still on the journey towards AGI, advancements like DeepMinds AlphaGo and MuZero show promising strides in that direction.

The realm of AI is vast and ever-evolving. As we continue to harness its potential, were not just reshaping technology but also redefining the boundaries of human-machine collaboration. Embrace the journey, for the future is AI!

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Understanding the World of Artificial Intelligence: A Comprehensive ... - Medium

On AI and the soul-stirring char siu rice – asianews.network

October 11, 2023

KUALA LUMPUR Limitations of traditional programming

Firstly, lets consider traditional computer programming.

Here, the computer acts essentially as a puppet, mimicking precisely the set of explicit human-generated instructions.

Take a point-of-sale system at a supermarket as an example: scan a box of Cheerios, and it charges $3; scan a Red Bull, its $2.50.

This robotic repetition of specific commands is probably the most familiar aspect of computers for many people.

This is akin to rote learning from a textbook, start to finish.

But this programmed obedience has limitationssimilar to how following a fixed recipe restricts culinary creativity.

Traditional programming struggles when faced with complex or extensive data.

A set recipe may create a delicious Beef Wellington, but it lacks the capacity to innovate or adapt.

Furthermore, not all data fits neatly into an A corresponds to Bmodel.

Take YouTube videos: their underlying messages cant be easily boiled down into basic algorithms.

This rigidity led to the advent of machine learning or AI,which emerged to discern patterns in data without being explicitly programmed to do so.

Remarkably, the core tenets of machine learning are not entirely new.

Groundwork was being laid as far back as the mid-20th century by pioneers like Alan Turing.

Laksa Penang + Ipoh

During my childhood, my mother saw the value in non-traditional learning methods.

She enrolled me in a memory training course that discouraged rote memorization.

Instead, the emphasis was on creating mind maps and making associative connections between different pieces of information.

Machine learning models operate on a similar principle. They generate their own sort of mind maps, condensing vast data landscapes into more easily navigated territories.

This allows them to form generalizations and adapt to new information.

For instance, if you type King Man + Woman into ChatGPT, it responds with Queen.

This demonstrates that the machine isnt just memorizing words, but understands the relationships between them.

In this case, it deconstructs King into something like royalty + man.

When you subtract man and add woman, the equation becomes royalty + woman, which matches Queen.

For a more localized twist, try typing Laksa Penang + Ipoh in ChatGPT. Youll get Hor Fun. Isnt that fun?

Knowledge graphs and cognitive processes

Machine learning fundamentally boils down to compressing a broad swath of world information into an internal architecture.

This enables machine learning to exhibit what we commonly recognize as intelligence, a mechanism strikingly similar to human cognition.

This idea of internal compression and reconstruction is not unique to machines.

For example, a common misconception is that our eyes function like high-definition cameras, capturing every detail within their view.

The reality is quite different. Just as machine learning models process fragmented data, our brains take in fragmented visual input and then reconstruct it into a more complete picture based on pre-existing knowledge.

Our brains role in filling in these perceptual gaps also makes us susceptible to optical illusions.

You might see two people of identical height appear differently depending on their surroundings.

This phenomenon stems from our brains reliance on built-in rules to complete the picture, and manipulating these rules can produce distortions.

Speaking of rule-breaking, recall the Go match between AlphaGo and Lee Sedol.

The human side was losing until Sedol executed a move that AlphaGos internal knowledge graph hadnt anticipated.

This led to several mistakes by the AI, allowing Sedol to win that round.

Here too, the core concept of data reconstruction is at play.

Beyond chess: The revolution in deep learning

The creation and optimization of knowledge graphs have always been a cornerstone of machine learning.

However, for a long time, this area remained our blind spot.

In the realm of chess, before the advent of deep learning, we leaned heavily on human experience.

We developed chess algorithms based on what we thought were optimal rules, akin to following a fixed recipe for a complex dish like Beef Wellington.

We believed our method was fool-proof.

This belief was challenged by Rich Sutton, a luminary in machine learning, in his blog post The Bitter Lesson.

According to Sutton, our tendency to assume that we have the world all figured out is inherently flawed and short-sighted.

In contrast, recent advancements in machine learning, including AlphaGo Zero and the ChatGPT youre interacting with now, adopt a more flexible, Char Siu Riceapproach.

They learn from raw data with minimal human oversight.

Sutton argues that given the continued exponential growth in computing power, evidenced by Moores Law, this method of autonomous learning is the most sustainable path forward for AI development.

While the concept of computers learning on their ownmight unnerve some people, lets demystify that notion.

Far from edging towardshuman-like self-awareness or sentience, these machines are engaging in advanced forms of data analysis and pattern recognition.

Machine learning models perform the complex dance of parsing, categorization, and linking large sets of dataakin to an expert chef intuitively knowing how to meld flavors and techniques.

These principles are now entrenched in our daily lives.

When you search for something on Google or receive video recommendations on TikTok, its these very algorithms at work.

So, instead of indulging in unwarranted fears about the future of machine learning, lets appreciate the advancements that bring both simplicity and complexity into our lives, much like a perfect bowl of Char Siu Rice.

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(Yuan-SenTinggraduated from Chong Hwa Independent High School in Kuala Lumpur before earning his degree from Harvard University in 2017. Subsequently, he washonoredwith a Hubble Fellowship from NASA in 2019, allowing him to pursue postdoctoral research at the Institute for Advanced Study in Princeton. Currently, he serves as an associate professor at the Australian National University, splitting his time between the School of Computing and the Research School of Astrophysics and Astronomy. His primary focus is onutilizingadvanced machine learning techniques for statistical inference in the realm of astronomical big data.)

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On AI and the soul-stirring char siu rice - asianews.network

Nvidias Text-to-3D AI Tool Debuts While Its Hardware Business Hits Regulatory Headwinds – Decrypt

Renowned for its technological prowess, Nvidia has arrived at the crossroads of innovation and entrenched interests. As the computer chip maker moves solidly into artificial intelligence, releasing a new application that could redefine 3D modeling, it concurrently faces geopolitical hurdles that threaten its dominance in hardware.

Nvidia joined forces with 3D software publisher Masterpiece Studio to release Masterpiece X, aiming to revolutionize the 3D modeling field by making it as easy as creating a two-dimensional image with MidJourney or Stable Diffusion.

"For years, we've worked hard to create cutting-edge 3D tools that are intuitive but also tools that would enable and empower more and more people to start creating 3D. Masterpiece Studio said in an official announcement, Generative AI enables entirely new possibilities.

The studio says its solution makes it possible to create 3D models with no local hardware or software required, as everything happens in the cloud. All you need is a keyboard, a browser, a little imagination, and just a few words," they wrote.

As a quick experiment, Decrypt took Masterpiece X for a spin. Our efforts to digitally sculpt our AI mascot Gen were not good. The envisioned "child robot" bore more resemblance to a chubby pigeon, while the render of an elegant teacher avatar seemed more like a tipsy vagabond.

Although far from perfect, these results hint at the software's vast potential and exciting advancements on the horizon. It is easier to reach a desired result starting from a pre-existing model instead of having to create a design from scratch.

The AI industrys dependency on Nvidia is notable. A considerable portion of the sector is tethered to Nvidia's cutting-edge technology in software and hardware, underscoring the firm's monumental influence in the sector.

This dominance has significantly contributed to Nvidia's financial performance, with the company becoming among the 10 top-performing stocks of 2023. Astonishingly, Nvidia's stock has surged by over 200% during the year, marking its all-time high in September 2023.

However, geopolitical challenges loom large. A recent report from Reuters highlighted the U.S. administration's efforts to tighten restrictions on AI chip exports to China. The restrictions have, in the past, hindered Nvidia from delivering its top-tier AI chips to Chinese consumers chips that are the gold standard for various AI applications.

In this case, the companys powerful H800 chips may be in the bullseye of the US government, even though Nvidia specifically designed them to comply with current export restrictions. They are less powerful and sophisticated than the current top-of-the-line H100 lineup. However, regulators seem determined to close any possible loophole to not give China any advantage in the AI race.

Undeterred by global challenges, China continues to showcase its technological resilience. The release of Huawei's Mate 60 series, equipped with the Kirin 9000S chip, exemplifies its determination. This phone features a 14nm chip designed to perform on par with its 7nm counterparts, and it boasts 5G capabilities. While the U.S. took measures to restrict Chinas access to certain technologies related to 5G and hardware development due to national security concerns, companies from that country managed to innovate and move forward.

Like a high-wire artist, Nvidia is walking a fine line between rising AI hype and geopolitical gravity. For now, Nvidia wobbles forward, with one foot planted in the promises of AI and the other mired in the perils of nationalism, while the whole AI industry is watching to see what happens.

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Nvidias Text-to-3D AI Tool Debuts While Its Hardware Business Hits Regulatory Headwinds - Decrypt

One step closer to the Matrix: AI defeats human champion in Street … – TechRadar

Researchers from the Singapore University of Technology and Design (SUTD) created a new software centered around reinforcement learning and phase-change memory thats designed to understand complicated movement design.

Previous work has applied this kind of deep learning to other games like Chess or Go, but they decided instead to expose the D-PPO algorithm to the rigors of Street Fighter Champion Edition II. The SUTD researchers trained its SF-R2 AI player on two days of consecutive play against the computer, before letting it loose on a human participant who the AI-powered system beat comfortably.

The work has implications for movement science more broadly, according to the research paper, and can possibly be fed into improving robotics and autonomous vehicles, for example. It paves the way for broadly applicable training in fields where machines may observe human norms and attempt to replicate and outperform them.

One of the major milestones that AI researchers have used to measure the effectiveness of the systems theyve built is by letting them compete with human players in different kinds of games. This has been happening for some time.

In 2017, an Alpha Go AI built by DeepMind beat the number-one human Go player in the world for the second time, following the first victory over Fan Hui the previous year. Microsofts AI, in June, achieved the worlds first perfect Ms. Pac-Man score, and in August we saw an OpenAI engine beating the best Dota 2 players of the time.

This latest milestone besting a Street Fighter champion was made possible due to reinforcement learning as well as phase-change memory. First developed by HP, this is a form of nonvolatile memory achieved by using electrical charges to change areas on chalcogenide glass. Its much faster than commonly used Flash memory.

"Our approach is unique because we use reinforcement learning to solve the problem of creating movements that outperform those of top human players, said principal investigator Desmond Loke to TechXplore. This was simply not possible using prior approaches, and it has the potential to transform the types of moves we can create.

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One step closer to the Matrix: AI defeats human champion in Street ... - TechRadar

The Vanishing Frontier – The American Conservative

Today, technology, state power, and development intertwine in a single Gordian knot that no liberal can untangle. After a few decades of geopolitical quiet, we have come to realize that threats to sovereignty have become more insidious, while decisions that lead to its preservation require subtle judgment, especially where the matters concern technology. Awareness of this new state of affairs is particularly acute in the two great powers: the U.S. and China.

The uniqueness of the circumstances was recognized by both Donald Trump and Xi Jinping. In Innovate to Dominate: The Rise of the Chinese Techno-Security State, Tai Ming Cheung recalls that in 2017, Xi gave a speech in which he claimed that if China wanted to ascend to the top, it had to become an innovation powerhouse. Later that year, the Trump administration released a national security strategy in which a national security innovation base is identified as a necessary condition for American prosperity and survival. Both leaders shared the premise that national security requires them to articulate and develop national systems of innovation.

One can view the confrontation between the U.S. and China not in terms of the Thucydides trap, but rather through the lens of the so-called Needham Question. Joseph Needham was a British biochemist and author of a 25-volume history of Chinese science. A key problem emerged from his research: how did it happen that China, which had been at the forefront of science and technology for most of human history, was outpaced by the West? The realization of its own technological weakness was painful, occurring during the first Opium War and after, during the so-called century of humiliation. Today, Chinas Communist elite not only wants to avoid technological vassalization, as Xi put it, but to take the lead in technological progress and astound the West, just as the West astounded China in the 19th century. Chinas national innovation system is meant to be a set of institutions and policies designed to achieve this goal.

According to Cheung, Xi Jinping is the opposite of Deng Xiaoping. In the 1970s, the latter reoriented the state away from military and security issues toward the challenge of capitalist reform. Large military budgets were slashed and priority was given to growth. While Xi doesnt ignore economic indicators, he places more significant focus on technological self-reliance, economic security and the modernization of the military. Cheung quotes him as saying that science and technology power determines changes in the balance of world political and economic power. Xi is a techno-nationalist.

Since the beginning of his rule, Xi has argued that China must accomplish a transition: abandon its role as an imitator and transform itself into an innovator. Only in this way will it become self-sufficient in critical fields of technology. In order to attain this, the Chinese leader is pushing military-civil fusion, a process of exchanging information, resources and capabilities between civilian and military actors in key technological and economic areas. Xi has had the MCF written into the CCP constitution as a national priority.

One of the most important initiatives under the MCF banner is the construction of a network of large laboratories where the civilian and military sides can exchange experiences and work together. Cheung notes, however, that the projects development has been painfully slow. He points to the bureaucratic opposition and fragmentation as the main reasons. Elsewhere, he suggests that the innovation strategy provides cover for the process of consolidating power and overcoming resistance embedded in the state apparatus.

There can be no doubt that, with Xi at the helm, China has ceased to adhere to Dengs maxim, Hide your capacities, bide your time. But did the transition from Deng's economic openness to the pursuit of self-reliance occur suddenly under Xi's leadership? Cheung does not provide compelling evidence for the thesis of a qualitative, rather than simply gradual, change under Xi regarding technological self-sufficiency and economic nationalism.

Over the past years of strained relations between Beijing and Washington, there has been much talk about a Sputnik moment. Opinions differed on what exactly constituted that moment. Some pointed to President Donald Trump, his tariffs and the pressure he applied to Huawei. Others mentioned year 2016, when AlphaGo, a program created by British machine learning experts, beat the best human player in the Chinese go game. Still others claim that the restrictions on semiconductor-related technologies imposed by President Joe Biden Biden last year unleashed an unprecedented mobilization in Beijing.

Cheungs book allows us to expand the historical perspective and propose a hypothesis of a certain continuity, more pronounced than the author himself would admit. In May 1999, the U.S. bombed the Chinese embassy in Belgrade. The CCP responded almost immediately by establishing the 995 New High-Technology Project. The ambition was to dramatically increase spending on weapon R&D. Influential military officials quoted by Cheung assert that without this program the modernization of Chinese military would not have happened on this scale or at this pace. We should be grateful to the Americans, admitted one general. Consider: Total Chinese defense R&D investment between 1999 and 2009 (the first ten years of the 995 program) was more than all the investment in defense R&D in the prior fifty years. Through this prism, Xi would represent only an intensification of a trend, not its source.

According to Dan Wang, a tech analyst who spent years in China, Xi has no real ambitions of a great reformer like Deng. The great power he has amassed in his hands is not matched by any great goal. Regarding critical technologies, his results have been disappointing so far: Beijing is fundamentally misunderstanding the chip industry if it can believe that semiconductors can be run as a national space project, estimates Wang.

As Wang notes, China has not made significant advances in either aviation or chips. However, it has managed to gain dominance in other sectors. In the case of EVs, huge subsidies have created a number of companies that are changing the landscape of the automotive market. As Fords CEO remarked, something monumental happened in our industry where China became the number one exporter of vehicles globally. It had always been the Germans and the Japanese. Chinese competition sows panic on the Old Continent; Carlos Tavares, the CEO of Stellantis, complained in an interview with Le Figaro that Europe rolled out the red carpet to Chinese companies, and now it will have to pay a steep price to compete with them. Westerners, says Tavares, are no longer comfortable with change, and rivalry with the Chinese will be Darwinian. How many will be able to adapt?, asks the CEO. China has accumulated unrivaled competence in the EV industry. According to Goldman analysts, in the PRC, construction of a EV factory takes 1/3 of the time required in other countries, while Chinese battery plants are 80 percent cheaper to build than American ones.

The PRC controls clean tech supply chains. Its grip on green technologies is so firm that many experts do not believe the EU can meet its green goals without maintaining close ties with China. When it comes to wind turbines, its share in the production of essential components amounts to over 70 percent, and in the case of solar panels, it reaches 80 percent. Beijings dominance in the area of rare earth metals particularly in refiningis equally strong. Of the 54 mineral commodities that the U.S. Geological Survey deems crucial to the country, America depends on China for as many as 35.

According to Jeffrey Ding, who studies the questions of technology and great power rivalry, it is not narrow technological developments that decide a countrys fate, but so-called general purpose technologies like the steam engine, electricity, or the computer. These are engines of growth that become engines of power, as the rise and fall of nations is ultimately determined by differences in economic growth.

Ding argues that the nature of the American innovation system presents a huge advantage to the U.S. Its decentralized character allows methods, information and technology to diffuse quickly and in multiple directions. At the turn of the 20th century, while other countries had the potential to rival the U.S. in terms of innovation, it was America that was able to diffuse interchangeable manufacturing methods because it had stronger connections between the frontier institutions, entrepreneurs, and engineers. This gave it the edge that would be key to the American Century. It was later to be repeated with computers, when Japanin contrast with the U.S.struggled to integrate them into its economy and institutional practice on a large scale. Ding contends that the U.S. should defend the status quo when it comes to its own innovation diffusion structures, as these are what will keep America ahead of the PRC, including in the coming age of artificial intelligence.

A number of voices suggest that the American innovation system needs a major overhaul. On the surface, it appears that there is a genuine renewal. The CHIPS and Science Act, the Infrastructure Act or the Inflation Reduction Act might imply that the state has regained its ability to mobilize resources and carry out industrial policy. Even if we set aside criticisms, such as the argument that the CHIPS Act doesnt represent a success for the semiconductor industry, but rather a win for the incumbents or simply a rescue plan for Intel, this whole effort may still lead to nowhere. As David Adler and William B. Bonvillian remark, Pathways necessary for diffusing new technologies and getting them to market are missing, including a lack of scale-up financing mechanisms. The vocational education system has withered as has the corporate lab system.

They argue that one of the main culprits was the fascination with information technologies. As a result, industrial policy was eventually dropped and deindustrialization allowed. Yet the problem boils down to more than a misguided notion of the countrys developmentAmerica is not going to turn into one big California, after all. The greatest barrier to technology implementation has become the lack of a technically trained workforce. This problem is also resurfacing in the context of the CHIPS Act. Of the 115,000 new jobs anticipated to be created in the semiconductor industry by 2030, 58 percent are projected to remain unfilled at the current graduation rate. If America wants to build an advanced manufacturing base at home, it must have the right workforce. Unfortunately, the vocational school system, as Adler and Bonvillian write, has collapsed. Without reconstructing it and reducing the gap between higher education and the manufacturing process, neither reindustrialization nor acceleration of technological progress to raise Americans standards of living will be attainable.

According to the head of the U.K. A.I. Foundation Model Taskforce, Ian Hogarth, we are entering an era when geopolitical relations will be transformed, if not destabilized by A.I. The U.S. innovation system will be put to a real test before our eyes with the advent of new general purpose technology.

Paul Scharres Four Battlegrounds: Power in the Age of Artificial Intelligence is a book about the age of A.I. nationalism. Its main premise is that an A.I. race between the U.S. and China is already underway. Translating this new technology into military or economic power will not be easy, but it will happen eventually. The author does not pay particular attention to the economic aspects of the race, such as the issue of explosive growth, which could give absolute economic advantage to one of the great powers. He is primarily interested in the effect that A.I. development will have on military power, as well as the conditions necessary for A.I. to produce such an effect.

Scharre does not define the essence of the A.I. race. Importantly, this arms race, as the economist Pradyumna Prasad explains, is indefinite: There is no clear finishing line, competitors relative position during the race is of utmost importance and there is always the possibility to reverse any win or loss. He believes that the most important edge that this technology could give in the future is the ability for total mobilization of economic resources in the event of war. What U.S. industry accomplished during World War II, could be achieved by A.I. in the near future. It could solve logistical problems, increase production efficiency, or magnify the effects of research and development.

Sharre describes in a very accessible manner the battlegrounds on which countries are competing in the age of A.I. nationalism. One of them is data. The claim that China is the Saudi Arabia of data is inaccurate; China has a wealth of a certain type of data, but lacks others. While their facial recognition systems can be trained on extensive material, it is unlikely to be used to train fighter jets.

The hardware aspect is overlooked by the public, and perhaps constitutes the most complex issue of the A.I. race, as it involves the globally dispersed semiconductor supply chains, where the Dutch ASML and Taiwans TSMC play such an outsized role.

Algorithms are not a scarce resource. People are, Scharre notes. Chinas potential in this regard is growing steadily: Between 2009 and 2019, the number of AI researchers increased twelvefold. PRC researchers are expected to surpass Americans in 2025 when it comes to ranking in the top 1 percent of most cited articles. However, it is still America that attracts the crme de la crme of A.I. engineers.

The non-obvious battleground in the A.I. race is institutions. Its not just the structure of diffusion of tacit knowledge and innovation that matters, but also their internal dynamism. Scharre cites the example of an American company that worked with the government in the field of A.I. and whose owner concluded that he had no other choice but to let his start-up be acquired by a larger corporation that had entire teams dedicated to government contract compliance. As Scharre writes, the fact that an AI start-up felt it needed to be acquired by a major defense contractor in order to succeed is a major problem for the DoD. The ossified bureaucracy needs reform if the U.S. doesnt want to lose its lead in the technological arms race.

Regulation is another facet of the competition for the best institutions. The European Union has thrown itself with ferocity into the race to regulate AI. It is widely believed that Brussels wants to be the quickest and the toughest, because that way, as in the case of GDPR, it will succeed in imposing AI standards around the world. That being said, its hard to imagine that the U.S., China and especially Big Tech will just stand by and watch. As Brookings analysts have convincingly shown, the so-called Brussels effect has little chance of working in the case of A.I. The regulations designed by the EU are so strict that supposedly none of the so-called foundation models (like GPT4 or LLaMa) meet them. One can risk the thesis that E.U. is well aware that the Brussels effect will not work this time around.The A.I. Act is, as I see it, the Maastricht of A.I.all member states will now be bound by an even tighter web of laws regulating a technology that will perhaps shape our future to a greater extent than the Internet.

The A.I. Act will also establish in each country a new agency associated with European A.I. regulation. In doing so, it will create a new interest group, the A.I. bureaucracy, which may align more closely with the E.U.s interest than with those of member states. A.I. is not an opportunity for Brussels to assert its regulatory power in the world, but to deepen the European integration.

The PRC has opted to be less strict. The Chinese state crafted a set of fairly rigid regulations and put them up for debate by companies working in the industry. In consequence, the initially tough restrictions have been loosened. Still, the most relaxed approach prevails in the United States. This attitudewhich one analyst described as laissez-faire and learnamounts to keeping a vigilant eye on AI, while acknowledging that lawmakers do not yet know enough about the technology to formulate regulations.

Washington's decision not to burden A.I. with excessive regulations demonstrates its confidence in Silicon Valley. Not only are U.S. companies spending more on A.I. than the government, they are also capturing all the available talent: In 2020, 70 percent of PhDs in A.I. were hired by the private sector. On the other hand, while China can count on the complete loyalty of its companies, the U.S. governments relationship with Big Tech is problematic.

Scharre reminds us how 3,000 Google employees protested their employers cooperation with the DoD. If support for American national security was controversial for Google, cooperation with the CCP on the Dragonfly project (it involved a search engine for the Chinese market that would meet Communist censorship requirements) was not. Googles CEO defended the cooperation with China to the very end.

Similar protests over cooperation with the U.S. government have also broken out at Amazon and Microsoft. However, there are companies in the field of AI for which national security is important, like Palantir. Its CEO, Alex Karp, rightly noted that entrepreneurs from Silicon Valley charge themselves with constructing vast technical empires but decline to offer support to the state whose protections and underlying social fabric have provided the necessary conditions for their ascent. They would do well to understand that debt, even if it remains unpaid. Ultimately, the U.S. will have to carve out a narrow path between Big Techs dominance and the governments heavy-handed grip, treating A.I.-building companies as it used to treat Big Oil: supporting them where their interest align with the advancement of the American national interest, while also curbing harmful monopolistic behavior. Nevertheless, the shift to techno-nationalism will be too painful for some techno-cosmopolitan incumbents will be replaced by companies that are not ashamed to work with the U.S. government.

The A.I. race carries obvious but asymmetric risks. It is to be feared that if A.I. gets out of hand, it will happen in the PRC. As Bill Drexel and Hannah Kelley write, Little accountability for mistakes means that business owners tend to play fast and loose with safety, as evidenced by Chinas grisly history of industrial accidents. In those cases where accidents have come to light and been met with public outragelike with the toxic toothpaste incident, the poisoned infant formula, or the collision of two high-speed trainsit has done nothing to improve public safety.

Achieving mutual restraint in this arms race seems to be no easy feat. If the hype proves true, the advantage in AI could dwarf all other factors, becoming, in the parlance of Jake Sullivan, a force multiplier. But even if AI does not translate into a military or economic revolution, it will significantly change both war and the economy, and no great power will miss the opportunity to gain an upper hand in those areas. Scharre mentions that there is a lively discussion in China about AI restraint. However, he cites a U.S. general arguing that those who talk the loudest about restraint are the ones who implement it the least. The trust gap seems unbridgeable.

To ensure that America never has to consider the Needham question in relation to its own development trajectory, it needs to ask itself a different one. What made it rise to first place in terms of technological progress? Many books have been written on the subject and complex arguments constructed to account for American exceptionalism in this regard.Still, there is a perspective that seems neglected. It reveals something about the American character. One study shows that CEOs born in frontier counties are more supportive of innovation than others, and their companies create more and higher quality patents than their competitors, building a culture striving after technological breakthroughs. Fixing the innovation system alone wont be sufficient: the technological frontier could still vanish from the horizon. The frontier spirit has shaped America and expressed a certain moral type, but it is not eternal and may fade away. The fifth battleground is to preserve and cultivate it.

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The Vanishing Frontier - The American Conservative