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As we move towards an intelligent world, information sensing, connectivity, and computing are becoming key. The better knowledge and control of matter, phenomena, life, and energy that result from these technologies are also becoming increasingly important. This makes rethinking approaches to networks and computing critical in the coming years.

In terms of networks, about 75 years ago Claude Shannon proposed his theorems based on three hypotheses: discrete memoryless sources, classical electromagnetic fields, and simple propagation environments. But since then, the industry has continued to push the boundaries of his work.

In 1987, Jim Durnin discovered self-healing non-diffracting beams that could continue to propagate when encountering an obstruction.

In 1992, L. Allen et. al. postulated that the spin and orbital angular momentum of an electromagnetic field has infinite orthogonal quantum states along the same propagation direction, and each quantum state can have one Shannon capacity.

After AlphaGo emerged in 2016, people realized how well foundation models can be used to describe a world with prior knowledge. This means that much information is not discrete or memoryless.

With the large-scale deployment of 5G Massive MIMO in 2018, it has become possible to have multiple independent propagation channels in complex urban environments with tall buildings, boosting communications capacity.

These new phenomena, knowledge, and environments are helping us break away from the hypotheses that shaped Shannon theorems. With them, I believe we can achieve more than 100-fold improvement in network capabilities in the next decade.

In computing, intelligent applications are developing rapidly, and AI models in particular are likely to help solve the fragmentation problems that are currently holding AI application development back. This is driving an exponential growth in model size. Academia and industry have already begun exploring the use of AI in domains like software programming, scientific research, theorem verification, and theorem proving. With more powerful computing models, more abundant computing power, and higher-quality data, AI will be able to better serve social progress.

AI capabilities are improving rapidly, and so we need to consider how to ensure AI development progresses in a way that benefits all people and ensures that AI execution is accurate and efficient. In addition to ethics and governance, AI also faces three big challenges from a theoretical and technical perspective: AI goal definition, accuracy and adaptability, and efficiency.

The first challenge AI faces is that there is no agreed upon definition of its goals. What kind of intelligence do we need?

If there is no clear definition, it is difficult to ensure that the goals of AI and humanity will be aligned and to make reasonable measurements and classifications and scientific computations. Professor Adrian Bejan, a physicist at Duke University, summarizes more than 20 goals for intelligence in his book The Physics of Life, including understanding and cognitive ability, learning and adaptability, and abstract thinking and problem-solving ability. There are many schools of AI, which are poorly integrated. One important reason for this is there are no commonly agreed upon goals for AI.

The second challenge AI faces is accuracy and adaptability. Learning based on statistical rules extracted from big data often results in non-transparent processes, unstable results, and bias. For example, when recognizing a banana using statistical and correlation-based algorithms, an AI system can be easily affected by background combinations and tiny noises. If other pictures are put next to it, the banana may be recognized as an oven or a slug. These pictures can be easily recognized by people, but AI makes these mistakes and it is difficult to explain or debug them.

The third challenge for AI is efficiency. According to the 60th TOP500 published in 2022, the fastest supercomputer is Frontier, which can achieve 1,102 PFLOPS while using 21 million watts of energy. Human brains, in contrast, can deliver about 30 PFLOPS with just 20 watts. These numbers show that the human brain is about 30,000 to 100,000 times more energy efficient than a supercomputer.

In addition to energy efficiency, data efficiency is also a major challenge for AI. It is true that we can better understand the world by extracting statistical laws from big data. But can we find logic and generate concepts from small data, and abstract them into principles and rules?

We have come up with several hypotheses to address these three challenges:

Starting from these hypotheses, we can begin to take more practical steps to develop knowledge and intelligence.

At Huawei, our first vision is to combine systems engineering with AI to develop accurate, autonomous, and intelligent systems. In recent years, there has been a lot of research in academia about new AI architectures that go beyond transformers.

We can build upon these thoughts by focusing on three parts: perception and modeling, automatic knowledge generation, and solutions and actions. From there, we can develop more accurate, autonomous, and intelligent systems through multimodal perception fusion and modeling, as well as knowledge and data-driven decision-making.

Perception and modeling are about representations and abstractions of the external environment and ourselves. Automatic knowledge generation means systems will need to integrate the existing experience of humans into strategy models and evaluation functions to increase accuracy. Solutions can be directly deduced based on existing knowledge as well as internal and external information, or through trial-and-error and induction. We hope that these technologies will be incorporated into future autonomous systems, so that they can better support domains like autonomous driving networks, autonomous vehicles, and cloud services.

Our second vision is to create better computing models, architectures, and components to continuously improve the efficiency of intelligent computing. I once spoke with Fields Medalist Professor Laurent Lafforgue about whether invariant object recognition could be made more accurate and efficient by using geometric manifolds for object representation and computing in addition to pixels, which are now commonly used in visual and spatial computing.

In their book Neuronal Dynamics, co-authors Gerstner, Kistler, Naud, and Paninski at cole Polytechnique Fdrale de Lausanne (EPFL) explain the concept of functional columns in the cerebral cortex and the six-layer connections between these functional columns. It makes me wonder: Can such a shallow neural network be more efficient than a deep neural network?

A common bottleneck for today's AI computing is the memory wall. Reading, writing, and migrating data often takes 100-times more time than computing itself. So, can we possibly bypass conventional processors, instruction sets, buses, logic components, and memory components under von Neumann architecture, and redefine architectures and components based on advanced AI computing models instead?

Huawei has been exploring this idea by looking into the practical uses of AI. First, we have worked on "AI for Industry", which uses industry-specific large models to create more value. Industries face many challenges when it comes to AI application development. They need to invest a huge amount of manpower to label samples, find it difficult to maintain models, and lack the necessary capabilities in model generalization. Most simply they do not have the resources to do this.

To address these challenges, Huawei has developed L1 industry-specific large models based on its L0 large foundation models dedicated to computer vision, natural language processing, graph neural networks, and multi-modal interactions. These large models lower the barrier to AI development, improve model generalization, and address application fragmentation. The models are already being used to improve operational efficiency and safety in major industries like electric power, coal mining, transportation, and manufacturing.

Huawei's Aviation & Rail Business Unit, for example, is working with customers and partners in Hohhot, Wuhan, Xi'an, Shenzhen, and Hongkong to explore the digital transformation of urban rail, railways, and airports. This has improved operational safety and efficiency, as well as user experience and satisfaction. The Shenzhen Airport has realized smart stand allocation with the support of cloud, big data, and AI, reducing airside transfer bus passenger flow by 2.6 million every year. The airport has become a global benchmark in digital transformation.

"AI for Science" is another initiative that will be able to greatly empower scientific computing. One example of this in action is the Pangu meteorology model we developed using a new 3D transformer-based coding architecture for geographic information and a hierarchical time-domain aggregation method. With a prior knowledge of global meteorological phenomena, the Pangu model uses more accurate and efficient learning and reasoning to replace time series solutions of hyperscale partial differential equations using traditional scientific computing methods. The Pangu model can produce 1-hour to 7-day weather forecasts in just a few seconds, and its results are 20% more accurate than forecasts from the European Centre for Medium-Range Weather Forecasts.

AI can also support software programming. In addition to using AI to do traditional retrieval and recommendation in a large amount of existing code, Huawei is developing new model-driven and formal methods. This is especially important for large-scale parallel processing, where many tasks are intertwined and correlated. Huawei has developed a new approach called Vsync which realizes automatic verification and concurrent code optimization of operating system kernels, and improves performance without undermining reliability. The Linux Community once discovered a difficult memory barrier bug which took community experts more than two years to fix. With Huawei's Vsync method, however, it would have taken just 20 minutes to discover and fix the bug.

We have also been studying new computing models for automated theorem proving. Topos theory, for example, can be used to research category proving, congruence reasoning systems, and automated theorem derivation to improve the automation level of theorem provers. In doing this, we want to solve state explosion and automatic model abstraction problems and improve formal verification capabilities.

Finally, we are also exploring advanced computing components. We can use the remainder theorem to address conversion efficiency and overflow problems in real-world applications. We hope to implement basic addition and multiplication functions in chips and software to improve the efficiency of intelligent computing.

As we move towards the intelligent world, networks and computing are two key cornerstones that underpin our shift from narrow AI towards general-purpose AI and super AI. To get there, we will need to take three key steps. First, we will need to develop AI theories and technologies, as well as related ethics and governance, so that we can deliver ubiquitous intelligent connectivity and drive social progress. Second, we will need to continue pushing our cognitive limits to improve our ability to understand and control intelligence. Finally, we need to define the right goals and use the right approaches to guide AI development in a way that truly helps overcome human limitations, improve lives, create matter, control energy, and transcend time and space. This is how we will succeed in our adventure into the future.

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Hypotheses and Visions for an Intelligent World - Huawei

Artificial intelligence (opens in new tab) continues to make headlines for its potential to transform businesses across various industries, and has been widely embraced as a technology that can help companies unlock new opportunities, improve efficiency, and increase profitability. At its most basic level, AI does this by analyzing inputted information to create intelligent outputs. The AI industry is currently valued at over $136 billion and is predicted to grow over 13 times in the next 7 years.

At its core, AI relies on data (opens in new tab) - specifically, large volumes of high-quality data to train machine learning algorithms. These algorithms analyze inputted information to identify patterns that can be used to make predictions, automate processes, or perform other tasks. Accordingly, while the power of AI applications (opens in new tab) across industries is immense, the benefits are entirely based on the information available to these systems.

Given that AI is so reliant on data, where this data is stored becomes an important concern. Businesses need to know that they can securely store a large volume of data and that this data is easily accessible for the AI systems to use. Moreover, for businesses, proprietary data for custom AI applications must be kept safe. With this in mind, the best way for businesses to keep large quantities of easily accessible data safely is by keeping at least one copy of it in the cloud.

AI systems need high volumes of data on hand to operate optimally. These systems have the capacity to improve their performance and enhance their learning speed as the amount of available data increases. For example, Google DeepMind's AlphaGo Zero had to play 20 million games against itself to train its AI to a superhuman level of play, demonstrating just how much data is needed for AI to work at its full potential.

Given that the success of AI implementation hinges on the amount of data AI systems can access, companies must thoughtfully consider their data storage options, whether that be on-premise, in the cloud (opens in new tab), or in a hybrid cloud system - and how that impacts their AI implementation.

Storing data on local hardware owned and managed by an enterprise, known as on-premises data storage, requires securing storage resources and maintaining systems. However, scaling in this way is difficult and costly compared to cloud-based storage, which is better equipped to handle increasing data volumes. On-premise scalability is also limited by ageing hardware and software, which often come with discontinued support plans and retired products. Therefore, for better scalability and security, the adoption of cloud storage services is becoming increasingly crucial for companies as they develop "AI first" strategies.

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David Friend is the co-founder and CEO of Wasabi.

Similar to the way businesses need to store a lot of data for AI, they also need to keep proprietary data should they wish to customize their AI to meet their organization's specific needs. For instance, an HR manager may be able to use AI to analyze years worth of company-wide survey data in minutes and predict employee responses to different kinds of company news, like new policies or team switchups. Similarly, an AI system could analyze company growth and economic data to inform major business decisions.

Incorporating proprietary data into an AI system improves the accuracy and relevance of insights leading to better decision-making and business outcomes. Customising AI applications using proprietary data can give businesses a competitive edge, however should they choose to take advantage of customised AI through proprietary data, its important that this data is stored safely.

Unfortunately, the rise of AI systems brings with it a host of new cybersecurity risks and the number and cost of cybersecurity attacks is expected to surge in the next five years, rising from $8.44 trillion in 2022 to $23.84 trillion by 2027. Particularly when storing critical company data, its key that AI systems are well-protected against ransomware attacks.

An important security advantage cloud has over on-premise solutions is that cloud infrastructure is separated from user workstations, bearing in mind hackers most commonly access company networks through phishing and emails (opens in new tab). Accordingly, having multiple copies of data with at least one version stored in the cloud is key to keeping company data safe and not compromising any critical AI systems.

The best way to protect against threats that may compromise the primary data copy is to keep a second, immutable copy of the AI system data. Immutable storage is a cloud storage feature that provides extra security by preventing data modification or deletion. Combined with comprehensive backup strategies, cloud storage (opens in new tab) providers offer high data security by storing immutable backups that can be retrieved if original data is compromised or deleted, ensuring availability, and avoiding loss of critical data.

For businesses, the value of AI is in its convenience and potential cost savings as it takes on tasks that would have previously taken hours of employee time and energy. By embracing cloud storage solutions for the reasons set out above, businesses can unleash the full power of AI for success.

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Cloud storage is the key to unlocking AI's full potential for businesses - TechRadar

The contemporary computer processor at only half the size of a penny possesses the extraordinary capacity to carry out 11 trillion operations per second, with the assistance of an impressive assembly of 16 billion transistors.[1] This feat starkly contrasts the early days of transistor-based machines, such as the Manchester Transistor Computer, which had an estimated 100,000 operations per second, using 92 transistors and having a dimension of a large refrigerator. For comparison, while the Manchester Transistor Computer could take several seconds or minutes to calculate the sum of two large numbers, the Apple M1 chip can calculate it almost instantly. Such a rapid acceleration of processing capabilities and device miniaturization is attributable to the empirical observation known as Moores Law, named after the late Gordon Moore, the co-founder of Intel. Moores Law posits that the number of transistors integrated into a circuit is poised to double approximately every two years.[2]

In their development, these powerful processors have paved the way for advancements in diverse domains, including the disruptive field of artificial intelligence (AI). Nevertheless, as we confront the boundaries of Moores Law due to the physical limits of transistor miniaturization,[3] the horizons of the field of computing are extended into the enigmatic sphere of quantum physics the branch of physics that studies the behavior of matter and energy at the atomic and subatomic scales. It is within this realm that the prospect of quantum computing arises, offering immense potential for exponential growth in computational performance and speed, thereby heralding a transformative era in AI.

In this article, we scrutinize the captivating universe of quantum computing and its prospective implications on the development of AI and examine the legal measures adopted by leading tech companies to protect their innovations within this rapidly advancing field, particularly through patent law.

Qubits: The Building Blocks of Quantum Computing

In classical computing, the storage and computation of information are entrusted to binary bits, which assume either a 0 or 1 value. For example, a classical computer can have a specialized storage device called a register that can store a specific number at a time using bits. Each bit is like a slot that can be either empty (0) or occupied (1), and together they can represent numbers, such as the number 2 (with a binary representation of 010). In contrast, quantum computing harnesses the potential of quantum bits (infinitesimal particles, such as electrons or photons, defined by their respective quantum properties, including spin or polarization), commonly referred to as qubits.

Distinct from their classical counterparts, qubits can coexist in a superposition of states, signifying their capacity to represent both 0 and 1 simultaneously. This advantage means that, unlike bits with slots that are either empty or occupied, each qubit can be both empty and occupied at the same time, allowing each register to represent multiple numbers concurrently. While a bit register can only represent the number 2 (010), a qubit register can represent both the numbers 2 and 4 (010 and 100) simultaneously.

This superposition of states enables the parallel processing of information since multiple numbers in a qubit register can be processed at one time. For example, a classical computer may use two different bit registers to first add the number 2 to the number 4 (010 +100) and then add the number 4 to the number 1 (100+001), performing the calculations one after the other. In contrast, qubit registers, since they can hold multiple numbers at once, can perform both operationsadding the number 2 to the number 4 (010 + 100) and adding the number 4 to the number 1 (100 + 001)simultaneously.

Moreover, qubits employ the singular characteristics of entanglement and interference to execute intricate computations with a level of efficiency unattainable by classical computers. For instance, entanglement facilitates instant communication and coordination, which increases computational efficiency. At the same time, interference involves performing calculations on multiple possibilities at once and adjusting probability amplitudes to guide the quantum system toward the optimal solution. Collectively, these attributes equip quantum computers with the ability to confront challenges that would otherwise remain insurmountable for conventional computing systems, thereby radically disrupting the field of computing and every field that depends on it.

Quantum Computing

Quantum computing embodies a transformative leap for AI, providing the capacity to process large data sets and complex algorithms at unprecedented speeds. This transformative technology has far-reaching implications in fields like cryptography,[4] drug discovery,[5] financial modeling,[6] and numerous other disciplines, as it offers unparalleled computational power and efficacy. For example, a classical computer using a General Number Field Sieve (GNFS) algorithm might take several months or even years to factorize a 2048-bit number. In contrast, a quantum computer using Shors algorithm (a quantum algorithm) could potentially accomplish this task in a matter of hours or days. This capability can be used to break the widely used RSA public key encryption system, which would take conventional computers tens or hundreds of millions of years to break, jeopardizing the security of encrypted data, communications, and transactions across industries such as finance, healthcare, and government. Leveraging the unique properties of qubitsincluding superposition, entanglement, and interference quantum computers are equipped to process vast amounts of information in parallel. This capability enables them to address intricate problems and undertake calculations at velocities that, in certain but not all cases,[7] surpass those of classical computers by orders of magnitude.

The augmented computational capacity of quantum computing is promising to significantly disrupt various AI domains, encompassing quantum machine learning, natural language processing (NLP), and optimization quandaries. For instance, quantum algorithms can expedite the training of machine learning models by processing extensive datasets with greater efficiency, enhancing performance, and accelerating model development. Furthermore, quantum-boosted natural language processing algorithms may yield more precise language translation, sentiment analysis, and information extraction, fundamentally altering how we engage with technology.

Patent Applications Related to Quantum Computers

While quantum computers remain in their nascent phase, to date, the United States Patent and Trademark Office has received more than 6,000 applications directed to quantum computers, with over 1,800 applications being granted a United States patent. Among these applications and patents, IBM emerges as the preeminent leader, trailed closely by various companies, including Microsoft, Google, and Intel, which are recognized as significant contributors to the field of AI. For instance, Microsoft is a major investor in OpenAI (the developer of ChatGPT) and has developed Azure AI (a suite of AI services and tools for implementing AI into applications or services) and is integrating ChatGPT into various Microsoft products like Bing and Microsoft 365 Copilot. Similarly, Google has created AI breakthroughs such as AlphaGo (AI that defeated the world champion of the board game Go), hardware like tensor processing units (TPUs) (for accelerating machine learning and deep learning tasks), and has released its own chatbot called Bard (also known as LaMDA).

Patents Covering Quantum Computing

The domain of quantum computing is progressing at a remarkable pace, as current research seeks to refine hardware, create error correction methodologies, and investigate novel algorithms and applications. IBM and Microsoft stand at the forefront of this R&D landscape in quantum computing. Both enterprises have strategically harnessed their research findings to secure early patents encompassing quantum computers. Notwithstanding, this initial phase may merely represent the inception of a competitive endeavor to obtain patents in this rapidly evolving field. A few noteworthy and recent United States patents that have been granted thus far include:

Conclusion

Quantum computing signifies a monumental leap forward for AI, offering unparalleled computational strength and efficiency. As we approach the limits of Moores Law, the future of AI is contingent upon harnessing qubits distinctive properties, such as superposition, entanglement, and interference. The cultivation of quantum machine learning, along with its applications in an array of AI domains, including advanced machine learning, NLP, and optimization, portends a revolution in how we address complex challenges and engage with technology.

Prominent tech companies like IBM and Microsoft have demonstrated their commitment to this burgeoning field through investments and the construction of patent portfolios that encompass this technology. The evident significance of quantum computing in shaping the future of AI suggests that we may be witnessing the onset of a competitive patent race within the sphere of quantum computing.

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The Quantum Frontier: Disrupting AI and Igniting a Patent Race - Lexology

An open letter recently signed by Elon Musk, researchers from the Massachusetts Institute of Technology and Harvard University, and more than a thousand other prominent people set off alarm bells on advances in artificial intelligence (AI). The letter urged the worlds leading labs to hit the brakes on this powerful technology for six months because of the "profound risks to society and humanity."

A pause to consider the ramifications of this unpredictable new technology may have benefits. But our enemies will not wait while the U.S. engages in teleological discourse.

"By combining our wealth of research capacity and industrial capabilities, Russia and China can become world leaders in information technology, cybersecurity, and artificial intelligence (AI)," declared Russian President Vladimir Putin on March 21 during his meeting in Moscow with Chinese President Xi Jinping. The two authoritarian leaders vowed to usher in a new, anti-U.S. world order, and as their joint statement noted a "Deepening the Comprehensive Strategic Partnership of Coordination in the New Era," highlighted cooperation between Russia and China on AI.

AI is regarded as part of the fourth industrial revolution, which also includes the Internet of Things, genetic engineering, and quantum computing. Here is how Americas top adversaries, China and Russia, plan to weaponize this powerful tool against America.

CHINA WILL REQUIRE AI TO REFLECT SOCIALIST VALUES, NOT CHALLENGE SOCIAL ORDER

China codified its AI ambitions in the New Generation Artificial Intelligence Development Plan, which it adopted in July 2017. China had its AI awakening moment a year prior, according to Kaifu Li, ex-director of Google China.On March 19, 2016, Google DeepMinds artificial intelligence program AlphaGo defeated South Koreas Lee Sedol, the world champion in Go, the ancient Chinese game, in a highly anticipated match at the Four Seasons Hotel in Seouls Gwanghwamun district. Most South Korean TV networks were covering the event as 60 million Chinese tuned in and 100,000 English-speaking viewers watched YouTubes livestream. That a computer could beat the world champion shocked the Chinese. Sixteen months later, the Chinese Communist Party vowed that Beijing will lead the world of AI by 2030.

Chinas AI strategy centers on three primary goals: domestic surveillance, economic advancement and future warfare. The Chinese government is already using AI-driven software dubbed "one person, one file," that collects and stores vast amounts of data on its residents, in order to evaluate loyalty and risk to the regime. A giant network of surveillance cameras the Chinese authorities call "sharp eyes" tracks everyone continuously. Americans who travel to China, especially business executives and government officials, need to be aware of the risks associated with this blanket 24/7 monitoring.

When it comes to military applications, Chinas strategic ambitions for AI are what the CCP calls "intelligentized" and "informatized" warfare. Chinas Ministry of National Defense has established two research centers to execute this mission the Artificial Intelligence Research Center and the Unmanned Systems Research Center. The Peoples Liberation Armys (PLA) tasked its Academy of Military Science with ensuring that the PLAs warfighting doctrine is fully capitalized on disruptive technologies like AI and autonomous systems.

AFTER XI-PUTIN MEETING, TEAM BIDEN STILL DOESN'T GET WHAT'S JUST HAPPENED TO THE UNITED STATES

The United States is the primary target of Chinas AI-enabled warfare doctrine, as it is the only country that stands in the way of Chinas long-held policy goal of securing control over Taiwan. The CCP has decided that instead of following the track of U.S. military modernization, something Chinese military theorists view as linear trajectory, China will pursue "leapfrog development" of AI and autonomous technologies.

The PLA views AI technology as a "trump card" weapon that could be used in multiple ways to target perceived U.S. vulnerabilities, including U.S. battle networks and Americas way of war in general. An AI-enabled "swarming" tactic, for example is one of the approaches China could use to target and saturate the defenses of U.S. aircraft carriers.

AI swarming is a high-tech version of flooding U.S. airspace, in the run-up to an invasion of Taiwan, with hundreds of weaponized air balloons, of the kind that it recently flew across America. This would overwhelm the detection and defense capabilities of the U.S. North American Aerospace Defense Command (NORAD.) How many F-22s and $400,000 AIM-9X Sidewinder missiles would be needed to down them all?

The speed of Chinas progress in AI is of grave concern to the Pentagon and U.S. intelligence. In March, the U.S. Defense Intelligence Agency warned that China is "investing heavily in its AI and ML [machine learning] capabilities."

The 2023 Annual Threat Assessment by the Office of the Director of National Intelligence characterized Chinas AI and big data analytics capabilities as "rapidly expanding and improving," saying China is on track to "expand beyond domestic use." China is already an "AI peer in many areas and an AI leader in some applications," according to the 2021 Final Report by the U.S. National Security Commission on Artificial Intelligence. The report warned that "Chinas plans, resources, and progress should concern all Americans" and highlighted the importance of winning the "intensifying strategic competition" with China, which is determined to surpass the United States in the next few years.

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Russia is lagging behind China and the U.S. in AI, but Moscow also seeks to become one of the world leaders in this novel technology. In 2017, Putin famously proclaimed "whichever country becomes the leader in artificial intelligence will become the ruler of the world."

In October 2019, Vladmir Putin approved Russia's "National Strategy for the Development of Artificial Intelligence to 2030" and directed his Cabinet to report annually about the progress of its implementation. Last year, Putin escalated his prioritization of AI. "Artificial intelligence technologies should be massively implemented in all industries in Russia this decade," he stated at the AI Journal Conference in Moscow in November 2022, urging Russia's researchers to "create breakthrough technologies of a new era." Russia's "place in the world, sovereignty, and security" depend on the results it achieves in AI, he said.

Russias AI strategy is primarily focused on robotics, robot-human interaction and counter-drone warfare. Russian military strategists believe that the expanding role of unmanned aerial vehicles (UAVs) in modern warfare necessitates the development of "UAV-killing UAV" systems. AI is also viewed by Russian strategists as a perfect technology to enable Moscows doctrine of "controlled chaos" as a way of deterring Washingtonfrom intervening in a conflict, such as the one in Ukraine. The doctrine envisions the targeting of the U.S. homeland with AI-enabled crippling cyber-attacks and spreading false information that could cause panic and disrupt the normal functioning of the society.

Russian doctrinal writings talk about "inspiring crisis" in an adversarys state by deploying AI-enabled cyber weapons and information operations in the run-up to a conflict. Using an "artificially maintained" crisis to trigger "aggravating factors such as dissatisfaction with existing government," would create a destabilizing effect on the opponent, pointing their focus inward and away from what Russia is doing, hypothesize Russian strategists.

As U.S. leaders make decisions regarding Americas pace of development in AI, they must remember that Russia and China are not only accelerating the speed of their AI research, they also plan to join forces to make critical gains in it. The goal is to create a new anti-U.S. world order, destabilize the U.S. from within, and defeat America on the battlefield if necessary. Now is not the time to cede our competitive advantage in AI to our top adversaries.

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Putin and Xi seek to weaponize Artificial Intelligence against America - FOX Bangor/ABC 7 News and Stories

[co-author: Geoffrey Vance, Perkins Coie LLP]

[Foreword by Geoffrey Vance: Although this article is technically co-authored by Jan and me, the vast majority of the technical discussion is Jans work. And thats the point. Lawyers arent supposed to be the Captains of the application of generative AI in the legal industry. A lawyers role is more like that of a First Officer, whose responsibility is to assist the primary pilot in getting to the ultimate destination. This article and all the exciting parts within it demonstrate how important data scientists and advanced technology are to the legal profession. The law firms and lawyers who dont reach that understanding fast will be left behind. Those who do will lead the future of the legal industry.]

Contents

Why is Human Language so Hard to Understand for Computer Programs?What are Large Language Models?The Zoo of Transformer Models: BERT and GPTChatGPTs LimitationsHow to Improve Large Language ModelsIntegrating LLM with existing Legal TechnologyUnderstand the Decisions; XAI

Human language is difficult for computer programs to understand because it is inherently complex, ambiguous, and context-dependent. Unlike computer languages, which are based on strict rules and syntax, human language is nuanced and can vary greatly based on the speaker, the situation, and the cultural context. As a result, building computer programs that can accurately understand and interpret human language is exceptionally complex and has been an ongoing challenge for artificial intelligence researchers since AI was first introduced. This is exactly the reason why it took so long for humans (in many of our lifetimes) to create reliable computer programs to deal with human language.

In addition, for many different reasons, early language models took shortcuts and none of them addressed all linguistic challenges. It was not until Google introduced the Transformer model in 2017 in the ground-breaking paper Attention is all you need that a full encoder-decoder model, using multiple layers of self-attention, resulted in a model capable of understanding almost all of the linguistic challenges. The model soon outperformed all other models on various linguistic tasks such as translation, Q&A, classification, text-analytics.

Before we dive into the specifics of large language models, lets first look at the basic definition. Large Language Models are artificial intelligence models that can generate human-like language based on a large amount of data they have been trained on. They use deep learning algorithms to analyze vast amounts of text, learning patterns and relationships between words, phrases, and concepts.

Some of the most well-known LLMs are the GPT series of models developed by OpenAI, BERT developed by Google, and T5 developed by Google Brain.

As encoder-decoder models such as the T5 model are very large and hard to train due to a lack of aligned training data, a variety of cut-down models (also called a zoo of transformer models) have been created. The two best known models are: BERT and GPT.

ChatGPT is an extension of GPT. It is based on the latest version of GPT (3.5) and has been fine-tuned for human-computer dialog using reinforcement learning. In addition, it is capable of sticking to human ethical values by using several additional mechanisms. These two capabilities are major achievements!

The core reason ChatGPT is so good is because transformers are the first computational models that take almost all linguistic phenomena seriously. Based on Googles transformers, OpenAI (with the help of Microsoft) has shaken up the world by introducing a model that can generate language that can no longer be distinguished from human language.

Much to our chagrin, ChatGPT is not the all-knowing General Artificial Intelligence most would like it to be. This is mainly due to the decoder-only architecture. ChatGPT is great for chatting, but one cannot control the factuality. This is due to the lack of an encoder mechanism. The longer the chats, the higher the odds that ChatGPT will get off-track or start hallucinating. Being a statistical process, this is a logical consequence: longer sequences are harder to control or predict than shorter ones.

Using ChatGPT on its own for anything else than just casual chit-chatting, is not wise. Using it for legal or medical advice without human validation of the factuality of such advice is just dangerous.

The AI research is aware of this, and there are a number of on-going approaches to improve todays models:

Currently, the Artificial Intelligence industry is working on all of the above improvements. In addition, one can also expect integrations with other forms of human perception: vision and speech. As you may not know, OpenAI is also the creator of Whisper, the state of the art Speech recognition for 100s of languages and DALL-E2, the well-known image generator, so adding speech to the mix is only a matter of time.

If you made it this far, you should by now understand that ChatGPT is not by itself a search engine, nor an eDiscovery data reviewer, a translator, knowledge base, or tool for legal analytics. But it can contribute to these functionalities.

Full-text search is one of the most important tools for legal professionals. It is an integral part of every piece of legal software, assisting lawyers in case law search, legal fact finding, document template search, among other tasks.

Todays typical workflow involved formulating a (Boolean) query, ranking results on some form or relevancy (jurisdiction, date, relevance, source, etc.), reviewing the results, and selecting the ones that matter. As the average query length on Google is only 1.2 words, we expect our search engine to find the most relevant hits with very little information. Defining the query can be hard and will always include human bias (the results one gets depends on the keywords used). What is more, reviewing the results of the search query can be time consuming, and one never knows what one misses. This is where Chatbots can help: by changing the search process into an AI-driven dialogue, we can change the whole search experience.

This is exactly what Microsoft does with the BING ChatGPT integration, but with a few risks in the current implementation:

As explained earlier, more focus on explaining where the results come from, the ability to eliminate information and a better understanding of the meaning of the text used to drive the dialogue is probably needed to get better results. Especially when we plan to use this for legal search, we need more transparency and understanding where the results come from.

Contract drafting is likely one of the most promising applications of textual generative artificial intelligence (AI) because contracts are typically highly structured documents that contain specific legal language, terms, and conditions. These documents are often lengthy, complex, and require a high degree of precision, making them time-consuming and expensive to produce.

Textual generative AI models can assist in the drafting of contracts by generating language that conforms to legal standards and meets specific requirements. By analyzing vast amounts of legal data and identifying patterns in legal language, these models can produce contract clauses and provisions that are consistent with legal norms and best practices.

Furthermore, AI-generated contract language can help ensure consistency and accuracy across multiple documents, reduce the risk of errors and omissions, and streamline the contract drafting process. This can save time and money for lawyers and businesses alike, while also reducing the potential for disputes and litigation.

But, here too, we need to do more vertical training, and probably more controlled text generation by understanding and incorporating the structure of legal documents in the text-generation process.

In all cases, it is important to note that AI-generated contract language should be reviewed by a qualified lawyer to ensure that it complies with applicable laws and regulations, and accurately reflects the parties intentions. While AI can assist in the drafting process, it cannot replace the expertise and judgment of a human lawyer.

We have serious doubts if generative Artificial Intelligence can be used as it is and provide help in providing meaningful legal advice. AI models lack the ability to provide personalized advice based on a clients specific circumstances, or to consider the ethical and moral dimensions of a legal issue. Legal advice requires a deep understanding of the law and the ability to apply legal principles to a particular situation. Text generation models do not have this knowledge. So, without additional frameworks capable of storing and understanding such knowledge, using models such as ChatGPT is a random walk in the court.

E-discovery is a process that involves the identification, collection, preservation, review, and production of electronically stored information (ESI) in the context of legal proceedings. While e-discovery often involves searching for specific information or documents, it is more accurately described as a sorting and classification process, rather than a search process.

The reason for this is that e-discovery involves the review and analysis of large volumes of data, often from a variety of sources and in different formats. ChatGPT is unable to handle the native formats this data is in.

The sorting and classification process in e-discovery is critical because it allows legal teams to identify and review relevant documents efficiently and accurately, while also complying with legal requirements for the preservation and production of ESI. Without this process, legal teams would be forced to manually review large volumes of data, which would be time-consuming, costly, and prone to error.

In summary, e-discovery is a sorting and classification process because it involves the review and analysis of large volumes of data, and the classification and organization of that data in a way that is relevant to the legal matter at hand. While searching for specific information is a part of eDiscovery, it is only one aspect of a larger process.

ChatGPT is neither a sorting, nor a text analytical or search tool. Models such as BERT or text-classification models based on word-embeddings or TF-IDF in combination with Support Vector Machines are better, faster, and better understood for Assisted Review and Active Learning.

Where Generative AI can help, is in the expansion of search queries. As we all know, humans are always biased. When humans define (Boolean) search queries, the search keywords chosen by human operators are subject to this bias. Generative AI can be very beneficial assisting users defining a search query and come up with keywords an end-user would not have thought of. This increases recall and limits human bias.

Legal documents can be lengthy and often contain boiler plate text. Summarization can provide a quick overview of the most important aspects of such a document. GPT is very good at summarization tasks. This can assist reviewers or project managers to get faster understanding of documents in eDiscovery.

As an AI language model, ChatGPT could be used to draft written responses to eDiscovery requests or provide suggested language for meet and confer sessions. However, it cannot provide personalized legal advice or make strategic decisions based on the specific circumstances of a case.

eDiscovery platforms enrich, filter, order and sort ESI into understandable structures. Such structures are used to generate reports. Reports can be in either structured formats (tables and graphs), or in the form of description in natural language. The latter can easily be generated from the ESI database by using generative AI to create a more human form of communication.

Here too, we can state that ChatGPT is not a text analytical or search tool. Straight forward search engines (using keyword, fuzzy and regular expression search), or advanced text-classification models such as BERT are better, faster and better understood for compliance monitoring and information governance purposes.

Nobody is more interested in explainable Artificial Intelligence (XAI) than DARPA, the Defense Advanced Research Projects Agency. Already in 2016, DARPA started an XAI program.

Ever since, DARPA has sponsored various research projects related to XAI, including the development of algorithms and models that can generate explanations for their decisions, the creation of benchmark datasets for testing XAI systems, and the exploration of new methods for evaluating the explainability and transparency of AI systems.

XAI is one of the hottest areas of research in the AI community. Without XAI, the application of artificial intelligence is unthinkable in areas such as finance, legal, medical or military.

XAI, refers to the development of AI systems that can provide clear and transparent explanations for their decision-making processes. Unlike traditional black-box AI systems, which are difficult or impossible to interpret, XAI systems aim to provide human-understandable explanations for their behavior.

XAI is not a single technology or approach, but rather a broad research area that includes various techniques and methods for achieving explainability in AI systems. Some approaches to XAI include rule-based systems, which use explicit rules to generate decisions that can be easily understood by humans; model-based systems, which use machine learning models that are designed to be interpretable and explainable; and hybrid systems, which combine multiple techniques to achieve a balance between accuracy and explainability.

The development of XAI is an active area of research, with many academic and industry researchers working to develop new techniques and tools for achieving transparency and explainability in AI systems. Ultimately, the goal of XAI is to promote the development of AI systems that are not only accurate and efficient, but also transparent and trustworthy, allowing humans to understand and control the decision-making processes of the AI system.

For legal applications, a full XAI framework is essential. Without XAI, there can also not be legal defensibility or trust.

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[1] Textual adversarial attacks are a type of cyber-attack that involves modifying or manipulating textual data in order to deceive or mislead machine learning models.

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