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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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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.

Vaswani, Ashish, et al. Attention is all you need. Advances in neural information processing systems 30 (2017).

Devlin, Jacob, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

Tenney, Ian, Dipanjan Das, and Ellie Pavlick. BERT rediscovers the classical NLP pipeline. arXiv preprint arXiv:1905.05950 (2019).

Radford, Alec, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language Models are Unsupervised Multitask Learners. (2019). GPT-2.

Language Models are Few-Shot Learners, Tom B. Brown et al., arXiv:2005.14165, July 2020. GPT-3.

Ouyang, Long, et al. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155 (2022).

Tegmark, Max (2017). Life 3.0 : being human in the age of artificial intelligence (First ed.). New York: Knopf.

Russell, Stuart (2017-08-31). Artificial intelligence: The future is superintelligent. Nature. 548 (7669): 520521. Bibcode:2017Natur.548..520R. doi:10.1038/548520a. ISSN 0028-0836.

Russell, Stuart, Human Compatible. 2019.

[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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The Future of Generative Large Language Models and Potential ... - JD Supra

Last fall, when generative AI abruptly started turning out competent high-school- and college-level writing, some educators saw it as an opportunity. Perhaps it was time, at last, to dispose of the five-paragraph essay, among other bad teaching practices that have lingered for generations. Universities and colleges convened emergency town halls before winter terms began to discuss how large language models might reshape their work, for better and worse.

But just as quickly, most of those efforts evaporated into the reality of normal life. Educators and administrators have so many problems to address even before AI enters the picture; the prospect of utterly redesigning writing education and assessment felt impossible. Worthwhile, but maybe later. Then, with last weeks arrival of GPT-4, came another provocation. OpenAI, the company that created the new software, put out a paper touting its capacities. Among them: taking tests. AIs are no longer just producing passable five-paragraph essays. Now theyre excelling at the SAT, earning a score of 1410. Theyre getting passing grades on more than a dozen different AP exams. Theyre doing well enough on bar exams to be licensed as lawyers.

It would be nice if this news inspired educators, governments, certification agencies, and other groups to rethink what these tests really meanor even to reinvent them altogether. Alas, as was the case for rote-essay writing, whatever appetite for change the shock inspires might prove to be short-lived. GPT-4s achievements help reveal the underlying problem: Americans love standardized tests as much as we hate themand were unlikely to let them go even if doing so would be in our best interest.

Many of the initial responses to GPT-4s exam prowess were predictably immoderate: AI can keep up with human lawyers, or apply to Stanford, or make education useless. But why should it be startling in the slightest that software trained on the entire text of the internet performs well on standardized exams? AI can instantly run what amounts to an open-book test on any subject through statistical analysis and regression. Indeed, that anyone is surprised at all by this success suggests that people tend to get confused about what it means when computers prove effective at human activities.

Read: The college essay is dead

Back in the late 1990s, nobody thought a computer could ever beat a human at Go, the ancient Chinese game played with black and white stones. Chess had been mastered by supercomputers, but Go remainedat least in the hearts of its playersimmune to computation. They were wrong. Two decades later, DeepMinds AlphaGo was regularly beating Go masters. To accomplish this task, AlphaGo initially mimicked human players moves before running innumerable games against itself to find new strategies. The victory was construed by some as evidence that computers could overtake people at complex tasks previously thought to be uniquely human.

By rights, GPT-4s skill at the SAT should be taken as the opposite. Standardized tests feel inhuman from the start: You, a distinct individual, are forced to perform in a manner that can be judged by a machine, and then compared with that of many other individuals. Yet last weeks announcementof the 1410 score, the AP exams, and so ongave rise to an unease similar to that produced by AlphaGo.

Perhaps were anxious not that computers will strip us of humanity, but that machines will reveal the vanity of our human concerns. The experience of reasoning about your next set of moves in Go, as a human player doing so from the vantage point of human culture, cannot be replaced or reproduced by a Go-playing machineunless the only point of Go were to prove that Go can be mastered, rather than played. Such cultural values do exist: The designation of chess grand masters and Go 9-dan professionals suggests expertise in excess of mere performance in a folk game. The best players of chess and Go are sometimes seen as smart in a general sense, because they are good at a game that takes smarts of a certain sort. The same is true for AIs that play (and win) these games.

Read: A machine crushed us at Pokmon

Standardized tests occupy a similar cultural role. They were conceived to assess and communicate general performance on a subject such as math or reading. Whether and how they ever managed to do that is up for debate, but the accuracy and fairness of the exams became less important than their social function. To score a 1410 on the SAT says something about your capacities and prospectsmaybe you can get into Stanford. To pursue and then emerge victorious against a battery of AP tests suggests general ability warranting accelerated progress in college. (That victory doesnt necessarily provide that acceleration only emphasizes the seduction of its symbolism.) The bar exam measuresone hopessomeones subject-matter proficiency, but doesnt promise to ensure lawyerly effectiveness or even competence. To perform well on a standardized test indicates potential to perform well at some real future activity, but it has also come to have some value in itself, as a marker of success at taking tests.

That value was already being questioned, machine intelligence aside. Standardized tests have long been scrutinized for contributing to discrimination against minority and low-income students. The coronavirus pandemic, and its disruptions to educational opportunity, intensified those concerns. Many colleges and universities made the SAT and ACT optional for admissions. Graduate schools are giving up on the GRE, and aspiring law students may no longer have to take the LSAT in a couple of years.

GPT-4s purported prowess at these tests shows how little progress has been made at decoupling appearance from reality in the tests pursuit. Standardized tests might fairly assess human capacity, or they might do so unfairly, but either way, they hold an outsize role in Americans conception of themselves and their communities. Were nervous that tests might turn us into computers, but also that computers might reveal the conceit of valuing tests so much in the first place.

AI-based chess and Go computers didnt obsolesce play by people, but they did change human-training practices. Large language models may do the same for taking the SAT and other standardized exams, and evolve into a fancy form of test prep. In that case, they could end up helping those who would already have done well enough to score even higher. Or perhaps they will become the basis for a low-cost alternative that puts such training in the hands of everyonea reversal of examination inequity, and a democratization of vanity. No matter the case, the standardized tests will persist, only now the chatbots have to take them too.

Read more from the original source:
A Chatbot Beat the SAT. What Now? - The Atlantic