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The article explores the phenomenon of people interacting with chatbots as if they were conscious beings, despite the fact that artificial intelligence (AI) systems have not achieved consciousness. The author discusses examples of griefbots, AI programs trained on a departed loved one's digital footprint, and their ability to mimic conversations. The article also delves into the philosophical and ethical implications of perceiving AI as conscious, as well as the potential benefits and risks of engaging with chatbots. Ultimately, the article highlights the importance of understanding human interactions with AI and the need to extend, rather than replace, human relationships. [Extracted from the article]

The article explores the current state of democracy and proposes ways to reimagine and revitalize it for the 21st century. It highlights the shortcomings of modern democracies, such as favoring the wealthy and producing legislative gridlock. The article suggests returning to the Athenian model of democracy, which involved citizen participation and collective decision-making. It discusses various approaches, including sortition, AI-augmented deliberation, and citizens' assemblies, as potential solutions to enhance democracy. The article also emphasizes the importance of civic education and the need for a mindset of openness and epistemic humility to foster democratic change. [Extracted from the article]

Common sense, often seen as universal knowledge or beliefs that are obvious to everyone, is actually more idiosyncratic than previously thought. A study conducted by Duncan Watts and Mark Whiting at the University of Pennsylvania found that common sense is not common at all, with very few statements being agreed upon by the majority of people. The study also revealed that demographic factors such as age, education level, and political leaning were not linked to an individual's common sense. The findings have implications for various areas, including artificial intelligence and public health, where common sense plays a significant role. [Extracted from the article]

This article describes the research conducted to further a prototype email editor, LaMPost, to support Dyslexia adults. LaMPost is a large language model-enabled artificial intelligence assistant for email-writing that includes subject generation and rewriting features. The research conducted involved the evaluation of LaMPost by 19 Dyslexia adults. Topics include a detailed overview of the development and features of LaMPost as well as the finding from the research generated by the user testing.

GROQ, a startup company, is challenging Nvidia's dominance in the AI chip market by designing purpose-built chips for AI from scratch. The company's chips are specifically designed for inference, which is the part of AI that applies learned information to new situations. With the increasing demand for computational power for AI models, Groq sees an opportunity to compete with Nvidia and other chip giants. Groq's chips, called Language Processing Units (LPUs), are faster, cheaper, and more energy-efficient than Nvidia's GPUs for inference. The company has already gained customers such as Argonne National Laboratory and Aramco Digital. Despite some skepticism, Groq is optimistic about its future in the AI chip market. [Extracted from the article]

Audrey Tang, a digital democracy pioneer and transgender minister in Taiwan, has played a key role in transforming the country's political landscape. Tang was involved in the creation of vTaiwan, a virtual platform for democratic deliberation, and implemented her vision of "radical transparency" in the government. She argues that technology can both erode and save democracy, with AI potentially assisting collective intelligence and fostering pro-democratic conversations. Tang's efforts have helped rebuild legitimacy in Taiwan's government and have attracted interest from other countries seeking to introduce digital democracy. [Extracted from the article]

The article discusses how data scientists can measure the behavior and performance of large language models (LLMs), and it mentions LLM-related skills and abilities. The concepts of emergence and effective scaling are examined, as well as generative artificial intelligence (AI) and LLM metrics. Information about the Chat GPT-3 and LAMDA LLMs is provided.

The increasing reliance on prompting as a primary interface for AI systems is problematic for both end users and experts. While prompting may seem intuitive, it often leads to confusion and inefficiency due to its sensitivity to slight variations in input. Furthermore, this approach risks fostering a replication crisis in AI research, as prompt-hacking may distort research outcomes. To enhance the usability, safety, and reliability of AI, it is essential to move beyond prompting and develop more natural, intuitive interfaces that support clear communication and robust evaluation across diverse applications.

The article explores how the rapid growth of large language models (LLMs) has led to significant increases in computing demands and operational costs, particularly for cloud-based AI services. To mitigate these challenges, researchers and industry leaders are exploring optimization techniques, such as model pruning, quantization, and knowledge distillation, which aim to reduce model size and enhance efficiency without sacrificing accuracy. Additionally, hybrid approaches that distribute workloads between user devices and cloud servers, as well as peer-to-peer computing models, are being investigated to improve performance and reduce the energy and financial costs of running LLMs. The ongoing research reflects a strong industry focus on overcoming the resource constraints and cost spiral associated with AI advancements.

This document provides brief summaries of various companies and organizations in different industries. It highlights the success and contributions of companies such as Rare Beauty, CoreWeave, Dyson, Frida, Depop, VinFast, Anthropic, Cerebras Systems, Saildrone, BYD, and Eisai. These companies are making strides in their respective fields, whether it be in beauty, technology, fashion, automotive, or pharmaceuticals. The document aims to provide library patrons with a quick overview of these companies and their recent developments. [Extracted from the article]

Artificial intelligence (AI) is making significant advancements in the field of mathematics, raising questions about the potential for AI to achieve human-level reasoning and even artificial general intelligence (AGI). AI systems have demonstrated the ability to solve complex geometry problems, assist with proofs, and generate new avenues of attack for long-standing mathematical problems. By using machine learning algorithms and neural networks, AI can spot patterns in complex datasets and make predictions that human mathematicians may not have considered. Additionally, the formalization of mathematical proofs and the use of AI to check and generate proofs could further enhance AI's mathematical capabilities. While some mathematicians remain skeptical about the possibility of AI achieving true mathematical creativity, others believe that AI's progress in mathematics could be a significant step towards AGI. However, the development of a superhuman AI mathematician by 2026, as some experts suggest, remains uncertain. [Extracted from the article]

The United Arab Emirates (UAE) is investing heavily in artificial intelligence (AI) to diversify its economy and increase its geopolitical influence. The country has released a powerful AI system called Falcon and has advantages such as cash reserves, abundant electricity, and the absence of income tax to attract top AI researchers. However, concerns have been raised about technology transfer and geopolitical tensions with the US and China. The UAE aims to improve the quality of life through AI and expand its economic footprint, but faces challenges in attracting researchers due to restrictions on civil liberties and labor abuses. The UAE is working with the US to expand semiconductor production and is committed to open-sourcing AI. [Extracted from the article]

The article offers an opinion regarding how a human-artificial intelligence (AI) collaboration can asses relevance. Topics include the improvements to large language models (LLMs) such as OpenAI's chatbot ChatGPT, the reasons why utilizing only LLMs can be problematic, and how human judgement can improve fairness, efficiency, and effectiveness.

This research article focuses on the science of detecting large language model (LLM) generated text. The authors discuss the advancement of natural language generation (NLG) technology, including OpenAI's ChatGPT, and explain how the two detection methods, black-box detection and white-box detection, work to mitigate the potential misuse of LLMs.

This article explores the concept of artificial general intelligence (AGI) and the limitations of current AI systems. It discusses the idea of modularity in AI systems and the potential role of consciousness in intelligence. The article suggests that current AI systems could benefit from a more selective attention mechanism and discusses the concept of a global workspace in AI systems. Various approaches to implementing a global workspace are proposed, and the article highlights the value of studying the brain's mechanisms for developing more intelligent AI. [Extracted from the article]

This article features insights from five experts in the field of artificial intelligence (AI) on their top priorities for the future. Edward Norton, co-founder and CEO of Runway, emphasizes the importance of clear communication and transparency in AI advancements. Sasha Luccioni, AI and climate lead at Hugging Face, highlights the need for measuring the sustainability and labor impact of AI and providing customers with information on the societal and planetary impacts of AI tools. Edward Norton also calls for an ethical baseplate to ensure that AI does no harm. Robert Wolfe, co-founder of Zeck, discusses the potential of AI to save time and create audience-specific content. Jack Hidary, CEO of Sandbox AQ, encourages leaders to explore the impact of large quantitative models (LQMs) in addition to large language models (LLMs) in their organizations. [Extracted from the article]

The article discusses how various microchip manufacturers are using artificial intelligence (AI) and generative AI to rethink and improve the design of microchips, and it mentions AI's impact on transistor models and processes involving microchip verification, testing, and simulation. AI's role in clock-tree synthesis is assessed, as well as the design of Meteor Lake processors. Reinforcement learning, large language models, and computer--aided design are mentioned.

The article discusses the impact that artificial intelligence (AI), large language models (LLMs), and tools such as Chat-GPT are having on computer science education and the job market. According to the article, AI and machine learning (ML) will make it less necessary for humans to learn computer programming (coding) in the future. It states that close to 90 percent of all jobs will be impacted in some way by generative AI.

The article discusses efforts to test the intelligence levels of large language models (LLMs), and it mentions how various LLMs such as GPT-4, GPT-3.5, and a chatbot named Eliza performed on the Turing test for machine intelligence. According to the article, GPT-4 passed as a human in 41 percent of the games that it played during the Turing test. The development of additional machine intelligence exams is assessed.

This article addresses the intensifying debate over open-sourcing AI with opinions divided on whether it will democratize AI or pose significant risks. Despite OpenAI not fully open-sourcing its flagship models, other initiatives like the AI Alliance and IBM’s efforts indicate a growing ecosystem for open-source Large Language Models (LLMs), emphasizing responsible and safe AI development. Public and private sectors are increasingly investing in AI infrastructure, highlighting a shift towards collaborative efforts to balance innovation with ethical considerations and transparency.

This article details the detection and evaluation of stereotypes and biases perpetuated by language technologies in Latin America. The article shows why Latin America needs its own unique investigations apart from research done in Global North countries focusing on the English language. The article describes the utilization of the EDIA, Stereotypes and Discrimination in Artificial Intelligence, tool applied to a diverse group including social scientists and machine-learning experts in five Latin American countries.

This article from Forbes presents the ninth annual list of the world's top private cloud computing companies. The list is created in collaboration with Bessemer Venture Partners and Salesforce Ventures. The top companies on the list include OpenAI, Databricks, Stripe, Canva, and Anthropic. The article highlights the growth and achievements of these companies in the cloud computing industry. [Extracted from the article]

The article discusses data quality and considers the building and scaling of data through open-source models. It mentions information duplication, degraded model quality, and data filtering in large language models (LLMs). Several research papers are cited including one titled "Textbooks are all you need" which focuses on Phi1, a transformer-based model which synthetically generates high-quality textbooks using output from sources such as GPT-3.5.

The Biden Administration has implemented a program to selectively declassify classified information as a strategic tool to combat disinformation, overclassification, and public distrust. This approach has been used to address international issues, such as deterring Russian aggression in Ukraine. However, critics are concerned about the potential misuse of declassified information and the impact on public trust in the intelligence community. The proliferation of classified information poses challenges, and the National Security Council has developed a process to handle requests for declassification that align with strategic objectives. While declassified intelligence has been used to expose potential Chinese, Russian, Iranian, and North Korean involvement in conflicts, there are calls for transparency, oversight, and the prevention of politicization and weaponization of intelligence to rebuild public trust. [Extracted from the article]

Millions of people are turning to AI-powered chatbots for emotional support and mental health assistance. While some experts believe that AI can express empathy more openly and tirelessly than humans, others question whether AI can truly be capable of empathy and worry about the consequences of relying on machines for emotional support. AI chatbots can identify human emotions and respond appropriately, but they lack the nuanced understanding and intuitive awareness that are essential components of empathy. The effectiveness of AI empathy also depends on how users perceive and emotionally connect with the AI, and there are concerns about the potential manipulation and exploitation of emotions by AI. Despite the convenience and accessibility of AI chatbots, some argue that relying on machines for empathy may change the nature of empathy itself and hinder opportunities for genuine human connection. [Extracted from the article]

The article discusses the limitations of large language models (LLMs) in truly understanding and participating in language. While LLMs, created through artificial neural networks and trained on vast amounts of text, can generate human-like responses, they lack essential human abilities associated with language. The author argues that language involves more than grammar and words; it encompasses expression, coordination, culture, customs, interpretation, and history, shaping our way of being in the world. LLMs, despite their linguistic capabilities, are considered to manifest an "alien intelligence" as they cannot match human ways of shaping and being shaped by language.