Artificial Intelligence/Robots – Page 10

"Evaluating the Efficacy of ChatGPT-4 in Providing Scientific References across Diverse Disciplines"

This work conducts a comprehensive exploration into the proficiency of OpenAI’s ChatGPT-4 in sourcing scientific references within an array of research disciplines. Our in-depth analysis encompasses a wide scope of fields including Computer Science (CS), Mechanical Engineering (ME), Electrical Engineering (EE), Biomedical Engineering (BME), and Medicine, as well as their more specialized sub-domains. Our empirical findings indicate a significant variance in ChatGPT-4’s performance across these disciplines. Notably, the validity rate of suggested articles in CS, BME, and Medicine surpasses 65%, whereas in the realms of ME and EE, the model fails to verify any article as valid. Further, in the context of retrieving articles pertinent to niche research topics, ChatGPT-4 tends to yield references that align with the broader thematic areas as opposed to the narrowly defined topics of interest. This observed disparity underscores the pronounced variability in accuracy across diverse research fields, indicating the potential requirement for model refinement to enhance its functionality in academic research. Our investigation offers valuable insights into the current capacities and limitations of AI-powered tools in scholarly research, thereby emphasizing the indispensable role of human oversight and rigorous validation in leveraging such models for academic pursuits.

https://arxiv.org/abs/2306.09914v1

"European Lawmakers Vote to Adopt EU AI Act"

European Union lawmakers have passed the EU AI Act that will govern use and deployment of artificial intelligence technology within the EU. . . . Changes introduced by MEPs to the original commission draft act include some top-level regulation of general-purpose AI tools such as ChatGPT. These foundation models will require mandatory labelling for AI-generated content and the forced disclosure of training data covered by copyright. . . . . Other changes include a fine-tuned list of prohibited practices, extended to include subliminal techniques, biometric categorisation, predictive policing, and internet-scraped facial recognition databases.

https://tinyurl.com/nhet5ckd

Congressional Research Service: Generative Artificial Intelligence: Overview, Issues, and Questions for Congress

The recent public release of many GenAI tools, and the race by companies to develop ever-more powerful models, have generated widespread discussion of their capabilities, potential concerns with their use, and debates about their governance and regulation. This CRS InFocus describes the development and uses of GenAI, concerns raised by the use of GenAI tools, and considerations for Congress. For additional considerations related to data privacy, see CRS Report R47569, Generative Artificial Intelligence and Data Privacy: A Primer, by Kristen E. Busch.

https://tinyurl.com/bdrpkzcj

"New ChatGPT Course at ASU Gives Students a Competitive Edge"

A new Arizona State University course will provide students with those skills, providing expertise that is becoming increasingly sought after.

Basic Prompt Engineering with ChatGPT: An Introduction is open this summer to students in any major, and despite the name, is not really about engineering.

https://tinyurl.com/rcfnt39d

"Guest Post — Accessibility Powered by AI: How Artificial Intelligence Can Help Universalize Access to Digital Content"

More than 1 billion people around the world have some type of disability (including visual, hearing, cognitive, learning, mobility, and other disabilities) that affects how they access digital content. No wonder we spend so much time talking about accessibility tools!

Digital transformation can revolutionize the world, turning it into an inclusive place for people with and without disabilities, with accessibility powered by artificial intelligence. This post provides an overview of how AI can improve accessibility in different ways, illustrated with real-world applications and examples.

https://tinyurl.com/3s64tvm7

"AI Is about to Turn Book Publishing Upside-down"

The latest generation of AI is a game changer. Not incremental change—something gentle, something gradual: this AI changes everything, fast. Scary fast.

I believe that every function in trade book publishing today can be automated with the help of generative AI. And, if this is true, then the trade book publishing industry as we know it will soon be obsolete. We will need to move on.

https://tinyurl.com/2p9z6pr6

"Top AI Researchers and CEOs Warn against ‘Risk of Extinction’ in 22-Word Statement"

The 22-word statement, trimmed short to make it as broadly acceptable as possible, reads as follows: "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war."

https://cutt.ly/EwqXnHn9

Pew Research Center: "A Majority of Americans Have Heard of ChatGPT, but Few Have Tried It Themselves"

However, few U.S. adults have themselves used ChatGPT for any purpose. Just 14% of all U.S. adults say they have used it for entertainment, to learn something new, or for their work. This lack of uptake is in line with a Pew Research Center survey from 2021 that found that Americans were more likely to express concerns than excitement about increased use of artificial intelligence in daily life.

https://cutt.ly/Ywqld1X7

"EU’s New AI Law Targets Big Tech Companies but Is Probably Only Going to Harm the Smallest Ones"

In a bold stroke, the EU’s amended AI Act would ban American companies such as OpenAI, Amazon, Google, and IBM from providing API access to generative AI models. The amended act, voted out of committee on Thursday, would sanction American open-source developers and software distributors, such as GitHub, if unlicensed generative models became available in Europe. While the act includes open source exceptions for traditional machine learning models, it expressly forbids safe-harbor provisions for open source generative systems.

Any model made available in the EU, without first passing extensive, and expensive, licensing, would subject companies to massive fines of the greater of €20,000,000 or 4% of worldwide revenue.

(Quote from Technomancers.ai.)

https://bit.ly/3ociZwo

Paywall: "Microsoft Says New A.I. Shows Signs of Human Reasoning"

When computer scientists at Microsoft started to experiment with a new artificial intelligence system last year, they asked it to solve a puzzle that should have required an intuitive understanding of the physical world. . . . The clever suggestion [by the AI] made the researchers wonder whether they were witnessing a new kind of intelligence. In March, they published a 155-page research paper arguing that the system was a step toward artificial general intelligence, or A.G.I., which is shorthand for a machine that can do anything the human brain can do.

https://bit.ly/42FkLp1

"The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces"

Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the need for new technology to support the reading process grows. In contrast to the process of finding papers, which has been transformed by Internet technology, the experience of reading research papers has changed little in decades. The PDF format for sharing research papers is widely used due to its portability, but it has significant downsides including: static content, poor accessibility for low-vision readers, and difficulty reading on mobile devices. This paper explores the question "Can recent advances in AI and HCI power intelligent, interactive, and accessible reading interfaces — even for legacy PDFs?" We describe the Semantic Reader Project, a collaborative effort across multiple institutions to explore automatic creation of dynamic reading interfaces for research papers. Through this project, we’ve developed ten research prototype interfaces and conducted usability studies with more than 300 participants and real-world users showing improved reading experiences for scholars. We’ve also released a production reading interface for research papers that will incorporate the best features as they mature. We structure this paper around challenges scholars and the public face when reading research papers — Discovery, Efficiency, Comprehension, Synthesis, and Accessibility — and present an overview of our progress and remaining open challenges.

https://arxiv.org/abs/2303.14334

Paywall: "Google is Changing the Way We Search with AI. It Could Upend theWeb."

At the same time, the talk of replacing search results with AI-generated answers has roiled the world of people who make their living writing content and building websites. If a chatbot takes over the role of helping people find useful information, what incentive would there be for anyone to write how-to guides, travel blogs or recipes?

https://cutt.ly/s6kmQpF

"Is There a Case for Accepting Machine Translated Scholarly Content in Repositories?"

Multilingualism is a critical characteristic of a healthy, inclusive, and diverse research communications landscape. However, multilingualism presents a particular challenge for the discovery of research outputs. Although researchers and other information seekers may only be able to read in one or two languages, they may want to know about all the relevant research in their area, regardless of the language in which it is published. Conversely, information seekers may want to discover research outputs in their own language(s) more easily. To facilitate this, COAR Task Force on Supporting Multilingualism and non-English Content in Repositories has been developing and promoting good practices for repositories in managing multilingual and non-English content. In the course of our work, the topic of machine translation (MT) has sparked a heated discussion within the Task Group and we would like to share with you the nature of this discussion.

https://bit.ly/42D1nbF

"Quick Poll Results: ARL Member Representatives on Generative AI in Libraries"

We conducted a quick poll of Association of Research Libraries (ARL) member representatives in April 2023 to gather insights into their current perspectives on generative AI adoption, its potential implications, and the role of libraries in AI-driven environments. In this blog post, we summarize, synthesize, and provide recommendations based on the survey responses, aiming to offer valuable insights for senior library directors navigating the AI landscape.

https://bit.ly/3M9yVc2

2023 EDUCAUSE Horizon Report: Teaching and Learning Edition

This report profiles key trends and emerging technologies and practices shaping the future of teaching and learning, and envisions a number of scenarios and implications for that future. . . .

Artificial intelligence (AI) has taken the world by storm, with new AI-powered tools such as ChatGPT opening up new opportunities in higher education for content creation, communication, and learning, while also raising new concerns about the misuses and overreach of technology. Our shared humanity has also become a key focal point within higher education, as faculty and leaders continue to wrestle with understanding and meeting the diverse needs of students and to find ways of cultivating institutional communities that support student well-being and belonging.

https://bit.ly/3panaJd

"Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond"

This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks.. . . Firstly, we offer an introduction and brief summary of current GPT- and BERT-style LLMs. Then, we discuss the influence of pre-training data, training data, and test data. Most importantly, we provide a detailed discussion about the use and non-use cases of large language models for various natural language processing tasks, such as knowledge-intensive tasks, traditional natural language understanding tasks, natural language generation tasks, emergent abilities, and considerations for specific tasks.We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios. . . . Furthermore, we explore the impact of spurious biases on LLMs and delve into other essential considerations, such as efficiency, cost, and latency, to ensure a comprehensive understanding of deploying LLMs in practice.

https://arxiv.org/abs/2304.13712

"AI Is Tearing Wikipedia Apart"

The current draft policy notes that anyone unfamiliar with the risks of large language models should avoid using them to create Wikipedia content. . . . The community is also divided on whether large language models should be allowed to train on Wikipedia content. While open access is a cornerstone of Wikipedia’s design principles, some worry the unrestricted scraping of internet data allows AI companies like OpenAI to exploit the open web to create closed commercial datasets for their models. This is especially a problem if the Wikipedia content itself is AI-generated, creating a feedback loop of potentially biased information, if left unchecked.

https://bit.ly/3NLrc50

Paywall: "’The Godfather of A.I.’ Leaves Google and Warns of Danger Ahead"

Down the road, he is worried that future versions of the technology pose a threat to humanity because they often learn unexpected behavior from the vast amounts of data they analyze. This becomes an issue, he said, as individuals and companies allow A.I. systems not only to generate their own computer code but actually run that code on their own. And he fears a day when truly autonomous weapons — those killer robots — become reality.

"The idea that this stuff could actually get smarter than people — a few people believed that," he said. "But most people thought it was way off. And I thought it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that."

https://bit.ly/3VoA9Dh

Is Artificial General Intelligence Closer Than We Think?: "Sparks of Artificial General Intelligence: Early Experiments with GPT-4"

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google’s PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4’s performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4’s capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.

https://doi.org/10.48550/arXiv.2303.12712

"The Viability of Using an Open Source Locally Hosted AI for Creating Metadata in Digital Image Collections"

Artificial intelligence (AI) can support metadata creation for images by generating descriptions, titles, and keywords for digital collections in libraries. Many AI options are available, ranging from cloud-based corporate software solutions, including Microsoft Azure Custom Vision and Google Cloud Vision, to open-source locally hosted software packages. This case study examines the feasibility of deploying the open-source, locally hosted AI software, Sheeko, and the accuracy of the descriptions generated for images using two of the pre-trained models. The study aims to ascertain if Sheeko’s AI would be a viable solution for producing metadata in the form of descriptions, or titles for digital collections in Libraries and Cultural Resources at the University of Calgary.

https://journal.code4lib.org/articles/17186

"Generative AI and Copyright Policy From the Creator-User’s Perspective"

As scholars Mark Lemley and Bryan Casey persuasively argue in their paper Fair Learning, we should generally permit generative AI tools that in effect learn from past works in ways that facilitate creation of new, distinct ones. While some claim that generative AI systems are simply engines for ‘collage’ or ‘plagiarism,’ copying previous expressions into new works, this isn’t an accurate description of how most tools work. Instead, generative AI extracts information that then is used to inform generation of new material; for instance, by looking at many pictures of dogs, it can extract information about what dogs look like, and can then help a user draw dogs, or by looking at many pieces of art labeled as Surrealist, it can help a user create new works in the style of Surrealism. In effect, these are tools that aid new creators in their learning and building on past works.

https://bit.ly/3GVNhK5

"EDUCAUSE QuickPoll Results: Adopting and Adapting to Generative AI in Higher Ed Tech"

Asked about their agreement with specific statements about generative AI, a strong majority of respondents (83%) agreed that these technologies will profoundly change higher education in the next three to five years (see table 1). These changes could be positive or negative. More respondents agreed than disagreed that generative AI would make their job easier and would have more benefits than drawbacks. However, more respondents agreed than disagreed that the use of generative AI in higher education makes them nervous, perhaps an acknowledgment of the potential risks of these technologies, however beneficial

https://bit.ly/3GTzdkf

Paywall: "Google Devising Radical Search Changes to Beat Back A.I. Rivals"

Google’s employees were shocked when they learned in March that the South Korean consumer electronics giant Samsung was considering replacing Google with Microsoft’s Bing as the default search engine on its devices. . . .Google’s reaction to the Samsung threat was "panic," according to internal messages reviewed by The New York Times. An estimated $3 billion in annual revenue was at stake with the Samsung contract. An additional $20 billion is tied to a similar Apple contract that will be up for renewal this year.

https://bit.ly/3MQjYfD

"How Generative AI Could Disrupt Creative Work "

In the face of technological change, creativity is often held up as a uniquely human quality, less vulnerable to the forces of technological disruption and critical for the future. Today however, generative AI applications such as ChatGPT and Midjourney are threatening to upend this special status and significantly alter creative work, both independent and salaried. The authors explore three non-exclusive scenarios for this disruption of content creation: 1) people use AI to augment their work, leading to greater productivity, 2) generative AI creates a flood of cheap content that drives out human creatives, and 3) human-made creative work demands a premium.

https://bit.ly/43pP4kh

"Surprising Things Happen When You Put 25 AI Agents Together in an RPG [Role-Playing Game] Town"

A group of researchers at Stanford University and Google have created a miniature RPG-style virtual world similar to The Sims, where 25 characters, controlled by ChatGPT and custom code, live out their lives independently with a high degree of realistic behavior. . . .

"Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day," write the researchers in their paper, "Generative Agents: Interactive Simulacra of Human Behavior."

http://bit.ly/3KSwc6b