Artificial Intelligence/Robots – Page 2

"Artificial Intelligence Assisted Curation of Population Groups in Biomedical Literature "

Curation of the growing body of published biomedical research is of great importance to both the synthesis of contemporary science and the archiving of historical biomedical literature. Each of these tasks has become increasingly challenging given the expansion of journal titles, preprint repositories and electronic databases. Added to this challenge is the need for curation of biomedical literature across population groups to better capture study populations for improved understanding of the generalizability of findings. To address this, our study aims to explore the use of generative artificial intelligence (AI) in the form of large language models (LLMs) such as GPT-4 as an AI curation assistant for the task of curating biomedical literature for population groups. We conducted a series of experiments which qualitatively and quantitatively evaluate the performance of OpenAI’s GPT-4 in curating population information from biomedical literature. Using OpenAI’s GPT-4 and curation instructions, executed through prompts, we evaluate the ability of GPT-4 to classify study ‘populations’, ‘continents’ and ‘countries’ from a previously curated dataset of public health COVID-19 studies.

Using three different experimental approaches, we examined performance by: A) evaluation of accuracy (concordance with human curation) using both exact and approximate string matches within a single experimental approach; B) evaluation of accuracy across experimental approaches; and C) conducting a qualitative phenomenology analysis to describe and classify the nature of difference between human curation and GPT curation. Our study shows that GPT-4 has the potential to provide assistance in the curation of population groups in biomedical literature. Additionally, phenomenology provided key information for prompt design that further improved the LLM’s performance in these tasks. Future research should aim to improve prompt design, as well as explore other generative AI models to improve curation performance. An increased understanding of the populations included in research studies is critical for the interpretation of findings, and we believe this study provides keen insight on the potential to increase the scalability of population curation in biomedical studies.

https://doi.org/10.2218/ijdc.v18i1.950

"NVIDIA: Copyrighted Books Are Just Statistical Correlations to Our AI Models"

Earlier this year, several authors sued NVIDIA over alleged copyright infringement. The class action lawsuit alleged that the company’s AI models were trained on copyrighted works and specifically mentioned Books3 data [a database of over 180,000 pirated books]. Since this happened without permission, the rightsholders demand compensation. . . .

The company believes that AI companies should be allowed to use copyrighted books to train their AI models, as these books are made up of “uncopyrightable facts and ideas” that are already in the public domain. . . .

“[AI] Training measures statistical correlations in the aggregate, across a vast body of data, and encodes them into the parameters of a model. Plaintiffs do not try to claim a copyright over those statistical correlations, asserting instead that the training data itself is ‘copied’ for the purposes of infringement,” NVIDIA writes [to the court hearing the case].

According to NVIDIA, the lawsuit boils down to two related questions. First, whether the authors’ direct infringement claim is essentially an attempt to claim copyright on facts and grammar. Second, whether making copies of the books is fair use.

https://tinyurl.com/mpa6e8jj

"Artists Claim ‘Big’ Win in Copyright Suit Fighting AI Image Generators"

In an order on Monday, US district judge William Orrick denied key parts of motions to dismiss from Stability AI, Midjourney, Runway AI, and DeviantArt. The court will now allow artists to proceed with discovery on claims that AI image generators relying on Stable Diffusion violate both the Copyright Act and the Lanham Act, which protects artists from commercial misuse of their names and unique styles. . . .

While Orrick agreed with Midjourney that “plaintiffs have no protection over ‘simple, cartoony drawings’ or ‘gritty fantasy paintings,'” artists were able to advance a “trade dress” claim under the Lanham Act, too.

https://tinyurl.com/yd27cvar

"Trade Dress Infringement"

"The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery"

One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aids to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community. We demonstrate its versatility by applying it to three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. To evaluate the generated papers, we design and validate an automated reviewer, which we show achieves near-human performance in evaluating paper scores. The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer. This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world’s most challenging problems.

https://arxiv.org/abs/2408.06292

"Wiley and Oxford University Press Confirm AI Partnerships as Cambridge University Press Offers ‘Opt-In’"

Wiley and Oxford University Press (OUP) told The Bookseller they have confirmed AI partnerships, with the availability of opt-ins and remuneration for authors appearing to vary. . . .

Meanwhile, Cambridge University Press has said it is talking to authors about opt ins along with ‘fair remuneration’ before making any deals.

Hachette, HarperCollins, and Pan Macmillan have not made AI deals.

https://tinyurl.com/bdzax5sk

"What Happens When Your Publisher Licenses Your Work for AI Training?"

In a lot of cases, yes, publishers can license AI training rights without asking authors first. Many publishing contracts include a full and broad grant of rights–sometimes even a full transfer of copyright to the publisher for them to exploit those rights and to license the rights to third parties. . . .

Not all publishing contracts are so broad, however. For example, in the Model Publishing Contract for Digital Scholarship (which we have endorsed), the publisher’s sublicensing rights are limited and specifically defined, and profits resulting from any exploitation of a work must be shared with authors. . . .

There are lots of variations, and specific terms matter. Some publisher agreements are far more limited–transferring only limited publishing and subsidiary rights. . . .

This is further complicated by the fact that authors sometimes are entitled to reclaim their rights, such as by rights reversion clause and copyright termination. . . .

We [the Authors Alliance] think it is certainly reasonable to be skeptical about the validity of blanket licensing schemes between large corporate rights holders and AI companies, at least when they are done at very large scale. Even though in some instances publishers do hold rights to license AI training, it is dubious whether they actually hold, and sufficiently document, all of the purported rights of all works being licensed for AI training.

https://tinyurl.com/53fnj9h7

"AI’s Future in Grave Danger from Nvidia’s Chokehold on Chips, Groups Warn"

Nvidia is currently “the world’s most valuable public company,” their letter said, worth more than $3 trillion after taking near-total control of the high-performance AI chip market. Particularly “astonishing,” the letter said, was Nvidia’s dominance in the market for GPU accelerator chips, which are at the heart of today’s leading AI.

According to the advocacy groups that strongly oppose Big Tech monopolies, Nvidia “now holds an 80 percent overall global market share in GPU chips and a 98 percent share in the data center market.” This “puts it in a position to crowd out competitors and set global pricing and the terms of trade,” the letter warned. . . .

https://tinyurl.com/y5c769nk

"European Artificial Intelligence Act Comes into Force"

The AI Act introduces a forward-looking definition of AI, based on a product safety and risk-based approach in the EU:

Minimal risk: Most AI systems, such as AI-enabled recommender systems and spam filters, fall into this category. These systems face no obligations under the AI Act due to their minimal risk to citizens’ rights and safety. Companies can voluntarily adopt additional codes of conduct.

Specific transparency risk: AI systems like chatbots must clearly disclose to users that they are interacting with a machine. Certain AI-generated content, including deep fakes, must be labelled as such, and users need to be informed when biometric categorisation or emotion recognition systems are being used. In addition, providers will have to design systems in a way that synthetic audio, video, text and images content is marked in a machine-readable format, and detectable as artificially generated or manipulated.

High risk: AI systems identified as high-risk will be required to comply with strict requirements, including risk-mitigation systems, high quality of data sets, logging of activity, detailed documentation, clear user information, human oversight, and a high level of robustness, accuracy, and cybersecurity. Regulatory sandboxes will facilitate responsible innovation and the development of compliant AI systems. Such high-risk AI systems include for example AI systems used for recruitment, or to assess whether somebody is entitled to get a loan, or to run autonomous robots.

Unacceptable risk: AI systems considered a clear threat to the fundamental rights of people will be banned. This includes AI systems or applications that manipulate human behaviour to circumvent users’ free will, such as toys using voice assistance encouraging dangerous behaviour of minors, systems that allow ‘social scoring’ by governments or companies, and certain applications of predictive policing. In addition, some uses of biometric systems will be prohibited, for example emotion recognition systems used at the workplace and some systems for categorising people or real time remote biometric identification for law enforcement purposes in publicly accessible spaces (with narrow exceptions).

https://tinyurl.com/32jy9pat

"AI and the Workforce: Industry Report Calls for Reskilling and Upskilling as 92 Percent of Technology Roles Evolve"

"The Transformational Opportunity of AI on ICT Jobs" report finds that 92 percent of jobs analyzed are expected to undergo either high or moderate transformation due to advancements in AI.

Led by Cisco, created by Consortium members, and analyzed by Accenture, the new report identifies essential trainings in AI literacy, data analytics and prompt engineering for workers seeking to adapt to the AI revolution.

The AI-Enabled ICT Workforce Consortium consists of Cisco, Accenture, Eightfold, Google, IBM, Indeed, Intel, Microsoft and SAP. Advisors include the American Federation of Labor and Congress of Industrial Organizations, CHAIN5, Communications Workers of America, DIGITALEUROPE, the European Vocational Training Association, Khan Academy and SMEUnited.

https://tinyurl.com/3hj8ypx2

"Is the AI Bubble about to Pop? Internal Documents Reveal OpenAI May Go Bankrupt within 12 Months"

Net losses for 2024 alone are expected to hit US$5 billion. . . .

The company spends US$7 billion on training its GPT models, with additional US$1.5 billion in staffing expenses.

It makes back anywhere between US$3.5 to US$4.5 billion in ChatGPT subscriptions and access fees. . .

https://tinyurl.com/y8hen3ep

"Copyright Office Releases Part 1 of Artificial Intelligence Report, Recommends Federal Digital Replica Law"

Today, the U.S. Copyright Office is releasing Part 1 of its Report on the legal and policy issues related to copyright and artificial intelligence (AI), addressing the topic of digital replicas. This Part of the Report responds to the proliferation of videos, images, or audio recordings that have been digitally created or manipulated to realistically but falsely depict an individual. Given the gaps in existing legal protections, the Office recommends that Congress enact a new federal law that protects all individuals from the knowing distribution of unauthorized digital replicas. The Office also offers recommendations on the elements to be included in crafting such a law. . . .

The Report is being released in several Parts, beginning today. Forthcoming Parts will address the copyrightability of materials created in whole or in part by generative AI, the legal implications of training AI models on copyrighted works, licensing considerations, and the allocation of any potential liability.

https://tinyurl.com/yc2fhthm

"AI Is Complicating Plagiarism. How Should Scientists Respond?"

A central question is whether using unattributed content written entirely by a machine — rather than by a human — counts as plagiarism. Not necessarily, say many researchers. For example, the European Network for Academic Integrity, which includes universities and individuals, defines the prohibited or undeclared use of AI tools for writing as "unauthorized content generation" rather than as plagiarism as such.

https://www.nature.com/articles/d41586-024-02371-z

"Capturing Captions: Using AI to Identify and Analyse Image Captions in a Large Dataset of Historical Book Illustrations"

This article outlines how AI methods can be used to identify image captions in a large dataset of digitised historical book illustrations. This dataset includes over a million images from 68,000 books published between the eighteenth and early twentieth centuries, covering works of literature, history, geography, and philosophy. The article has two primary objectives. First, it suggests the added value of captions in making digitized illustrations more searchable by picture content in online archives. To further this objective, we describe the methods we have used to identify captions, which can effectively be re-purposed and applied in different contexts. Second, we suggest how this research leads to new understandings of the semantics and significance of the captions of historical book illustrations. The findings discussed here mark a critical intervention in the fields of digital humanities, book history, and illustration studies.

https://tinyurl.com/bdvjespp

"AI and Medical Images: Addressing Ethical Challenges to Provide Responsible Access to Historical Medical Illustrations"

This article examines the ethical considerations and broader issues around access to digitised historical medical images. These illustrations and, later, photographs are often extremely sensitive, representing disability, disease, gender, and race in potentially harmful and problematic ways. In particular, the original metadata for such images can include demeaning and sometimes racist terms. Some of these images show sexually explicit and violent content, as well as content that was obtained without informed consent. Hiding these sensitive images can be tempting, and yet, archives are meant to be used, not locked away. Through a series of interviews with 10 archivists, librarians, and researchers based in the UK and US, the authors show that improved access to medical illustrations is essential to produce new knowledge in the humanities and medical research, as well as to bridge the gap between historical and modern understandings of the human body. Improving access to medical illustration can also help to address the "gender data gap", which has acquired mainstream visibility thanks to the work of activists such as Caroline Criado-Perez, the author of Invisible Women: Data Bias in a World Designed for Men.

https://tinyurl.com/3jek7ey4

"Academic Authors ‘Shocked’ After Taylor & Francis Sells Access to Their Research to Microsoft AI"

One of the biggest concerns raised by Clemens [Dr Ruth Alison Clemens] is over whether it is possible for Taylor & Francis’ authors to opt out of the AI partnership with Microsoft. Clemens told The Bookseller: "There is no clarity from Taylor & Francis about whether an opt-out policy is in place or on the cards. But as they did not inform their authors about the deal in the first place, any opt-out policy is now not functional."

Taylor & Francis was paid around $10 million for the license.

https://tinyurl.com/3yyarxnj

"Meta Releases the Biggest and Best Open-Source AI Model Yet"

Meta is releasing Llama 3.1, the largest-ever open-source AI model, which the company claims outperforms GPT-4o and Anthropic’s Claude 3.5 Sonnet on several benchmarks. It’s also making the Llama-based Meta AI assistant available in more countries and languages while adding a feature that can generate images based on someone’s specific likeness. . . .

Meta’s own implementation of Llama is its AI assistant, which is positioned as a general-purpose chatbot like ChatGPT and can be found [in a few weeks] in just about every part of Instagram, Facebook, and WhatsApp.

https://tinyurl.com/2cs552p4

Paywall: "Exploring the Use of Generative Artificial Intelligence in Systematic Searching: A Comparative Case Study of a Human Librarian, ChatGPT-4 and ChatGPT-4 Turbo"

The findings suggest that AI could expand the scope of search terms and queries, automating the more repetitive and formulaic aspects of the systematic-review process, while human expertise remains crucial in refining search terms and ensuring methodological rigor. Meanwhile, challenges remain for AI tools’ capacity to access subscription-based or proprietary databases and generate sophisticated search strategies.

https://doi.org/10.1177/03400352241263532

AI Is Running Out of New Training Data: Consent in Crisis: The Rapid Decline of the AI Data Commons

General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training corpora. . . .Our longitudinal analyses show that in a single year (2023-2024) there has been a rapid crescendo of data restrictions from web sources, rendering ~5%+ of all tokens in C4, or 28%+ of the most actively maintained, critical sources in C4, fully restricted from use. For Terms of Service crawling restrictions, a full 45% of C4 is now restricted. If respected or enforced, these restrictions are rapidly biasing the diversity, freshness, and scaling laws for general-purpose AI systems.

https://tinyurl.com/4k56axzk

"STM Statement Regarding Unlicensed Use of STM’s Members’ Content in the Training, Development, and Operation of AI Models"

The unlicensed use of STM’s members’ content in the training, development, and operation of AI models is of great concern to STM and to our members. Because STM’s members do not share a single jurisdiction, the particular actions and practices of a given AI developer with respect to a given domestic copyright law are too varied to enumerate here. However, regardless of legal nuances among jurisdictions, STM considers the conclusion to be the same — the collection of our members’ content and its use in AI training without authorization, compensation or attribution, amounts to infringement. We support the statements about third parties’ use of content in generative AI training and development that have been made by our sister organizations the International Publishers Association and the UK Publishers Association.

https://tinyurl.com/5n6zh9sy

"Google’s Wrong Answer to the Threat of AI — Stop Indexing Content"

"Google is no longer trying to index the entire web," writes Schmalbach [Vincent Schmalbach, SEO expert]. "In fact, it’s become extremely selective, refusing to index most content. This isn’t about content creators failing to meet some arbitrary standard of quality. Rather, it’s a fundamental change in how Google approaches its role as a search engine." The default setting from now on will be not to index content unless it is genuinely unique, authoritative and has ‘brand recognition’.

https://tinyurl.com/32t98fhu

"ARL & CNI Release Deluxe Edition of AI-Influenced Future Scenarios for Research Environment"

This Deluxe Edition of the ARL/CNI AI Scenarios includes:

The Final Scenario Set: This final scenario set explores potential futures where AI plays a pivotal role, providing critical insights into the evolving challenges and opportunities for the research environment.

The Strategic Context Report: This report summarizes community feedback gathered through focus groups and interviews about an AI-influenced future for the research environment that were held in winter 2023–24 and spring 2024.

The Provocateur Interview Report: Featuring forward-thinking dialogues with industry leaders, these interviews challenge conventional wisdom and stimulate stretch thinking with regards to an AI-influenced future.

https://tinyurl.com/5n7xwc8c

"A Real-World Test of Artificial Intelligence Infiltration of a University Examinations System: A ‘Turing Test’ Case Study"

The recent rise in artificial intelligence systems, such as ChatGPT, poses a fundamental problem for the educational sector. In universities and schools, many forms of assessment, such as coursework, are completed without invigilation. Therefore, students could hand in work as their own which is in fact completed by AI. Since the COVID pandemic, the sector has additionally accelerated its reliance on unsupervised ‘take home exams’. If students cheat using AI and this is undetected, the integrity of the way in which students are assessed is threatened. We report a rigorous, blind study in which we injected 100% AI written submissions into the examinations system in five undergraduate modules, across all years of study, for a BSc degree in Psychology at a reputable UK university. We found that 94% of our AI submissions were undetected. The grades awarded to our AI submissions were on average half a grade boundary higher than that achieved by real students. Across modules there was an 83.4% chance that the AI submissions on a module would outperform a random selection of the same number of real student submissions.

https://doi.org/10.1371/journal.pone.0305354

"RIAA Sues Suno & Udio AI Music Generators For ‘Trampling’ on Copyright"

Major recording labels of the RIAA have filed a pair of broadly similar copyright lawsuits against two key generative AI music services. The owners of Udio and Suno stand accused of copying the labels’ music on a massive scale and the labels suggest that they’re already on the back foot. In pre-litigation correspondence, both were ‘evasive’ on content sources before citing fair use, which the RIAA notes only arises as a defense in cases of unauthorized use of copyright works.

https://tinyurl.com/p9tnycte

Paywall: "AI Is Exhausting the Power Grid. Tech Firms Are Seeking a Miracle Solution."

In addition to fusion, tech giants are hoping to generate power through such futuristic schemes as small nuclear reactors hooked to individual computing centers and machinery that taps geothermal energy by boring 10,000 feet into the Earth’s crust. . . .

A recent Goldman Sachs analysis of energy that will power the AI boom into 2030. . . found data centers will account for 8 percent of total electricity use in the United States by 2030, a near tripling of their share today. New solar and wind energy will meet about 40 percent of that new power demand from data centers, the forecast said, while the rest will come from a vast expansion in the burning of natural gas. The new emissions created would be comparable to that of putting 15.7 million additional gas-powered cars on the road.

https://tinyurl.com/5fhwpc36

"Empowering Knowledge through AI: Open Scholarship Proactively Supporting Well Trained Generative AI"

Generative AI has taken the world by storm over the last few years, and the world of scholarly communications has not been immune to this. Most discussions in this area address how we can integrate these tools into our workflows, concerns about how researchers and students might misuse the technology or the unauthorised use of copyrighted work. This article argues for a novel viewpoint that librarians and publishers should be encouraging the use of their scholarly content in the training of AI algorithms. Inclusion of scholarly works would advance the reliability and accuracy of the information in training datasets and ensure that this content is included in new knowledge discovery platforms. The article also argues that inclusion can be achieved by improving linkage to content, and, by making sure that licences explicitly allow inclusion in AI training datasets, it advocates for a more collaborative approach to shaping the future of the information landscape in academia.

https://doi.org/10.1629/uksg.649